I wrote a year-in-review post for 2019 about leaving fintech, joining Entrepreneur First, and starting a new company. That year felt like a hinge point – a move from the known to the unknown. 2025 had a similar feel, but the shift wasn’t personal. It was industry-wide.
AI stopped being a special project. It became infrastructure.
That sentence sounds obvious in December. It wasn’t obvious in January. At the start of the year, most organizations I had worked with were still treating AI as an experiment. A side initiative. Something the “AI team” owned. By the end of the year, the successful ones had woven it into delivery pipelines, support tooling, and internal operations where reliability matters more than novelty.
The unsuccessful ones are still running pilots.
From demos to systems
The biggest shift was organizational, not technical. Projects moved from isolated demos to systems with owners, budgets, and maintenance plans. Evaluation and monitoring became part of deployment, not afterthoughts. Rollback plans existed before launch, not after the first incident.
This isn’t glamorous work, but it’s the work that matters. The teams that won in 2025 weren’t the ones with the cleverest prompts. They were the ones with the most disciplined operations.
Governance stopped being a dirty word
One thing I pushed hard for: governance as enablement, not bureaucracy. Clear rules for data handling, model selection, and access controls made teams faster. Guardrails reduced rework. Policy embedded in CI pipelines unblocked adoption in regulated contexts where teams had been stuck for months.
The pattern is simple. If governance is a checklist in a SharePoint, teams work around it. If governance is a set of automated checks in the delivery pipeline, teams rely on it. It’s the same lesson I learned running infrastructure at scale: make the right thing the easy thing.
Cost became a design constraint
Early in the year, teams treated model costs like someone else’s problem. By mid-year, the bills arrived. Suddenly, cost and latency were architectural decisions, not afterthoughts.
Small models for simple tasks. Large models for complex reasoning. Routing by task type and risk level. Caching repeated requests. Treating token spend like any other variable cost and engineering it down. These are infrastructure patterns, not AI magic. The teams that figured this out early controlled their economics. The teams that waited got surprised.
This reminded me of the early cloud days, when teams learned that “spin up more instances” isn’t a cost strategy. The discipline is the same: measure, optimize, budget. The only difference is that the unit of cost went from compute hours to tokens.
The throughline
On a personal note, 2025 was also the year I started proving out ideas I’ve carried since my early ventures. Building tools that reduce operational complexity, and make the right thing the easy thing, applies directly to AI infrastructure. The overlap between what I learned building cloud tooling and what teams need now for AI operations is almost one-to-one. Different surface area, same principles.
What actually worked
AI delivered best when scoped to a well-defined job with measurable outcomes inside existing workflows: drafting, summarization, classification, data extraction, and assisted analysis. Human review was explicit. Responsibility for quality was assigned to a specific person, not “the AI team.”
The three patterns that held up all year: evaluation-first rollout, human-in-the-loop for consequential actions, and model routing instead of one-model-fits-all.
What didn’t work
Broad, underspecified mandates. “Use AI to transform our customer experience.” That isn’t a spec. That’s a wish. Deployments without visibility into quality, security, or cost. Optimistic assumptions substituting for measurement.
I watched one organization burn an entire quarter on an “AI-powered” feature that had no eval suite, no monitoring, and no clear definition of success. When leadership asked why quality was inconsistent, the team had no data to answer with. They had anecdotes. Anecdotes don’t survive a quarterly business review.
The organizations that struggled most were the ones that mistook enthusiasm for strategy.
What stayed hard
Ambiguity. When success criteria are unclear, AI outputs drift and debates replace decisions. This is a product management problem, not an AI problem.
Trust. Users lose trust faster than teams regain it. One bad incident – a confidently wrong answer, a data exposure, a weird hallucination – and the credibility deficit takes months to recover from.
Drift. Small changes to prompts, data, or models shift behavior in ways that are hard to notice without measurement. This is why evaluation isn’t a launch activity. It’s a continuous operation.
High-stakes automation. The closer a feature gets to irreversible actions, the more you need review, auditability, and rollback. This constraint isn’t going away. Nor should it.
The story of 2025 isn’t that AI is unreliable. It’s that reliability is engineered, not assumed.
The internal shift that mattered most
Inside organizations, the biggest change was process maturity. Prompts and routing rules got versioned and reviewed like code. Evaluation moved earlier in the lifecycle. Platform teams became enablement functions instead of gatekeepers.
This is what turned AI from “experimentation” into “infrastructure.” It happened not because of a model breakthrough, but because engineering leaders insisted on treating AI systems with the same rigor as everything else in production.
Looking at 2026
The trajectory is continuation, not revolution. Better reliability. Tighter governance. Deeper integration. MCP and similar protocols making tool integration more standardized. Agents getting more practical for bounded workflows. Regulation becoming a real deployment constraint rather than a theoretical discussion.
I expect 2026 to be the year when the gap between “AI-capable” organizations and “AI-mature” organizations becomes impossible to ignore. Capable means you can build a demo. Mature means you can run it in production, measure it, fix it when it breaks, and explain it to a regulator. That gap is where the real competition happens.
The most valuable progress will come from operational discipline. Not a single breakthrough. Not a new model that changes everything. Just the steady, unglamorous work of making AI systems predictable, auditable, and maintainable.
2025 was the end of the novelty phase. The work now is execution.
The teams that understand this will win 2026. The teams that are still waiting for the next model release to solve their operational problems will keep waiting.