The year pilots met reality
For most enterprises, 2025 was the year generative AI had to prove itself. The experiments of the previous two years had produced plenty of impressive prototypes, chat assistants and internal hackathon winners. Boards and finance teams started asking a harder question: what has it changed?
The honest answer, in many organizations, was "less than we hoped". Pilots that delighted a small group struggled to scale. Assistants that answered questions well were used for a few weeks and then forgotten. Costs that looked trivial in testing grew uncomfortably at volume.
That is not a sign that generative AI is overhyped. It is a sign that the easy part is over. The organizations pulling ahead are the ones that have learned what it takes to move from a promising pilot to a dependable part of the business. Here is what we expect that to look like in 2026.
What separates the deployments that stick
Looking across the projects that have made it into everyday use, a few traits come up again and again.
- A specific job. Not "an AI assistant for the sales team", but "a first draft of every proposal, based on our pricing and past wins, in under five minutes".
- Access to the right data. The model is grounded in the organization's own documents and systems, with permissions respected, so its answers are specific rather than generic.
- A way to measure quality. A set of real examples with known good outcomes, used to test every change before it reaches users.
- An owner. Someone accountable for the result, who keeps improving it after launch.
- A place in the workflow. The output appears where the work already happens, not in a separate tool people have to remember to open.
None of these is about choosing the most powerful model. All of them are about treating AI as a product rather than an experiment.
Chat was the demo; embedded is the product
The chat window was the perfect way to show what generative AI could do. It is rarely the best way to deliver value at scale, because it depends on every employee knowing what to ask, and when.
The next phase is quieter. AI will increasingly sit inside existing tools and processes: summarizing a case before an agent picks it up, drafting a reply inside the ticketing system, filling in a form from an attached document, checking a contract against policy as it is uploaded. Users will often not think of it as "using AI" at all. That is when it starts to change the numbers.
Agents arrive, with a short leash
AI agents, systems that can plan a series of steps and use tools to complete a task, were the most talked-about idea of 2025. They are real and they are useful, but the early lessons are consistent: they work best on narrow, well-defined tasks, with limited permissions, and with a person approving anything that cannot be undone.
In 2026 we expect most enterprises to deploy agents in exactly that form. Triage, document processing, research and routine operational tasks are good candidates. Fully autonomous agents running core business processes are not, yet. The organizations that build the guardrails, evaluation and monitoring now will be the ones ready to give agents more responsibility later.
The model matters less than you think
It is tempting to make the choice of model the central decision. In practice, capable models are increasingly interchangeable for most business tasks, and the best choice changes every few months.
The smarter architecture uses a mix. Large frontier models handle the hardest reasoning. Smaller, faster and cheaper models, including open-weight models that can run inside your own infrastructure, handle the high-volume, well-defined work and the tasks involving sensitive data. A routing layer decides which model handles what. Designed this way, switching or adding a model is a configuration change rather than a rebuild.
Inference becomes a line item
As usage grows, so does the bill. Generative AI costs scale with every request, every long document and every retry. In 2026, AI spend will move from innovation budgets into operating budgets, and it will get the same scrutiny as any other running cost.
The good news is that most of it is controllable through design: using smaller models where they are good enough, caching repeated work, keeping prompts and context lean, and processing in batches where real-time answers are not needed. Cost per task deserves a place on the dashboard next to quality.
Governance becomes operational
In Europe, the AI Act is now being phased in, with obligations for general-purpose AI models applying from August 2025 and more requirements following over the next few years. Regardless of where you operate, customers, regulators and employees increasingly expect organizations to know where AI is used, on what data and with what oversight.
That calls for practical governance rather than policy documents alone: an inventory of AI use cases, clear rules on data, human oversight for consequential decisions, and records that show how each system was tested. Done well, it speeds adoption up, because teams know what is allowed and do not have to ask every time.
People remain the multiplier
The most successful organizations we see do not frame AI as a way to replace people. They use it to remove the repetitive parts of skilled work, so people spend more time on judgment, relationships and creativity. They invest in training that is specific to each role, they involve teams in redesigning their own work, and they are open about what is changing.
Where people are brought into the change, adoption follows. Where AI is imposed on them, it quietly fails.
Five priorities for 2026
- Pick fewer, bigger bets. Retire pilots that have not shown value and concentrate on a handful of use cases with real volume.
- Build evaluation into everything. No AI feature should reach users without a test set that reflects the real work.
- Get your data ready. Clean, accessible, well-governed information is the biggest accelerator of all.
- Design for cost and choice. Use a mix of models, keep switching easy and measure cost per task.
- Make governance practical. An inventory, clear rules and human oversight where it matters.
Generative AI is moving from the exciting phase to the useful phase. That is good news for any organization prepared to do the unglamorous work of turning potential into results, and it is the work our AI Transformation team does every day.
