The Real AI Market Is Taking Shape
10 Shifts That Matter More Than the Model Hype Cycle
Today’s AI narrative has been dominated by model breakthroughs, funding rounds, and product launches. It has felt like a glitzy, fast-moving, parallel universe. At times, more like watching the Oscars than understanding how work will actually change. Understanding, not shaped by Silicon Valley or Wall Street, has taken time to arrive.
I think what is starting to shift now is not the pace of innovation, but the quality of market thinking. The centre of gravity is moving away from the builders and toward the users who actually have to make this work.
From that perspective, whether you are an enterprise buyer or an institutional investor, these are ten observations about where I think the real AI market is heading, and why they will matter far more than the next model release or benchmark comparison.
1. The model is no longer the centre of the story
It initially looked like the model was the product. I think we are past that. Models are becoming powerful components. But they are increasingly interchangeable, increasingly abstracted, and increasingly hidden behind other layers. The centre of gravity is shifting upward into how models are applied, governed, and embedded into work, and the platforms that allow this to happen.
2. Software is still the distribution mechanism for AI
No matter how advanced the model becomes, the customer won’t just interact with a model. They’ll interact with software that relies on interfaces, workflows, approvals, forms, notifications, and dashboards. Bascially the same things that have always defined enterprise value. Nothing about that has changed yet. Because AI is an amplifier. It reinforce not replace the importance of the software systems through which work actually happens.
3. The “death of SaaS” is overstated, but the weak will disappear
We can debate the scale of the SaaS apocalypse, but surely all agree that there will be (and should be) casualties. A decade of proliferation has created a long tail of thin, single-purpose tools that exist largely because they could. Many of them will not survive. I’ve described this phase as “the application frat party” or “whatever-as-a-service.” Core systems, however, are not going away. If anything, systems of record become more important. They provide the structure, authority, and constraints that AI needs to operate safely. What disappears is not software itself, but the fragmentation. And that is long overdue.
4. The real enterprise problem is no longer integration. It is coordination
Connecting systems has been an observable challenge for most of my career, from the early days of “what the hell is middleware?” to today where APIs have largely solved that problem. But the harder challenge was never just about connecting systems. It was about following how work actually moves through a business. Defining who approves what, what triggers the next step, where decisions branch, how exceptions are handled, and how multiple functions interact in real time is still incredibly difficult. Where integration connects systems, coordination connects work, and delivers coherence back to the business. So to be valuable, AI has to sit inside a coordination layer.
5. Context matters more than data, and most organisations have lost it
At the exact moment AI arrives as a context-driven capability, most organisations have never had less clarity about themselves. Over the past decade, the rise of as-a-service has fragmented ownership, spread decision-making across functions, and steadily weakened enterprise architecture. Systems have multiplied, the CIO’s influence has diminished, and organisational coherence has declined as a result. At the same time, organisations have invested heavily in data lakes, warehouses, and pipelines, assuming that centralising data would unlock value. AI is now exposing the limits of that thinking. What remains is data without structure, systems without alignment, and organisations that struggle to explain how they actually operate end to end. That is the context gap, except it no longer a gap. It is a chasm.
6. Enterprise architecture is coming back, whether organisations are ready or not
The rise of SaaS decentralised technology decision-making (see #3). Buying groups emerged everywhere. Systems were acquired function by function. Architecture, as a discipline, quietly weakened. But AI does not tolerate that fragmentation. It forces organisations to confront how everything fits together and it exposes gaps, duplication, and incoherence. Architecture, that lost art, is no longer optional. It is being reintroduced by necessity.
7. The service layer is not shrinking. It is moving up the stack.
The belief that AI will shrink the services market and that automation will replace people, and the need for services will decline is just plain wrong. The service layer is actually becoming more valuable than the model layer. So the familiar pattern still holds. The one where the product arrives first, but the real value follows in services. The organisations that understand this will capture far more value than those focused purely on building models. Because AI doesn’t simplify the enterprise but it does make its complexity unavoidable. So the work moves, not disappears. It shifts toward far more demanding activities like stitching together models, workflows, governance, identity, and data into something that actually works inside an organisation. It is not implementation in the traditional sense but still services engineering.
8. Closed ecosystems will persist longer than expected
The case for open ecosystems built on a soft core and hardened shell has always been clear. But the market is not rational. Closed systems continue to win because many organisations are not equipped to manage multi-vendor complexity, so they default to simplicity. I see this consistently, particularly in government environments. That creates a paradox. Closed platforms are structurally weaker, but commercially durable. It is what keeps ERP vendors in the game. That durability buys time, but it is also finite. As the market evolves, remaining closed becomes harder than transitioning.
9. The AI bubble may need to dim before real progress accelerates
Right now, the model spotlight is too bright. It is drawing attention away from the harder, slower work required to make AI useful. That work is not in the models. It sits in process redesign, governance, data meaning, and organisational alignment. These are not headline topics, but they are the ones that determine outcomes. So the constraint is no longer capability, but focus. Progress will accelerate when the spotlight shifts, either because it fades, or because buyers (and investors) start dimming it themselves.
10. Most organisations are being sold the penthouse, not the front door
Vendors are pitching end-state visions replete with fully autonomous workflows, agentic enterprises and self-optimising systems. But organisations do not start there. They have always needed entry points in the form of clear, grounded, level one use cases that light the pathways into more mature adoption. Without that, the gap between ambition and execution remains way too wide (commercially and technically). The next phase of AI adoption will be defined by those who can show customers where to begin, not just where they could end up.
That’s it for now. So despite the tendency to believe that everything has changed (because this technology is so amazing), in reality, what is happening in the real economic markets, Monday to Sunday, is far more subtle. Almost recognisable?
What sits underneath these shifts is not a technology story at all, but a clear decision framework about where to invest, what to rationalise, how to architect, and how to execute. Things like “treat models as components, not strategy”, and “shift services investment up the value chain” and “rationalise the long tail of SaaS” are very practical steps to take.
AI extends what is possible, but it doesn’t rewrite the story. The fundamentals of software still apply, the complexity of organisations still matters, and the difficulty of execution hasn’t gone anywhere.
If anything, this may be the crowning achievement of the last 50 years of technology. It gives every organisation the potential to be great. But what happens next is not a technology question. It’s an organisational one.