One of the architects of the modern internet, Vint Cerf, went on to become Google's chief internet evangelist from October 2005 through to July 2026, making several key predictions on the technologies he played a role in crafting. But, casting his eye back, he also once made a staggering admission about his view on privacy.

The need for privacy

Cerf was speaking during a media Q&A session at a Federal Trade Commission (FTC) workshop on the Internet of Things (IoT) when he made these off-the-cuff remarks.

There's no single official source of truth on the exact words he uttered, but The Verge reconstructed his words as best they could, with Cerf essentially highlighting that throughout the span of human existence, our desire for privacy is, relatively, a modern blip.

The comments may be perceived as a means of undermining what's become an aggressive desire for privacy, especially in the wake of revelations on government surveillance and a reliance on using personal data in the business models of companies like Google. But Cerf cast his eye much further and wider, cutting to human nature itself.

Human nature

Cerf saw people as being naturally inclined to living in much smaller communities, like villages, in which only a few thousand people would ever interact.

There, he said, everybody would know what everybody else was doing, and the postmaster would know who everyone was getting their mail from, as he put it.

Since the Industrial Revolution, there's been a massive growth in much larger population centres like cities, with the global population also massively exploding.

But there's also an argument to make that times move on, and our nature has shifted to adapt to the world we know, not the world in which our ancestors inhabited. By that nature, our need for privacy is still innate, with big tech companies like Google seeking to undermine this by exploiting our personal data for private gain.

As a Batman fan, I was disappointed to learn last week that The Batman Part 2 has been delayed until February 18, 2028, another four-month wait to add to add to its last (delayed) intended release date of October 1, 2027. This latest holdup is actually the fourth time the long-awaited sequel to The Batman has been delayed, with its initial release originally set to be October 2025.

Thankfully, there are plenty of ways to enjoy Batman content at home while we eagerly await The Batman: Part 2’s 2028 release. From Christopher Nolan’s iconic Dark Knight Trilogy to the awesome Batman: The Animated Series, there are tons of choices. And there’s no better way to enjoy Batman’s many adventures than with a 4K Blu-ray. I’m a 4K Blu-ray fan and collector, so I’m always testing the latest releases for the Blu-ray Bounty and finding new discs to test the best TVs and best soundbars.

Below, I’ve picked three of my favorite Batman-related 4K Blu-rays, not just because they’re my personal picks, but because they’re also great examples to show what 4K can do, especially for your home theater.

The Batman

Where else to better prepare for The Batman: Part 2 than with its predecessor, The Batman. Matt Reeve’s The Batman follows Bruce Wayne (Robert Pattison) in his second year as Batman, where he must face The Riddler (Paul Dano), a revolutionary who uses a series of riddles and puzzles to test Batman, as Riddler plans to expose Gotham’s corrupt government and elite.

The Batman has become one of my main 4K Blu-rays for testing TVs. Due to its low brightness, some TVs can really struggle to accurately display the movie, compromising its dark tones. But, there’s also plenty of excellent-looking scenes with powerful contrast, balancing light and dark tones, that can really show off a TV.

The opening 20 minutes of the movie itself is all a big demo-reel for contrast. From the dark subway platform illuminated by overhead lamps as Batman fights a gang of thugs, to where we first see Bruce decoding Riddler’s clues in the Batcave, The Batman delivers truly dynamic and strong contrast, which I've found look superb on the best OLED TVs in particular.

There’s also a chance to show a display’s shadow detail, with plenty of scenes making use of shadows to create a captivating image. The aforementioned subway scene is one of these, with truly dark shadows creating a foreboding atmosphere that fits the gloomy tone of the movie.

There’s excellent detail throughout, with crisp textures that look refined and lifelike. Close-up shots show scars, wrinkles and stubble on people’s faces with impressive clarity, and texture in environments, like Mayor Mitchell’s house or the Batcave, are given a near-3D level of detail.

The Batman is an audio showcase as well as a visual one. The Batmobile chase has become a reference scene I use for showing off a sound system. The ignition of the Batmobile’s engines deliver room-rattling, powerful bass that through a subwoofer can create some serious roar. The 4K disc uses a Dolby Atmos soundtrack that creates an immersive and detailed sound.

As the Batmobile chases Penguin across a busy freeway, serving traffic and blaring horns are accurately mapped to the action. As Penguin fires a spray of bullets at the Batmobile, the sound glides around a surround system, as I found when using soundbars such as the talented Samsung HW-Q990H. There are plenty of Atmos effects too, as rain regularly falls throughout the movie, effectively using the height channels of any sound system.

The Dark Knight

Arguably the most popular Batman movie ever made, The Dark Knight is the second movie in Christopher Nolan’s trilogy. It’s one that’s stuck with me ever since I saw it as a teenager in theaters in 2008. Bruce Wayne (Christian Bale) is a couple of years into his time as Batman and he faces his biggest challenge yet, when he squares off with the unpredictable and chaotic Joker (played brilliantly by Heath Ledger).

Unsurprisingly, where this disc shines is in its IMAX sequences. These IMAX scenes show impeccable detail and crisp, 3D-like textures. In the opening bank heist scene, every detail is refined, from the creases in the robbers’ masks, to the texture in buildings of the densely packed city. Oh, and don’t be surprised when the ratio changes from widescreen to fullscreen: that’s normal for IMAX on home media.

Black tones and shadow detail are excellent during the film's night scenes too (of which there are plenty), and they look superb on an OLED TV. Contrast is also strong, particularly during the prison escort scene. As trucks move under the freeway, the overhead lights balance well with the dark, shadowed areas of the tunnel to create dynamic contrast.

This disc is also an audio powerhouse. With a DTS-HD 5.1 MA soundtrack, there’s some fantastic detail. During the tunnel escort scene, the whir of Batman’s bike riding through the tunnel is mapped perfectly to every channel. As perspective changes, so does the sound. Bullets are precise, flying from the front to rear channels when the movie is from Batman’s point-of-view, and every individual effect is audible, such as flying debris and swerving tyres.

Bass throughout the movie is truly powerful. Bullets have serious impact and the roar of the Batmobile’s engines delivers tightly controlled, but hefty low-end that reverberated through our testing room when I watched.

The score is a showcase in and of itself, with superb separation across different channels of the Samsung HW-Q990C soundbar I was using. In the opening scene, the scratching strings came from the left rear, the ticking sound from the right, and the heavy bass exploded from the subwoofer. Crucially though, every element was perfectly balanced, within the score itself and with all the action.

Batman Forever

This pick will raise eyebrows, I’m sure. But, honestly, Batman Forever is the Batman movie I grew up with and despite its very corny nature, I enjoy it. It scratches the exact part of my brain that occasionally craves a nostalgic Batman fix. The movie follows Batman (played by Val Kilmer) as he does battle with the Riddler (Jim Carrey) and Two-Face (Tommy Lee Jones) as they attempt to take over Gotham.

While this may be a surprising choice for a lot of people, this is a seriously good-looking 4K presentation. Batman Forever uses a lot of bold and vibrant colors, and in 4K, they really pop on screen. The green of the Riddler’s gaudy outfit and the purple of Two-Face’s injured side compliment each other well. There’s also a lot of neon-esuqe lighting used in the movie, with light tubes, red detail on Two-Face’s minion’s guns and the shimmering greens of Riddler’s hideout and event space. All these colors have a gorgeous punch that looks sharp and dynamic throughout the movie.

Shadows are used a lot, with characters often standing over lamps or in shaded rooms to create strong contrast in images. The LG G6 OLED on which I watched this movie in our testing room celebrates these rich and inky dark tones, with the leather of Batman’s armor showing nice depth, but also balancing well as the light reflects off it. Shadow detail is excellent too, with textures in dark areas on screen legible (yes, even the strange texture of Batman’s armor). Skin looks realistic in the many close-up shots used, too.

This 4K disc also supports Dolby Atmos. There’s some excellent use of height channels throughout, with the sound of helicopter Two-Face uses to escape in the intro coming from overhead as I watched. Explosions have plenty of meat to them, producing some lovely low-end rumble during the movie’s more action-packed scenes. The sound of the Batmobile’s engines firing is another great demo for the powerful bass in the mix.

More subtle details are also fully audible. The sound of Batman’s batarang as he throws it is precisely mapped, with the sound eagerly following its trajectory. Bullets have great punch to them, and again these are accurately posited within the surround channels. Speech is consistently clear throughout too, even during the most chaotic scenes. You might not necessarily expect this movie to be such an audio showcase, but it really is.

As noted in my Ninja Creami Swirl review, with the right ingredients, your frozen dessert can help you meet your protein goals. I have tried dozens of recipes and found that I really don't care for the taste or texture of the ready-made protein shakes and powders that many high-protein recipes call for. Plus, they are quite expensive, often around $3 / £2.50 / AU$4.50 per serving, not counting any other ingredients you might add.

Many recipes also call for pudding mix, which is supposed to help with texture, taste, and added sweetness, but I'm not a fan of those either, so I searched out some options that provide a decent amount of protein without any of those products — just real food.

Just yogurt

The obvious option is just to take a tub (or two) of your favorite yogurt, dump it into the Ninja Creami pint, and freeze. Spin it on the Yogurt setting and you get a nice ice cream texture. The key is to use a yogurt that you really like, because the flavor doesn't magically change into ice cream, just the texture! The image above is two tubs of simple vanilla Greek yogurt. Keep in mind that freezing anything dulls the sweetness, so you might want to add a touch of your sweetener of choice before freezing, or add a sweet mix-in, such as cookies or chocolate.

Cherry-vanilla yogurt

Yogurt (or cottage cheese) and fruit is always a great combo. One of my favorites is this simple recipe:

Cherry-vanilla frozen yogurt:

3/4 cup dark sweet frozen cherries

1 cup Greek vanilla yogurt

Blend first and then freeze for 24 hours. Spin it on the Frozen Yogurt cycle. If desired, add more cherries for the Mix-in cycle.

I rarely actually blend mine first, but it's probably better for your machine if you do. This is a favorite I make over and over, and it's simply delicious. Sometimes I add 1/4 tsp of almond extract before freezing for a heightened flavor. I also sometimes add more protein and texture by running a small handful of nuts in the Mix-in cycle.

Pear-vanilla yogurt

This one sounds a bit odd, but it's so good.

Pear-vanilla yogurt:

2 tubs of Greek vanilla yogurt

1 tub pears

No need to blend, just mix and freeze for 24 hours. Spin on the Frozen Yogurt cycle.

Canned fruit is very soft, which is why you don't need to blend it. You can use any fruit here, it doesn't have to be pears. They all work well. You can try different yogurt flavors, too. Canned pineapple with coconut yogurt is a tasty combo, for example. And if you don't have tubs like I used, you can just measure out a roughly 2:1 yogurt to fruit ratio to the max fill line.

Chocolate Cottage Cheese Ice Cream

I have tried many chocolate 'protein ice creams' and haven't yet hit on that perfect recipe. This one is pretty good, with great texture. Be sure to taste it before freezing, in case you want to add more sweetener. User a sugar-free sweetener if you want to cut calories. If you prefer whole foods, try honey, maple syrup, or agave syrup. You could even just use a few dates.

Chocolate Cottage Cheese Ice Cream:

1 cup cottage cheese

3 tbsp cacao powder

2-3 tbsp sweetener of choice

2 tbsp almond butter

1 tsp vanilla extract

Blend well and freeze for 24 hours. Spin on the Light Ice Cream setting.

Add nuts, cookies, and/or chocolate on the Mix-in cycle if desired. I mixed in a few chocolate chips. If the cottage cheese is too salty for you, or you just don't like the taste, you could use milk or Greek yogurt instead. You can even do some combination of the three. The cottage cheese does give a nice texture, but you don't need the full cup to get it. Even a quarter cup helps.

And more

I have several high protein recipes in my recent article, healthier recipes for your Ninja Creami, including a pistachio ice cream recipe I particularly like and a banana nut ice cream I eat quite regularly. I also have some high protein options in my article about eating ice cream for breakfast!

I know my recipes tend to be dairy-forward, because that's what I like, but there are other options out there. I've seen bean-based and tofu-based recipes, but haven't had the guts to try them.

There are other additions to you can blend into any favorite recipe, such as hemp seeds, chia seeds, and flax seeds. Just a tablespoon added to any recipe will increase the protein and other nutrients without doing much of anything to the flavor of your ice cream.

The AI buildout has not just captured the attention of the biggest technology companies in the world, but also the global superpowers, including the US and China in particular. The growth of this technology, which is still nascent, is seen as not just a competitive advantage for domestic businesses, but a fundamental development that can be harnessed to dominate the world stage in the future.

AI prescience

The Russian president, Vladimir Putin, was speaking to Russian students shortly after Google scientists published their seminal study that outlined the transformer architecture that defines modern AI systems.

His comments came in the context of national security and the balance of geopolitical power, with many world leaders long understanding that AI would be at the heart of not just commercial technologies but military capabilities.

His thoughts also balanced the notion of benefits that may arise with the threats, both by humans wielding AI tools as well as from the AI systems themselves. Elon Musk, for example, had previously highlighted the idea that AI could lead to a third world war.

Modern warfare

It's become increasingly clear just how prominent the role of AI and autonomous weapons is playing in international conflicts nearly ten years on, including in the Russian invasion of Ukraine in 2022 — and the battles that have ensued since.

Geopolitically, both the US and China are also each defining their own AI strategy, with both nations desperately building out their capabilities in a way that ensures dominance while respecting and maintaining ongoing international trade and commerce.

Although the technology hasn't reached the point where one single country or superpower can dominate the era to come, the US has begun strategically restricting the release of AI models, like Mythos, on the basis that they're becoming too powerful to get into the wrong hands.

Marc Andreessen was a prominent figure in the early internet era, creating some of the earliest web browsers, including Netscape and Mosaic, which he authored. In recent years, he's weighed in on subjects including AI — but it was his comments on software that really summarised how technology evolved during the early 21st century.

Inside Intel

Andreessen was running his venture capital firm, Andreessen Horowitz, when he published his era-defining essay in the Wall Street Journal.

The essay highlighted the dramatic shifts underway across the economy, with Andreessen centering his argument on the observation that software companies were poised to take over longstanding industries via the massive shift among consumers to online and digital spaces.

One example he highlighted was a misstep by the book retailer Borders, which closed all its US stores in 2011. In 2001, it handed over its online business to Amazon because, he argued, it saw online book sales as "non-strategic and unimportant".

Well, as Andreessen put it, "oops." He then highlighted Amazon's role as a software company with a core engine capable of selling virtually everything online.

Dog eat dog

In the cut and thrust of business over hundreds of years, there have been wave after wave of revolution, with new technologies overhauling and refreshing the landscape. Many businesses have persisted by adapting and pivoting, but the rapid proliferation of software over the last 30 years is now threatening to give way to an entirely new era.

Andreessen highlighted that, in 2011, the stock market "actually hates technology", and there's an argument to make that the stock market has once again turned its back on some of the biggest software giants, with AI threatening to make them redundant.

A few years on, Jensen Huang, the CEO of Nvidia, even built on the idea that the Netscape co-founder set out 15 years ago by suggesting that "AI is going to eat software".

Google DeepMind chief Demis Hassabis has spent years convincing the world that artificial general intelligence will be one of humanity’s greatest achievements. His latest essay on X.com is no less gushing about AI in many ways:

“This is a pivotal moment in human history. Artificial General Intelligence (AGI), a system that exhibits all the cognitive capabilities the brain has, is probably only a few short years away,” he wrote. “When we look back on this time in the decades to come, I think we will realize we were standing in the foothills of the singularity.”

But there’s a tinge of nervousness to the purple prose that sets it apart from some of the similar essays he has produced. He actually calls for some form of regulation.

“Urgent action is needed to address risks that might arise as we get closer to AGI. We’ve already seen the challenges frontier models pose for cybersecurity, and other threats including nuclear and bio risks may soon emerge as capabilities continue to advance.”

Coming from one of the people leading the race, it is a striking admission that even the builders are starting to worry about where the road leads. The problem is that this has become the defining tone of the AI industry. Every few weeks another executive tells us that machines capable of transforming civilization are just around the corner, then immediately follows it with a warning that society needs to move much faster to prepare. It is a sensible message, and probably a necessary one, but it is also becoming increasingly surreal.

Excitement and fear

“AGI cannot be compared to standard technological breakthroughs, not even ones as consequential as the internet or mobile,” Hassabis wrote. “It is much more akin to the discovery of electricity or fire. If you stop to think about it, we’ve essentially found a way to make sand think. It’s miraculous.”

It’s a vivid way to illustrate how silicon chips begin with an abundant mineral, yet now power machines that can map proteins, compose music, and hold fluent conversations. The unsettling part is that the miracle is being pursued inside a commercial and geopolitical contest whose participants cannot agree on where the finish line is.

Hassabis is an optimist about what AGI could accomplish. He expects it to discover medicine, cleaner energy, and produce advanced materials that could loosen the limits imposed by scarcity. He describes its impact as ten times that of the Industrial Revolution at ten times the speed, which makes a five-year business plan look adorably quaint.

His warning is that the race is outpacing our understanding. Current models already pose cybersecurity problems, while biological and nuclear risks may become more serious as systems gain capabilities. More autonomous agents could learn to bypass safeguards, conceal their intentions, or improve themselves in ways their creators struggle to keep up with. OpenAI recently experienced this when one of its models escaped a sandbox and breached Hugging Face.

“Nobody in the world knows for sure what is going to happen from here, and even the experts disagree. When there is a large degree of uncertainty and the stakes are this high, proceeding with cautious optimism is the sensible and correct strategy.”

AI referee

Hassabis proposes a new US-led standards body. It would be funded largely by industry but overseen federally, staffed by elite technical experts and equipped to test advanced systems. Labs would voluntarily submit models for review up to 30 days before release. If the system proved effective, approval could become mandatory in the United States.

Evaluations would probe cybersecurity, biological threats, deception and attempts to evade guardrails. The body could update its tests, commission independent assessments and coordinate a slowdown if the danger became severe enough.

Pre-release testing makes more sense than waiting for millions of users to discover dangerous behavior by accident. Independent benchmarks would be harder for labs to train around, while shared standards could stop safety becoming a branding exercise. The difficult part is creating a watchdog independent enough to challenge the companies paying for it and legitimate enough to matter beyond America.

Hassabis knows technical safeguards will not settle what AGI means for employment, wealth, purpose or political power. Those questions cannot be delegated to engineers employed by firms with enormous financial stakes in the answers. Society must decide who benefits from abundance, who controls advanced systems and what happens if productivity rises much faster than wages. The public debate is trailing the technology by an uncomfortable distance.

“There is both huge excitement and uncertainty around AI, and both are warranted. But the future is not yet written, we must use this precious window before AGI arrives to shape this technology for the benefit of all humanity. What we collectively do now will determine how the next phase of civilization."

The advent of AI marks a new era in terms of how digital infrastructure is built, connected, and scaled. Each new generation of AI model demands exponentially greater GPU power, storage capacity, and interconnectivity.

As these workloads rise in complexity, the network responsible for moving data between GPU clusters and across data centers, also referred to as “scale-across”, faces mounting pressure.

Traditionally seen as a straightforward background utility, the transport layer is fast emerging as a strategic foundation for AI-driven infrastructures.

It is evolving from simply moving packets to intelligently orchestrating massive data flows with deterministic performance, low-latency, and seamless scalability.

AI-optimized data centers, built for training and deploying large-scale models, require dense GPU fabrics, vast storage, and, most importantly, high-bandwidth, ultra-low latency connectivity. As workloads increase, so too does the critical importance of Data Centre Interconnect (DCI) solutions.

The transport layer is no longer just a dumb pipe, but the backbone of the AI era, enabling real-time updates of AI LLMs (Large Language Models), continuous learning of those models, and the efficient movement of intelligence across a global digital landscape.

The demands placed on traditional networks by AI

AI training and inference pipelines generate overwhelming volumes of data. One large language model alone requires thousands of GPUs operating simultaneously, continually exchanging parameters, gradients, and checkpoints.

The outcome is a relentless demand for ultra-high-bandwidth, low-latency connectivity between clusters, whether across campus environments or regions, or out on the edge.

However, the traditional transport architectures developed to facilitate predictable enterprise or content delivery traffic buckle under the weight of emerging patterns.

East-west traffic has surged, bandwidth per node has soared, and latency requirements have tightened to microsecond precision. Simultaneously, operators face mounting pressure to control power and space consumption at the metro and edge layers, primarily because AI workloads are currently most ubiquitous here.

Scaling optical systems is clearly inadequate. Therefore, we must look at re-engineering the entire transport layer to support performance, agility, and efficiency.

What does it take to design an AI-first transport layer?

Next-generation transport layers need to evolve beyond moving bits quickly and adapt to the fluctuating demands of AI workloads. In practice, this means delivering ultra-high capacity and consistently low latency, while enabling the network to reconfigure itself as AI tasks switch between training and inference. This ensures that the transport network is transformed into an ecosystem as flexible and intelligent as the workloads it supports.

In addition to bandwidth scaling to meet demands, latency determinism is equally important. Distributed AI training depends on precise synchronization across thousands of GPUs, given that even marginal timing variations can stymie performance. Transport systems must therefore ensure reliable and constant optical path behavior, beyond simply average low delay.

Automation and energy efficiency are also critical to creating an AI-first transport layer. When power becomes a key enabler of data center growth, an AI-ready transport layer needs to sense, adapt, and optimize in real time, allocating capacity where it’s most needed, and shutting down redundant channels, seamlessly integrating with orchestration layers above.

Engineering architectures of the future

Today, overcoming the challenge of developing an AI-first architecture requires a new approach. The next generation of transport systems must be built on coherent optical technology capable of scaling above 400G and 800G. These advances allow operators to extract maximum capacity from existing fiber infrastructure while maintaining the low-latency performance which is essential to distributed AI training and inference workloads.

The evolution of software-defined optical control is equally important. Embedding intelligence into the transport layer facilitates real-time telemetry, closed-loop automation, and predictive optimization. Networks can monitor performance, anticipate congestion before it occurs, and dynamically re-route traffic to preserve stability and efficiency. Effectively, the optical layer evolves to be self-aware, adaptive, and capable of responding to changing workload demands.

IP-optical convergence is another area of advancement, unifying packet and optical transport under a unified control and management framework. This convergence reduces latency, rationalizes operations, and accelerates service provisioning. The advantages for hyperscale DCI environments include fewer network elements, reduced complexity and a more deterministic performance profile.

At the metro and edge levels, where space and power constraints are most stringent, compact modular DCI systems are emerging as a critical component of the AI ecosystem. Such platforms introduce high-capacity optical connectivity closer to compute resources, enabling real-time inference, analytics, and automation at the edge. Augmenting optical performance beyond the hyperscale core helps operators support increasingly distributed AI workloads and shorten the distance between data-generation and decision-making.

Such transformations are also supported by greater automation and more open orchestration frameworks. Modern transport networks are evolving into API-rich, software-driven environments that allow telemetry data from the optical layer to feed directly into higher-level AI and cloud management systems. This creates a feedback loop between application and infrastructure that, in practice, enables an adaptive network: one that learns, optimizes, and responds dynamically to workload patterns in real time.

5G + AI: the formula for success

The convergence of AI and 5G represents a new milestone in network evolution. As operators accelerate the deployment of 5G Standalone cores and distributed edge computing, the demand for high-bandwidth, deterministic transport now extends beyond data centers to cell sites, aggregation hubs, and metro edges.

AI is increasingly used to automate and optimize 5G operations, from traffic prediction and spectrum management to self-healing and real-time orchestration. These capabilities rely on the same transport tenets that power AI training: massive data movement, ultra-low latency, and intelligent routing.

As AI and 5G ecosystems grow in sophistication, they’re forming a unified, intelligent network infrastructure that seamlessly connects core clouds, AI clusters, and edge nodes. And today, optical transport systems that move terabits of training data between data centers will be able to support time-sensitive control traffic and AI-driven services at the edge in the near future.

Reimagining transport for the era of AI

AI is having a profound effect on all layers of digital infrastructure, but nowhere is this more evident than in transport. The optical layer is evolving from a passive conduit into an intelligent, agile system that anticipates demand, reconfigures dynamically, and continuously optimizes for performance.

This transformation is being propelled by advances in optics, automation, and converged architectures. These elements combined enable self-optimizing networks capable of sustaining AI and data-intensive services at a global scale.

Fundamentally, to meet the expectations of an AI-first world, transport networks must evolve into intelligent systems that are as dynamic and responsive as the workloads they carry. By rearchitecting the optical layer with agility, automation, and convergence at its core, the industry can lay the foundation for scalable, distributed intelligence across the global digital landscape.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

Despite the noise around frontier AI models, most of the non-tech economy still runs on spreadsheets, manual lookups, and legacy systems.

This is even more so the case in automotive repair, where the gap between what AI promises and what it actually delivers costs the industry real money.

The global auto repair market is worth over $1 trillion but the US alone accounts for more than $180 billion of that annually, spread across more than 250,000 businesses, with no single operator holding more than 5% market share.

The average American vehicle is now 12.6 years old, the oldest fleet on record, meaning more frequent repairs, more complex parts, and more pressure on workshops already running on thin margins.

