Organizations are racing to infuse artificial intelligence (AI) into their operations to unlock data insights, automate business processes, and enable better customer engagement experiences. But outdated network architectures are throttling performance and chilling ROI.
According to a global industry survey, data center experts predict AI workloads will place increasing demands on network infrastructure over the next 2–3 years — with 53% of respondents saying AI will drive more network demand than cloud or big data analytics.
Leads Cyber Security and Resiliency, Network and Edge Practice at Kyndryl.
As AI traffic scales faster than network capacity, these shortfalls will exacerbate existing infrastructure weaknesses and act as a powerful amplifier of operational risk. Currently, such shortfalls contribute to prolonged outages that average more than 75 days per year of system downtime, at a whopping annual cost of nearly $400 billion.
How legacy networks hold AI back
AI applications require speed, scale, and low-latency and high-bandwidth connectivity. So, when enterprises try to run them on legacy systems — especially creaky networks — the results can be a drag on security, growth and innovation. Bottlenecked data paths can widen automation gaps and result in slower decision making.
These frictions then can be responsible for missed insights leading to diminished customer experiences. They also can delay decision making in areas like forecasting and supply chain planning — resulting in higher costs and reduced revenues.
In essence, inadequate networks limit the ability of AI “blood” to nourish the body of an organization — weakening it and stifling its growth.
Many enterprise networks developed incrementally over time, with successive layers of technology implemented over time. Mergers, divestitures, and one-off projects to solve immediate problems have left organizations with a patchwork of architectures, vendors and configurations.
Then add on end-of-life (EOL) and end-of-support (EOS) equipment, and network downtime and security vulnerability risks escalate quickly.
This fragmentation makes it far harder to enforce uniform security policies, maintain visibility, or respond to threats at speed. As AI traffic increases across data centers, clouds, and the edge, blind spots multiply.
Once-manageable technical debt becomes an active security liability, expanding the attack surface and undermining Zero Trust initiatives as AI-driven traffic increases.
What needs to happen to achieve AI success
There is a symbiotic relationship between AI and network modernization. In fact, 97% of IT leaders say IT modernization — everything from updating IT systems and networks to implementing robust cybersecurity measures — is critical to enterprise AI success.
AI is not a standalone investment. Its ability to deliver value depends on modern, resilient networks that increasingly connect data, cloud, edge and security layers.
Network improvements that can help AI deliver faster insights, improved automation and stronger security & resiliency include:
- Replacing outdated MPLS (multiprotocol label switching) with SD-WAN and SASE architectures that are foundational to cloud-first strategies
- Implementing real-time visibility to ensure AI workloads run efficiently and securely
- Automating cybersecurity enforcement across hybrid cloud environments
These approaches unify connectivity and security across on-premises environments and multiple cloud platforms, delivering consistent performance, visibility, and policy enforcement capabilities across distributed environments.
Cloud networking services reduce the need to reconfigure networks for each environment, allowing consistent connectivity and security as applications move across cloud, data center and edge locations.
The quantum clock Is ticking
While many organizations are still grappling with the infrastructure demands of AI, another transformational technology is rapidly approaching. Experts predict that quantum capabilities will begin impacting security within five years.
Quantum computers could break today’s encryption standards, exposing sensitive financial, healthcare and operational data. Worse, attackers are already engaging in “harvest now, decrypt later” strategies — stealing encrypted data today to exploit tomorrow.
The relevance to networking and AI issues is straightforward. Preparing for the challenges (and opportunities) of quantum computing will be an incremental, multi-year project that needs to start now. Enterprise IT infrastructures must be able to adapt and scale to quantum computing developments as they evolve.
Companies will need to be able to “skate to where the puck will be,” and then skate again!
While becoming quantum-safe may seem daunting, organizations don’t have to do it all at once. They should focus on their most important vulnerabilities and look for opportunities to introduce quantum security into ongoing projects.
Preparing for quantum security should begin now, and will require:
- Forming leadership-level task forces, with buy-in and collaboration from the top down
- Taking inventory of public-key cryptography to prioritize vulnerable systems
- Migrating to post-quantum cryptography (PQC) by adopting NIST-approved algorithms to integrate Zero Trust principles
- Collaborating on IT modernization and quantum-safe strategies with cross-industry and technical experts
The path to AI and quantum resilience
Leveraging AI to create value, prepare for resiliency in the face of quantum threats, and modernize IT infrastructure are part of the same conversation. Without a modern, scalable infrastructure, organizations will be unable to realize the full potential of AI — including its ability to help defend against future quantum-enabled attacks.
Enterprises can develop solutions by integrating several approaches to managing these challenges. They must know the capabilities and vulnerabilities of their own organizations.
They also must collaborate with experts who have the cross-industry knowledge and engineering chops to build fit-for-purpose solutions rather than buying and adapting off-the-shelf products. Most importantly, they must acknowledge the looming threats of the digital landscape and commit to taking immediate action.
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