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Somewhere right now, a customer is repeating themselves. They are explaining their problem for the third time, to the third person, because the organization on the other side has no shared memory of the previous two conversations. It is an infrastructure problem that AI is making harder to ignore.

It is also becoming impossible for policymakers to ignore. Just in April, the Mayor of London launched a new AI and Jobs Taskforce to examine how AI is changing work across the capital, signaling that the conversation has moved well beyond investment announcements and into the harder question of what AI does inside organizations.

It is also shining a spotlight on a memory crisis inside modern business.

As UK organizations rush to deploy AI in the workplace, many are layering it onto fragmented systems that were never designed to preserve institutional memory in the first place.

According to research published in Harvard Business Review, knowledge workers toggle between applications and tools roughly 1,200 times per day, a pattern known as "toggling tax". That figure alone tells the story: we aren’t short of tools, but there is no coherence among them.

The result is a new kind of productivity paradox. Work is moving faster, but clarity is not improving.

This is where much of the current enterprise AI conversation unravels. A surprising amount of what is marketed as AI today still relies on humans to do the synthesis work themselves. The system retrieves documents. It summarizes conversations. It surfaces links. But employees still carry the burden of reconstructing meaning, and so do the customers and end-users waiting on the other side of those decisions.

Notably, when these types of AI tools do the retrieval, but humans skip the synthesis, the output feels hollow. That creates a trust and credibility problem - not just for the individual, but for AI as a category. People start associating "AI-assisted" with "low-effort".

When context is lost internally, the effects aren't invisible. They surface as slow responses, repeated requests for information that customers already provided, support experiences that feel fragmented, and sales teams reconstructing account history manually before every renewal, escalation or executive review.

The AI models themselves are becoming more capable, but the organizational foundation beneath them remains fragmented.

Most AI systems today are fundamentally stateless. They generate outputs based on temporary context windows rather than durable organizational memory. Every interaction requires the system to repeatedly reconstruct understanding from fragments.

Consider how databases work. We do not recompute everything from scratch every time a query arrives. We cache and index, then preserve relationships between entities, because continuously recomputing context is computationally irrational.

Yet much of enterprise AI is still being deployed exactly this way and the industry has started mistaking activity for intelligence.

What I believe organizations should focus on is whether they have structured, durable memory that lets AI and humans reason from the same shared context. Without that foundation, AI outputs remain generic.

Most collaboration systems multiply this problem in two ways. First, they encode knowledge into naming conventions and tribal memory – the kind that lives in channel names nobody can decode and folder structures only three people understand. New employees are not learning the business, they are learning the conventions.

Second, even when information exists, it remains inaccessible. The same decision appears as "PostgreSQL migration", "database move Q3", and "backend infrastructure change" across three different channels. They are semantically identical but textually invisible to any system trying to surface it.

This problem becomes even more acute in distributed organizations. I don’t believe you can build modern global companies on a "you had to be there" culture. Yet many businesses still operate as though important context naturally transfers through proximity and synchronous communication.

Search was designed to discover information, whereas modern enterprise work requires systems that understand the relationships in data.

A customer escalation is not just a support ticket. It is connected to product decisions, engineering discussions, account history, contractual obligations, and revenue impact. A sales opportunity is tied to customer sentiment, historical support patterns, product usage, and internal stakeholder alignment.

Traditional collaboration systems flatten these relationships into disconnected channels and documents, whereas AI knowledge graphs preserve them.

Researchers call this a transactive memory system: the collective understanding of who knows what, how decisions were made, and how work is coordinated across teams. The same logic now extends to AI. Intelligent systems can participate in that process too by encoding context, surfacing relevant history, and routing knowledge to the right people at the right time.

The Office for National Statistics has consistently flagged weak productivity growth as one of the UK's most persistent economic challenges. Since 2010, UK productivity has grown at 6.2%, compared with roughly 10% across the euro area and nearly 15% in the United States over the same period. AI is increasingly being positioned as a mechanism to help close that gap.

But productivity does not improve because your business has added more AI agents to the workflow. If every important decision still requires humans to manually reconstruct fragmented context, organizations just accelerate confusion.

What UK businesses need are systems capable of preserving context, maintaining institutional memory, and grounding AI systems in trusted organizational knowledge. Better AI infrastructure starts with a simple question: Does your organization remember anything? For most, the honest answer is no.

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In the early 2000s, IP VPN was the enterprise networking technology of choice for IT leaders.

MySpace was the go-to social network, we used Skype for video calls, we listened to music on our new MP3 players and the Nokia 1100 was the most popular mobile handset.

It feels like a different era entirely, yet many businesses are still running on legacy networks that were perfect for their needs back then but are now holding them back.

By today’s terms, networks were built for low levels of traffic. Cisco estimates global IP traffic levels were around 175 petabytes per month in 2001. Compare that to today’s figure, which is around 522,000 petabytes per month, or approximately 3000 times higher than 2001 levels, and you can understand why 87% of businesses in an Accenture study believe their legacy network is compromising their ability to advance on cloud, data and AI and digital transformation.