Since 2022, repair costs have risen by 25%, well above general inflation. Despite all of this, the industry still matches parts largely through manual catalogue lookups, experience, and guesswork. This results in wrong parts getting ordered, vehicles sitting in shops and workshops absorbing the cost of returns, rework and lost technician time.

That problem exists across every market, but the US is where it is most apparent; the market is enormous, deeply fragmented, and has operated without AI-native infrastructure designed specifically for it. That reflects a genuine technical challenge that general-purpose AI has not solved yet and cannot solve in its current form.

Why general-purpose AI models fall short

General-purpose models are built for optimizing language, reasoning and creativity, but not to be ruthlessly precise within one specific industry. In industries like automotive repair, tolerance for error is close to zero. For example, if a model recommends a headlight that isn't the right fit, it holds up the technician repairing the car, delays the customer and costs the insurer paying for the repair considerably more.

When you benchmark leading general-purpose models against domain-specific automotive parts tasks, the results are stark. The best general models achieve around 5% precision on parts identification benchmarks. A well-trained human does considerably better, but is constrained by memory, catalogue complexity and time.

A model purpose-built for this problem, trained on proprietary original equipment manufacturer (OEM) data, normalized catalogue schemas, fitment rules and live transactional feedback, achieves over 90% precision on the same benchmarks - not just a marginal improvement but a different category of capability.

The data and architecture problem

There are structural reasons why the gap is that large. Automotive parts and repair data is not a larger version of generic text, it is a constantly evolving graph of relationships:

VINs, trims, sub-models, region-specific variants, supersessions, aftermarket substitutions and workshop-specific preferences. Much of this lives in fragmented OEM catalogues and proprietary formats not available on the open web.

The architecture required is different too; a parts decision in a real workshop needs to account for vehicle history, insurer regulations, supplier inventory, contractual pricing and technician preferences simultaneously, requiring a model built to ingest structured catalogues and enforce hard constraints, not one optimized for broad conversational usefulness.

Building the infrastructure that makes scale possible

Building a domain-specific foundation model is not a matter of fine-tuning a general base as the dataset has to be assembled through years of OEM agreements, schema normalization and continuous integration of repair, claims and inventory data.

The model needs to be wired into the systems that run the business, so that every accepted recommendation, return and job outcome feeds back in and compounds accuracy over time. The competitive advantage builds over time through the combination of proprietary data, deep integration and continuous learning. The longer the model runs, the better it becomes.

For the US market, this matters more than anywhere else; the scale of the opportunity, 250,000 repairers, a fleet getting older every year, costs rising faster than inflation, means the compounding value of getting parts right the first time is enormous.

The markets where this technology has been deployed in Europe and Asia-Pacific have demonstrated that the productivity gains are real and measurable. The US has, until now, had no equivalent infrastructure to access them.

What buyers and investors should be asking

For technology buyers in complex verticals, the right question to ask of any AI vendor is not how capable their model is in general, but how it performs on the specific failure modes that cost your industry money. In automotive repair, that means first-time-right rates, return rates and cycle time compression.

The same logic applies to investors: the size of a model is not a measure of how hard a business is to displace. The relevant questions are how hard the dataset is to assemble, how embedded the model is in operational workflows and how general the reasoning ability is within the domain.

General-purpose models will continue to improve and will remain valuable for a wide range of tasks. But the deepest, most durable value in AI will be created where models stop being generic assistants and start becoming invisible infrastructure, optimized for one hard problem at a time.

In automotive repair, that means fixing the parts problem that has taxed the industry for decades. In the US in particular, where scale amplifies both the cost of the problem and the value of solving it, that shift is overdue.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

In 2025 alone, US private AI investment reached $285.9 billion, backing nearly 2,000 newly funded AI companies in a single year. That capital fueled the first era of AI. The harder question now is where organizations actually run these workloads, and under whose legal jurisdiction. The answer to that question is redrawing the global cloud map.

Rather than raw silicon, the next era of AI success is now being determined by which infrastructures can support it best. The markets moving the fastest today aren't necessarily the largest or the wealthiest economies; they are the ones treating AI infrastructure as a mission-critical utility and dismantling the barriers to its deployment.

Regulation and the rise of alternative clouds

Like AI itself, the battle to define the next generation is constantly shifting in approach to combat new challenges and requirements. Whilst the path might once have been to build a centralized mega data-center that you use to serve your global users, shifting regulation and desires for true data sovereignty have, and are, changing that.

As governments scramble to regulate AI development without stifling it, some regions have managed to entangle data center development in years of energy and administrative gridlock. The regions moving faster are treating infrastructure permissions as a competitive asset, enabling neoclouds and alternative cloud providers to build, scale, and operate data centers at a pace the giants will struggle to match.

These alternative networks are already winning enterprise contracts that hyperscalers cannot touch, not because of price, but because of where the data sits and who can legally access it. They are doing so by meeting developers exactly where they are, in environments unaffected by the legacy architecture of traditional hyperscalers.

The compliance problem and "geo-repatriation"

The crux of the problem is jurisdiction. The regions getting ahead are those where operations are not stifled by regulatory gridlock. While the US have led the AI race since its eruption, this very progress is what may now be fueling the regulatory hole that some hyperscalers now find themselves in.

The impending deadlines of the EU AI Act, which have been recently adjusted, and similar global mandates, are triggering a wave of “geo-repatriation”, as organizations realize that housing AI workloads on centralized, US-governed clouds is becoming a compliance liability.

For enterprises deploying high-risk systems, compliance requires auditable data governance and human oversight mechanisms. In the EU, the legal and financial damage of non-compliance can trigger fines of up to €35 million or 7% of a company’s global annual turnover.

Faced with these penalties, organizations are realizing that housing AI workloads on centralized, US-governed clouds is a compliance liability. Under the 2018 US Cloud Act, US-based hyperscalers can be compelled to provide US authorities with data stored on their servers, no matter where that data physically resides.

To help handle this friction, enterprises are actively undergoing "geo-repatriation", which is seeing companies move data off US-centric public clouds and transition to region-isolated infrastructure. To avoid regulatory penalties, the models of tomorrow must be trained and deployed on localized networks that offer absolute sovereignty within the borders that they serve.

The great public cloud exodus

As proof of this regulatory pressure, 86% of Chief Information Officers are currently actively planning to migrate at least some workloads away from traditional public clouds. Many are finding the advantages of alternative cloud networks extend well beyond compliance.

The reality is that the legacy hyperscaler architecture was designed for a completely different function than what enterprises now need. As AI usage has developed and increased globally, the regulation and demands of networks have also shifted to match.

For enterprises that are completing complex and constant AI tasks, such as training a Large Language Model, issues can arise when data is bottlenecked by virtual layers and remote servers. To optimize these workloads, enterprises are moving away from traditional public clouds towards alternative cloud providers, who can offer distinct advantages in addition to regulatory compliance:

Bare-metal performance - Traditional, non-regionalized public clouds that run workloads through a hypervisor are costing enterprises performance, speed and money. Alternative cloud providers are offering direct access to bare-metal infrastructure, which, for compute-heavy AI training and inference, has upside over traditional networks.

Decentralized locales - Legacy cloud giants route data through massive regional hubs, resulting in high latency for global users. Alternative cloud networks are deploying agile, high-density data centers in localized regional markets worldwide. This allows enterprises to process data precisely where it is generated, satisfying data-sovereignty mandates and delivering a better experience for users.

Winning the next era of AI

The hyperscalers were built for a world where data could move freely across borders without legal consequence. That world no longer exists. For organizations operating in regulated sectors such as healthcare and finance, contractual promises of security are not sufficient. Data sovereignty must be built into the physical infrastructure, not written into a service agreement.

The next decade of AI infrastructure will be won by providers who built for sovereignty first. Enterprises that recognize this and act before compliance deadlines force their hand will hold a structural advantage over those that do not.

The global AI map is being redrawn. Alternative, sovereign cloud networks built for the realities of modern AI are at the center of that shift, offering enterprises something the hyperscalers cannot: genuine, jurisdictionally enforced control over where their data lives and who can reach it.

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The AI fever of the last few years has stalled slightly, despite companies investing mind-boggling sums in tools and infrastructure.

But the problem is not with technology itself. Many companies are focused on experimentation and expansion, with only 9% of UK organizations reaching augmentation stage, according to ServiceNow’s most recent AI Maturity Index research.

The pattern is all too often the same: after an initial wave of experimentation, companies find that early enthusiasm fails to translate into sustained usage.

For example, employees stop using AI assistants, perhaps out of fear of ‘augmenting themselves out of work’.

It’s clear that the problem here is not that AI systems do not work: it’s that the cultural foundations needed to support them are lacking. The problem of finding real value from AI is often a cultural one.

This requires careful thought to build creativity back into people’s work lives, so they can re-engineer legacy processes to take control and truly reap the benefits of AI.

By doing so, they set the stage for a whole new way of working, and a new way to grow businesses.

Losing control

A key issue is that when employees feel like they are losing control over how they work, they tend to disengage. If employees feel like they are training their replacements, or losing any sense of agency they once had, they tend to push back or revert to older processes.

This is seen most clearly in organizations that have invested heavily in AI platforms: teams try the tools, then revert to familiar processes, manual workarounds, and even back to spreadsheets. This is a cultural problem.

People’s concerns over job erosion and loss of autonomy are real, and dealing with this requires trust, new skills, and redesigned workflows. Teams must be empowered to shift to new growth-focused ways of working.

Employees need to engage creative muscles in a way they may never have before, one which previous workflows and employment structures often barred from them.

Shifting mindset

This mindset shift needs to start at the top. All too often, CEOs still see AI as a ‘magic pixie dust’ which can be used for one reason: to cut costs. They are marooned in a post-COVID protectionist mindset, and the only thing that matters is shrinking the cost base.

What needs to happen is a shift to a growth mindset, using the economies of AI to find new approaches to the market. Rather than cutting costs or getting rid of people, organizations need to reshape how teams work. Bosses should identify new sets of use cases and go after bigger targets.

Rather than allowing people to make themselves redundant, they should retrain and invest in those people, pivoting that human capital to something different and more valuable. Your people know your business: you should invest in them.

That retraining cannot be treated as a one-off exercise. Organizations need to create structured pathways for continuous learning and AI skill development, helping employees build confidence and adapt alongside technology. Employees need personalized learning and hands-on experience that helps them move from experimentation to real-world application.

A new mindset

What is required to succeed with AI is a radically different mindset, focused on growth and the customer. This change needs to come from the board level, from the Chief Information Officer (CIO), Chief Digital Officer (CDO), or Chief Technology Officer (CTO).

Organizations need to move employees from doing one specific task to a new focus on growth and move beyond simple digital transformation to something bigger.

Culture and change management, rather than technology alone, will be what dictates the winners of the next phase of AI.

Leaders who have the foresight and vision to upskill their employees and drive towards new ways of working will see huge productivity gains and will position their companies for future success.

Culture, not technology

The solution to stalling AI, or a failure to find value from the technology is not more AI, bigger models and more pilots. It is a cultural solution, based on the best asset you already have: your people.

That means superpowering existing teams, not replacing them, and finding new business models. This requires upskilling staff, rebuilding workflows and engaged leadership that actively encourages new ways of working.

To build companies fit for the future, culture and change management are just as important as AI licenses.

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Not every AI workload belongs in the same place. Large language models often fit logically in the cloud because they can serve as general-purpose engines that improve with scale and draw value from broad, up-to-date knowledge.

But the AI workloads moving into production are not just text and language, and we’re increasingly seeing enterprises adopt multimodal models for AI video, audio, and image generation and seeing massive advantages across compute usage, control of IP, and ability to customize the look and feel of creative output.

For these types of creative production, the raw material they’re using to build is not the open web. Instead, it’s often footage, branded assets, or unreleased IP that already lives within the organization’s walls. In these instances, there’s a clear need for running the models closer to where that content already resides.

That’s because creative production is iterative by nature, and that volume of iteration and generation brings with it real cost pressures when drawing on the cloud.

As a founder, I’ve watched this transition play out repeatedly: companies adopt AI pilots, usage skyrockets, and suddenly finance teams are trying to understand which teams, workflows, or model calls are driving up the bill.

Cost predictability becomes an infrastructure question

Once AI tools become part of daily work, usage no longer behaves like an experiment. Every generation, agent action, video render, or workflow step carries a cost. The equation becomes much harder to forecast once adoption spreads across teams and AI agents.

For companies with high-volume creative workloads, running more of their inference locally, at the edge, or in private environments gives greater control over unit economics and makes AI spending easier to manage over time.

This is particularly important in creative production environments, like filmmaking and gaming, all the way to marketing campaign creation and internal training, where teams often generate dozens of variations of an asset, sequence, campaign concept, or interface.

In an environment where a single workflow can generate thousands of API calls per day, the difference between cloud and local inference can determine whether an AI strategy is sustainable or requires constant budget justification.

Data control will shape deployment choices

Long-term, data control has potential to be a primary driver for enterprises to move toward more flexible AI architectures. Businesses have become increasingly sensitive about where and how their information is stored, how long it stays there, who has access to it, and how it can be used.

Those questions become more serious when AI is mapping physical environments, working with unreleased creative assets, production files, or other material that was never meant to move freely outside controlled systems.

When it comes to AI video generation, which can involve multiple iterations on sensitive creative assets and IP, teams may prefer to run their models within their own environments. In these cases, local or private deployments are less about rejecting the cloud and more about giving companies a way to use AI without handing over access to sensitive information.

As AI becomes more embedded in business-critical work, these choices will involve more than IT architecture because they affect what a company can build, what risks it takes on, and how much control it keeps over the systems producing its work.

The future is optionality, not a single deployment model

The cloud has proven to be essential for many AI workloads, especially when companies need elastic compute, access to frontier models, or the ability to support highly variable demand.

A more realistic future is one in which enterprise AI becomes hybrid by necessity, with different workloads running in different environments based on the needs of the business rather than the convenience of a single deployment model.

Some workloads will run in the cloud because scale matters most, while others will run locally because latency, interactivity, and iteration matter more, and still others will run on-prem to prioritize privacy, compliance, customization, or ownership.

The organizations that prepare for this transition will be the ones that stop treating deployment as a binary choice and start asking which workloads require which level of control.

This pressure only intensifies when we consider where creative production is heading. The same models that teams use to generate video are now evolving into world models: systems that can predict and simulate the physical world, moment to moment, in real time.

Workloads like these will be defined by interactivity and latency, and a generation that waits on a round trip from the cloud and back won’t be able to cut it.

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Before COVID-19, fraudsters largely operated in line with the standard workweek, active between 9 am – 5 pm and tapering off at the weekends. Then, during the pandemic, fraud started to spike at off-peak times, including late at night and on weekends.

This suggests that fraudsters started to intentionally target businesses when staff weren’t manning the systems, or that they started viewing it as more of an opportunistic or recreational activity.

Today, the pattern has been upended again. In the ever-increasing digital era, AI-assisted tools are not only automating processes for criminals, enabling them to scale operations quickly, but they are also making fraud cheaper and harder to detect. Fraud has now evolved into an ‘always-on’ threat that flows like water, seeking cracks to exploit.

Yet, the latest AI attack vectors have also become somewhat of a distraction for businesses. Simpler, low-tech methods still persist but have become a blind spot in many companies’ security.

The focus on solving the high-tech issue has drawn attention away from the low-tech issue, creating a renewed vulnerability to rudimentary attacks. The key is learning to combat AI-powered identity-based attacks, while also tightening loopholes that are enabling rudimentary fraud attempts to slip through the cracks.

Ultimately, that means implementing a broad set of layers – combining identity verification, biometric authentication, liveness detection, and so on – to catch the different spectrums of attack.

The new face of fraud

Modern fraud is coordinated and intentionally mimics legitimate users and devices to evade detection.

Fraud rings now use automation and device emulation to run high-volume attacks. But rather than launching hundreds of fraudulent applications at once, attackers are submitting small batches over time, enabling them to disappear before issues are identified.

We’re also seeing slight spikes in fraudulent activity from 2-4 am. This suggests deliberate coordination, where attackers are exploiting times when both users and security teams are least likely to respond quickly. This creates a larger window to abuse compromised identities before detection and remediation occur.

Fraudsters are also increasingly exploiting human behavior. There’s been an upward trend in phishing, deepfake impersonations, and romance scams, with each tactic targeting a different vulnerability, whether that’s trust, emotional attachment, or even fatigue. Thames Valley Police has warned that romance fraudsters deliberately keep victims talking late into the night, using exhaustion to erode judgment.

However, businesses have been slow to adapt to the new face of fraud. Because modern users demand speed and simplicity, companies continue to drive UX changes that reduce friction during critical times, like onboarding.

But in many cases, this has become an Achilles heel. Too many organizations are optimized for minimizing user friction without balancing that with continuous protection. They set automated controls which alone are no match for fraudsters who are exploiting human behavior and deliberately operating below detection thresholds.

Two security changes are having a big impact in bolstering defenses against these attacks. First, more adaptive security approaches that detect social engineering signals through behavioral and contextual cues, for example. Second, inserting more varied and unpredictable identity checks that make it significantly harder for attackers to rely on automated or scripted tactics.

Proactive approaches, such as red teaming or fraud simulation exercises, can also help organizations identify weaknesses before they are exploited.

AI has made fraud cheaper, not just smarter

As far back as 2019, AI was used to mimic a CEO’s voice for financial exploitation. Deepfake AI technology has come a long way since then. Injection attacks, where manipulated or synthetic media is fed directly into systems to bypass the camera, surged by 40% in 2025 compared to 2024, and deepfakes now account for one in every five biometric fraud attempts.

That’s because AI has helped to commoditize fraud at a massive scale. AI-assisted tools have lowered the barrier to entry, making it available to anyone with access to a laptop and a credit card.

Fraud-as-a-service platforms sell ready-made kits, credential dumps, deepfakes, and stolen data online. This is one reason why digital forgeries, often created using open-source models, now make up 35% of all fraudulent document submissions.

As these techniques become standardized and widely available, static identity checks (like selfies) are no longer sufficient. An identity that appears legitimate at onboarding can be compromised later on, requiring organizations to continuously verify trust rather than treating identity as a one-time event.

Yet low-tech methods still find success

Despite the increased volume of AI-powered threats, the most common points of entry remain startlingly simple. Many fraudsters still rely on remarkably low-tech methods. For example, in selfie-based identity verification, 90% of attacks involve basic presentation methods, such as using someone else’s photo on a screen, an image of an ID, or showing a printed copy to the camera.

Organizations that focus exclusively on advanced techniques leave themselves exposed to the most basic ones.

By requiring users to move in a specific, unplanned way, systems can distinguish real individuals from 2D images, masks, injected media such as deepfakes, or simple spoofing attempts such as a video of a video or a photo. Partnered with behavioral analysis and real-time risk signals, these tactics provide a strong countermeasure.

As fraud becomes distributed and industrialized, the organizations best positioned to respond will be those that pursue an identity-centric security approach. We need to stop viewing identity checks as a one-and-done moment and start seeing it as an ongoing process throughout the customer journey.

The goal is to make identity security invisible to legitimate users and unavoidable for fraudsters. In a world where fraud no longer sleeps, security strategies can’t afford to either.

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Accenture's decision to expand its fiscal 2026 share repurchase program by $2 billion, bringing the full-year total to $7.5 billion, comes with a clear statement from CEO Julie Sweet: the current share price does not reflect where Accenture stands on AI-driven reinvention, nor the underlying strength of the business.

With year-to-date shareholder returns already at $8.2 billion and total planned returns for the year heading towards $11.5 billion, the numbers speak for themselves.

The press commentary surrounding Accenture's share price decline has not been fully grounded in what Accenture actually does, and it is having a direct effect. A single poorly-reasoned piece gets picked up, simplified, and recirculated across news aggregators, social channels, and even some analyst notes, each iteration stripping away a little more nuance.

Brokerage downgrades follow. Ironically, AI is making this worse, not better. The same shallow take reproduced at scale, with no one checking it against the underlying reality.

The narrative that has taken hold across the press, financial and otherwise, is that AI has rendered consulting obsolete, that chatbots can now produce what partners charged hundreds of pounds an hour to deliver, and that firms like Accenture are watching their reason to exist disappear. There is a version of this argument with some merit. There is also a version that is sloppy analysis, and we are increasingly finding the two conflated.

A misunderstanding of Accenture

Accenture is not a strategic advisory firm. It is not primarily in the business of producing slide-deck output that a large language model can approximate. It is an organization of 800,000 people that integrates and implements complex technology programs at enterprise scale, often in mission-critical systems, regulated environments, and legacy estates that predate modern software architecture.

Recent analysis reveals just 11% of Accenture's UK revenues come from consulting, much of which will be technical advisory rather than strategic advice. The remaining 89% sits in solutions – building things – and operations – running them.

Treating it as interchangeable with the Big 4 or the strategy houses like McKinsey and Booz Allen reflects a fundamental misunderstanding of the business. Lumping them all together because they are sometimes called consultants is roughly as useful as lumping together a GP and a neurosurgeon because they both work in healthcare.

The reality of AI adoption

Press commentary has also made some odd claims about what AI can and cannot do. Some pieces have argued AI is useless for creative work like advertising copy, which is simply not true, while simultaneously suggesting it can replace the kind of deep organizational understanding required to tell a large, complex enterprise something useful about its own situation. It is closer to the other way round.

This is important when it comes to the agentic AI picture, which is where the real story sits. Most agentic work remains narrow in scope and is still working its way from pilot to production. UK businesses are adopting AI faster than they can embed it, and the gap between enthusiasm and operational reality remains wide.

As that closes over the next few years, the organizations best placed to benefit are those with the integration capability, the regulated sector relationships, and the enterprise delivery track record to move programs from experimentation to operational reality at scale. Our analysis places Accenture ahead of its peers on exactly those measures.

There is also a human dimension to this that the press commentary has largely ignored. The biggest brake on AI adoption at enterprise scale is not technology; it is people. Fear of becoming obsolete, sometimes referred to as FOBO, is shaping how workforces engage with AI tools, how quickly organizations can genuinely embed new ways of working, and ultimately how fast clients can realize value from their investments.

Change management at that scale is complex, and it is exactly the kind of work that Accenture has built deep capability in over many years. As agentic AI moves from pilot to production, getting people through the transition is as hard as the technology itself.

Getting the message right

None of this means the business is without pressure. Like every major IT services firm, Accenture is reworking how it structures and prices its services in a world where AI productivity changes delivery economics. The margin trajectory on AI work will need to prove out over time.

But there is a meaningful difference between a business reinventing its delivery model to embed AI and one whose work is evaporating because AI has replaced it. The current press narrative has not been careful about that distinction.

There is an irony in this that Accenture might want to sit with. It spends significantly on marketing and is generally considered good at it. Yet the dominant narrative about what it does and why AI threatens it has been allowed to take hold largely unchallenged.

Communicating what its role looks like in an AI-driven market is arguably as pressing as any of the operational changes it is making. The Reinvention Services branding is a start, but the message has not cut through in the way that matters most right now: with investors and the financial press.

The broader issue here extends beyond Accenture. We are in a period where the volume of commentary on AI and its impact on the tech sector has vastly outpaced the quality of analysis behind it.

Working out what is actually happening, as opposed to what the latest round of recirculated opinion says is happening, requires understanding the technology, understanding the services market, and understanding how large organizations change in practice. The firms and investors that base decisions on the current press narrative rather than the underlying reality are the ones most likely to get this wrong.

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As football fans across the globe tuned in to the world's largest football tournament, they saw teams compete on the sport's biggest stage.

What they didn't see were the countless decisions being made behind the scenes, which are now increasingly being informed by data and artificial intelligence.

Today's elite teams have access to an unprecedented range of tools. Real-time player tracking, opponent analysis, injury-risk assessment, and performance insights have become part of the modern game.

Coaches and analysts can now draw from vast amounts of information to help prepare for opponents, evaluate player performance, and inform decisions before and during matches.

Yet as these capabilities become more widely available, technology itself is becoming less of a competitive advantage.

The more pressing question is not who has access to data, but who can truly harness that information to make better decisions under pressure.

Access Is No Longer the Advantage

This challenge is not unique to football. Across industries, businesses are doubling down on artificial intelligence and advanced analytics. Many have access to similar technologies, similar datasets, and similar capabilities. Yet outcomes often vary dramatically. The organizations that consistently outperform their peers are often the ones that can make better decisions faster.

Football provides a compelling example because the margins between success and failure are so small. At the highest levels of competition, a single decision can influence the outcome of a match. A tactical adjustment at halftime, a substitution or a change in formation can make the difference between advancing and going home.

Data can help identify patterns, opportunities, and risks, while AI can surface insights more quickly than ever before. But insights alone do not drive outcomes. Success depends on the ability to separate meaningful signals from noise, make decisions with confidence, and act decisively.

The frontier of sports analytics is moving away from passive data collection and toward active, intelligent synthesis. The goal today isn't to accumulate more data points, but to eliminate the friction between a raw data silo and an executive or coaching decision.

