Unleashing AI: Why AI needs a better cloud strategy

Speed has always been one of the key metrics of business success. The need for speed has been amplified in recent years, with time-to-market, streamlined production, seamless logistics, and round-the-clock customer service all now expected as a baseline for companies that want to compete. We’ve reached the limit of what hard work and elbow grease can achieve in these fields, and that’s where artificial intelligence (AI) takes the baton and runs with it.

AI models are no longer a fringe technology. They’re now commercially available, and companies can train them to suit their own specific use cases. In no small part, this new-found accessibility is due to the cloud environment and the agility, flexibility, and scalability that comes with it. The marriage of AI and the cloud has paved the way for AI-as-a-Service (AIaaS) solutions – just as the majority of businesses now benefit from Software-as-a-Service (SaaS) to avoid lengthy vendor lock-ins and dependence on outdated platforms, AIaaS brings the benefits of AI modeling, natural language processing (NLP), and large language models (LLMs) to the masses.

These benefits are difficult to ignore. According to a report by Accenture, AI adoption has the potential to boost profitability for businesses by an average of 38% by 2035. However, for businesses to reap the benefits of this now highly accessible technology, they will need to overcome one of the most common bottlenecks – connectivity. Not only is the speed and robustness of connectivity critical for harnessing AI, advanced use cases also demand an expertly configured multi-cloud environment that guarantees security and performance.

Putting the “P” in KPI

All businesses use key performance indicators to measure their success, from first call resolution (FCR) to the time it takes to get a product to market. The scope and ambition of these KPIs are naturally limited by what the business can realistically achieve, but AI is changing that. Take Amazon, for instance, which leverages AI to create tailored product recommendations, optimize pricing strategies on a regional basis, and detect fraudulent reviews and transactions. All of these functions tie directly to KPIs such as sales volume, recurring custom, and risk mitigation – all made possible through the deployment of AI. One of Facebook's most high-pressure KPIs in recent years has been the moderation of content on its platform. It too has leveraged AI and machine learning to flag and remove inappropriate content, allowing it to increase its KPI targets and meet them to preserve trust in its brand.

The car manufacturing industry is another great example. Car manufacturers will have countless KPIs around driver and passenger safety, which can now be bolstered using AI. Onboard infotainment systems can use real-time sensors to detect things like driver tiredness – something that would not have been possible without the inclusion of artificial intelligence.

As tools such as OpenAI’s ChatGPT and Google’s PaLM increase in sophistication and availability, the opportunity for businesses of all shapes and sizes to optimize their KPIs and achieve a higher level of performance is now within reach – but only if they avoid the connectivity bottleneck.

Connectivity is the bedrock of cloud-based AI

Adding more traffic to an already congested road will only lead to more problems, particularly when that traffic needs to move at lightning speeds in order to be useful. Adopting AIaaS with basic public internet connectivity is like setting racing cars on a busy urban road. It’s technically possible, but won’t yield the desired results. If businesses are going to invest in the race car (AIaaS), they need to ensure there’s a dedicated highway for them to use. Interconnection technology can offer that highway.

The speed of data transfer (the race car) to and from the cloud is crucial, and the public internet's unpredictable routes can slow down traffic and, perhaps worse, risk the exposure of sensitive data. Interconnection achieves optimal performance by using direct connections from company IT infrastructure to cloud services that bypass the public Internet. Businesses can still use the urban road (public cloud) for non-critical traffic, and use the dedicated highway offered by interconnection technology for AI processing. This setup avoids the unreliability of the public internet, offering near real-time insights and lightning-fast processing. Implementing a cloud routing service on an interconnection platform also allows direct cloud-to-cloud communication, eliminating the need for data to travel back to the company infrastructure. This service boosts application performance across systems and ensures seamless interoperability between clouds – perfect for multi-cloud setups.

So, the race is on. According to IBM, 35% of businesses globally have already adopted AI in some form, and the AIaaS market is set to grow by close to 40% per year until 2030. To be part of this upward curve, businesses need to be in the cloud – but more than that, they need to have the connectivity infrastructure in place to thrive in the cloud.

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