Deploy. Integrate. Adopt. How Enterprise AI Becomes Business Value

The conversations we are having with customers have shifted. A year ago, the question was "should we be doing AI?" Today it is "why aren't we further along?"

According to McKinsey's State of AI 2025, 88% of organisations are now using AI in at least one business function, yet only 1% of C-suite leaders describe their gen AI rollouts as mature — meaning AI is fundamentally changing how work gets done and driving real business outcomes, according to separate McKinsey research. The path from pilot to production is long and uncertain — IDC research finds only 4 out of every 33 AI pilots ever reach production.

The gap between ambition and impact is usually not a technology problem. It is almost always a strategy and sequencing problem,. And it is the problem WWT and our partners are focused on solving. We also see a big shift from business thinking about the use of chatbots and frontier models for customer services or internal tools. While these use cases still dominate most AI Pilots in 2026, we see progressive businesses looking for competitive advantages beginning to shift towards inference applications; including robotics, research, scientific enhancement, fraud and risk management, and new standards in Finance.  We also see a significant focus on Private AI.

That's the conversation we're bringing to AI Summit London this June, alongside four world-class technology partners who each bring deep, differentiated capability to the challenge.

Start with the business. Build the stack that supports it.

At WWT, we organise the work of becoming AI-ready as a journey across three layers, and the order matters.

The first is AI foundations: the strategic groundwork that determines whether an AI programme succeeds before a single model is deployed. That means identifying which use cases are worth your focus and in what order, building a business case that accounts for the full cost of production (not just the pilot), and establishing a governance model, typically an AI Centre of Excellence, that gives the programme the decision rights and accountability it needs to move forward.

The second layer is AI experiences: where AI actually shows up for people. Whether that's your workforce using AI tools to work smarter, your developers writing code faster, your customers getting better service, or your physical operations becoming more intelligent at the edge, every AI investment ultimately has to deliver a better outcome for a person or a process.

The third layer is the technology stack: infrastructure, data, intelligence, applications, and security, the five dimensions that have to work together at production scale.

"Executive leadership is the critical factor in moving beyond pilots. Waiting on the sidelines may be the biggest risk of all."  - Jim Kavanaugh, Co-Founder and CEO, WWT

Business questions precede technology answers. That sequencing rule runs through everything we do and it's the single most common reason strong AI programmes pull ahead while others stay stuck.

A CTO perspective: Build for where you're going, not just where you are

Ginna Raahauge, International CTO - EMEA:

I spend a lot of time with CTOs across Europe, and the conversations have a new quality to them. The urgency has sharpened. It's no longer just about getting AI into production, it's about whether the architectural decisions being made today will still serve those organisations in five years. That is a different question, and it requires a longer lens.

The infrastructure choices made now on data sovereignty, compute architecture, cryptographic standards, governance models are not simply enabling today's AI workloads. They are setting the conditions for everything that follows. In a piece I recently wrote for Information Age, I argued that CTOs need to be thinking about quantum readiness now, before it arrives. The lesson from the AI era is instructive: organisations that deferred the hard foundational work are the ones stuck in pilot purgatory today. 

One of the clearest lessons from the AI era is what happens when security becomes an afterthought. According to IBM's 2025 Cost of a Data Breach Report, 13% of organisations experienced breaches of AI models or applications — and 97% of those lacked proper access controls. The data you are protecting today — intellectual property, customer records, financial history — has a long exposure lifespan. Post-quantum cryptography is the most essential immediate step; it is also a commercial signal that your organisation is a trustworthy, long-term steward of data.

But the deeper principle applies equally to AI. The organisations pulling ahead are not necessarily the ones moving fastest. They are the ones moving deliberately at the foundation level that give them the optionality to scale when the business demands it. That is precisely what Mike and the team have built into WWT's three-layer framework, and it is why that framework resonates with CTOs who are thinking about the long game.