Moving from Analysis to Co-Innovation

We are seeing this shift from pure data tracking to active AI integration play out on the pitch right now. For instance, we’re seeing teams actively piloting generative AI solutions to revolutionize match analysis.

By utilizing AI to instantly synthesize complex match footage, scouting data, and player metrics, coaching staff can cut down on hours of manual video review. This allows sports analysts to deliver highly tailored, digestible tactical insights directly to players exactly when it matters most.

Importantly, this is not an isolated experiment limited to a handful of elite franchises; it is rapidly becoming the new operational standard across professional sports. While football is driving much of the innovation, we're seeing the same AI-powered approach gain traction across our sports customers worldwide.

From ice hockey and basketball to handball and beyond, organizations are embracing data and AI not just as analytical tools, but as collaborative partners in decision-making, demonstrating that this model is both scalable and largely sport-agnostic.

Why Insights Alone Aren't Enough

Too often, though, conversations about AI narrowly focus on the tools. Companies ask themselves which platform to adopt, what capabilities to implement or how quickly they can deploy new technologies. While those questions are important, they can overshadow a more fundamental challenge. Organizations must build the culture and processes necessary to transform insights into action.

In football, successful teams recognize that collecting data is only the first step. They create environments where coaches, analysts, medical staff, and players can work from a shared understanding of performance and objectives. Information flows across teams, insights are discussed and challenged, and decisions are made with both data and human expertise in mind.

The same principle applies in business. Data often remains siloed across departments, making it difficult to establish a shared understanding of corporate priorities. Even when valuable information is available, companies can struggle to align stakeholders around what actions to take and when to take them.

Decision-Making Culture Matters

As AI continues to evolve, these challenges may become even more significant. The ability to generate insights is becoming increasingly democratized. Capabilities that were once limited to a select group of organizations are now accessible to many. As a result, competitive advantage is shifting away from access and toward execution.

The business that stands out are often those that can make decisions with confidence and adapt quickly when circumstances change. Whether on the field or in the boardroom, leaders often have to make decisions before they have all the answers, balancing risk, opportunity, and competing priorities. Technology can support that process, but it cannot replace human judgment.

The teams that foster curiosity, encourage cross-functional collaboration, and empower employees to act on insights are often better positioned to realize the value of their technology investments. They create environments where data becomes a catalyst for action.

Lessons Beyond the Field

As the tournament captured the attention of fans around the world, it also served as a reminder that success is rarely determined by technology alone. Talent, preparation, and execution still matter. So does the ability to learn, adapt and make informed decisions under pressure.

As AI becomes increasingly accessible, the question is no longer who has the technology. The question is who can use it to make better decisions. Whether on the field or in the boardroom, that may be the competitive advantage that matters most.

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The Garmin Cirqa is a strong new addition to Garmin's ecosystem of devices. It's a screenless fitness tracker like a Whoop band, working with the Garmin Connect app to track sleep, workouts, and health metrics. You can read all about what happened when I tested the Garmin Cirqa against a heart rate monitor during its UK launch event here.

One under-the-radar announcement most people have glossed over is that even though most features are subscription-free, you need a Garmin Connect+ subscription to access Garmin Coach. This is Garmin's digital coaching feature, offering personalized training plans for its users to help achieve their strength, running, or cycling goals.

The reason this is important is that it's the first time Garmin Coach has been locked behind a paywall. It used to be that more expensive watches came with Garmin Coach features, while less expensive watches didn't. That was it. This latest development means we could see future budget-friendly Garmin offerings, such as the successor to the Garmin Forerunner 70, get Garmin Coach capabilities but keep them locked behind Connect+ in order to keep costs down.

While this is very much in line with how Fitbit and Apple run things on their fitness apps, it's a big change for Garmin and one that could rile existing users. We've seen how the Garmin community reacted to Connect+ when it was first introduced last year — negatively, with many people fearing new watch features would be locked behind a paywall, as ongoing subscriptions are very profitable for tech companies.

At least it's still substantially better value than Whoop's pay-annually mandatory subscription model.

Garmin acquires TrainingPeaks and TrainHeroic

Garmin has acquired TrainingPeaks and its sibling platform TrainHeroic. TrainingPeaks is an incredibly popular cross-platform programming software where users can download and use fitness plans and communicate directly with coaches. It's available on lots of different devices, not just Garmin watches.

Naturally, many people are afraid this means TrainingPeaks will become a Garmin-exclusive piece of software, and anyone who currently uses, for example, an Apple Watch Ultra 3 will be left in the cold. Even those that use it on Garmins may fear another subscription will be required.

Our homes editor and ex-fitness editor Cat Ellis uses TrainingPeaks to train for her marathons. She said: "I use TrainingPeaks and a Garmin watch, so I’m interested in what the future will hold as the two become intertwined — but also concerned for owners of non-Garmin devices. It seems plausible that owners of devices from other brands — such as Polar, Suunto, and Amazfit — might be left out if Garmin folds TrainingPeaks plans into Garmin Connect+.

"I’m also curious how Garmin might integrate TrainingPeaks. Will there be a separate Connect+ membership tier that unlocks access to TrainingPeaks plans? What about plans from coaches that TrainingPeaks users currently pay for separately? It’s going to be an interesting time."

Watch our Garmin Cirqa hands-on on our TikTok below:

Cross-platform confirmation

TrainingPeaks & TrainHeroic MD Lee Gerakos told DC Rainmaker that "TrainingPeaks will continue to operate as a cross-platform ecosystem, enabling athletes and coaches to use the devices, platforms and services that best suit their needs."

TrainingPeaks users can breathe a sigh of relief, for now — but it's not hard to connect the dots. Garmin's most recent, highest-profile product puts its coaching features behind a paywall at the same time it announces the acquisition of one of the most popular third-party coaching platforms. Garmin could be aiming to improve its coaching platform to get more people to join Connect+.

This isn't a bad thing per se — much of the criticism levelled at Connect+ was that its offering was a bit thin — but the pressure will be on for Garmin to keep its existing users, along with existing TrainingPeaks and TrainHeroic users, happy with how the new coaching features are implemented.

Listen up, music fans: the best streaming service in the world* just got even better.

I'm talking, of course, about Qobuz — the French streamer that we consider to be the best music streaming service for high-res audio and which has just rolled out a bunch of updates that fix some of its biggest problems.

And, to be clear, it did have a few. While Qobuz is without question the best music streamer if you care more about audio quality than anything else (and are you really a music lover if you don't?), it has traditionally lagged behind the likes of Spotify when it comes to user experience and features.

This update brings a bunch of nice quality-of-life changes that make it a slicker, easier platform to use, as well as adding some sorely missed features that most of us have come to expect these days — for instance, lyrics.

Let's dig in.

* According to me, not TechRadar as a whole

Fixing a hole

There were two big things that jumped out to me when I switched from Spotify to Qobuz in late 2025.

One was the incredible leap in audio quality; oh my word, you have not lived until you've heard Wilco's Yankee Hotel Foxtrot in glorious 24-bit / 192kHz quality on a decent hi-fi.

The second was how bare bones the app experience was. Spotify is many things, and not all of them good, but its app is generally a joy to use. In contrast, the Qobuz interface felt distinctly lacking, as if so much effort had gone into making the music sound good that they hadn't spent much time making it look good too.

This new update changes that as follows:

1. The Qobuz player has a fresh new look

The first change is on the face of it a relatively superficial one: the player that displays when you're listening to music has been redesigned. However, I'd actually rate this as pretty consequential.

For instance, the player now changes color to match the artwork of whatever you're listening to. Who cares? Well, me. Immersion is a massive part of the music-listening experience, and opening the app to see that its background is of a similar hue to the album art helps to keep you in the mood. After all, if you're listening to Joy Division or The Cure, you don't want a bright yellow sunny screen to snap you out of your intentional gloom.

There's more to the overhaul than just the colors, though. The fact that the player stays visible during navigation is appreciated, while the split-screen tablet mode — which I haven't tried yet — promises to make multi-tasking easier still.

2. You now get lyrics!

This is a big one. I've been on the Qobuz Beta for several months and so have had access to lyrics on the app for a while — and frankly, I can't imagine (musical) life without them. After all, there's only so many times you can listen to Total Football by Parquet Courts before you really, really need to know whether he's genuinely saying something rude about Tom Brady at the end. (He is.)

It is fairly astonishing that it's taken this long for Qobuz to add lyrics to the service, admittedly, but better late than never eh?

The lyrics also scroll as the song is playing, with the current line highlighted, and there's even translation of foreign-language lyrics via single button press.

One caveat: your experience may differ depending on what you listen to. My tastes are geared more towards the relatively obscure alternative rock side of things, and some of my favorite tunes either don't have lyrics at all or don't have the fancy scrolling-and-highlighting-and-translating options. But hopefully the catalogue will expand over time.

3. There's a new Explore section

One of the big differences between Spotify and Qobuz is in how rarely the latter service tries to persuade you what to listen to. In many ways this is a plus point; for all of its supposed algorithmic expertise, Spotify never really knew me too well. And I definitely didn't need to be told that I should be listening to some high-energy party playlist just because it was a Friday, thank you very much.

Qobuz's new Explore section shows how it should be done. Simply click the icon when listening to any song and you'll be shown an excellent overview of related content: albums by the artist, playlists that feature them, magazine articles about them (yes, Qobuz has a built-in magazine), more from the same label, similar artists, similar releases, and genre classics.

It's a one-stop-shop for music you might well want to listen to next, and its contextual nature really lifts it above the competition and makes it far more useful than most.

4. You can easily go deeper

Want more than what the Explore section offers? You can go deeper still by clicking the Info icon. This already existed on Qobuz, serving up credits for the musicians on the track and the label behind it. But it's now been expanded to give easy access to an artist bio, label bio, info about the audio quality and the ability to follow artist and label.

5. The queue finally makes (some) sense

Qobuz's queue feature was definitely not its strongest point in the past, although many of the criticisms I've seen aimed at it are based entirely on the fact that it doesn't work exactly like Spotify's; not worse, it's just slightly different.

Anyway, it's another area that's received a freshening up here: it's easier to see what's coming up next, you can easily add or remove tracks from the queue with a swipe, you can reorder by dragging and dropping. Simple things, but things that make it slicker to use and so are welcome all the same.

What's next?

Qobuz probably still has some work to do on the user-experience front if it wants to convert more people from Spotify, Apple Music and Tidal. The desktop apps lack some of the enhanced features now found in the mobile versions, for instance, and I still have some major frustrations around playlists in particular — not least the 2,000 track limit that has broken some of my favorite collections.

But that's for the future. For now, I can rejoice in the fact that the gap between Qobuz's audio chops and its user experience has been sharply narrowed. If you love music I recommend you give your ears a gift and try it out.

The new Qobuz features are available now on the service's iOS and Android apps for mobile and tablet.

Intel is one of the most ubiquitous companies in the world, let alone the technology industry, with its technologies powering many of the computers most people have used for generations. Central to its success was a philosophy from its former CEO Andy Grove, who was a part of the company from day one and ran it from 1987 to 1998.

Inside Intel

Andy Grove was setting out the foundational theme for his management book 'Only the Paranoid Survive', which he wrote toward the latter years as CEO of Intel.

Grove, who is widely recognised as one of the most successful tech executives of the late 20th century, advocated strongly for putting paranoia at the heart of business. Worrying about various risks that can affect your business — from new competitors to macroeconomic shocks — means you can prepare for them.

But what's worse, as he described it, are 'strategic inflection points' that signal a massive shift in fundamentals that could either lead to staggering growth or a grave decline by an order of magnitude. It's fair to say, in the years since he left Intel, that the company has endured its ups and downs, putting it mildly.

Sliding doors

Although Intel enjoyed incredible success in the 20th century and into the early 2000s, the company stuttered in recent years with rival chipmakers including Nvidia and AMD gaining ground and eating into its market share.

Ironically, from the outside, it appears that the company had succumbed to the sort of complacency that its former CEO had been warning against.

That said, the company is in the midst of a remarkable comeback under the leadership of new CEO Lip-Bu Tan, who has overseen a dramatic surge in the company's stock price that underlines its improved overall outlook as well as new ventures to support the AI buildout.

Having just watched the Samsung Unpacked event, I think the Korean company has just fixed every major problem that existed with foldable phones. They’re now thinner, wider and more normal-looking, and yet even at the point where most of the kinks have been ironed out… I still find them a bit uncool.

There’s still something about unfolding a phone in 2026 that feels unnecessary and a bit like a throwback from the 90s. It takes something that has been refined into a seamless sheet of glass and puts a hinge back into the middle of it.

Sure, retro is cool, but foldable phones still feel stuck between two worlds to me.

Cheaper in Korea

Foldable phones are also still pretty expensive — I wasn’t the only one in the office raising an eyebrow at the Z Fold 8 Ultra’s eye-watering $2000+ price when it was announced— although at this point I’m tempted to blame AI for making everything more expensive, even if it's up to 48% cheaper in Korea.

The new foldable phones look amazing, but it’s taken Samsung a while to get here — it launched its first foldable phone, the Galaxy Fold , back in 2019 — at the time we called it, “The most forward-thinking phone you shouldn't buy”.

But the real cultural test of the foldable phone may not arrive until Apple does what Apple does best — walks into an established category, removes a few visible seams, adds a name that sounds like it came from a yoga retreat, and behaves as if everyone else had merely been beta-testing the future.

Under Steve Jobs, being best, not first, was Apple’s greatest trick. The iPod was not the first MP3 player. The iPhone was not the first smartphone. The iPad was not the first tablet. Apple’s genius was letting other companies prove there was a category, then making the version people could imagine actually buying.

It remains to be seen if the incoming CEO John Ternus has the ability to work the controls of Steve Jobs’ reality distortion machine in exactly the same way and convince people that Apple’s version of a folding phone will make them cool.

Enter the iPhone Ultra

That rumoured Apple foldable phone is the iPhone Ultra, which could arrive in September this year. I’d also be surprised if it wasn’t well over the $2,000 mark, too. Some reports suggest $2,500.

I haven’t gotten hands-on with the new Z Fold 8 Ultra yet, but I’ve used foldable phones before and they always felt incredibly geeky to me. They’re impressive but slightly awkward to use, especially in public, and mostly bought by people who like explaining their phone to strangers.

I understand that the foldable form factor makes it better for watching videos using that massive inner screen, or by using the design to prop the phone up on a desk so you can watch on its outer display without having to hold it or carefully balance it against something.

I get that a large screen is better for gaming, too, but I can’t remember the last time I played a game on my iPhone.

My problem is that I don’t want to attract attention to myself. Phones are expensive and waving a larger screen around in public seems like it could make you more of a target for thieves, or the simple curiosity of strangers. I’d rather be left alone to enjoy my journey in peace with a phone that doesn’t advertise my presence.

The real problem isn't the hinge

All of which makes me think that in September, Apple’s biggest challenge might not be around making the perfect, seamless hinge, camera or screen aspect ratio.

It might be simply to convince people that foldable phones can be cool, even at their ridiculous prices, and that’s going to be a tougher task than any of the technical hurdles it will need to overcome.

Saying that, if anybody can make us think that folding phones are for everyone, it’s Apple. Samsung has done the brave work of making foldables exist. Apple’s job is to make them feel less like a magic wallet and more like an iPhone that has learned a party trick.

Recruitment has always reflected the realities of the labor market it serves. For decades, those realities were relatively stable.

Roles were well defined, career paths followed familiar patterns, and organizations could afford to move at a measured pace when bringing in new talent. That context has changed, but many hiring practices have not.

Today, skills evolve quickly, business priorities shift frequently, and candidates expect a level of speed and transparency that traditional processes struggle to provide.

The result is a growing mismatch between what organizations need and how they go about finding it.

Hiring cycles stretch out, decisions are made with incomplete information, and opportunities to secure the right talent are often missed.

It is within this context that artificial intelligence has entered the conversation, often accompanied by a degree of unease. Questions about automation, control, and fairness are not only understandable but necessary. However, focusing too narrowly on whether AI might replace elements of HR risks overlooking the more important development taking place.

There has been a rapid rise in the technology, with 87% of companies now adopting AI to support recruitment . AI is reshaping how decisions are made, and in doing so, it is redefining the contribution HR can make to an organization.

Establishing talent architects

For many HR teams, a significant proportion of time is still absorbed by coordination and administration. Screening applications, arranging interviews, and managing workflows are essential tasks, but they rarely represent the highest-value use of expertise.

A recent study three quarters of recruiters are using AI to save time and improve sourcing candidates . AI has the potential to absorb much of this operational load, processing large volumes of information quickly and consistently, while identifying patterns that would be difficult to detect through manual methods alone.

The benefit generates efficiency, while also creating the opportunity for HR leaders to operate with greater strategic intent. As organizations adopt AI-enabled approaches, the role of HR begins to move away from managing transactions and towards shaping the workforce more deliberately.

The concept of the HR leader as a “talent architect” is increasingly relevant in this environment. It reflects a responsibility not just to fill roles, but to design teams around capability, potential, and long-term direction. This requires a different balance of skills, combining an understanding of data and technology with a strong grounding in judgement, experience, and organizational culture.

Improving fairness through greater visibility

Concerns around bias often sit at the center of discussions about AI in hiring. There is a legitimate fear that automated systems could reinforce existing inequalities if left unchecked. Yet it is also important to recognize that bias is not introduced by technology alone. It is already present in many traditional hiring processes, often in ways that are subtle and difficult to measure.

What AI offers, when implemented with care, is greater visibility. Patterns in decision-making can be analyzed, inconsistencies can be identified, and outcomes can be assessed against clear criteria. This does not remove the responsibility from organizations to act, but it does provide a stronger foundation for doing so. Effective governance, transparency in how systems are trained, and ongoing review are essential if these benefits are to be realized.

Alongside considerations of fairness, there is a broader question of how AI influences the human aspects of hiring. The risk is not that technology replaces judgement, but that it is relied upon too heavily without sufficient oversight. The most effective organizations will be those that treat AI as a source of insight rather than a substitute for decision-making.

When this balance is achieved, the impact is tangible. Hiring processes become more responsive, with fewer delays between stages. Decisions are supported by a richer evidence base, allowing for greater confidence in outcomes . Candidates experience a process that is more consistent and easier to understand, which in turn strengthens the organization's reputation as an employer.

A better outcome for businesses and candidates

There are also clear links to business performance. When hiring decisions are better aligned with the capabilities required, organizations are more likely to see improvements in productivity and retention. Teams are built with an eye on future needs as well as immediate demands, which is particularly important in sectors where change is constant.

At the same time, candidates benefit from a process that is more transparent and consistent. Decisions are grounded in evidence, and opportunities are matched more closely to individual strengths. The experience becomes more predictable and, ultimately, more credible.

For HR leaders, the priority now is to approach AI with both ambition and discipline. Introducing new tools without a clear understanding of the problem they are intended to solve will deliver limited value. Equally, adopting a cautious stance that delays progress may leave organizations at a disadvantage in a competitive talent market.

Clarity of purpose is therefore essential. Whether the objective is to reduce time-to-hire, improve the quality of matches, or strengthen diversity, the role of AI should be defined in relation to those outcomes. This should be supported by investment in capability, ensuring that HR teams are equipped to interpret and apply the insights generated.

Perhaps most importantly, there needs to be a shift in how the role of HR is perceived within the organization. The introduction of AI does not diminish the importance of human expertise; it places a greater emphasis on it. As routine tasks are streamlined, the expectation is that HR will contribute more directly to strategic decision-making, bringing a deeper understanding of talent, culture, and organizational dynamics.

A new chapter for hiring

Companies integrating technology into their recruitment operations have seen their hiring processes become faster, data-informed, and more closely aligned to business priorities. AI is a significant factor in that transition, but it is not the defining feature. Rather, HR existing with AI support depends on how organisations choose to integrate technology with human judgement.

For those that do this well, the outcome is greater than a more efficient hiring process. It is a more thoughtful and effective approach to building teams, one that recognises both the value of data and the importance of human insight.

That balance will shape the next phase of hiring, and it will determine the extent to which organizations are able to adapt to the demands of an increasingly dynamic labor market.

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Engineering discipline in software development has been under the spotlight since AI started being used by devs to generate code, with many teams worried it’s causing unnecessary risk. Headlines have been full of cautionary tales about bugs created by hastily shipped code or senior executives taking vibe coding into their own hands.

Nevertheless, teams are finding ways to build trust in AI-generated code. Google’s 2025 DORA survey concluded that AI has an amplifying effect on organizations' processes - quality processes enhanced by AI lead to higher quality outputs, and more of them.

Our own research shows that most teams aren’t being haphazard with safeguards or accepting AI outputs at face value. Most teams are being more rigorous and giving their outputs the same scrutiny as the work of a teammate. That tells us a lot about how the best-performing teams are learning how to work with AI.

Seventy-six percent of enterprise teams are reviewing AI-generated work at least as rigorously as human-written work. Teams using AI to accelerate their work are seeing results from using proven engineering practices to ensure code is reviewed and tested sufficiently.

Rather than being inherently unstable, the rise of AI-generated code has emphasized that guardrails are the bedrock of consistently reliable software.

Treat AI as a virtual teammate

Teams treating AI-generated code as if it was produced by a human tells us something about how mature teams are using AI. The engineers using AI most effectively treat it as a virtual teammate. They stay in charge of the decisions and they use AI to accelerate the execution.

To make AI adoption an iterative process that improves over time, starting with low-stakes, repeatable tasks and applying stringent checks to outputs gives the tech a chance to work properly without expecting immediate results.

As MIT’s Computer Science and Artificial Intelligence Laboratory reported, AI coding’s widespread adoption doesn’t mean it can handle all aspects of large-scale software engineering by itself. Releasing to production is higher stakes than writing code, for example. Scrutiny from real humans is vital to prevent errors and hallucinations from impacting the business.

At this early stage of AI being used in workflows, it’s positive that 43% of teams are treating AI code like human-written code, with 33% applying even stricter checks. Even the best developers on a team aren’t above the processes and guardrails that guarantee reliable software deployment at speed. AI should be approached in the same way.

Trust in AI follows the risk curve

As with any new technology, introducing AI to the software lifecycle is a learning process. Our data shows trust in AI varies greatly depending on the stage of the software lifecycle, indicating that teams are building confidence with appropriate caution.

A pragmatic approach is being taken at the best-performing enterprises. Thought leaders from Gartner, Forrester and Google’s DORA team all highlight that organizations seeing success from AI-assisted development are strengthening their engineering controls and building up success over time.

This tallies with where AI is trusted to perform. The vast majority (82%) now use AI during the build stage, dropping to 58% at release where production risk is highest. Using AI primarily for earlier stages of software development is a rational step to make sure failures don’t impact the wider business.

This doesn’t betray a lack of confidence in the technology. Almost half (46%) of teams are confident in the performance of AI-generated code, indicating that its more cautious use in production is a measured business decision rather than skepticism.

Outputs are much easier to review and refine before the release stage. With many businesses still lacking full observability, teams often only hear about mistakes in live code once users notify them. Despite claims that software development could eventually be fully automated, teams are showing a clear awareness of where human oversight is essential.

Rather than making blanket judgments about AI’s capabilities, they are taking a more nuanced view, which bodes well for the future of AI-assisted software delivery.

Combine speed with discipline to win

There is continuity in how mature teams are making sure AI-generated code is fit for purpose, but it’s still having a seismic impact on the role of software engineers. When I speak with developers, their feedback is unanimous: the time AI saves is invaluable for focusing on neglected parts of their process.

AI moves the cognitive load from writing code and building configuration to reviewing and directing it. With time freed up, teams have more scope to prioritize observability, test coverage or disaster recovery scenarios - crucial elements of software hygiene that too often get overlooked.

AI adoption is increasing documentation quality according to the DORA report, suggesting that many teams are taking the opportunity to improve the broader engineering practices that support reliable software delivery.

As it takes on more of the mechanical aspects of software development, engineers become increasingly responsible for the work that matters most: validating outputs, understanding risk, and ensuring outputs align with business objectives. If someone asks “why did we build it this way?” it’s a problem if no one can answer the question without asking AI. Humans must be in the loop.

The organizations that benefit most from AI will not be those with more automation, they will be the ones that combine AI-driven speed with engineering discipline and a strict adherence to repeatable processes.

AI adoption is a DevOps challenge

The debate around AI-generated code often focuses on whether the technology can be trusted in DevOps. In most cases, teams have been building this trust iteratively without throwing away human judgment to get the best results.

As AI capabilities continue to improve, the gap between high-performing teams and everyone else is unlikely to be determined by access to the latest model.

The engineers and teams that will thrive are the ones who pair AI’s capabilities with their own judgement.

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Last year, thousands of customers at one of the UK's biggest banking groups were locked out of their accounts. This cut people off from their own money, leading to declined cards, missed payments.

Across the UK, major banks and building societies racked up more than 800 hours of unplanned tech and systems outages last year. That's more than a month of disruption spread across the financial services millions of people rely on every day.

I don't believe this is bad luck or just a one-off error. It's something deeply structural and predictable. And it won’t fix itself.

The structural problem underneath the headlines

The headlines zero in on the disruption or the compensation bills or the apologies over social media. What gets missed is why it keeps happening in the first place.