Untangling and replacing the complex web of enterprise networks built up over years is an unavoidable and costly necessity. It’s a bit like replacing the windows in your home - you know you’ll improve security, stormproof your home and cut energy costs by upgrading, but the process feels like a hassle.

Today, IT leaders aren’t just ‘replacing the windows’ by modernizing outdated networks; they’re going further and building high capacity, low latency, secure architectures designed to withstand the explosive demands of AI.

Millions of businesses are switching from IP VPN to Software-Defined Wide Area Networks, or SD WAN. Strong market growth is forecast in SD WAN, with one market forecast anticipating SD WAN CAGR of almost 40% (38.9%) from 2023 to 2030.

This growth is being driven by multiple factors including a shift to cloud-native architectures; a change in workplace practices and rise in remote working environments; and strong demand for network architectures that can manage current and future AI-related applications and services.

SD WAN is faster, more cost effective and more secure, with built in zero trust protection. It’s purpose built for distributed users and for managing cloud, AI workloads, data flows, and SaaS traffic.

But, to be truly AI ready, IT infrastructure must be software driven, and this is where SD WAN excels: it gives your business the security, flexibility, and reliability needed to operate confidently in an AI driven future. Here are five ways switching to SD WAN will help you build an AI-ready network:

High-bandwidth, low latency SD WANs are critical for the delivery of AI workloads, particularly as businesses move towards AI inference. They provide fast access to cloud services and dynamic bandwidth allocation as they monitor network conditions and reroute over the best available path.

For example, imagine a drive-through restaurant that uses an AI voice to take and relay orders or a supermarket that uses an AI model to scan shelves in its store, to detect gaps in stock, alert staff and predict which items will run out next. A high-performance, low latency network is essential here to guarantee a seamless customer experience.

SD WAN’s application-aware routing levels this up even further, prioritizing AI traffic and deprioritizing the transfer of, for example, bulk file transfers or back-ups.

The global cyber attack surface has expanded dramatically. AI now plays a dual role, enabling more sophisticated attacks while also powering new, advanced defense capabilities. Traditional IP VPNs offer traffic encryption but lack native security features. In contrast, SD WAN is built to protect modern networks from today’s high volume, highly sophisticated cyber threats:

- Zero trust access protects users, devices and applications

- Traffic is encrypted end to end, so that all data between sites, platforms and applications is secure

- Threat prevention at the edge protects core infrastructure from threats, with features such as intrusion detection and prevention, malware scanning and DNS security

- Automated real-time security updates with threat intelligence pushed globally within minutes

SD WANs provide direct, optimized access to major cloud environments, such as Microsoft Azure, AWS, and Google Cloud, by using automated secure tunnels and intelligent path selection.

This ensures cloud and AI services run with lower latency, higher performance, and more reliable connectivity. Also important to note is that SD WANs provide high levels of autonomy and automation, so it’s easy to make changes quickly and easily as businesses navigate dynamic market conditions.

SD WAN captures real-time data including latency, packet loss and application usage patterns – data which can be fed into AI-based network monitoring, automation and predictive maintenance management tools, so that networks become self-optimizing, self-healing and proactively secure.

When combined with Secure Access Service Edge (SASE), SD WAN creates a single, secure, high performance network foundation that’s built to drive AI opportunities while protecting against cyber risks with integrated security solutions including zero trust, secure web gateways and cloud firewalls.

SASE is a cloud based networking and security framework that combines SD WAN with integrated security services (like Zero Trust, secure web gateways, and cloud firewalls) into a single unified architecture. It’s the gold standard of AI-ready architecture.

As enterprises accelerate toward an AI driven future, the networks that once served them well are now becoming a barrier to progress. SD WAN offers a clear path forward: a software defined, secure, high performance foundation built to handle the scale, speed and complexity of modern cloud and AI workloads.

By making the shift now, businesses can replace aging infrastructure with an agile, intelligent network that not only supports today’s demands but unlocks the full potential of tomorrow’s AI innovation.

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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.

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As enterprise AI systems evolve, the limiting factor is shifting. Model quality still matters, but it’s no longer the main issue holding systems back. Increasingly, what constrains performance, scalability, and cost is context.

Large language models are now expected to support long conversations, multi step reasoning, and complex workflows that span time, users, and systems.

Every one of those interactions generates tokens, and those tokens produce key value (KV) cache — the working memory that allows models to reason efficiently without constantly recomputing prior steps.

Most AI architectures still treat this context as temporary. KV cache typically lives in GPU memory, is tied to a single inference process, and is discarded as soon as resources are exhausted.

That approach might be acceptable for small scale experimentation, but it quickly breaks down in enterprise environments where context lengths grow, concurrency increases, and recomputation becomes expensive.

Inference context has quietly become one of the largest bottlenecks in enterprise AI.

To understand why this matters, it helps to stop thinking about KV cache as “just a cache.”

Enterprises have spent decades building strategies around structured data and unstructured data, but AI introduces a third class that deserves just as much attention: AI native data. This is data generated by model execution itself, and KV cache is one of its most important forms.