-Ginna Raahauge, International CTO - EMEA, WWT

For European enterprises in particular, this is not abstract. Data residency obligations, regulatory frameworks, and sovereignty requirements mean that the infrastructure stack cannot be an afterthought bolted on after a model is deployed. The AI journey and the quantum-readiness journey are happening at the same time, on the same infrastructure, with the same data.

Theory is easy. Integration is where enterprise AI either proves itself or falls apart and that's exactly why the partner ecosystem matters.

The ecosystem that makes it real

No single vendor solves enterprise AI. The workloads are too varied, the requirements too specific, and the stakes too high to rely on any one technology stack. What organisations need is a partner who can navigate that landscape honestly, one with deep expertise across multiple ecosystems and the independence to recommend the architecture that fits your strategy, not one that fits a preferred supplier.

At AI Summit London, WWT will be joined by four partners who each bring world-class capability in their domain.

Cisco - Critical infrastructure for the AI era

Cisco approaches enterprise AI from the network out, with security and intelligent connectivity at the foundation. As organisations move from AI assistants to AI agents — systems that can plan, act, and make decisions across enterprise systems — the governance and security requirements become substantially more complex. Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in early 2025. For organisations where security posture and network architecture are foundational requirements, Cisco's AI ecosystem is built around those principles from the outset.

NVIDIA — the accelerated compute backbone for AI 

NVIDIA's full‑stack accelerated computing platform is the engine powering AI workloads at production scale. Whether running on‑premises, in the cloud, or at the edge, NVIDIA's AI infrastructure makes it possible to meet the unprecedented compute demands of modern AI factories and enterprise‑scale AI programmes. WWT and NVIDIA work together on AI Factory solutions: full‑stack, purpose‑built infrastructure tailored for the specific performance, density, and operational requirements of today's AI workloads. 

Dell Technologies – Business outcomes at enterprise scale 

Dell Technologies delivers a comprehensive, integrated platform for enterprise AI, unifying high-performance, scalable compute and storage with end-to-end data management, enterprise-grade security, and global support. Dell's open, modular ecosystem lets customers run AI workloads on proven Dell infrastructure alongside existing environments, simplifying deployment and operations. Together, Dell and WWT provide validated designs, deep partnerships across the AI stack, and global services that help enterprises move from pilots to production quickly and reliably - turning data into measurable business outcomes at scale. With this foundation, Dell and WWT are the right partners to help organisations design, deploy, and operationalise agentic AI with the reliability, governance, and performance enterprises require. 

Digital Realty - where AI lives

Every AI workload, every model, every dataset has to live somewhere. Digital Realty provides the data centre and colocation infrastructure that makes AI deployments real and repeatable at global scale. For European enterprises in particular, where data sovereignty, latency requirements, and regulatory residency obligations are increasingly shaping infrastructure decisions, Digital Realty's footprint across the UK and Europe is directly relevant to any serious AI programme.

WWT as the navigator

What WWT brings to this conversation is not loyalty to one partner or one architecture. It is depth across all of them, combined with the experience of having helped organisations at every stage of the AI journey — from initial use case analysis through to production-scale deployment.

That includes our Advanced Technology Centre, where customers can test and validate AI solutions across different vendor stacks in a working environment before committing to production. It is the difference between choosing infrastructure on paper and knowing it works for your specific workloads, data requirements, and performance expectations.

Where are you on your journey?

We know the challenges are real. IDC research shows that Global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027 — almost always because token costs, integration overhead, and change management were not modelled at the start. According to the Intapp 2025 Technology Perceptions Survey, half of professionals have already adopted AI tools without organisational authorisation. And access to AI tools has grown rapidly, but meaningful daily use has not kept pace — McKinsey finds that despite near-universal AI adoption, only one-third of organisations have begun scaling their programmes.

These are not reasons to slow down. They are reasons to get the foundations right before scaling - to sequence the work properly, build on an infrastructure that fits your requirements, and make sure your AI investments are tied to outcomes you can actually measure.

That's what WWT and our partners are here to help you do.