Here's what I think is going on: In a lot of these cases, data is sitting in silos, systems don't stay properly aligned and no one has clear ownership of the key domains. So when something changes in one place, it doesn't stay contained. It ripples through other systems in ways that aren't always obvious.

A dependency breaks somewhere downstream, a third-party service doesn't behave as expected and the impact becomes harder to trace than it should be. Recovery slows because teams are spending time working out what actually changed before they can even start fixing it. This isn't a legacy technology problem or a budget problem. It's a data ownership problem, and the people running these organizations know it.

The UK's particular problem

Banks aren't short on ambition, and there’s no shortage of investment or early deployment. In fact, the vast majority of AI initiatives are now moving from pilot to production.

But complete rollout is a totally different story. In the US, around a third of banking executives say their AI initiatives consistently reach full deployment. In the UK, barely one in ten can say the same, and UK respondents are more than twice as likely to say projects never make it past the pilot stage. I don't think this is a technology gap. It's an organizational one.

UK institutions have spent years digitizing on top of legacy infrastructure, acquiring new capabilities without properly integrating them. Each layer adds complexity, more handoffs and more unclear ownership. That's the accumulated cost of organizational decisions that were easier to defer than resolve.

At that rate, UK banks risk getting lapped not just by their US peers but by the fintechs who are already drawing customers away.

The governance bottleneck

There's a second structural problem looming beneath the data fragmentation issue. Decision-making in financial services is still highly centralized. The majority of major tech investments still require C-suite approval, with only a handful of these delegated to the data or digital leaders closest to the systems and best placed to act. By the time approval arrives, the moment to act decisively has passed.

Those who've got it right have named who owns what. Decision rights sit closer to the people actually doing the work, governance is folded in from the start, not as an afterthought. And when things go wrong, which is inevitable, recovery comes faster because nobody has to spend the first hour working out who's responsible. That's the kind of margin most banks can't currently rely on.

What the minority are doing differently

Only a handful of financial services firms are planning to move to product-led, cross-functional team structures with clear data ownership and accountability. I'd argue that's the operating model most likely to produce faster delivery, clearer accountability and fewer of the outages that result in front-page headlines.

Among firms that have embedded intelligence directly into live customer journeys, results are already showing up. Real-time fraud alerts, payment resolution support and event-driven interventions are all cited as direct drivers of customer loyalty. These are the outcomes when data is clean and teams have the authority to act on it.

In a market where switching has never been easier, that's the difference between keeping a customer and losing one.

What the outages are really telling us

Last year's outage headlines prompted understandable focus on compensation and consumer protection. But I think that's the wrong question. The more important one is why these incidents keep happening, and what it would take to stop them.

That comes down to knowing who owns what, giving teams the authority to act at speed and building governance in from the start rather than adding it on when something breaks.

Banks can't keep treating these as isolated incidents. They're symptoms of operating models that haven't kept pace. Until that changes, the next outage isn't a question of if. It's when.

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Middleware has traditionally been treated as a background process. Enterprises could afford to have this layer connect systems and data with minimal oversight – but that autonomy is no longer compatible with the scale and complexity of modern data flows.

Large enterprises, retailers, and financial institutions consist of transactions sprawled across a disconnected web of middleware.

IBM MQ, Apache Kafka, Apache ActiveMQ, RabbitMQ, and TIBCO are only a few of these environments, and each creates a step that introduces failure points where data can back up or stall completely.

This reality no longer aligns with the direction enterprises are heading. As organizations accelerate investments in agentic AI, the effectiveness of those systems increasingly depends on their ability to understand the operational environments in which they operate. Many large organizations are steadily advancing toward petabyte-scale volumes and agentic AI embedded throughout their processes.

Yet they overlook the need to secure the foundation on which these additional investments are built. These technological improvements depend on a deeper understanding of operational systems and a strong application base. As AI-powered operations and high transaction volumes become the norm, middleware teams can no longer afford to rely on disjointed oversight and fragmented structures for their middleware systems.

Closing the Context Gap for AI Operations

Across industries, agentic AI needs system awareness to function effectively. In a recent McKinsey & Company article, 33% of organizations cited data limitations, and 29% cited tech platform limitations as among the top three roadblocks to scaling AI.

These challenges are especially pronounced for businesses with complex, fragmented middleware landscapes. In many enterprises, these limitations stem not from a lack of AI investment, but from fragmented operational environments that prevent systems from accessing complete business context.

Without interconnected views across an organization, agentic AI struggles to understand how brokers, queues, topics, routes, and dependencies fit together across live environments. This means AI could generate insights, but they might not fully reflect the realities of the operational environment. In practice, this could look like a middleware system surfacing a “small” discrepancy that is actually a larger issue beneath the surface.

For example, a queue backlog in one broker may appear isolated, but it could signal a larger downstream application failure or potential architecture limitations. In a retail environment, this could manifest as delayed order processing, while in financial services, it could impact payment workflows and customer experiences. Misinterpreting these signals is problematic because middleware teams operate with little to no room for error.

Much of the information needed to build operational context already exists throughout growing enterprise telemetry. The challenge lies in the scale of the information operators would have to evaluate.

From Data Overload to Operational Intelligence

Enterprises are building increasingly large telemetry repositories, which offer significant potential. There is no shortage of data, and if organizations analyze it correctly, technology leaders could use it to move away from reactive monitoring and toward predictive insight generation. The issues surface when trying to manage this data in real time and detect signals with enough time to act.

The volume of this middleware telemetry is often too dense for operators to evaluate by hand, making its potential obsolete without practical ways to analyze it. Enterprises need ways to tie this information back to actual system behavior at scale. Raw data can show what happened, but enterprises need a broader understanding of what those signals mean for the overall operational picture.

Making AI and Telemetry Work Together

Organizations must correlate operational activity across traditional and real-time telemetry to prevent growing data volumes from hindering issue detection. This means establishing a telemetry foundation that allows businesses to view the entire production environment.

With a strong foundation, predictive intelligence is positioned to ingest, index, and query data without disrupting production workflows. Historical operating data, including communication paths and processing routes, could help AI read signals, identify issues early, and respond quickly and accurately. AI could interpret individual events in the context of applications, message flows, transaction paths, and business outcomes.

With operational context, agentic AI can identify anomalies more accurately and provide recommendations grounded in a deeper understanding of system behavior across platforms and environments. It could gather data over time and surface optimization opportunities for queue depths and message flows, alongside other functions such as automating repetitive connection testing and log analysis.

This framework would yield recommendations grounded in business realities and give middleware teams a chance to break away from routine logging and focus on designing scalable architectures for enterprise data flows.

Building for an AI-Driven Future

Enterprises can no longer treat the middleware layer as an afterthought. Fragmented integration architectures provide little transaction-level visibility and allow errors to compound unnoticed.

Data growth and AI-powered analysis are exposing the limitations of these legacy middleware standards. Organizations need full oversight of the interconnected systems that carry a transaction from one step to the next, as well as established connections between telemetry and business activity.

When enterprises build a strong foundation of operational context, AI can help businesses transition from reactive awareness to insights that predict what might go wrong in the future. In this environment, the organizations that pull ahead will be those that leverage AI and telemetry to turn raw data into meaningful business outcomes.

As enterprise AI adoption matures, competitive advantage will increasingly depend not only on the sophistication of AI models, but also the quality of operational context available to them.

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Within a short space of time, AI has made the leap from experimental technology to everyday business tool. Enterprises across sectors are today deploying AI to take on routine, time-intensive tasks to allow their teams to focus on more strategic tasks or work that requires human judgement. According to McKinsey, most organizations are using AI in at least one business function.

Network management and monitoring is one of the fastest-growing areas of interest. The timing is no coincidence. Technologies like cloud computing are driving significant increases in both network traffic and complexity, making modern infrastructure far harder to manage than it was even a few years ago.

To stay ahead, IT teams are embracing AI and automation - but how widely is AI-based network monitoring used today and where is it headed next?

Relieving the pressure on IT staff

The task of monitoring network activity is complex and requires continual attention. It involves keeping infrastructure healthy, identifying faults, and reacting swiftly to unusual activity. These tasks were once handled manually but growing network scale and an increasingly hostile cyber threat environment have made traditional approaches hard to sustain.

IT professionals now find themselves spending a disproportionate amount of time on repetitive work such as firewall management, network provisioning, and routine monitoring.

AI addresses this directly by automating large portions of network supervision. Machine learning models can continuously process enormous volumes of network data, identifying anomalies such as traffic spikes, suspicious access patterns, or behaviors associated with known threats - and doing so in real time. This means teams can intervene before a problem becomes an outage or a security breach.

AI also sharpens focus. Rather than requiring specialists to wade through endless logs and alerts, AI-powered systems sift swiftly through these, filtering out the noise and drawing attention to only the issues that pose genuine concern. False positives are reduced, and teams can direct their energy toward real risks - including fast-moving threats that rule-based tools simply cannot keep pace with.

Scalability is another advantage. As demand fluctuates, AI monitoring systems can expand their coverage automatically, without the need for additional headcount. In environments where networks are growing larger and more dynamic by the day, this kind of elasticity is no longer a luxury.

Adoption today

AI-enabled network monitoring is already embedded across a wide range of industries. For many networking professionals, automation and AI are now considered core operational capabilities rather than nice-to-haves. A meaningful and growing share of network management activity - covering design, deployment, maintenance, and troubleshooting - is already handled through automated processes.

However, adoption does not automatically guarantee success. Many organizations are actively deploying AI features within their network tools, and some are even training models on their own IT and security data. Yet far fewer report achieving fully successful outcomes. The gap between using AI and genuinely benefiting from it reflects the real-world difficulty of moving beyond pilots to reliable, production-grade operations.

Two challenges consistently hold organizations back. The first is data quality - incomplete records, inconsistent formats, and poor documentation undermine AI model performance before it even gets started.

The second is skills. Many IT teams simply do not have the in-house expertise required to deploy, train, and manage AI-driven networking tools effectively, which slows progress and erodes confidence in outcomes.

Agentic AI: what's coming next

Despite these hurdles, the direction is clear. AI-based monitoring is a crucial component in networks management, and the next evolution, agentic AI, is already beginning to take shape.

Where conventional AI tools focus on detection and recommendations, agentic AI goes further. These systems can identify anomalies, diagnose root causes, predict capacity issues, and take corrective action - either autonomously or with minimal human sign-off. Rather than simply flagging problems, agentic AI is built to analyze, decide, and act, moving networks toward genuinely autonomous operations.

Consider a practical example. On a hospital campus, a staff member unknowingly connects an unauthorized access point to the network. A rogue SSID appears - a classic vector for man-in-the-middle attacks.

An agentic network management system detects the anomaly instantly, classifies the threat based on policy, and presents the administrator with a targeted remediation action: block the port or quarantine the MAC address. In sensitive environments, human sign-off is preserved by design. The AI does the analysis; the human makes the call. This is not a future concept - it is in production today.

Industry analysts expect agentic approaches to gain significant traction over the next few years, particularly in large and complex network environments. For most organizations, however, getting there will require a phased approach.

The immediate priority is deploying AI-based monitoring solutions that integrate cleanly with existing infrastructure, while ensuring teams are trained and confident in using them. As organizations build trust in their data quality and in the reliability of AI-generated insights, they can progressively introduce more autonomous capabilities.

The direction is clear: the organizations that treat AI-driven network management as a strategic investment today will operate faster, more resilient networks tomorrow - while those that wait will find the gap increasingly difficult to close.

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Most business leaders are aware that AI adoption in their organizations has moved faster than the governance around it. When we surveyed 250 finance decision makers in mid-market organizations, we found that 83% of teams were already using AI, yet only 53% had a formal framework for its safe use.

If finance - a function with some of the highest standards for data accuracy and compliance - is operating with that kind of governance gap, it is reasonable to ask whether other departments across the business look any different.

That gap represents a risk - and it's a risk that sits squarely with the business and its leadership. So how do you encourage innovation when it comes to AI without losing control of the risks it brings?

There is an international standard designed specifically to address this: ISO/IEC 42001. It is not a compliance exercise to tick a box; it is a practical framework for governing AI responsibly, with direct implications for how business leaders evaluate the software they rely on.

For SaaS businesses, ISO 27001, the standard for information security management systems, has become the norm. It's a key requirement, providing assurance to customers about how the business ensures Confidentiality, Integrity and Availability for the data it hosts and processes on their behalf.

ISO/IEC 42001, published in 2023, is its counterpart for AI: the first international standard governing how organizations develop, deploy and oversee AI systems. Whether or not you pursue certification yourself, it should be a key reference point when evaluating any AI-powered software you use.

Why we need a standard for AI security

It's not even four years since the public launch of ChatGPT heralded the boom in use of generative AI. Not only has the technology moved at an incredible pace since then but so has its adoption in business.

We've all been told that AI will transform productivity and change the world of work forever. So it's not surprising that many organizations have been racing to buy licenses and get people using it. In organizations that haven't done so, it's likely that staff are using it on the side anyway - which creates another problem: "Shadow IT".

"Shadow" AI use seems to be rife. Among the finance decision makers we surveyed, in almost half (46%) of organizations where AI hadn't been officially adopted, people were using AI assistants anyway and 30% were using AI-powered forecasting and analysis. It would be surprising if the picture looked much better elsewhere in the business.

So it's worth asking: where is the data we have spent so much effort protecting, through ISO standards such as ISO 27001, now going - and do I have visibility and control over it?

The scale of ungoverned AI use matters in any organization. ISO 42001 provides a structured way to ask the right questions, whether you are reviewing your own internal AI use or evaluating a software vendor.

How ISO 42001 helps business leaders

Certain parts of an organization have particularly high standards for data accuracy and integrity - finance teams producing regulatory reporting, legal teams managing case records, HR functions handling sensitive employee data. ISO 42001 helps ensure those standards are reflected in the AI tools and processes you use, whatever your context.

Transparency: ISO 42001 requires that AI outputs can be explained and traced. Whether AI is producing a report, performing an automated workflow or flagging an anomaly, you as the human in the loop should be able to explain what the system did and why. That human in the loop is a key component of the standard.

Accountability: ISO 42001 stresses clear ownership of AI systems and their outputs. That means the use of any AI in business-critical workflows should have a defined owner who is responsible for its performance and governance.

Risk management: ISO 42001 requires ongoing risk assessment for AI systems over and above those in place for ISO 27001. This doesn't usurp what you have currently in place. It complements current risk evaluation through a focus on AI and the implementation of controls to help manage that identified risk.

That means identifying what could go wrong, how likely that event is and ensuring the right controls are in place to mitigate it. Those risks might include data accuracy, model performance degrading, security of sensitive business data and the risk of AI acting on outputs that have not been adequately verified.

The questions you should ask

ISO 42001 offers you a useful way into important conversations with software vendors, whether or not they've achieved formal certification. It prompts some important questions.

- How are the vendor's AI systems developed, tested and monitored? The ideal response would show documented processes for keeping things accurate and trustworthy, with the human in the loop clearly built into the processes.

- How does the AI product produce its output? What happens when an output is incorrect or unexpected? It's worth understanding whether the AI outputs come from a layer bolted onto a core system and drawing on verified data from that system - or whether they are generated predictively, the way a large language model would work.

- How does the vendor manage the risk of AI model performance changing over time? One of the frustrations of using AI is that LLMs can become less good at a task they did well before. You need to know your vendor is on top of this issue.

- What accountability exists within the vendor's organization for these AI capabilities? You need a relationship with a vendor that's prepared to take responsibility for its product and the data that flows from it.

- Does the vendor use your data to train or improve its AI models? This is a question that more business leaders are asking, and rightly so. Your data should never be used to improve a third-party model. Look for vendors who operate a zero-retention policy, meaning your data is used only in a live, read-only state and is never fed back into AI training processes.

However capable AI gets, it will not be replacing human decision-making and accountability at the top of organizations in the foreseeable future. That remains the job of leaders who need to know they are putting their names to decisions grounded in complete, accurate and trustworthy data from their own systems.

ISO 42001 is the mechanism by which you can hold AI to the same standards of accuracy, transparency and accountability that rigorous organizations have always required. It will not slow down AI adoption. But it will ensure that adoption does not come at the expense of the controls that protect your organization and your reputation.

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A new acronym is starting to appear in boardrooms, investor conversations and executive debates: MANGOS - Meta, Anthropic, NVIDIA, Google, OpenAI and SpaceX.

It is being used as shorthand for the new power cluster of the AI era, and perhaps as the successor to FAANG as a symbol of where technological gravity now lives.

Here is my take: MANGOS is not the destination. It is the opening move.

Over the next five years, some companies that dominate their markets today, icons of an era, businesses that once seemed structurally unassailable, may lose relevance, decline, or be replaced by new leaders. At the same time, new companies will emerge from places nobody is currently watching.

This has happened before. It will happen again. What is different this time is the speed and the depth of structural change required to survive it.

The world is being reshaped by AI. But the companies that will win are not the ones that simply add AI to what they already do. They are the ones who rebuild themselves around it entirely.

The tool-adoption trap

There is a seductive but dangerous interpretation of AI transformation that goes something like this: deploy a copilot here, add a chatbot there, automate a few workflows, report efficiency gains to the board, and move on.

That approach may create efficiency gains, but it does not create an AI-native company.

The companies building genuine competitive advantage right now are not asking, “Where can we use AI?” They are asking a harder question: “If we were starting this company today, with AI as a native capability rather than a retrofit, what would we look like?”

The answer, almost always, is dramatically different from what they look like now.

This is not a technology shift. It is a company redesign. And the gap between those two ways of thinking is where many organizations are currently losing.

What AI-native actually means

The term AI-native is used loosely, so let me be precise about what I mean by an AI-native mindset. It has concrete, structural implications that go far beyond which tools a company deploys.

The first is re-engineering the org chart to unlock AI outcomes, not to preserve legacy structures.

Most organizational hierarchies were designed for a world of information scarcity, where value came from controlling access to data, expertise and decision-making authority. AI inverts that logic. Information is becoming abundant. The constraint is now judgment, creativity and the ability to act on signals faster than competitors.

An org chart built for the old world can suppress the outcomes the new world makes possible. Redesigning for AI is not about simply cutting headcount. It is about rethinking where human judgment actually adds value, and structuring the organization so that AI amplifies it rather than working around it.

The second is increasing talent density with genuine intentionality.

AI adoption does not happen organically at the pace this moment demands. When transformation requires a cultural shift as fundamental as this one, where every employee’s relationship with their work is changing, organic adoption is too slow.

It has to be a CEO mandate, driven from the top, resourced seriously and measured against real outcomes. The companies that treat AI training as an optional benefit will be outrun by the companies that treat it as a core operating requirement.

The third, and the one I find most underappreciated, is eliminating before automating.

There is a reflexive impulse in most organizations to reach for automation as the response to inefficiency. But the highest-value process is often the one that should no longer exist.

Before you make something faster, you need to ask whether it needs to happen at all. Automating a broken process at scale just produces broken outcomes faster. The discipline of elimination, of genuinely questioning whether a workflow, a layer of approval, a reporting structure or a product feature actually needs to exist, is harder than automation, and more valuable.

The fourth is relentless commercial focus.

AI investments must drive measurable revenue growth or profitability. This sounds obvious, but in practice, it is where many transformation programs lose their way. The excitement of the technology can become its own justification. It cannot be.

Every significant AI investment should have a clear line of sight to a commercial outcome, not eventually, not in theory, but in a timeframe that the business can actually hold itself accountable to.

In commerce, this distinction is already becoming visible. The winners will not simply add AI tools to search, customer service, merchandising or operations. They will rethink how decisions are made across the entire business, from product discovery to fulfilment, customer experience, media and profitability.

The hidden risk for today’s market leaders

If you are a market leader today, you face a specific and uncomfortable dynamic.

The capabilities that got you to dominance, your processes, your organisational structure, your institutional knowledge, your partner ecosystem, are also the things that can make transformation hardest. They are not only assets. If left unquestioned, they can become anchors.

New entrants do not have this problem in the same way. They are building AI-native from day one, without legacy infrastructure to protect or organizational politics to navigate. They will not necessarily announce themselves before they are ready. By the time many incumbents recognize the threat, the gap may already be significant.

This is not a reason to panic. It is a reason to move with urgency that many large organizations are not currently demonstrating.

MANGOS is the beginning, not the ceiling

The MANGOS companies are defining much of the infrastructure of the AI era, and their influence on how the next decade of business is conducted will be profound.

But the history of technology tells us something important: infrastructure enablers and application-layer winners are usually not the same companies.

The railroads did not own all the businesses that the railroads made possible. The internet infrastructure companies of the 1990s did not capture most of the value that the internet created. MANGOS is building the rails. The question for every enterprise leader is what they are going to build on them.

The winners of the next era will be the companies that take AI infrastructure as a given, build organizational and commercial models designed specifically for an AI-native world, and execute with relentless focus.

They will come from industries nobody is currently betting on. They will emerge from markets that look mature or even declining. They will be led by people who understood earlier than everyone else that this is not an upgrade cycle. It is a redesign.

The game is on. Execution based on experimentation driving bold outcomes is what will make the difference.

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The 2026 FIFA World Cup is the largest sporting event ever staged, being hosted in 16 cities across three countries. The event revenue is projected to reach $10.9 bn, and from a cybersecurity standpoint, this makes for an unprecedented attack surface that threat actors are desperate to get their hands on.

What makes the 2026 World Cup genuinely different from recent sporting events isn't only the scale but the geopolitical context it is happening in.

The recent U.S.-Israel-Iran conflict has fundamentally reordered what a US-hosted mega-event means for threat actors based in the Middle East.

Against the backdrop of the ongoing NATO conversations with Russia, with all three host nations being either members or close allies of NATO, it means Russian affiliated adversaries will be keeping a close eye out for an opportunity too.

Overall, this means that the tournament is taking place inside an environment where state-backed adversaries are already actively operating.

As the world looks on with eager eyes, the tournament is defined by three primary threats looming over it: state-backed espionage, infrastructure disruption, and large-scale consumer fraud.

Understanding how these risks might manifest is critical for organizers, local authorities, and visitors alike.

The Iranian groups to watch out for

The group immediately relevant is Handala Hack Team, which has been assessed by the FBI and threat intelligence firms to be a front for Iran's Ministry of Intelligence and Security. This year alone, the group wiped systems across Stryker, a Fortune 500 medical technology company, and breached the personal email of the current FBI director. This is not a group testing waters; they've demonstrated the capability by repeatedly breaching high-value targets.

But of even deeper concern for the World Cup is the Iranian group CyberAv3ngers. The group has a proven track record of targeting industrial control systems at US water, energy, and municipal facilities. Each match is being run on a layered, ring-based tournament network grafted onto a permanent stadium environment.

These networks depend on a temporary commercial supplier ecosystem and pull on host-city public services that FIFA does not own. And this IT infrastructure isn't all hardened. It is instead managed by often under-resourced local authorities with legacy systems and, in many cases, remote access tools that were never designed for the threat environment today.

Russia's playbook

Russia has been running cyber interference in global sport for years. At the Pyeongchang Winter Olympics in 2018, a wiper attack took down Wi-Fi during the opening ceremony, killed ticketing systems and grounded broadcast drones. It took twelve hours to restore operations. It was not financially motivated - the goal was to cause chaos at a moment of maximum visibility.

That instinct hasn't changed, but the technique has. Groups aligned with Russia have conducted thousands of DDoS attacks against NATO member states and infrastructure since 2022, with surges timed to politically symbolic moments. And they are not just doing website takedowns but targeting the kind of operational remote-access services that run physical infrastructures.

The threat to public safety and why it matters at scale

Fraud during massive public events has always been of utmost concern and remains so.

During the Qatar World Cup in 2022, more than 16,000 fraudulent domains appeared, fan accounts were compromised, and fake apps and social profiles proliferated across app stores and social media. When millions of fans are navigating unfamiliar transit systems and scanning QR codes for everything from parking to shuttle passes, there is a great opportunity for attackers to cause chaos and threaten stability.

The MGM Resorts breach a few years ago showed how quickly a well-executed social engineering campaign can collapse a major hotel operator's guest-facing systems, from reservations, digital keys, and POS going down simultaneously. The same scenario run across multiple host-city hospitality sectors and transit networks during the World Cup presents a shiny, vast attack surface that has reputational and operational damage extending well beyond financial motive.

The historical record of securing such large-scale events is actually encouraging, and shows how serious and sustained preparation is key. Paris Summer Olympics faced more than 140 documented cyber events, including 22 confirmed intrusions and a ransomware attack on the Grand Palais venue, but none of it reached the field of play. That outcome required years of coordinated preparation between ANSSI (National Cybersecurity Agency of France), government agencies, and private industry.

While the 2026 World Cup happens on the ground, the infrastructure that promises to run it seamlessly for one of the biggest sporting experiences will be under threat. The single most important defense posture is to assume the attacks will come. As seen during the Paris Olympics, sustained preparation works. However, success will depend on a coordinated security stance that prioritizes the resilience of both digital and physical systems.

Simple measures like treating the IT help desk as the first line of defense, using VPNs when on public networks, and buying tickets only on the official platforms can go a long way in preventing phishing and fraud risks. It is worth noting that the window to do it properly is limited, and the adversaries already have their eyes peeled for an opportunity.