KV cache directly determines inference latency, throughput, energy consumption, and cost. As context windows get longer and reasoning chains become deeper, the volume and importance of this data grow faster than token counts alone. When KV cache is constantly thrown away, systems pay for it through rising latency, lower GPU utilization, lost reasoning context, and higher inference costs.

At scale, this inefficiency becomes structural rather than incidental.

KV cache also exposes a mismatch with traditional infrastructure design.

GPU memory delivers exceptional performance, but it is scarce and local to a single server. CPU memory extends capacity but remains volatile. Local NVMe storage adds scale yet keeps context trapped at the node level. Traditional shared storage provides durability and resilience, but it wasn’t designed for highly dynamic, inference time state.

This leaves enterprises with a fragmented memory hierarchy where context is either fast but fragile, or persistent but difficult to access efficiently. No amount of tuning can fully resolve this, because the problem isn’t optimization — it’s architecture.

What enterprise AI needs is a way to treat inference context as system memory rather than disposable state.

That shift is what we describe as an inference context memory layer.

Instead of forcing all KV cache to live and die inside GPU memory, this approach allows context to be created close to the GPU for low latency, then managed across a hierarchy of memory and storage tiers designed explicitly for inference workloads. Inactive context can move out of high cost memory without being discarded, while relevant context can be restored on demand without recomputation.

This changes the behavior of inference systems in a fundamental way. Inference is no longer a series of isolated executions that start from scratch each time. It becomes a continuous, stateful process where knowledge accumulates, moves, and is reused across sessions, agents, and nodes.

Making this work places new demands on storage.

Inference context is large, mostly immutable, and technically recomputable — but regenerating it at scale is costly and inefficient. A storage architecture for inference context must preserve locality when performance matters, enable sharing without manual replication, and provide resilience so context isn’t lost when hardware fails.

When storage is designed this way, it stops being just a place to store data and becomes an extension of AI memory itself. That shift has real economic consequences: faster time to first token, higher GPU utilization, support for much longer sessions, and dramatically lower cost per query.

For enterprise workloads like tax advisory, legal analysis, healthcare reasoning, financial planning, and customer support, this is critical. These systems depend on preserving reasoning history and conversational context, not repeatedly rebuilding it from scratch.

Enterprise AI is entering a new phase.

Models will continue to advance, but the systems that scale successfully will be defined by how well they manage the intelligence those models produce. Tokens are no longer fleeting artifacts, and context is no longer something enterprises can afford to lose.

KV cache is AI native data. It represents system state. And increasingly, it must be treated as infrastructure.

The architectural principle is simple: generate context once, manage it intelligently, and reuse it wherever possible. That shift is foundational to making enterprise AI reliable, efficient, and scalable — and it’s why storage once again plays a central role in the future of computing.

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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.

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• Microsoft CEO Satya Nadella has warned AI companies are training their models on the business secrets of their customers
• These secrets are then used to train new, more powerful models, that are sold to their customer's competitors
• But, Nadella says there is a way to remain competitive without being locked in to one AI vendor

Microsoft CEO Satya Nadella has warned the big players in the AI industry are using their proprietary models to learn the business secrets of their customers, which they can then use to train and deploy more advanced AI models.

The crux of the issue, Nadella said in a blog post, is that, “You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!”

What Nadella is saying in essence, is that AI companies are harvesting sensitive business data from their customers, using it to make training their models cheaper, and then launching these models for use by their own customer’s competition.

“Models learn from ‘exhaust,’ the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how,” Nadella explained.

Nadella also criticized how AI companies are increasingly complaining about how their models are being distilled by their own competition. For example, Anthropic accused retailer and e-commerce company Alibaba for using thousands of Claude prompts to distill their own models. By figuring out how a proprietary model works, you don’t have to spend the enormous amount of capital needed to source training data and create your own AI model.

This, for Nadella, is a major contradiction in how AI companies work. “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation,” he said.

It is also therefore hypocritical for AI companies to accuse other companies of distilling their own product, and then include within their AI usage contracts clauses that allow AI companies to “reserve the right to learn from customer usage and interaction data.”

“In consuming intelligence, you are creating intelligence. And what you create should belong to you,” Nadella added.

Nadella’s fix for this growing problem? It’s time to move back to on-prem. Nadella encourages businesses to “retain ownership” of the data they feed AI models by switching to the use of “proprietary learning environments” built on the cloud.

The added benefit of moving to these environments is that they allow businesses to switch between different AI models provided by different companies using “orchestration layers” and AI gateways.

There is also a growing trend of businesses switching to using open source technologies, which goes hand in hand with businesses operating in the cloud. Businesses can train open source AI models using their data that is already available in cloud environments to do much of what the proprietary models do, for far cheaper — and without handing over that same sensitive data to be used by AI companies to train their own models.

The on-prem solution also has additional benefits. AI models operated on-site within manufacturing plants, stores, and other premises are far cheaper and require less specialized hardware. Businesses that operate using a centralized cloud are increasingly encountering issues with data egress fees, storage bloat, and idle specialized hardware.

Google Cloud recently released a report about these very issues, and also encouraged businesses to move towards using AI gateways and on-prem models to reduce latency, improve resilience, and cut per-token costs by switching to local, highly optimized models.

Via TechCrunch