Six shifts drawn from working with organisations that want either to lead on AI or follow safely.
Any attempt to predict the future of AI is only useful if it looks beyond models and GPUs and into how organisations are actually adopting it. Buying an enterprise licence is nowhere near “job done”.
The six shifts below are drawn from discussions with clients from across a wide range of organisations, from highly regulated organisations to fast-paced, growth-focused businesses. They’re not a master theorem of AI, but recurring patterns that manifest themselves once you get past the pilot theatre.
Treat them less as predictions and more as prompts: what are you actually changing in how you talk about AI, how you measure it, how you treat data, how you orchestrate work, how you invest, and who or what you trust to make decisions?
1. New buzzwords will keep appearing in our AI lexicon, but the story willkeep moving forward
We’ve already moved from AI to Generative AI to Agentic AI, and in 2026 we should expect to hear fresh labels like “compound AI systems” or “AI fabrics”. The interesting shift is that attention is moving from the model and individual agents to the whole system around it: orchestration, guardrails, monitoring, experts-in-the-loop and how value is captured.
This means AI is no longer just a technology responsibility; it’s a business and people responsibility too. Successful investments will be those jointly owned by leaders in the business who are willing to change how work gets done, in technology who build and run the stack, and in people and change who are accountable for skills, adoption and new ways of working.
If 2024 was the year of Generative AI and 2025 the year of agents, 2026 will start to become the year of Stacked AI: multiple specialised agents wrapped around data products and orchestrated into workflows, that operations genuinely depend on, and would miss if switched off.
2. AI success will be measured by real adoption, not by how many POCs have been run
Since 2023, AI inside organisations has generated plenty of pilots and agents, but in many cases, it still isn’t embedded in the core workflows that really run the business.
Products like Copilot have been rolled out to thousands of employees, helping people draft emails and documents faster, but this is nowhere near the kind of universal, habitual usage we’ve seen on the consumer side with ChatGPT. Inside companies, these solutions are still treated as productivity of convenience, as optional accelerators that sit on the edge of real work, so behaviour and core processes haven’t fundamentally shifted.
In 2026, that won’t be enough. Business leaders will start asking much simpler questions:
- What % of my revenue or cost base is now touched by AI every day?
- Which decisions are measurably better or faster because of AI?
- Where have we actually switched off legacy ways of working?
We’ll see a clearer split between cosmetic adoption, (“Our people have access to a chatbot if they feel like it”) and structural adoption, (“If the AI is down, operations genuinely slow or stop”). The pivot will be from individual productivity tools to process-embedded agents wired into the places where value actually flows.
3. AI will be the catalyst for targeted data transformations,and “good enough” data willunlock orchestration
There’s a familiar tension in most organisations. Everyone is excited about AI agents running whole workflows, but the moment you talk about orchestration, someone says “our data isn’t ready yet”, and everything is pushed into the future.
In 2026, that stalemate starts to break. Organisations will stop waiting for a fully standardised, perfectly modelled enterprise dataset before doing anything useful with AI.
Instead of spending years “tidying the whole house”, high performers will start from a handful of high-value workflows and let AI be the catalyst for data change. The question shifts from “When will our data be ready so we can use AI?” to “Which three to five workflows are worth targeted data improvements so AI can run reliably on the data we actually have?”
In practice, that means keeping data closer to where it already lives, and moving away from the religion of a single golden data model and towards federated, task-centric data: strong standards where it matters, and flexible, in-context access everywhere else.
Agent and workflow orchestration platforms will grow fast and become the new integration middleware, but only in places where leadership accepts that data will always be messy and design their AI and guardrails to work with that reality.
4. AI use case hunting will give way to AI blueprints and factories
In 2026, building a giant backlog of AI use cases will start to lose its shine. More mature organisations will shift from chasing individual use cases to building AI blueprints and factories tied to a handful of big strategic bets.
Today, most AI roadmaps can look like laundry lists: each use case treated as a bespoke project, each with its own tech stack and governance. That makes it hard to realise the promise that learning in one area lowers the cost or risk in the next.
The pivot to AI factories means codifying the entire path from idea to impact: a consistent way of evaluating where AI can realistically move the Profit & Loss, standard criteria for data readiness and technical feasibility, a repeatable lens on responsible AI and risk, and a playbook for how use cases are designed, simulated, built, deployed and monitored.
Once that factory exists, use cases stop becoming bespoke one-off projects and run through the same proven path: ideas in and AI agents embedded in real workflows out. That’s how organisations move from scattered pilots to a platform that develops and runs agents at scale.
5. Organisations will be forced to pick an AI laneor risk getting stuck in neutral
By 2026, the real differentiator won’t be who has the best model, but which leadership teams are honest about their ambition. It’s easy to talk like a pioneer and behave like a fast follower. That’s how you end up with one foot on the accelerator and one on the brake, and AI quietly stuck in “nice-to-have” territory.
AI also isn’t something you can do purely top-down. You need clear signals from leaders on ambition, risk appetite and where AI is expected to move the P&L, and teams on the ground with the mandate to redesign workflows and retire old ways of working. When leadership won’t pick a lane, the implicit message is: experiment if you like, but nothing important is really going to change.
The real leadership questions for 2026 are: where are we truly all-in, where are we deliberately cautious, and are we honest enough to say both out loud?
6. 2026 is when companies start rewriting whoactually makes decisions
We’ll look back and realise that “AI to GenAI to Agentic AI” was only the technical half of the story. The real breakpoint is when leaders stop asking “Where can we add AI?” and start asking “If AI is a permanent capability, how would we redesign our decision-making, our org chart and our change process from scratch?”
That’s the shift from AI projects to AI-shaped organisations. In practical terms, the organisations that pull ahead in 2026 will do something uncomfortable. They will redraw the map of who actually makes decisions. Instead of a slide full of AI use cases, they’ll take their most important decisions and sort them into four clear buckets:
- Human-only
- AI-assisted (human decides)
- AI-first (human override)
- AI-autonomous (with guardrails)
Job descriptions will become less about tasks and more about which decisions, guardrails and exceptions people own. Roles will shift from doing the work to defining how the work is done by humans and AI together. This will be the moment AI stops being an experiment and quietly starts rewiring how the company actually runs.
Why I’m optimistic
AI might be the first wave of technology that forces business and IT into the same conversation about value, not just cost. No one says, “We won that £50m deal because our CRM was nicely configured”, and businesses won’t rush to admit they landed a contract because a bid-writing agent did half the work either.
You have to design attribution into the system from day one (e.g. uplift metrics, decision logs, before/after comparisons), because, outside obvious operational efficiencies, people rarely volunteer that AI made the difference, especially for things like win rates, revenue or quality. That’s how AI stops being “an IT thing” and becomes a shared asset both sides can point to and say, “That’s where the value came from.”
If there’s one test worth carrying into 2026, it’s this: if your models quietly became twice as good overnight, would you be ready to take advantage, or still debating about pilots, licences and “data not being ready”? The tech curve will keep bending either way; the only thing you control is how quickly you turn it into different decisions, different workflows and different expectations of your people.
So perhaps use these six shifts as a practical checklist: pick one critical decision or one high-friction workflow and design the AI-enabled version now. If, by the end of 2026, you can point to real progress on this, you’re no longer experimenting with AI, you’re actively reshaping your business into an intelligent enterprise.