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Computing is so ubiquitous that we often see more than one in most households, including laptops, desktops, all-in-ones, smartphones, tablets, and so on. But there was once a time when that certainly wasn't the case. In that context, some comments made by industry stalwarts of the past may appear like massive miscalculations.

Ethics in AI

The late Ken Olsen was the co-founder and CEO of Digital Equipment Corporation (DEC), a major company in the mid-20th century that produced a series of minicomputers over the decades in which it operated until its decline in 1998.

Despite being a key part of the computer industry, Olsen was adamant that the computer was something isolated to the enterprise — delivering his infamous comments in 1977 at a convention of the World Future Society. There are, however, two very important caveats.

Firstly, computers at the time were either gigantic mainframes or minicomputers that needed specialized staff to manage. Secondly, Olsen later claimed his words were taken completely out of context.

Smart living

Olsen claimed he wasn't referring to the sort of computers we see today in homes like desktop PCs or laptops, but actually the then-futuristic proposal of a master computer that would automate different functions around the house.

What the executive was referring to was what we now understand as the smart home. However, regardless of the context, his comments still missed the mark — as the rise of smart home technology has been a very exciting development during the 21st century.

That said, the vision of a fully automated and intelligent home has proven massively prohibitive, partially due to cost, partially due to hardware, and finally due to slow interoperability and integration. That latter element, however, is finally something that's been changing thanks to the Matter smart home standard.

As TechRadar’s AV tester, I’m always on the lookout for new 4K Blu-rays to test the best TVs and best soundbars with. While I have a pretty healthy catalog, with some top-notch discs, as a 4K Blu-ray fan, it’s exciting to check out new discs to keep testing fresh.

Last month, I tested the Speed Racer 4K Blu-ray and it was a powerhouse. Not only did it deliver staggeringly vibrant colors, but it also had a superb Dolby Atmos soundtrack that demonstrated power and precision, particularly during race sequences. It was one of my top discs of the June 2026 Blu-ray Bounty, and it’s easily one of the most impressive I’ve tested.

This month, a new disc has caught my eye: The Super Mario Galaxy Movie. The second entry in the new Mario Bros set of films, first started in 2023 with The Super Mario Bros., I’m anticipating that The Super Mario Galaxy Movie will have bright, crisp visuals and, hopefully, a great Dolby Atmos soundtrack.

While this was one of my most anticipated discs of July 2026, a first impressions Reddit thread on r/4K Blu-ray from user u/nacthenud got me even more hyped. Not only did they rate the movie’s HDR performance, but also mentioned it in the same breath as Speed Racer. So, I decided to check out how the disc fared on our reference LG G6 OLED TV, one of the best OLED TVs I’ve tested this year.

Phenomenal colors

From the moment The Super Mario Galaxy Movie starts, it’s clear just how stunning the 4K disc’s color reproduction is. As Princess Rosalina reads a bedtime story to the Lumas, the small creatures dazzle on screen. Every color, including yellow, green, red and so on, look natural and punchy in equal measure. Thanks to the high HDR highlight brightness, coupled with Dolby Vision support, the Lumas really popped on the LG G6’s screen. If your TV has high brightness with good highlights, you’ll be rewarded in this scene alone.

Soon after, as Rosalina battles Bowser Jr. in his giant robot, the sparkle from her magic, which comes out in vibrant blues, pinks and purples, shimmers on screen. This is also true later as the yellow Luma turns into a star, with the glittery stardust jumping on screen. Again, it’s another showcase of how impactful HDR highlights can look.

Later, as we see Mario and Luigi meet Yoshi for the first time, the 4K presentation gets to show the authenticity of its colors. Yes, Mario’s red jumpsuit and the green of Luigi’s overalls and Yoshi’s skin are striking, but the colors look realistic too. While I expected the colors in this movie to explode on screen as I’d seen in animated flicks like Elemental, these particular colors reminded me more of the lifelike colors I’d seen in The Wild Robot, exhibiting the wonderful depth and realism I liked in the latter.

In complete opposition to that, the neon signs of the games in the casino Peach and Toad arrive at really burst with color. This is where the G6’s highlights and peak brightness were really tested, and it did a great job. If you have a TV with high peak brightness, then again you’ll be rewarded. The same is true with the gold detail on the furnishings around Peach’s castle, as they sparkle as well.

Peach and Rosalina’s dresses are highlights in themselves, really delivering that candy-colored pop alongside the shimmer they both seem to produce. While they’re colors aren’t as natural as the Mario Bros and Yoshi, they’re accurate for the movie and really deliver a nice impact.

Dolby Atmos power

While I anticipated this disc to be a showcase for color, I didn’t expect it to be such a perfect audio powerhouse. Bass is frequently hefty, pushing the limits of the subwoofer of the Samsung HW-Q990C soundbar I was using. As Bowser Jr’s robot stomps towards Rosalina, the rumble from the Q990C’s subwoofer rattled the room and reverberated through the floor. Later, as Fox ignites the engines of the Arwing, this produces some meaty bass, akin to the igniting of the Batmobile’s engine from The Batman, my go-to audio testing scene.

The Dolby Atmos soundtrack here is also very precise. As Luigi launches fire in a panic in a cave, the flames’ trajectory was accurately mapped to the front channels, travelling from right to left across the soundbar. In another scene, as Peach and Toad first fly into space, the sound of the twinkling flight path of the trail behind them is not only mapped accurately, but clearly audible among the rousing, horn-led score, showing the soundtrack’s excellent balance.

Surround and height channels are also fully utilized. During the climactic space battle, as Fox pilots the Arwing towards Bowser Jr’s ship, the Arwing travels around the whole ship. This was mapped with real precision first to the front channels, then the rear right, to the rear left and back again, creating a fully immersive feel. It reminded me of the time-bending music sequence in Sinners, which also moved around each channel.

Throughout, there are numerous scenes where characters fly through space, using the star to move from galaxy to galaxy. These are a great demonstration for Atmos, with overhead flight paths actually coming from overhead. The same was true with Fox’s Arwing. As it avoided Bowser’s minions in the space battle, anytime the Arwing moved to the top of the screen, Atmos rendered accurate overhead flight.

Another reference disc

After watching The Super Mario Galaxy Movie, it’s definitely entering my testing rotation. Not only does it deliver on color and sound, but animation is crisp throughout, with clean-looking animation. Contrast is also powerful, with the dark blues of space contrasting well with the stars, Lumas and even the bright outfits of the main characters.

There are also plenty of bright scenes that will show off a TV’s HDR highlights and brightness, plus there’s some lovely shadow detail. Any scenes with strong light, such as Mario and Luigi with their flames in the cave, or at sunset as Peach gets frustrated, there’s strong shadows cast that look realistic.

This really is a superb 4K disc, and if you’re looking for a bold, colorful and dynamic movie to show off your home theater system, this is one to consider.

AI was supposed to level the playing field. So far it has done the opposite. The enterprises that already had the most resources are pulling further ahead, while the small businesses that stand to gain the most are still waiting for tools built for them and their workflows.

The appetite is there with 72% of small business owners in the US see AI as a way to support their staff and work more efficiently. What is missing is access. Most of what ships today is built for enterprise scale and enterprise budgets.

That is a design problem as much as a pricing one. Tools built for large organizations assume infrastructure, technical teams, and time to onboard.

Most small businesses have none of the three. The result is a widening gap between what AI can do and what a small business can actually use.

The admin tax on small teams

Ask any founder where their week goes, and the answer is rarely the work they started the business to do. It goes to outreach, follow-ups, meeting prep, and the dozens of small tasks that keep the lights on. In a small team, where roles blur, most of it lands on the founder.

Most founders have adapted by becoming more efficient with menial tasks. They get faster at outreach, better at juggling calendars, and sharper at the administrative work that never required their judgement or insight in the first place.

None of it is optional, and all of it competes with the time that should go to customers and growth. For a small business, winning that time back is the difference between surviving and building something that lasts.

AI is becoming part of the team

For most of the past two years, AI has played a supporting role. It drafted copy, summarised documents, answered questions, always useful but also always waiting for instructions.

That is changing. The newest systems do not just help with tasks. They take them on. They can hold their own queue of work, make decisions, and run multi-step jobs from start to finish with little supervision. The software starts to behave like a teammate that owns a workload, rather than a tool that waits for the next prompt.

The shift also changes what good looks like. When AI was viewed as a glorified writing assistant, the standard was defined by its output quality. When it owns a workload, the measure is reliability or how well it follows through with tasks with minimal human input. This is what separates tools that earn trust from ones that create a new challenge.

From orchestrator to strategist

As AI takes on more of the operational load, the founder's job changes. Less time supervising the day-to-day means more time on the work only a founder can do: setting direction, building partnerships, planning past the next quarter. In a cautious economy, that shift is what separates the businesses that merely survive from the ones that compound.

It only works if the tools earn their place. AI saves time only when it does not create more of it. Anything that adds friction, complexity, or one more thing to manage will not deliver an advantage. It will breed resentment.

The founders who adapt to adoption early will be the most technical who know where their judgment is genuinely needed and where it has just been the only option available. That distinction is harder to make honestly than it sounds. The burden of time and limited resources make this necessary.

Built for small teams

A new class of platforms is being built around how small businesses actually operate, rather than as scaled-down enterprise software. They assume no technical team and no lengthy onboarding. The ones that win will fit the messy, improvised way small teams really work, and make people more capable without asking them to learn a system first.

Pricing and access are not the only barriers to adoption. Trust is a big factor. Many small business owners have fallen victim to software that overpromises and underdelivers. The bar for small businesses goes beyond functionality. There must be transparency with any AI tools about what it is doing and why.

Bridging the gap

Enterprises had the head start on AI. They had the budgets, the engineers, and the time. Small businesses had none of that, and they are the ones who need the leverage most.

Closing that gap is the real opportunity in front of this technology. Meet small teams where they already are, give them tools that work on day one, and you hand them something they have never had: a fair shot at competing with companies many times their size. That is the version of AI worth building.

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Businesses are moving quickly to bring artificial intelligence (AI) into the workplace, exploring how it can support everything from recruitment and workforce planning to performance management and employee services.

Employee confidence in this technology, however, is struggling to keep up. Research suggests only 46% of people say they trust AI systems, while almost a third (31%) of employees are concerned they could be replaced by AI.

As AI becomes more involved in decisions around hiring, performance and progression, employees are asking more questions about how those tools are being used and where the boundaries should sit.

Without confidence in how AI is integrated into these processes, even the most promising AI initiatives can struggle to gain acceptance.

Employees aren’t necessarily resistant to AI

There is often an assumption that employees are reluctant to embrace AI. But in reality, many workers would welcome technology that helps tackle the admin burdens or tedious tasks that consume their time each day.

European employees lose an average of 15 hours every week to administrative tasks. Only 43% say they spend most of their working day focused on work that delivers direct value, while more than a quarter (26%) say they’re spending most of their time on administration outside their core role.

That helps explain why AI is attracting so much interest in the workplace. Used well, it can automate repetitive tasks, simplify processes and make it easier for employees to access the information they need.

Employees do not necessarily want technology to do their jobs for them. They want more time to focus on the work they were hired to do. Almost three in ten (29%) of employees say they would enjoy their job more if they had greater freedom to focus on creative work.

The challenge is that discussions around AI are no longer limited to productivity. As organizations introduce AI into more workplace processes, employees naturally want to understand what role it is playing and what influence it has over decisions that affect them.

Clear boundaries build confidence

Recent headlines have done little to ease those concerns. Stories involving AI analysis of workplace communications and AI-enabled employee monitoring have prompted wider discussions about where organizations should draw the line. While these examples do not reflect every organization's approach, they have contributed to growing uncertainty about how AI could be used in the workplace.

Those concerns become even more pronounced when AI moves closer to areas such as hiring, performance management and career progression. Most employees are comfortable with AI helping them complete a task, find information or reduce administrative work. They become less comfortable when they are unsure how much influence the technology has over decisions that shape their careers.

This is why businesses need to be clear about where AI fits into workplace decision-making. Employees should understand where AI is supporting decisions, where human judgement remains essential and who is ultimately accountable for outcomes.

AI can help identify patterns, analyze information and provide recommendations. It can help managers make better-informed decisions and reduce administrative effort. Responsibility for decisions that affect an individual’s career, development or wellbeing, however, should remain with people.

The clearer organizations are about where AI supports work and where people remain accountable, the easier it is for employees to feel confident about its role in the workplace.

Bringing employees into the conversation

Employees are more likely to embrace new technology when they understand how it works, why it is being introduced and how it can help them in their role.

That means businesses need to invest in communication, training and skills development alongside technology deployment. Employees should have opportunities to learn, experiment and develop confidence in using AI themselves.

This is particularly important at a time when concerns about replacement remain widespread. People are far more likely to view AI positively when they see it helping them become more productive, develop new skills or spend more time on higher value work.

The conversation should not simply focus on what AI can do but also focus on how employees can work alongside it.

Trust has to be earned

AI has enormous potential to improve employee experience and reduce the administrative burden that continues to frustrate many workers. But successful adoption depends on more than introducing new technology.

Employees do not need every answer about AI, but they do need honesty about where it is being used, where decisions remain human and what role they have in the process.

Ultimately, confidence is built when employees can see that technology is helping them do their jobs better, not quietly making decisions on their behalf. AI can automate work, but building trust still requires people.

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Quantum computing has fascinated the tech world for several decades. That’s because, while classical computing is binary, quantum bits (qubits), made from atoms, electrons and superconductors, can be in both “0” and “1” at the same time, providing computational power that is inaccessible to classical computers.

The implications of this computational power will be far-reaching, such as accelerating drug discovery, optimizing global supply chains, and revolutionizing material science for clean energy. If that’s not interesting enough, the new frontier of hybridizing quantum and classical computing generates yet another source of excitement for the field.

Humanity has embedded computing into the fundamental fabric of daily life. It underpins our progress in an enormous number of fields, as well as impacts our day-to-day lives more than almost anything else.

While quantum computers are fascinating on their own, it is becoming increasingly clear that their integration into data centers - treating them as a crucial piece of the broader computing puzzle - represents one of the most interesting frontiers in science and technology today.

Hybrid computing: already a reality and key in the future

The idea that quantum and classical computing will work together is not a future vision. Quantum computers already rely on tight integration with classical computing, which plays a critical role in enabling their operation.

For example, extensive classical computing is used for generating the control signals that operate the quantum hardware and to calibrate quantum hardware and control systems. In this scenario, classical computers are finding thousands of different system parameters and retuning them constantly as they drift over time.

Very compute-intensive classical algorithms are also used for decoding errors during quantum error correction, which is critical for enabling stable quantum computation and the scaling of quantum systems. Another key use case is that hybrid quantum-classical algorithms rely on interleaving quantum and classical computation to solve complex problems.

In the future, this hybridization will have a growing impact on computing applications. QPUs will act as specialized accelerators within classical HPC and AI workflows, complementing CPUs and GPUs rather than replacing them. Quantum systems will sit alongside CPUs and GPUs in data centers and be deployed for exactly the type of problems they are best suited to solve.

In fact, this is already starting to happen - some of the most important recent quantum demonstrations have relied on quantum and classical systems operating in close coordination. For example, RIKEN and IBM scientists recently achieved one of the largest quantum simulations of iron-sulfur clusters through closed-loop data exchange between a co-located IBM Quantum Heron processor and RIKEN's Fugaku supercomputer.

This kind of hybrid exchange between quantum and classical systems, rather than quantum computing operating as an isolated, standalone resource, is the model we expect to become standard as the technology matures.

The final, and perhaps most intriguing frontier in quantum-classical hybridization is its relationship to Artificial Intelligence. Compute infrastructure is being used to train AI models, while hardware capabilities limit what models can do in various ways. Integrating quantum hardware into this compute fabric may allow new data to be generated for AI models as well as new engines for AI inference.

Moreover, AI tools may help us break some of the abstraction layers we have created, which can remove further limitations on how we use quantum hardware to solve problems.

Quantum computers don't run themselves

Given the above, as quantum computing moves from laboratory demonstrations toward practical applications, success will depend not only on better quantum processors but on how effectively quantum and classical computing work together.

The latency and bandwidth of the quantum-classical links will play a critical role in how performant this integration is and will determine how deeply connected the quantum and classical processing can become. This matters because qubits hold their state only briefly - the faster the classical system can read, process and respond, the more complex a calculation it can support before that state is lost.

Another critical aspect is software. Building infrastructure, tools, compilers and applications that orchestrate hybrid quantum-classical workflows efficiently, with high performance as well as developer experience in mind, will be critical for the industry to succeed in its mission to deliver real-world value in a timely manner.

This requires investment in the orchestration layers that allow developers to calibrate and control quantum resources as easily as they would HPC today, abstracting away much of the underlying hardware complexity.

The next phase of quantum computing will depend on bringing together advances in physics, engineering and computer science to cross the barrier from lab prototypes to useful computers.

There are many challenges ahead, from scaling the hardware and performing error correction at scale to finding the right applications to push for. The clear message is that hybrid quantum-classical systems are not a temporary stage in this journey. They are the foundation on which practical quantum computing will be built.

Enterprises and IT leaders exploring quantum shouldn’t focus on the number of qubits, but on how systems integrate with classical infrastructure. When it comes to scaling useful quantum computing and implementing hybrid computing, the control systems, error correction and software orchestration will determine whether a system can deliver reliable performance and repeatable results.

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Cybersecurity risks in water infrastructure have consequences that reach far beyond systems and networks.

Across the sector, the systems responsible for treatment and distribution are becoming more connected. Pumps, sensors and control environments that once operated in isolation are now linked to wider networks, often in the name of efficiency or modernization.

The problem is that these systems were never built with continuous exposure to cyber threats in mind and connecting them has introduced new attack surfaces that are difficult to maintain visibility of and control.

At the same time, many facilities have crept towards an increasingly blurred line between IT and operational technology (OT). Driven by incremental needs and very practical limits on systemic refresh, these systems have been added to and grown more complex over time rather than being designed securely from the ground up.

As a result, they become more tightly interconnected, with access stretching further across networks and systems than it should. Once an attacker breaches a system, it becomes far easier to move laterally across the network and get closer to critical infrastructure.

Meanwhile, cyber threats continue to encroach on the water sector, with attacks becoming increasingly frequent and their potential impact is hard to ignore. Disruptions to drinking water treatment or control systems can quickly escalate, interrupting supply or affecting water quality and, in turn, the communities that depend on them.

Structural challenges in securing water systems

Securing water infrastructure is made more difficult by the operational realities many providers face. Technology environments have expanded over time, often without dedicated cybersecurity resources growing at the same pace, making it harder to maintain consistent oversight across increasingly ageing and disparate systems.

Many organizations are also balancing modern hyper-connected digital management expectations with the ongoing operation of originally isolated, long-established systems. This places additional pressure on teams responsible for maintaining both resilience and day-to-day continuity.

Another persistent challenge is the divide between IT and operational technology (OT) teams. Because these environments have traditionally evolved separately, with different design approaches, responsibilities, priorities and expertise, they are not always closely aligned, which can slow decision-making and create gaps in visibility during an incident.

In smaller providers, cybersecurity responsibilities may sit with operational staff whose primary expertise lies in running facilities rather than managing cyber risk. Larger organizations may have more specialized cyber teams, but greater separation between functions still introduces coordination challenges and the risk of operational blind spots.

Connectivity without constraint

The growing use of cloud computing platforms and remote access tools has brought clear operational advantages to critical infrastructure like water systems. However, it has also reinforced a default position of keeping systems online at all times, often without a genuine operational imperative for continuous connectivity.

This “always-connected” approach can unnecessarily increase exposure, particularly as more assets become reachable across wider networks. Without clear control over the time windows when systems need to be accessible, organizations may be creating more risk than expected, certainly more than is required.

A stronger, resilient approach starts with recognizing that security grows by making connectivity intentional. Not every system needs to remain online continuously, and limiting unnecessary access significantly improves security outcomes.

This can be achieved by creating stronger separation between critical systems and the wider network, using controls that allow connections to be enabled only when required while maintaining essential operations. In this model, connectivity is actively managed to define resilience on demand.

Containment as a first line of defense

In the event of a vulnerability or compromise, response speed is critical, notably in environments where interconnected systems enable threats to spread rapidly across the network. Without effective connection controls in place, attackers can exploit this unconstrained accessibility to extend their reach before a full response is underway.

The ability to isolate systems in real time helps change this state. Segmenting critical parts of the network helps limit lateral movement, and deeply segmenting down to high criticality digital elements enables organizations to contain threats far more substantially and focus their efforts on speeding up the incident response.

Having this level of control helps limit the spread of disruption and supports a more structured response. It also creates clear, demonstrable evidence of how risk is being managed - something that’s becoming increasingly important as regulatory scrutiny and cyber insurance requirements become more demanding.

Moving to controlled access

The most resilient model possible with physical connection control treats access to critical systems as fully conditional. Rather than keeping them permanently online, connections can be limited to where, when and why they are required for business reasons. As risk levels change, this can be refined or tightened at will.

This lowers both risk and potential impact, minimizing loss, while preserving the flexibility required for day-to-day operations.

For water providers, deliberately managing connectivity and segmenting networks at an infrastructure level should be a priority for resilience. Clearer boundaries and reduced unnecessary access make it easier to protect infrastructure that plays a vital role in public safety.

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Across industries, organizations are increasingly tracking AI usage through dashboards, token utilization and platform engagement metrics. Some of the most visible companies in the world have stood up leaderboards ranking employee AI use; others have tied AI adoption directly to raises and promotions.

The intent is reasonable - leaders want a signal that the investment is landing.

But we’ve reached a point where managers, executives and boards carry a false assumption that AI usage means a more AI-ready workforce. Usage and capability are not the same thing.

Recent research, based on a survey of 2,000 workers across the U.S. and U.K., suggests many organizations are measuring AI adoption faster than employees are learning to use it effectively. While 46% of employees report using AI tools at work, nearly half have received no formal AI training and 56% have no clear path for developing AI-related skills.

Perhaps most concerning, 17% admit they are pretending to use AI at work. If organizations mistake AI usage for capability and readiness, they risk building strategies and processes for a workforce that doesn’t actually have the skills to execute them.

The gap isn't a talent problem; it's a systems problem between technology adoption and workforce development. Solving it means CIOs and HR leaders must move beyond coordination and take joint accountability for translating AI usage into true workforce capability.

The measurement trap

As AI becomes embedded into everyday work, leaders are looking for ways to track progress. Dashboards, usage reports, prompt counts and engagement metrics seem to offer an obvious way to demonstrate momentum. But activity is not the same as capability.

An employee generating 10 prompts daily may appear highly engaged. That doesn’t mean they know how to provide effective inputs, evaluate outputs, recognize hallucinations, or apply AI responsibly in ways that meaningfully improve performance.

When organizations treat activity as a proxy for competency, leaders develop a false sense of confidence about workforce readiness while critical capability gaps remain hidden beneath the surface.

Don't just agentify the mess

There’s a parallel trap on the technology side, where there’s a race to “agentify” everything, wrapping an agent around every existing process and SKU.

But automating a broken workflow simply produces a faster broken workflow. The point isn’t to agentify the mess. It’s to rethink the work first, then apply AI to what matters.

The same discipline applies to how we measure return. Automation and the productivity gains are real, but treating efficiency as the finish line badly undersells the opportunity.

The larger prize is transformation: moving the topline and the bottom line, not just shaving cost per task. Organizations that aim only at incremental productivity will capture a fraction of what AI can actually deliver.

The visibility gap nobody is talking about

This creates a new challenge for CIOs and HR leaders. Most organizations can now see who is using AI tools. Far fewer can see whether employees are using them effectively.

The next phase of AI transformation will not be determined by access to tools, which most organizations have already solved. It will be determined by whether employees possess the judgment, confidence and skills necessary to use those tools productively and impactfully.

Without that visibility, organizations risk optimizing for adoption metrics while underinvesting in the development that generates long-term business value. Technology procurement lives with one team. Learning and skills data lives in another. Performance data often lives somewhere else entirely.

Consequently, organizations struggle to connect AI usage with business outcomes.

This is a CIO problem as much as an HR one.

Joint accountability, not coordination

The conversation I’m having with peers is about moving from coordination to joint accountability. Coordination means IT and HR talk to each other. Joint accountability means they own the same outcome together; specifically, whether the workforce can execute the organization’s AI strategy.

Forget using AI. Are employees using it effectively enough to have a measurable impact on the business?

That reframe changes where decisions get made and who makes them. HR leaders understand what capabilities the business will need and where the development gaps are widening. CIOs understand how AI tools are deployed, where agents sit in the workflow, and where technical infrastructure can support learning at the point of work.

Neither function can solve the problem alone.

The skills that endure

Through all this churn - new models, new tools, new agents every quarter - one thing stays durable: domain expertise expressed as work. The specific, task-level skills that make someone effective at their core job don’t depreciate the way a given tool does. As AI transforms how work gets done, those domain-grounded skills are what compound and carry forward.

When organizations examine why AI adoption often stalls or remains shallow, the same issue tends to surface: AI is deployed without being meaningfully anchored to the skills and tasks of the workforce. Adoption becomes activity - visible, but not compounding.

Addressing this requires a shift in focus. AI needs to be connected directly to how work is actually performed and improved. When adoption is tied to real tasks and outcomes, it becomes a mechanism for continuously strengthening underlying skills, rather than just increasing tool usage.

What CIOs need to own

The AI-readiness conversation has largely focused on technology deployment. The harder question is whether organizations are building the workforce capabilities necessary to translate adoption into results.

For CIOs, that means taking ownership of something that extends beyond technology infrastructure. It’s creating the systems, partnerships and feedback loops that allow their organizations to build capability at the speed AI is evolving, with visibility into the AI skills that their people are developing.

The most successful organisations will have CIO and HR leaders jointly turning AI usage into sustained workforce capability and measurable business value. They give employees not just the tools, but the support to use them effectively. That is what the AI-empowered workforce of the future looks like.

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Data leaks and corporate breaches have become routine. In many cases, stolen credentials, databases, or attack tools eventually appear on the dark web, where they are traded and reused in future attacks.

This raises a question for businesses: if stolen corporate data ends up on the dark web, does it make sense to engage with this environment directly — by buying information, paying for services, or negotiating with attackers?

The short answer is no.

Not because the dark web doesn’t matter — quite the opposite: it is a core part of today’s cybercriminal infrastructure. The problem is that doing business with the dark web rarely reduces the immediate risks and systematically strengthens the very market that creates threats.

The nature of the dark web

The dark web — often used interchangeably with the term darknet — refers to parts of the internet intentionally hidden from search engines and accessible only through tools such as Tor or I2P.

It is not a single network but a collection of platforms and communities gated by encryption, nonstandard protocols, or restricted access. While some resources are relatively neutral, others are directly tied to criminal activity. From a cybersecurity perspective, the dark web matters primarily as a mature cybercrime marketplace.

Technically, many platforms resemble early internet forums. Functionally, however, they operate much like B2B marketplaces — except the products include stolen data, compromised accounts, malware, exploit kits, and attack services.

The economics of cybercrime

A key function of the dark web is simplifying the monetization of cybercrime. More importantly, it enables specialization and the formation of complex supply chains.

Instead of building operations end-to-end, cybercriminals now focus on specific roles: some identify vulnerabilities and gain initial access, others develop and distribute malware, while others specialize in monetization through data sales, extortion, or attacks-for-hire.

This division of labor has created a full-fledged cybercrime economy. Attackers no longer need advanced expertise or their own infrastructure — they can purchase the necessary tools and services, lowering the barrier to entry and increasing the scale of attacks.

A clear example is the Ransomware-as-a-Service (RaaS) model, where core groups develop malware and manage negotiations, while affiliates carry out attacks for a share of the ransom. This model has enabled large-scale incidents such as the 2021 Colonial Pipeline attack, which disrupted fuel supplies across the U.S. East Coast and resulted in a $4.4 million payment.

Dark web intelligence and false signals

As the dark web evolved into a cybercrime marketplace, businesses naturally became interested in monitoring it for early warning signals.

In practice, this approach works only partially. The problem with dark web intelligence is that it comes from an environment with virtually no reliable verification mechanisms.

Like any anonymous and unregulated market, the dark web contains a significant amount of noise, manipulation, and outright fraud. Listings may be outdated, fabricated, or recycled from old leaks, while reputation signals can be artificially inflated.

The problem becomes even more pronounced when monitoring is outsourced to third-party vendors. Weak or unverifiable signals can easily be exaggerated, misinterpreted, or presented as evidence of major threats.

As a result, dark web monitoring rarely provides the level of certainty businesses expect. At best, it can highlight a potential issue that still requires verification.

Never pay cybercriminals

Direct engagement with the dark web is even more problematic — whether through ransom payments, purchasing leaked data, or hiring anonymous actors to test infrastructure.

The most obvious issue is that paying cybercriminals offers no guarantees. Attackers may simply demand another payment or leak the data anyway.

Uber learned this in 2016 after paying attackers $100,000 following a breach affecting 57 million users, only for the incident to become public later and trigger regulatory fallout.

A similar pattern appeared in the 2017 breach of HBO, when attackers stole 1.5 TB of Game of Thrones-related data, including unreleased episodes and internal documents. HBO reportedly transferred $250,000, but the material leaked anyway.

The broader problem, however, is structural: every payment flowing into the dark web economy directly finances its further growth. The more businesses participate in that market, the stronger the incentives for attackers to discover vulnerabilities, compromise systems, and scale operations.

Common mistakes when dealing with the dark web

When dealing with the dark web, organizations tend to repeat the same mistakes regardless of industry or size.

Trying to pay their way out of the problem. Companies often approach ransomware or leaks as negotiation problems. In reality, paying a ransom guarantees neither recovery nor safety. According to a 2021 study by Cybereason, 80% of organizations that paid ransoms were attacked again, often by the same groups.

Treating dark web monitoring as insurance. Monitoring services are often marketed as proactive protection. In reality, if company data appears for sale on the dark web, the compromise has already happened. Monitoring can provide signals, but it cannot replace actual security controls.

Hiring dark web hackers to test infrastructure. Unlike legitimate penetration testing, anonymous dark web “audits” offer no accountability, verification, or compliance guarantees. Even worse, the hired hacker may establish unauthorized access and later resell it.

Panicking after seeing the company name on the dark web. Many leaks and listings are outdated, recycled, or entirely fabricated. Without proper verification, rushed decisions can worsen the situation.

Delegating the entire issue to “dark web specialists.” Many companies delegate dark web monitoring to external vendors without the ability to independently assess the quality of the results. This creates a dangerous information asymmetry and increases dependence on unverifiable claims.

What businesses should do instead

Dark web intelligence can be useful as one additional source of signals, but it requires cautious interpretation and independent validation. Treating it as a reliable source of truth — or outsourcing the entire function without oversight — is risky.

More importantly, businesses should avoid directly financing criminal ecosystems through payments or participation in underground markets.

Cyber resilience is built internally. Rather than attempting to “buy security” on the dark web, organizations should invest in systematic defense: resilient architecture, vulnerability management, monitoring, incident response, and technologies capable of mitigating attacks while maintaining continuity of critical services.

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Across the UK, cybersecurity incidents have become a familiar feature of the business landscape.

Disruptions affecting manufacturing and logistics over the past year have underlined how exposed organizations can be when physical operations are connected and digitalized.

Despite this growing awareness, boardroom conversations on cyber risk still tend to center on corporate IT and not operational technology (OT).

That focus leaves a significant gap. Operational technology, the systems that run factories, manage supply chains and underpin essential services, is now a primary target for attackers. When these environments are compromised, the consequences extend far beyond lost data, affecting safety, revenue and in some cases an organization's ability to operate at all.

For many boards, this is less a question of indifference and more one of framing. Cyber risk is still commonly understood through an IT lens, shaped by experiences with data breaches or malware attacks that take down websites or enterprise IT systems. Operational disruption behaves differently in both scale and impact, and it demands a different level of governance attention.

Why OT risk is routinely underestimated

Much of today’s operational infrastructure was designed long before connectivity and remote access became standard. These systems were engineered for reliability and safety, not for defense against hostile actors. As they have become more connected and digitalized, exposure has increased without always being matched by equivalent security practices.

The result is that many of the most serious business risks now sit within operational environments that boards rarely examine in detail. This creates a structural blind spot. While IT incidents are often measured in hours or days, failures in OT environments can take longer to mitigate while halting production, disrupting critical services and generating losses that compound rapidly over time.

Boards tend to engage more effectively when risk is grounded in tangible business terms. Understanding what a facility produces in a day, or what a week-long shutdown would mean for customers and partners, brings operational risk into sharper focus. Without that context, OT security can remain abstract and under-prioritized.

When cyber incidents stop operations

Recent incidents have shown how quickly cybersecurity events can escalate into operational crises. Last year, a leading British automotive brand publicly confirmed a cyber incident that led to a precautionary shutdown of systems. Manufacturing and retail operations were halted for weeks and disruptions rippled through suppliers, logistics partners and dealerships

Similar lessons can be drawn from cyber incidents affecting the UK’s water sector, where attackers targeted environments connected to the operational systems that control treatment and distribution. Beginning in 2024, multiple incidents reached systems close enough to operational control to raise concerns about safe operation.

Taken together, these examples point to board-level issues beyond preventing down time or service outages. They are also about maintaining operational continuity, understanding how quickly localized disruptions can cascade across an organization, and factoring in safety concerns and reputational risk.

A risk landscape shaped by geopolitics

Operational technology risk is increasingly shaped by global forces. Geopolitical tension, trade restrictions and supply chain uncertainty now influence how organizations plan and prioritize security investment.

At the same time, governments are raising expectations around resilience and incident reporting, particularly in sectors linked to national infrastructure. Boards are therefore required to consider regulatory and geopolitical pressures alongside technical risk, adding another layer of complexity to cyber governance.

Bringing direction and discipline to governance

Stronger oversight depends on education and structure. Boards should expect cyber leaders to explain operational risk in clear business terms and to reference recognized best practice. Focusing on a prioritized and manageable set of critical controls that deliver the greatest risk reduction provides a practical foundation without overwhelming the organization.

Governance cadence is just as important as control selection. Regular, structured engagement with senior management create space to track how security investment supports operational resilience and wider business outcomes. Treating cyber risk as a standing governance issue, rather than an occasional update, reinforces accountability and sustained attention.

Clear prioritization models can further support decision-making. Categorizing actions into those that must happen now, those that can follow next and those that should not be pursued helps align technical, operational and financial perspectives. A shared language of priority reduces ambiguity and supports more consistent execution across sites.

A leadership obligation

Operational technology security can no longer be treated as a technical niche. It has become a leadership responsibility shaped by operational dependence, external pressure and increasingly capable adversaries. Boards that recognize this shift are better positioned to protect continuity, revenue and trust.

Looking ahead, resilient organizations will be led by teams that engage directly with the realities of their industrial environments. Asking sharper questions, demanding clearer insight and ensuring governance structures keep pace with operational risk remain among the most effective safeguards leaders can provide.

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The current state of AI adoption in UK businesses paints a decidedly mixed picture.

For many organizations, it’s full speed ahead: they’re using the technology to transform operations and unlock growth. For others, progress has stalled; they remain stuck in the sandbox, struggling to translate AI’s promise into tangible business outcomes.

In this environment, the government’s £200 million investment to support AI adoption and scaling is a welcome step towards turning theoretical use cases into reality.

Crucially, the inclusion of workforce training signals recognition that AI success isn’t just about technology, but about people and skills. Together, these measures underline AI’s potential to drive long-term economic growth in the UK.

However, investment alone will not be enough to close the gap between ambition and impact. To realize meaningful returns, businesses must take a more grounded approach that connects AI initiatives directly to operational reality and resists the temptation to implement AI for AI’s sake.

This means rethinking operating frameworks, balancing innovation with strong governance and establishing the right foundational architecture from the outset.

When done well, this creates the culture and processes needed to drive AI adoption, ensuring AI is not only deployed, but properly tested, governed, and scaled for sustained value.

Start simple to scale faster later

Businesses are often swept up in AI’s promise, treating it as a universal solution to enterprise-wide challenges but the reality is more nuanced. While the technology offers significant potential, value only comes from use cases with clearly defined outcomes, not from deploying it for its own sake.

A more effective approach is to start small and stay focused. Identifying two or three priority business processes where AI tools can deliver measurable impact is more likely to generate meaningful ROI, as once an initial pilot proves its value, organizations can build the credibility and confidence needed to expand.

With tangible results to point to, momentum builds, making it easier to scale further use cases and embed AI more widely across the business.

Equally, businesses need to be realistic about the journey. Results are rarely immediate and well-defined, accurate processes take time to refine. Building an AI-ready operating model is a long-term process, and the leap from successful pilot to deployment can introduce new questions and insights around where AI can deliver value.

Don’t build AI on shaky foundations

Businesses eager to get AI projects off the ground often move too quickly, approving projects before the right technical foundations are in place.

From data pipelines and model integration to reusable agent frameworks, these building blocks are critical. Without them, what should be a seamless transition from isolated AI pilots to enterprise-wide deployment instead stalls before it can scale.

Perhaps the most costly mistake is rushing straight into model development while neglecting data foundations. AI is only as strong as the data underpinning it and if that data is incomplete, inconsistent or inaccessible, even the most advanced tools will fail to deliver reliable outcomes.

The result is often inaccurate outputs, hallucinations and missed errors, which erode trust and limit impact. To mitigate this, businesses must prioritize data quality from day one and build in robust quality controls to catch issues early.

Governance isn’t just a tick-box exercise

Organizations that scale AI successfully build governance frameworks before writing a single line of code. This establishes clear ownership, consistent standards, and the organizational buy-in needed to drive AI transformation.

It also embeds testing and regulatory readiness from the outset, ensuring businesses have the operational discipline required to be compliant with evolving AI regulations.

Recent research shows that governance challenges can ultimately determine whether AI delivers value or introduces risk. By 2027, 60% of organizations are expected to fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks.

Building these frameworks from day one removes key barriers and helps answer any employee questions around trust, accountability and responsible use.

Rethinking operating models

AI success rarely comes down to technology alone, it hinges on organizational alignment. Too often, data scientists develop models that don’t quite meet business needs, while leadership sets expectations that aren’t grounded in real user experience, resulting in a disconnect that stalls progress before it scales.

Closing this gap requires more than upskilling alone. While building AI capability across the workforce is critical, real impact comes from rethinking operating models and culture, enabling a shift away from siloed specialists towards “human-in-the-loop" teams that actively manage, refine and scale AI across the organization.

This shift enables AI to move out of isolated use cases and into day-to-day operations, with continuous feedback loops that improve performance over time. Without it, even well-trained teams can struggle to translate technical capability into measurable business value.

At the same time, the pace of change can create its own challenges, as with new AI tools and developments emerging constantly, it’s easy for teams to mistake activity for progress. Without a collaborative operating model underpinning these efforts, perceived gains often lack the data and validation needed to prove real value.

Just the beginning

Businesses are only just starting to grasp AI’s true potential and the scale of opportunity it represents but investment alone is no guarantee of success. Without the right operational framework, culture, and data foundations in place, even the most ambitious initiatives will struggle to deliver impact.

The journey involves starting slow and scaling, ensuring governance frameworks are in place, and investing in an operating model that includes clearly detailed team ownership of projects.

Leadership will be critical in determining whether those investments translate into real value. That starts with reframing AI not as a standalone technology project, but as a business transformation effort that will fundamentally shape how the organization operates for years to come.

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The biggest publicly traded tech companies in the world, commonly known as the "Mag 7" — or magnificent seven — may not be enjoying their stranglehold at the top of the US economy for much longer. That's, of course, if the leaders of a swathe of new AI-centric tech firms, including OpenAI and Anthropic, have their say as they plan to IPO in the coming months. But positioning these businesses in the new big tech landscape has been a major challenge.

Ethics in AI

The Anthropic CEO Dario Amodei was reflecting on his history in the AI industry during an interview with Bloomberg that aired earlier this year.

In an exchange themed around the success of Anthropic's enterprise-centric AI tools like Claude Code and Claude Cowork, Amodei took the opportunity to opine on the nature of doing business and the importance of business models that align with your values.

Commenting that many in the tech industry prioritize models that tap into elements like engagement, advertising, and the promotion of AI slop, Amodei noted that compromising your own values is not a long-term and sustainable way forward.

This is at least as far as he's concerned. That's why, he suggested, he's attempting to make Anthropic more "useful" to the world by targeting enterprise customers.

Bad blood

Amodei, an ex-OpenAI executive, co-founded Anthropic in 2021 largely as a rejection of the values that drove OpenAI at the time and the paths the company had taken.

In particular, reports suggest that Amodei was frustrated and disturbed by the willingness to bypass what he considered to be crucial safety measures, like the slowing of updates to prevent malicious use of AI.

Critics of Anthropic, however, also point out that despite positioning itself as a safety-first AI company, engineers are releasing increasingly powerful models — including Mythos lately — that threaten to undermine safety if they get into the wrong hands.

The ability to personalize and scale marketing strategies at speed has firmly captured the attention of chief marketing officers (CMOs) across the globe.

Yet, in the relentless rush to automate and optimize, some brands may be jeopardizing the very thing that fosters genuine connection with consumers: authentic storytelling, real human emotion and a distinct creative vision.

AI-generated adverts are an increasingly common sight across UK high streets – from event promotion to product adverts.

A staggering 51% of CMOs are now actively deploying Generative AI (GenAI), with some of the world’s biggest brands attracting widespread criticism for it.

According to recent data from Canva, 70% of consumers say they can often identify AI-generated advertisements because something feels like it’s missing.

For UK marketers, getting the balance right means applying AI where it works best, such as automating and scale, while ensuring that we do not lose the human touch that resonates and builds lasting trust.

The AI automation trap

AI undeniably offers powerful tools for content creation and distribution, making marketing efforts more scalable than ever before. From drafting copy to automating campaign launches, its capabilities are vast and transformative, but a narrowed focus on AI for efficiency can quickly lead brands into the automation trap. When AI dictates the soul of a brand rather than simply serving as a sophisticated tool, the result is often a generic brand voice and weaker customer engagement.

The danger lies in homogeneity. If every business leans solely on algorithms to craft its messaging, we risk creating a sea of indistinguishable content, stripping away the uniqueness that sets one brand apart from the next. Marketing then becomes a mechanical, predictable exercise. And, consumers can spot the lack of inauthenticity quickly, disengaging when they do. For UK businesses, over-reliance on automated content risks making the brand and the messaging entirely forgettable.

Authenticity as the ultimate differentiator

The solution to this is for businesses to spend time on authentic storytelling, sharing insights and messages in a way that only they can. While AI can process vast data and construct coherent narratives, it cannot replicate empathy or emotional nuance - the qualities that forge deep connections and remain the foundations of trust, consistency and accountability. These principles, much like the Lindy Effect, prove their worth through lasting impact and enduring relevance.

Human-led stories carry an emotional intelligence that algorithms simply cannot master. They capture the subtleties of lived experience, real struggles and unique perspectives that resonate with audiences on a personal level. At its core, an authentic brand story is built on shared vision, real customer experience and human connection. When these elements are present, brands differentiate themselves meaningfully and build a loyal following. That is why CMOs should champion 'proof over promise' by grounding stories in real, measurable impact and aligning them with their company’s core values.

The importance of "human-only zones"

Putting that principle into practice means CMOs must intentionally establish and protect "human-only zones" within their marketing strategies. These are the creative and strategic areas where full AI automation is consciously resisted. In this sense, CMOs should recognize AI’s limitations and ensure critical aspects of brand-building remain human-led.

These essential human-led zones include:

Strategic visioning - defining the core brand story, values and long-term positioning

Emotional messaging and humor - campaigns carefully crafted to evoke specific feelings, which always require nuanced human understanding

Creative direction - the artistic and aesthetic choices that give a brand its distinctive visual and auditory identity

Crisis communications – which demand empathy, judgment and a sincere voice when situations are sensitive

Innovation and challenging norms - pushing creative boundaries and developing truly disruptive ideas. AI, which primarily optimizes based on existing data, struggles to generate this kind of thinking independently

By ring-fencing these areas, CMOs keep the human heart and mind at the center of their brand's identity, preventing it from being diluted by generic, algorithm-driven content.

CMOs as guardians of creativity

That protective role is reshaping the job of CMO itself. Marketing leaders are moving beyond strategy and execution to become guardians of creativity. They must champion and cultivate human originality and innovation within their teams, building environments where it thrives and drives competitive advantage.

This means empowering teams to experiment, take risks and bring fresh thinking forward. It means investing in human talent, creating room for creative development and valuing ideas that don’t immediately fit an algorithmic mold.

Ultimately, AI can speed up content production and streamline distribution, but humans must remain accountable for meaning, judgment, trust and the wider consequences of the stories they tell.

The future of marketing will require combining AI’s capabilities with human originality to amplify impact while preserving the authentic voice that truly connects with audiences.

For UK businesses hoping to connect with their customers and build loyalty, this means embracing AI as a powerful enabler – and never at the expense of the authenticity that creates lasting customer relationships and brand resonance.

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There's a pattern many organizations know well. A new technology arrives, adoption accelerates faster than governance can keep up, and a few years later, the finance team is staring at a spreadsheet, wondering how the bill got so large and who signed off on it.

The SaaS era exposed what happens when technology adoption outpaces financial oversight. And with agentic AI embedding itself into everyday workflows, organizations risk heading down a similar path.

Recent reporting on Amazon employees 'tokenmaxxing' - gaming internal AI metrics to inflate adoption figures, is an early signal of what that looks like in practice.

But the dynamic is not specific to Amazon. When AI usage isn't fully visible, and the incentives favor showing more activity rather than less, accountability tends to disappear quietly. It points towards a governance failure - and governance failures require structural solutions.

The opportunity is clear. The governance isn't.

The need for visibility

AWS's Banking on the Cloud 2026 report makes the strategic case for agentic AI in financial services clearly and compellingly. Cloud computing infrastructure and AI agents are positioned as the foundation of next-generation banking, which means faster decisions and more responsive customer experiences.

What the report focuses on is what AI can save. The other part of the equation is what AI itself costs to run at scale, and who's accountable for that.

The cost model for AI agents behaves differently from anything most enterprise finance teams have managed before. When you license a conventional software tool, there's usually a fixed price and a user count. The spending is visible even when it isn't well-controlled.

AI agents work differently as they run continuously, calling on external services and triggering actions across systems as they go. Each step consumes resources, and because agents operate autonomously, often handling tasks that would previously have required human judgment, that consumption can scale quickly and unpredictably. There's no contract line that captures it cleanly and no renewal date that forces a review.

Getting ahead of this requires visibility that most organizations are only now beginning to build.

The sprawl problem

The SaaS management challenge is a familiar one to most IT and finance leaders. Application estates grew faster than procurement could track them, governance lagged behind adoption, and many enterprises spent years rationalizing software stacks they never intended to build. It’s an ongoing problem that businesses are still managing, years down the line.

AI agent sprawl will likely develop differently, but the underlying problem is similar. The critical difference is pace.

A SaaS tool that gets deployed and forgotten sits there, quietly billing at a fixed rate. An AI agent generating outputs in real time is actively consuming resources from the moment it runs, and that financial exposure, left unmonitored, compounds in ways a forgotten software subscription simply doesn't.

Organizations that get the right comprehensive visibility in place early will be in a significantly stronger position than those treating cost governance as something to formalize later.

There's also a regulatory dimension that's coming into sharper focus, particularly in financial services. AI agents frequently depend on external model providers and third-party data sources. Each dependency introduces a potential point of failure - and in regulated industries, potential compliance exposure.

Regulators are paying attention, the EU AI Act's full obligations for financial services AI land in August 2026, and DORA audits are already underway, which means the question of who owns that chain of accountability will need a cleaner answer than most organizations currently have.

What good governance actually looks like

The encouraging part is that none of this requires building new disciplines from scratch. It requires applying familiar ones to a new context and doing it early.

The right starting point is understanding cost in relation to outcome. What does it actually cost to complete a task using an AI agent, and what is that task worth to the business? Answering it means connecting AI spending data to the broader picture of how technology is used and what it delivers, so that finance and engineering are working from the same information rather than talking past each other.

Controls also need to be built into the infrastructure rather than layered on top of it. As agent deployments grow, no team can realistically review individual workflows by hand. Policies that depend on someone remembering to check a dashboard aren't really policies; they're suggestions. When a budget review turns difficult or a regulator asks questions, suggestions don't hold up.

Most importantly, ownership needs to be established from day one. Which budget carries this deployment? Who reviews it when consumption shifts? Right now, many AI agents are being deployed by engineering teams without meaningful involvement from finance. That gap is entirely closable. Closing it before the bill arrives, rather than after, is where the real advantage gets built.

The organizations that navigate agentic AI well will be the ones that treat governance as part of the deployment decision rather than an afterthought to it. Cleaner accountability means faster decisions and AI investments that can actually be defended against the board or a regulator. Throughout the rest of this year and beyond, that is the key differentiator between organizations that scale AI confidently and those that are still untangling the bill.

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The conversation about AI in space keeps arriving at the same image: a floating supercomputer processing the world's information from 400 miles up. It’s a compelling narrative, but there’s a gap between what companies are hoping to build and what is being built today.

Today, satellites run on fixed power budgets measured in watts with strict constraints. Bandwidth is scarce enough that every byte reaching the ground has to earn its place.

Those limits push the field toward architecture that looks more like a nervous system, than a data center – lightweight models running onboard that interpret sensor data in near real time and convert observations into structured events. The event reaches the ground.

The raw pixel doesn’t.

From hours to minutes

In a conventional Earth observation pipeline, a satellite captures an image, downlinks it to a ground station, ground systems process the raw data, and the result reaches whoever needs it. Best case that happens in hours, but often it’s closer to a day.

For a disaster response team managing a flood in a low-lying river delta, or a conservation authority trying to locate the origin point of a wildfire in a remote national park, that day comes at a cost.

A satellite running inference onboard changes the math. Detection happens in seconds. What gets downlinked is a position, timestamp, or risk score. The bottleneck shifts from the space segment to the ground distribution, which is a comparatively manageable problem.

The use cases where this matters most are not the visible disasters people are watching. They’re the methane leak on a pipeline with no weekly inspection schedule, an oil spill beyond the reach of coastal patrols, a wildfire that began in a remote area before anyone had reported smoke. Onboard inference turns a passive imaging asset into an early warning system.

What orbital constraints teach edge architects

The tradeoffs being resolved in orbit are an extreme version of the same constraints facing any organization deploying AI outside a well-provisioned data center.

Cloud-native AI development often has a back up plan: when the model is too large add compute; when bandwidth is constrained, increase it; when latency is a problem, move the processing closer. In orbit, none of these options exist. You build within the envelope, or the system doesn’t function.

The result is a forcing function that enterprise architects rarely encounter at the same level. Industrial IoT deployments face intermittent connectivity. Autonomous systems can’t afford round-trip latency to a central server at decision time.

The shift from 'send everything, process centrally' to 'process locally, transmit what matters' is happening across multiple industries. Space is where that shift ran without a safety net.

The bandwidth math

The data reduction numbers transmitted in real time is not just 80-90 percent. Once processing happens on the spacecraft, the reduction for the real-time layer exceeds 99 percent. This is semantic compression. The satellite sends the meaning of what it saw, not the measurement it produced.

A conventional operator downlinking hundreds of terabytes of raw imagery daily is paying bandwidth cost for data that largely contains nothing of interest. With onboard inference, what's transmitted in real time is a structured detection event: a position, a timestamp, a risk score, and perhaps a small compressed image. That is hundreds of kilobytes, not terabytes.

An operator downlinks only what warrants examination, rather than blindly dumping the full data stream.

Architecture decisions that preview what's next

A model making decisions before a human is in the loop carries different requirements than one generating recommendations for human review. Ambiguity tolerance is lower. Inference behavior needs tighter scoping. This is the same conversation that medicine and finance have been having for years.

AI-assisted diagnostics and accountability distributed across platform, model, training data, and end customer rather than concentrated in a single layer. The space industry is joining a conversation other sectors have already been having for years.

In a cloud environment, model size and computational efficiency are optimization targets. Meaning they are important, but secondary to capability. In a constrained orbital environment, they are the primary design constraint from which everything else follows. A model that cannot run within the available compute envelope is not a model that gets deployed. There is no option to add a larger instance.

A maritime patrol aircraft that previously ran random vessel inspections now works from a ranked list of targets with risk scores attached. Some alerts will be false positives which is a physical reality of any probabilistic system. But the aircraft's operational effectiveness improves substantially compared to random patrolling or no monitoring at all. The AI narrows the search.

The scaling problem is familiar

One satellite running an onboard model is a proof of concept. A constellation of hundreds running distributed inference is a different infrastructure problem as orbital AI scales.

Centralized orchestration becomes the bottleneck when constellations grow. Every decision can’t route through a ground station. Distributed inference is a requirement. Enterprise architects hit the same wall when a pilot deployment expands to thousands of edge nodes. The centralized model that worked in development becomes the thing that breaks in production.

The cloud infrastructure analogy supports this. Nobody builds a data center before launching an application. The pattern is shared infrastructure, with control at the model, mission logic, and decision layer. Those can be sovereign regardless of who owns the underlying compute.

A design principle worth carrying

It’s hard to develop constraint-based thinking in environments where adding compute is always on the table. The organizations that have built it tend to have faced conditions where it wasn’t.

The strategic advantage in edge AI over the next decade will not just be measured in the amount of compute available. It will also be measured in code deployed to the right place in the stack. Satellites are running that experiment first.

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The 2026 World Cup is already under way. Billions of eyes are on the pitch. The story that matters to anyone running a supply chain, though, is playing out in warehouses, customs terminals and distribution centers spread across three countries.

For the first time, the tournament spans three host nations: the United States, Canada and Mexico. That means millions of product lines, thousands of supplier handoffs and cross-border compliance requirements across three distinct regulatory environments, all compressed into a window with zero tolerance for error.

The operational scale of this tournament is without precedent.

Now that the group stages are live, the pressure on supply chains is real and immediate. The lessons surfacing are worth paying attention to, because they apply well beyond sport.

A live packaging stress test

Official merchandise for an event of this scale moves across multiple countries, customs jurisdictions and retail channels simultaneously. Product identification has to work at every stage of that journey, from the manufacturer's floor to the stadium vendor's shelf. The label that cleared customs in Los Angeles may face entirely different requirements in Toronto.

The deeper problem is structural. Today's supply chains still largely operate as disconnected islands. Each site, supplier, co-packer and carrier maintains its own systems and repeatedly re-enters the same product and compliance data. That fragmentation creates built-in waste at every handoff: redundant setup, duplicate records and inconsistent label versions. Under normal conditions, these inefficiencies are costly but can be masked by day-to-day operations. Under the pressure of a live global tournament, they become critical.

Demand shifts are happening in real time. A host city that reaches the knockout stages sees fan merchandise demand surge overnight. Supply chains built on static, batch-processed labelling data are finding they cannot respond at that pace.

The speed of these shifts can be surprisingly tangible. In Atlanta, shortly after the Spain-Cape Verde match, I walked through the airport and was struck by how many people were wearing Cape Verde jerseys. In the space of a few hours, merchandise that had been relatively low-profile had become highly visible, underscoring how quickly demand signals can emerge and spread during a global event.

From labels to live data

What the World Cup is making visible in real time is a shift that has been under way for several years. Product identification is no longer a print-and-forget exercise. It is a live data problem.

The industry is moving from fragmented, internal systems to connected, multi-partner ecosystems. The organizations managing the tournament's supply chain most effectively are those that have made this shift: rather than each stakeholder operating in isolation and recreating the same product and compliance data from scratch, they are working within a shared, real-time environment where information flows seamlessly across systems, suppliers, customers and geographies.

The benefits of this approach are measurable. Organizations that can operate with real-time visibility and trusted data across their extended value chain can reduce delays, prevent errors at source and respond faster when disruption hits. In sectors where production downtime can exceed $1-2 million per hour, that responsiveness is not a nice-to-have but operationally critical.

The cost of disconnected systems

The consequences of siloed product data are well understood in theory. A tournament of this scale is making them visible in practice.

When product data does not flow seamlessly across sites and trading partners, the failure surfaces in predictable ways. Rejected shipments at customs. Compliance failures that stall distribution. Production downtime while teams manually reconcile data across systems. Against the backdrop of a global event with fixed deadlines, those failures are not recoverable.

The organizations absorbing those costs right now are those still operating inside the organization perimeter - managing product identification as an internal function rather than a network-level capability. The distinction matters. Supply chain resilience increasingly depends on the ability to coordinate accurate product data across every site, trading partner, and customer - creating a connected ecosystem in which product identity can be shared, trusted, and acted upon seamlessly.

What happens after the final whistle

Connected, network-driven approaches to product identification are no longer a future aspiration. The World Cup is demonstrating their value in real time, at a scale most supply chains will never encounter but from which every supply chain can learn.

The direction of travel is clear. Organizations that can rapidly adapt labelling requirements across plants and partners, share trusted product data in real time and eliminate the manual rework that comes with disconnected systems will outperform those that cannot. That is as true in retail, pharma and automotive as it is in a stadium in Los Angeles.

The World Cup will be over in a matter of weeks. The infrastructure challenges it is exposing will still be there when it ends. The organizations that use this moment to address those fundamentals will be better placed for whatever high-pressure deadline comes next.

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If you’ve done any shopping for marketing automation tools these days, you’ve probably noticed they all claim to be “powered by AI.” Apologies for splitting hairs, but that’s just not true.

A significant portion still rely primarily on rule-based automation, work identically to the platforms they replaced, triggering if/then workflows created by human engineers years ago. Give their systems an edge case to parse and you’ll soon see them send an inappropriate email, crash, or output some stale nonsense that wouldn’t matter to any living person.

Same label. Same old architecture. The problem? A rule bottleneck.

Traditional marketing automation relies on knowing rules ahead of time. A lead hits a score? Send an email. A prospect completed behaviors A, B, and C? Trigger sequence Y. Wait Z days, send the follow-up email.

A rules engine can execute these commands flawlessly – but it can only execute what it knows. When a situation arises that doesn’t fit the rules, what do you do? You update the rules.

Why this matters

Marketing is messy. Prospects take unpredictable journeys, trends come and go overnight, and audiences who loved your message last week don’t care about it this week. But rule-based systems can only improve when given new rules to fire. Engineers can’t possibly keep writing rules faster than the world changes.

The industry has been papering over this problem with AI buzzwords. Sprinkle some Neural Network magic on a rule engine, and suddenly you’ve got yourself an “AI platform.” Engineers who look past the updated sales brochures still find the same good old-fashioned if/then statements, patched up with trendy new nomenclature for the latest round of funding.

The difference between legacy automation and true agentic AI is that true agentic AI won’t just patch up the last generation of marketing automation tools – it will replace them. Agentic AI isn’t defined by capabilities so much as by the way decisions are made.

Rules engines ask, “what rule should fire next, given this input?” Agents ask, “what action should I take to get closer to my goal?” This is subtle but critical. Agent theory holds that the system knows its goal, its current context, and a list of available actions it can take.

Based on those three pieces of information, it can reason as to which action will bring it closer to accomplishing its overall objective. This extends far beyond executing canned responses - it’s deciding what to do.

Agentic systems maintain goals, reason over available actions, invoke tools, evaluate intermediate results, and adapt their plans as new information becomes available. The architecture is fundamentally iterative rather than purely reactive.

You know where this is going.

An agent can adapt if a campaign stops performing. It can coordinate with other agents who manage different subsets of that workflow. And it can do so without a human engineer going back into the system to rewrite the rules every time the world changes. The system manages its goals.

Why specialization matters

One important architectural decision that separates good agent implementations from the rest is specialization. Should you build one big generalist AI system to handle everything or many specialized agents, each performing their own task?

Specialization comes up often in discussions around AI, from medical doctors to Renaissance men. There is broad utility in generalization, but singular accuracy in specialization. The family doctor can handle any symptoms you throw at them. But when you need to be absolutely certain about your diagnosis, you see a specialist.

That’s because specialists aren’t smarter than the generalist – they’re just trained on narrower data. Likewise, generalist AI models aren’t going to produce great results for highly specific use cases. OpenAI’s models can write you a marketing strategy. They can craft creative assets. But they can’t produce marketing assets that:

• Fit the pixel ratio requirements of a given publisher
• Match your brand’s color palette
• Align with your target audience’s emotional affinity profile
• Incorporate mentions of trending topics from the previous day

They can’t do all of those at once, either. And you shouldn’t expect them to. For hard problems with specific solutions, you should build specialized agents (sometimes called “agent crews”) that own a narrow subset of your workflow.

One crew might specialize in strategy generation, while another focuses on creative writing. One might select publishers while another analyzes performance. Separately, these crews create atomic workflows that a generalist system would struggle to manage.

How not hosting your models affects data privacy

There’s another argument for specialized, privately hosted models that isn’t made enough: data privacy.

Whenever you use a public large language model (LLM) to write marketing copy, your data is being uploaded to someone else’s infrastructure. “We don’t use customer data for training” is easy to say but barely offers any assurance. Inputs are still being ingested, processed, stored, and handled according to what that provider’s internal policies dictate.

And those policies can change… most corporate lawyers have never looked at the data use section of public AI providers Terms of Service, let alone dissected it line-by-line.

But what about controls your organization can enforce? Do your developers scrub data for PII before generating content with an LLM? That only works if everyone in your company memorizes your data policies and uses tools responsibly. One rogue employee attaching a spreadsheet full of internal pricing to a prompt breaks your compliance.

But if the model itself is hosted privately, that’s one major source of exposure that goes away. Your data never leaves your infrastructure. There’s no ingestion point to transmit it to a third-party, no training feedback loop that will process it, and no agreement to parse about how that company will handle your data “moving forward.”

Governance is a system property

Because AI in the enterprise has reached a maturity level where governance is a legitimate concern, many teams treat it as a bulk edit at the end of AI-generated content. Have humans review and approve. That’s fine, and many teams require this today. But governance should be built into the system at a fundamental level.

Well-built agents have guardrails at every stage of the decision-making process. That means models that make predictions within set bounds. That means observability that can trace every word generated back to its origin.

That means third-party benchmarking to prove your models perform well against industry standards, not just internal testing. Governance shouldn’t just be applied to outputs – it should be inherent in the architecture.

What enterprise buyers should actually be asking about

Buying criteria for agentic AI will vary by company, but as requests for proposal accelerate to keep pace with innovation in the industry, here are a few considerations every enterprise buyer should ask about:

• Goals vs. rules - Is this system actually agentic? Or is it just automating workflows with AI tools bolted on? The first step is asking vendors point blank what their system does when it encounters data it doesn’t know how to parse. Rules engines will point to specific fallback rules that execute. Agents will talk about reassessing their goal and weighing their available actions until they decide on the next best step.
• Models and hosting - Where are the models hosted? Are they specialized and trained on domain-specific data? This answers two questions at once – vendor capability, as well as data privacy concerns.
• Long-term memory and context - Enterprise agents become dramatically more useful when they retain organizational context over time. Rather than treating every interaction as a new conversation, they can accumulate institutional knowledge, remember previous decisions, and personalize future actions while remaining within governance boundaries. Persistent memory allows agentic systems to improve continuously without requiring engineers to encode new rules after every edge case.
• Hallucination - No current LLM is immune to hallucinations. The important architectural question is how the system detects, bounds, and mitigates them before they affect downstream business processes. Specialists hallucinate less in their domain of expertise. Prediction window guardrails limit how far an AI system can go “outside the data.” Human approval gates before sending anything live catch anything that slips through.
• Governance / auditability - Can the system provide traceability for every output it generates? Is the system’s accuracy benchmarked against a third-party, or just internally verified?

The economic case for getting this right

There's an additional argument that often gets overlooked in discussions focused on capability: cost structure.

Token-based pricing from large model providers creates a fundamentally unpredictable cost model for enterprise deployments. Every question, every generation, every iteration costs tokens — and iterating toward an acceptable output for a complex campaign task can consume a significant volume of them.

Enterprise subscriptions impose usage caps that create their own operational friction. The more AI-dependent your workflows become, the more acute this pressure grows.

Organizations that own and host their own specialized models are not subject to this dynamic. There is no token meter running. The economic relationship is closer to infrastructure than to a metered service - you bear the cost of building and maintaining the system, and in return you have predictable marginal cost. For organizations at scale, that math changes substantially.

Don’t fall victim to marketing speak

AI marketing platforms will continue to flood the market with AI-sounding languages attached to rules engines. But for enterprises who truly want to deploy agentic AI, there’s a far better solution. Domain specific, privately hosted agents that don’t leave your organization exposing itself to risk.

As agentic systems mature, the organizations that differentiate between genuine autonomous architectures and AI-enhanced workflow engines will be better positioned to capture sustainable competitive advantage.

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Data center design has been shaped by a familiar set of priorities for years: keep systems available, resilient and predictable in any condition. Just like the electrical grid that powers these sites, they have been engineered to provide a highly consistent service regardless of what happens, even when individual components fail.

This has meant operators build layers of redundancy into power, cooling and network infrastructure.

However, artificial intelligence has changed the story. While organizations are racing to roll out AI tools at scale, the data center industry is discovering that not all workloads have the same infrastructure requirements.

For instance, training a large language model, running real-time inference, supporting enterprise applications and processing business-critical transactions each place very different demands on the underlying infrastructure.

Today, one data center doesn’t need to serve every purpose equally and we’re increasingly seeing that facilities can be both flexible and tailored to specific workload requirements.

The end of the traditional model

Historically, 99.999% uptime was non-negotiable. Data centers have traditionally powered systems like banks, emergency networks and customer-facing digital services, requiring continuous availability.

In these types of environments, where outages could have an extreme impact (from high financial losses to putting real lives at risk) this approach makes sense. Since operators couldn’t always predict which applications would be truly mission-critical, many facilities were built to the highest resilience standards by default.

But AI has changed this. “One-size-fits-all” redundancy isn’t necessary anymore. Different models, training and inference processes each require totally different service levels. For example, facilities for AI training workloads are being designed without backup generators, complex redundancy systems or high-tier architecture.

The good news is that there is a growing understanding of the distinction between environments needed for AI training and AI inference. Training facilities are increasingly being located wherever power is available.

The primary constraints are energy supply, cooling capacity and speed of deployment. In many cases, maximizing compute density and accelerating delivery timelines are more important than achieving the highest possible redundancy levels.

Inference infrastructure presents a different set of priorities. These workloads are often deployed closer to users and support services that people interact with daily. In these scenarios, latency, availability and customer experience become notably more important, creating a stronger case for resilient infrastructure and geographically distributed architectures.

Precision resilience to support an industry under pressure

It’s clear, therefore, that reliability still matters. However, infrastructure requirements vary significantly depending on the service being supported. In today’s age of AI, ‘precision resilience’ should be the focus, e.g., redundancy matching how workloads actually behave, rather than relying on legacy design assumptions.

The key challenge here for operators is determining where resilience delivers genuine business value and where it simply adds cost and complexity.

In a time when developers are facing a huge amount of pressure amid labor shortages, with demand outpacing supply, defaulting to ultra-resilient, high-tier designs for every AI deployment only intensifies challenges.

The industry is also expected to deliver capacity faster than ever before, while battling an ongoing power gap, meaning large-scale developments are increasingly difficult to execute. In this landscape, overengineering infrastructure can have unintended consequences.

Every additional layer of redundancy consumes capital and increases complexity. This is triggering an increased focus on efficiency, not just in terms of energy consumption, but in how capital is allocated throughout a project. Operators are looking to design infrastructure that maximizes the value generated by every watt of available power.

The role of upgradability

As operators move away from this one-size-fits-all redundancy to optimize their bottom line, it’s crucial that their facilities can adapt as workload requirements change.

While inference is expected to account for a growing share of AI demand, the landscape continues to evolve and it’s difficult to predict which workloads, densities and cooling requirements will dominate in the future. Infrastructure that can accommodate changes in compute technologies will be better positioned to support the next generation of AI applications.

Flexibility and fungibility are therefore the new non-negotiables in data center design. How is this made possible? Increasingly, developers are using ‘building blocks’ constructed off-site in factory environments, and then later assembling them on site to create an adaptable facility that can forever evolve, grow and shift.

This approach reduces the need to make every resilience decision upfront and builds with tomorrow’s changes in mind. In the coming years, we will see a shift towards multiple types of facilities, each developed for a different purpose.

These will range from energy-optimized training campuses built close to power sources, to distributed inference sites where uptime and latency directly affect user experience, alongside hybrid environments supporting both AI and traditional workloads. Yet they should all be built with flexibility front of mind to ensure they can evolve as requirements change.

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AI is now part of the operating system of SaaS. It shapes how products are built, how teams collaborate, and how quickly ideas move from concept to launch.

But speed isn’t the most important shift. The real change is that AI is reducing the coordination cost inside organizations.

Work that once required multiple layers of approvals, handoffs, and alignment can now move more directly between the people closest to the problem. And as that friction drops, something more fundamental starts to change: how companies are structured.

Projects that once demanded large teams, heavy investment, and long development timelines can now be delivered by smaller groups using AI tools to accelerate execution.

The rise of micro-SaaS businesses is one clear example, with small teams able to build and scale products with a level of speed that would have been difficult to imagine a few years ago.

This isn’t just about building faster. It’s changing what scale actually looks like.

From experimentation to infrastructure

Today, AI is embedded directly into product development, engineering, growth, and support. It’s no longer something teams experiment with on the side.

This has brought about a fundamental change: individual contributors can move faster, make decisions earlier, and deliver more on their own.

That has a direct impact on how teams scale.

And increasingly, the companies with an edge are not the ones with the biggest teams, but the ones that can remove friction and make better decisions faster.

Why scale no longer means more layers

Traditionally, growth came with added complexity. More customers meant more people. More people meant more managers, more processes, and more coordination.

At a certain point, coordination becomes a job in itself.

AI starts to break that pattern and bottleneck, freeing up time for quality decisions. When a product manager can analyze user feedback, draft a roadmap, and collaborate more directly with engineering using AI tools, you reduce the need for multiple handoffs. When a growth team can produce, test, and iterate on campaigns faster, execution accelerates without increasing headcount at the same pace.

It doesn’t remove the need for structure. But it does reduce the need for layers whose main role is coordination.

And that opens the door to a different model of scaling: one that is lighter, more direct, and more focused on making the right decisions, not just executing faster.

The return of the “contribution era”

What we’re starting to see is a shift back toward what could be called a contribution-led model. For a long time, SaaS organizations leaned heavily into management structures. That made sense when scaling meant handling more complexity across teams, regions, and products.

Now, as AI lowers the cost of execution, the balance starts to shift again: the biggest advantage AI creates is not productivity but organizational simplification.

Strong individual contributors who can own a problem and drive it to completion become even more valuable. They don’t need to wait for as much coordination. They can test, build, and iterate independently. And they can do it while staying closely connected to the outcome. Importantly, this isn’t about removing managers. It’s about rebalancing the system.

What builder-led really looks like in practice

A builder-led model doesn’t mean everyone is an engineer, and it doesn’t mean structure disappears.

It means the people closest to the work have more autonomy to move it forward.

You see this already across teams:

• Product teams prototyping faster using AI-assisted tools
• Growth teams running more experiments with shorter feedback cycles
• Support teams handling higher volumes while focusing human attention where it matters most

In each case, AI is not replacing people. It’s increasing their speed and range.

And when that happens consistently, the bottleneck shifts. It’s no longer capacity. It’s clarity and decision quality: knowing what to work on, what to prioritize, and where to invest time.

This is where leadership becomes even more important, not less.

Instead of focusing on overseeing activity or managing layers of communication, leaders have to focus on creating the right conditions for execution.

In practice, it often looks like:

• Fewer approval steps
• More direct communication between teams
• More emphasis on outcomes rather than process

Leaders still set the direction and make the hard decisions. But they rely more on capable contributors to carry things forward.

In many cases, the most effective leaders are those who can still contribute when needed, not just coordinate others.

Hiring for ownership, not just specialization

This shift also changes how companies think about hiring.

Specialists remain essential. But, if smaller teams can deliver more, the focus moves toward people who combine expertise with ownership, and have a strong ability to make good decisions in fast-moving environments. There’s growing value in hiring people who can operate with autonomy, make decisions, and adapt as things change.

In a builder-led environment, the question is less “what is your lane?” and more “how effectively can you solve the problems in front of you?”

That doesn’t mean everyone needs to do everything. It means teams benefit from individuals who can connect dots, move across boundaries, and take responsibility for outcomes.

Building smarter, not just bigger

It’s important to stay grounded in how this shift plays out. AI won’t fix weak strategy or unclear thinking, and layering it onto already complex processes can sometimes create new friction rather than remove it.

At Weglot, we've seen teams ship projects with significantly fewer handoffs than two years ago. Marketing can prototype ideas faster, product teams can validate concepts earlier, and engineers spend less time on repetitive tasks.

Our support team is another good example. Over time, they've built a suite of AI-powered tools including a case summarizer, customer profiler, drafting assistant, internal copilot, knowledge base, and AI chatbots. Together, these tools help agents access context faster, learn from previous cases, and resolve more requests independently.

The biggest change isn't speed itself. It's the reduction in coordination overhead, which is ultimately a more sustainable way of scaling.

Smaller, highly capable teams with clear ownership tend to stay closer to the product and the customer and can adapt more quickly when things change. We’re already seeing that in micro-SaaS businesses, but the same thinking applies more broadly.

AI will continue to evolve, but one direction is becoming clear. The companies that will stand out are not necessarily the ones that grow headcount fastest. They’re the ones that stay focused, reduce friction, and make it easier for their best people to build and deliver impact.

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Every enterprise I talk to right now has the same complaint dressed up in different language.

Their AI bills are climbing faster than anyone budgeted. Their model invoices look reasonable when viewed on their own.

But somewhere between boardroom approvals and the monthly cloud statements, money is disappearing in ways that nobody can fully explain.

Here’s the central issue that people struggle to understand: the most expensive part of AI isn't always the model itself. It's the infrastructure, orchestration, retries, idle GPUs, oversized context windows, and inefficient routing decisions that sit between a user's prompt and the final response.

This is something I call the hidden tax on AI adoption, and it's growing faster than most organizations realize.

Three numbers that should change how you think

Recently, at FinOps X in San Diego, a Goldman Sachs projection appeared on the main stage. Current enterprise token consumption globally sits at around six quadrillion tokens. The three-year projection: 120 quadrillion. That is not a rounding error… it is a 20x expansion, and it is arriving faster than the governance frameworks to manage it.

The same conference saw our launch of the Tokenomics Foundation (I’m fortunate to be a governing board member). It’s a vendor-neutral body inside the Linux Foundation dedicated specifically to the economics of AI token consumption.

The FinOps community, practitioners who have spent the better part of a decade building discipline around cloud computing spend, recognized that tokens represent a fundamentally different problem. Not a harder version of cloud cost optimization. A different one.

Here is why. When your organization runs a cloud workload, the cost is relatively legible. You provision compute, it runs, and you receive a bill. The relationship between action and expense is traceable. With AI tokens, that relationship fractures across three layers, and most organizations have visibility into only one.

Production → consumption → value: the three layers most teams ignore

The first layer is production. Before any AI model responds to any prompt, your infrastructure has to manufacture the tokens. GPU clusters, inference nodes, autoscaling policies, Kubernetes configurations: these are your token factories. Their efficiency, or lack of it, determines the base cost of everything that follows. A GPU node at 30% utilization is an expensive factory running at a third of capacity.

The second layer is consumption. This is where counterintuitive economics live, and it is what I spend most of my time thinking about. Two organizations can send identical prompts to different coding agents and arrive at radically different costs depending on how they manage context, caching, retries, routing, and the infrastructure supporting inference.

Prompt length, context window usage, caching strategy, and model routing decisions all compound. The common assumption that routing a task to a cheaper model always saves token cost, turns out to be wrong often enough to matter. A routing decision that invalidates a warm cache can make a "cheaper" model call more expensive than the frontier option it was meant to replace. These are second-order effects. They do not appear in standard dashboards but they appear in your monthly bill.

The third layer is value. This is the one that FinOps teams are comfortable with, and the one that matters least until you have the first two under control. Mapping token spend to business outcomes is a legitimate and important discipline. But you cannot govern at the value layer without instrumentation at the production and consumption layers. You are doing math with incomplete inputs.

Why 85% of your AI spend is probably misallocated

Here is a pattern I see consistently. Organizations treat frontier AI models, the most capable, most expensive models available, as their default infrastructure. Every task goes to the same model. Every prompt is constructed the same way. There is no routing logic, no tiering, no architectural distinction between work that genuinely requires the full capability of a frontier model and work that does not.

Based on my observations across organizations deploying AI at scale, roughly 15% of software development tasks actually require frontier model capabilities. The remaining 85% of routine coding, summarization, classification, and retrieval work can be handled by smaller, faster, and less expensive models, if you have the infrastructure to make those decisions intelligently and automatically.

The unlock is not picking better models manually. Manual model selection does not scale and degrades the developer experience by introducing friction at the moment when a developer needs to move fast. The unlock is building infrastructure that makes routing decisions for you: one that understands the task, routes it to the appropriate model tier, evaluates output quality, and escalates if needed. You specify the outcome you need. The system handles the economics of achieving it.

This is the direction the industry is moving, and the organizations that build this capability first will have a structural cost advantage that compounds over time.

The invoice arrives last. And you realize something has gone horribly wrong

There is a phrase I have started using with customers that captures the core problem: the invoice arrives last.

By the time you see the model provider bill, the cost decisions were made weeks ago in infrastructure configurations, autoscaling policies, and prompt architectures that nobody has reviewed since the initial deployment.

The retry logic runs silently when an upstream service slows down. The GPU nodes were reserved for peak traffic that never came. The agentic workflow, where a single user request fans out into dozens of model calls beneath it, each billed separately, none visible in the tool that generated the original request.

These costs do not live in the model invoice. They live in the infrastructure layer, in the consumption layer, and in the gap between how teams think their AI systems work and how they actually behave in production. You cannot govern what you cannot see. And right now, most teams are looking at one layer of a three-layer problem.

When AI goes from copilot to coworker, the stakes multiply

There is a shift underway that makes all of this more urgent. The AI deployments most enterprises built over the last two years were assistants, tools that accelerated individual work by handling the first draft, the next suggestion, and the boilerplate. A human remained in the loop at every consequential step. The economics were bound by how many people were using the tool and how often.

Autonomous agents change the economic profile entirely. When an AI system can receive a goal, build a plan, execute multi-step work, evaluate its own outputs, and iterate to completion without human intervention at each stage, you are no longer running an assistant. You are running something closer to a coworker, one that operates continuously, scales horizontally, and generates token consumption at rates that individual user interactions never approached.

The transition from copilot to coworker has already happened. And the governance implications are significantly more serious. A copilot with poor token economics costs you some efficiency. An autonomous agent with poor token economics runs that inefficiency at scale, continuously, without generating the natural friction that would cause a human user to pause or change approach. Infrastructure discipline and token optimization need to be in place before autonomous workloads scale rather than retrofitted afterward when the bill arrives.

The mandate for infrastructure teams

The right answer to this problem is not more dashboards. More visibility into a system you cannot control is just a more detailed invoice; it arrives with the same lag, and it changes nothing about the decisions that were already made upstream.

What infrastructure teams actually need is control that operates at the layer where costs are determined, not where they are reported. That means autonomous management of GPU and inference workloads, continuously rightsizing to match actual demand rather than peak assumptions, absorbing the bursty consumption patterns that agentic jobs produce, and moving compute capacity across providers when one environment becomes the bottleneck.

This is especially urgent now, because the transition from copilot to coworker does not give you a grace period to retrofit discipline. Autonomous agents do not pause. They do not get frustrated and choose a different approach. They run the inefficiency you built into them at scale, continuously, until something external stops them.

So the next phase of enterprise AI is defined by who can deploy models efficiently. As AI systems become more autonomous and token consumption accelerates, competitive advantage comes from understanding the full economics of AI, and not just the price of a model call.

Because by the time the invoice arrives, the decisions that shaped it have already been made.

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Alan Turing is widely revered as one of the most important scientific and technological figures in modern history. Following his work on cracking the Enigma Machine during the Second World War, Turing published his thoughts on the future of machine intelligence – and specifically on the pathway that we can one day take to achieve proficient AI.

Eating machines

Turing concluded his seminal 1950 study 'Computing Machinery and Intelligence' with musings on how to fulfill what he saw as achieving the great promise of machine intelligence in the future.

The pioneering mathematician and computer scientist suggested, in his concluding passage, that even though you may hold some vision for the future, or long-term ambitions, it's key to solve the immediate challenges you face now. True progress isn't achieved in one leap, but through a series of smaller and seemingly less significant steps.

In the context of his paper, Turing suggested that scientists in the future need not strive to achieve human-like intelligence in machines, but build the very first iterations of what he described as "thinking machines" so that this work can be iterated on in the future.

Beyond the Turing Test

When the study was published, the term "artificial intelligence" was not in widespread use, with scientists instead opting for terms like cybernetics or machine intelligence.

What followed over the next decades was a cascading series of breakthroughs – including the birth of neural networks in the 80s and the transformer architecture in 2017 – that have led to today's widely used large language models and generative AI services.

Although true human-like intelligence is still some distance away, we've advanced to such an extent that many have even claimed the Turing Test – where somebody cannot tell the difference between interacting with a human and AI – has already been beaten.

Digital commerce has historically focused on optimizing the customer experience through interface design, structured navigation, and carefully engineered conversion processes.

Over time, merchants have improved these experiences to reduce friction and support decision-making at every step of the purchasing lifecycle.

While AI historically played a limited role, restricted to basic chatbots or shopping assistants, rapid advancements mean AI agents are now prepared to execute transactions directly on behalf of customers.

This shift is driving a fundamental transformation across the ecommerce sector.

This new ecosystem is agentic commerce, where software agents initiate and complete transactions within clearly defined constraints.

To accommodate this, payment infrastructure is moving closer to the point where intent becomes execution, introducing protocols that make platforms machine-readable so AI can safely complete transactions in real time.

It is a major leap forward for retail technology, and enterprise readiness is urgent.

The Enterprise Blueprint: Brand vs. Non-Brand Agents

For large enterprise merchants, this shift introduces both an immediate opportunity and a critical defensive necessity. Enterprise adoption will likely begin on a merchant’s own digital properties through brand agents, which are dedicated AI assistants designed to improve conversion, capture valuable data, and keep the consumer firmly within the merchant’s ecosystem.

Eventually, these properties must open up to external, non-brand agents controlled by consumers or procurement departments. As AI agents become the primary interface for ecommerce, enterprise merchants who fail to make their platforms discoverable and transactable risk losing market share to competitors who are ready.

Securing the Payment Layer, Navigating Discovery

To capture these autonomous sales, a merchant's infrastructure must interact seamlessly with software systems. When autonomous agents handle procurement, they process data directly rather than navigating traditional user interfaces.

While optimizing product catalogs and metadata for LLMs is vital for discovery, enterprise merchants do not need to tackle this layer alone; they can solve this through specialized discovery and platform partners. The core operational challenge for the merchant remains the payment layer.

Because AI agent activity makes transaction volumes highly dynamic, minor inefficiencies or a single failed authentication step can terminate an entire chain of transactions.

The priority for merchants is establishing a robust payment architecture capable of verifying human intent and explicit consent, recognizing and authenticating the specific AI agent, and processing transactions securely across multiple rails in real time.

Shifting to Modular Business Models

The applications of agentic commerce vary across sectors due to distinct transaction models, regulatory systems, and the structural maturity of different digital verticals. However, a recurring theme is the transition from rigid, packaged bundles to highly granular, modular transactions.

For example, instead of requiring a fixed monthly or annual subscription for software-as-a-service (SaaS), an AI agent can dynamically subscribe a user to a platform precisely when needed. The agent continuously assesses usage and pays for the appropriate tier automatically. This allows subscription models to match real-time demand, aligning perfectly with customer utility.

Similarly, online learning platforms can deploy micro-transaction frameworks. Rather than purchasing full courses, users can access a single lesson or group of lessons at a bespoke price point. Agents can combine individual lessons from multiple providers to create a tailored learning experience, while the underlying payment infrastructure fragments and distributes the value seamlessly across all accessed merchants.

Programmable Monetary Flows

At the heart of agentic commerce is the transition from traditional payment infrastructure to programmable monetary flows. In this environment, systems execute transactions based on continually evaluated conditions of intent and permission. Confirming human consent is vital, as it serves as the primary defense protecting merchants from claims of unauthorized or fraudulent AI activity.

To enable this, payment environments must interpret delegated instructions, enforce spending constraints, and execute transactions instantly. Agent-bound payment credentials facilitate these purchases, allowing agents to act autonomously while preserving financial control and traceability for the end user. Global card schemes are already formalizing the components of this model, signaling a broader evolution of delegated payment frameworks.

Security infrastructure must also evolve to counter malicious actors attempting to counterfeit legitimate agent behavior. Regulatory updates, such as the upcoming PSD3/PSD4 frameworks in Europe and the EU AI Act, are setting clearer expectations for accountability, making compliance more critical than ever.

From Complexity to Simplification

Navigating this new terrain involves balancing real-time agent authentication, compliance with shifting regulations, and programmable credentials, all of which introduce undeniable operational complexity. But preparing for it doesn't mean overhauling your existing systems.

The path forward lies in a single integration point that abstracts this protocol churn away from your business. By partnering with the right payment layer expert, enterprise merchants can simplify the complex backend architecture, shielding their operations from technical friction while ensuring they are ready to accept machine-to-machine payments seamlessly.

The protocol-level reset for digital trade is already accelerating. Competitive advantage belongs to organizations that secure their payment infrastructure early, start by assessing whether your payment architecture can verify intent and authenticate agents today.

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The best IT experience is the one employees never have to think about.

A business laptop stays fast. A video call does not freeze. A virtual desktop launches without delay. An application works when it is needed most.

When everything runs smoothly, employees do not praise IT. They simply keep working.

That quiet experience is the goal. It is also becoming an increasingly important objective for enterprise technology teams.

For years, IT teams have been trapped in a reactive operating model. A user reports a problem. Support gathers logs, investigates the issue, identifies a root cause, and applies a fix after productivity has already been affected.

That model worked when environments were simpler, users were mostly in the office, and technology change moved at a different pace.

Today’s environments look very different. Employees now work across physical devices, virtual desktops, cloud workspaces, SaaS applications, home networks, office networks, identity systems, security layers, and collaboration tools. The experience can break at any point. When it does, employees rarely care where the issue originated, they simply know that work has stopped.

As workplaces become more distributed and interconnected, visibility remains important, but organizations increasingly need systems that can interpret data and act on it. The next phase of IT operations will not be defined by better dashboards alone.

It will be centered around intelligent systems that can detect issues, understand what is happening, and fix problems before employees ever need to report them.

From visibility to action

Digital Employee Experience (DEX) changed the way IT teams understood the workplace. DEX gave organizations a clearer view into performance, reliability, sentiment, device health, application behavior, and the friction employees face every day.

Visibility on its own does not solve problems. Dashboards, experience scores, and alerts help IT understand where issues exist, but they still rely on people to investigate and respond. The larger opportunity is using those insights to identify likely causes, recommend corrective actions, and automate routine remediation where appropriate.

This shift is driving the emergence of Autonomous Endpoint Management (AEM). While DEX helps organizations understand employee experience, AEM extends that capability by using AI and automation to identify root causes, recommend corrective actions, and resolve many common issues without waiting for manual intervention.

The goal is not to eliminate IT oversight, but to reduce the time between detection and resolution while preventing avoidable disruptions from affecting employees in the first place.

Why AI matters now

The challenge for IT has never been a lack of data. Most enterprise environments generate enormous amounts of telemetry. The problem is that the data is fragmented, noisy, and often disconnected from the user experience.

A device may show high CPU utilization, a virtual desktop may show latency, an application may crash, a network path may degrade or a user may report that everything feels slow. Each signal matters, but the value comes from connecting them quickly enough to understand the real cause.

This is where AI changes the equation. AI can correlate signals across devices, applications, networks, identity, sessions, and infrastructure. It can identify patterns that would take humans much longer to spot. It can summarize what changed, why it matters, and what action is most likely to resolve the issue. It can help IT move from searching for clues to acting with confidence.

The value of AI in IT operations extends beyond conversational interfaces. Context matters more than conversation. The ability to understand what changed, determine likely causes, and recommend the most appropriate action can dramatically improve operational efficiency.

Combined with real-time telemetry and automation, AI becomes part of the operational framework used to manage the digital workplace rather than simply another productivity tool.

The power of self-healing IT

The most meaningful advances in IT operations may be largely invisible to employees. A device that begins slowing down because of excessive memory consumption can be identified and corrected automatically before the user contacts support.

The same approach applies to recurring application crashes, virtual desktop performance issues, and other common disruptions. Rather than waiting for tickets, platforms can identify patterns, determine likely causes, and initiate corrective actions before productivity is affected.

Similarly, when a virtual desktop session begins showing signs of degraded performance, AI-assisted analysis can help determine whether the cause is resource contention, network latency, profile corruption, or application behavior, allowing IT teams to address the issue more quickly.

This is what self-healing IT looks like. The objective is not to create more visibility for employees. It is to reduce disruptions before they interfere with work.

For IT organizations, success increasingly means fewer outages, fewer support tickets, fewer escalations, and fewer situations where employees are forced to troubleshoot their own technology problems.

IT teams remain central

AI does not replace IT professionals. It gives them leverage to work more efficiently.

The modern IT organization is being asked to do more than ever. IT teams are expected to support more devices, secure increasingly distributed environments, manage a growing portfolio of applications, improve employee experience, control costs, support hybrid work, and enable AI adoption, all while the complexity of the digital workplace continues to increase.

AI helps reduce noise, accelerate root cause analysis, automate repetitive tasks, and direct human expertise toward areas where judgment, governance, and strategic decision-making are required. The goal is not to remove people from the process. It is to eliminate unnecessary effort that prevents teams from focusing on higher-value work.

Where the industry is heading

Across the industry, enterprise IT is moving toward platforms that can see what is happening in real time, understand context, and act safely at scale.

Digital Employee Experience remains foundational. Organizations still need deep visibility into how employees are experiencing technology across physical endpoints, virtual desktops, cloud workspaces, applications, networks, and infrastructure. But visibility alone is no longer enough. The next layer is intelligent, autonomous action.

The digital workplace has become the front door to productivity. When that experience breaks, the business feels it. When it runs smoothly, employees stay focused on their work, customers are served better, and IT becomes a strategic enabler of the business.

The next chapter of IT

IT operations are becoming more predictive and increasingly automated, but the objective remains unchanged: keeping employees productive.

Organizations seeing the greatest value from AI are building systems that can identify issues, understand context, and resolve common problems before users are affected.

When technology works as expected, employees stay focused on their jobs rather than the systems supporting them. AI is helping IT teams achieve that goal more consistently and with less manual effort.

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If your AI rollout is making people more exhausted, the architecture is wrong. That’s not a provocative claim; it’s the logical conclusion of what the data shows. With 69% of UK businesses implementing AI assistants, these tools have become part of everyday working life.

But deployment without the right operational structure is leaving employees exposed to the harmful effects of ‘AI brain fry’. AI overload is not a failure of the individual, but a failure of system design. And it is the responsibility of technology and business leaders to fix it.

Researchers recently coined the term ‘AI brain fry’ to describe the cognitive fog and loss of concentration that results from excessive oversight and orchestration of AI tools; i.e. the mental load of managing the systems themselves. The problem - as our own data makes clear - is not that employees are using AI skills too much.

Rather, it is that most UK businesses have deployed AI tools without building the structured ways of working needed to make them productive. The issue does not lie in use alone; it lies in how that use is managed.

The organizations deploying these tools have a duty to ensure they deliver genuine efficiency gains – not a new category of cognitive burden.

The adoption gap is creating more work, not less

Only 31% of businesses are using multi-agent workflows, leaving their employees stuck in a cycle of tedious labor – the kind that AI is supposed to reduce. The majority of UK businesses currently employing AI tools are expecting employees to still do most of the heavy lifting.

Employees are finding themselves writing prompts, manually checking whether the answers are reliable, and interpreting outputs.

This type of work was supposed to be eased by AI. Instead, the tools have made it worse.

The consequence? More admin, not less. Employees who were promised that AI would lighten their workload are instead finding it has added a new layer of tasks including prompt management, output validation, error correction on top of the day job.

Few businesses have moved towards a structured multi-agent workflow where AI systems handle the orchestration burden directly – routing tasks, validating outputs and managing agent-to-agent handoffs without requiring constant human supervision.

That is the architecture that relieves the cognitive load. Without it, employees are not using AI – they are managing it. And there is a significant difference between the two.

Moving towards an ‘adaptive operating model’

UK organizations need to move beyond AI deployment and towards an adaptive operating model. One that is deliberately architected, not organically grown. That means clearly defining which tasks AI can be trusted to handle autonomously, where human judgement remains the critical control point, and how work moves between the two. In practice, this could look like:

AI agents handling first pass research, data synthesis and output drafting. Humans setting direction, making judgement calls and reviewing exceptions, rather than every output.

The distinction between “AI does the work” and “human manages the AI doing the work” is where most current deployments get stuck. To protect employees and exact real productivity, businesses must build in human oversight at the right level – not at every level.

This means developing genuine domain expertise so that employees can interrogate AI outputs critically, not simply accept them. It also means investing in the technical literacy to design workflows that are robust, not just functional. An AI deployment that requires constant human supervision to remain reliable has not been properly engineered.

The organizations that get this right will also be better protected as the employment landscape shifts. With the UK government’s Employment Rights Act 2025 set to reduce the qualifying period for unfair dismissal claims and remove the compensation cap from January 2027, the cost of poorly managed AI-driven workforce change, both in human and legal terms, is rising.

Businesses that have embedded clear human-AI accountability structures will be far better placed than those that have not.

Without making these structural changes, AI adoption will continue to add effort rather than remove it. Thereby accelerating burnout at the very moment businesses are depending on these tools to drive productivity.

Protection and transformation go hand-in-hand

It is entirely possible to realise the productivity potential of AI whilst protecting employees from its cognitive costs. Multi-agent workflows, properly designed, keep humans in the seats that matter - strategy, judgement and decision-making – and had the orchestration burden to the systems built for it.

AI brain fry is not an inevitable side effect of AI adoption. It is a signal that the implementation architecture needs re-thinking. That is a technical and organizational challenge, and it belongs with the people who built the system – not the people using it.

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The World Cup has arrived, bringing a wave of anticipation, celebration, and distraction that stretches far beyond the stadiums. For employers, it also serves as a real-time test of workforce operations as schedules, staffing needs, and employee engagement are all put under the spotlight.

During the six-week tournament, millions of employees will be watching matches, adjusting schedules, arriving late, swapping shifts, requesting time off, or turning up tired after late nights.

New UKG research of 8,000 employees across Australia, Canada, France, Germany, Mexico, the Netherlands, the UK, and the US estimates the tournament could drive at least £12.6 billion in lost productivity.

In the UK alone, the impact could exceed £680 million, with employees not just planning to follow matches during working hours, alter their schedules, or miss work altogether --many admitted they would go to work hungover, secretly stream matches, and push the limits of what their employer would allow.

If England wins it all, almost a third of UK employees said they would take the day off whether that absence had been approved.

What the World Cup Reveals About the Future of Work

That creates a real challenge for employers. But the bigger lesson is not about football. It is about how work now happens.

The World Cup is a high-profile example of something organizations face every day: unpredictable employee behavior, sudden changes in demand, last-minute absences, weather disruption, supply chain delays, new regulations, and shifting customer expectations. For frontline-heavy industries in particular, disruption is not an exception to the operating model. It is the operating environment.

The question is whether workplace technology is built for that reality.

Many workforce systems were designed around predictability. They record schedules, track attendance, and report what happened after the fact. That matters, but it is no longer enough. When conditions change by the hour, organizations need more than systems of record. They need systems of action that help managers see risk earlier, make better decisions faster, and keep work moving in real time.

Three Workforce Strategies That Help Employers Stay Ahead of Disruption

The employers that come out ahead during the World Cup, and during the everyday disruption that follows it, will do three things differently.

1. See the disruption before it happens

Too often, workforce disruption becomes visible only once it has created a gap. Someone does not arrive. A shift is suddenly under-covered. A manager starts calling around for support. The business reacts after the damage has begun.

The World Cup gives organizations a chance to get ahead of that pattern.

Employees are already signaling intent. They know which matches matter to them. They know when they are likely to want flexibility, when they may need time off, and when they are more likely to be distracted or unavailable. Organizations that capture that intent early can turn it into useful operational data.

That does not mean monitoring employees or trying to control their behavior. It means giving people approved ways to communicate availability, preferences, and likely conflicts before they become last-minute absences. When that information is combined with historical absence patterns, demand forecasts, local schedules, and workforce data, managers can identify where risk is most likely to emerge.

A retailer, manufacturer, logistics operation, or hospitality business does not need to know every individual decision. But it does need to know where coverage pressure is building. High-interest match days, late kick-offs, local celebrations, and major national fixtures can all create predictable patterns of disruption.

Seeing that risk early allows organizations to plan differently. They can adjust staffing levels, open additional shifts, prepare contingency cover, or communicate expectations before managers are forced into crisis mode.

2. Design flexibility into the operating model

The instinctive response to disruption is often to tighten control. But rigid rules can push behavior underground.

If employees believe there is no fair or practical way to adjust work around major life moments, they are more likely to find informal workarounds. That can mean last-minute sickness calls, unapproved absences, shift swaps that managers do not see, or colleagues covering gaps without the right skills, rest periods, or compliance checks.

The better approach is to make flexibility visible, fair, and operationally safe.

That means giving employees clear, approved ways to request time off, swap shifts, volunteer for extra hours, adjust availability, or pick up open shifts. It also means giving managers the tools to assess those requests against business need, skills, fatigue, labor rules, and fairness.

This is especially important on the frontline, where the margin for error is small. A missed shift in an office may delay a meeting. A missed shift in healthcare, retail, manufacturing, hospitality, or logistics can affect safety, service, cost, and compliance.

Flexibility cannot sit outside the workforce strategy. It must be built into it.

For employers, that shift is powerful. Flexibility becomes less of a concession and more of an operating capability. Employees get more transparency and control. Managers get fewer surprises. The business gets a better chance of protecting service levels without treating people like variables in a spreadsheet.

3. Act in real time when the plan changes

Even the best World Cup plan will not survive unchanged.

A match goes to penalties. Demand spikes unexpectedly. More employees call in sick than forecast. A local team advances further than expected. A manager discovers at short notice that the people available do not have the right skills or certifications.

This is where many workforce systems fall short. They can show the scheduling. They can record the absence. But they do not always help managers decide what to do next.

Modern workforce management has to move from static planning to real-time execution. Managers need to know where gaps exist, who is available, who is qualified, who is approaching overtime or fatigue limits, and what action will create the best outcome for the business and the employee.

That is where data and AI can play a practical role. Not generic AI layered onto old processes, but intelligence that understands workforce context and helps recommend the next best action. Should a manager offer an open shift? Redeploy someone from a lower-demand area? Approve a swap? Escalate a compliance risk? Adjust breaks? Bring in contingent support?

The value is not simply in having more data. It is in turning workforce data into action while there is still time to influence the outcome.

The real stress test for workplace technology is not whether an organization can create a schedule weeks in advance, but whether it can adapt that schedule minutes after conditions change.

The World Cup will pass. The operating lesson will not.

Every organization will face its own version of this disruption: seasonal demand, illness, weather, regulatory change, major events, economic pressure, and shifting employee expectations. The companies that treat these moments as one-off exceptions will keep solving them manually, shift by shift and manager by manager.

We review the best employee management software.

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