The prediction game is easy. Models get better. Context windows get longer. Multimodal improves. Agents get more capable. Legal and compliance teams get more involved. None of this is surprising.
The harder question: what should you actually do differently?
Here’s my short answer, based on a year of working on AI across multiple organizations and watching the gap between teams that shipped and teams that stalled.
Stop Experimenting. Start Measuring.
If you’ve been running AI “experiments” for more than a quarter without a clear evaluation framework, you aren’t experimenting. You’re procrastinating. Experiments have hypotheses, metrics, and endpoints. Pilots have owners, success criteria, and deadlines.
Pick two or three use cases closest to production. Define success in numbers, not narratives. Build an evaluation set. Ship to real users with monitoring. Learn from data, not opinions.
This isn’t glamorous. It’s effective.
Build the Operational Foundation
The teams that will move fastest in 2025 are the ones building the plumbing now. Not new models. Not new frameworks. Plumbing.
- An evaluation loop that runs regularly, not when someone remembers
- Cost tracking with per-feature attribution so you know where money goes
- Security controls for model access and data handling that satisfy your legal team
- Model-agnostic interfaces so you can swap providers without rewriting your stack
Every one of these is boring. Every one of these is a prerequisite for scaling anything in 2025. Through Q4, I’ve been helping teams set up exactly this kind of infrastructure, and the teams that have it in place are already iterating faster than teams that built flashy demos without it.
Governance Isn’t the Enemy
AI governance has a reputation problem. Engineers hear “governance” and think “bureaucracy that slows us down.” That framing is wrong.
Lightweight governance – clear ownership for use case intake, a simple review path for legal and security risks, a cadence for measuring value and retiring weak experiments – actually accelerates shipping. It removes the ambiguity that causes teams to stall waiting for implicit approval.
The companies that move fastest all have some version of this. Not a committee. Not a 50-page policy document. A clear owner, a simple process, and a regular review. That’s it.
What I’m Betting On
Personally, I’m betting that 2025 is the year AI stops being a separate initiative and becomes part of how software gets built. Not a team. Not a project. A capability that lives inside existing workflows, owned by existing teams, measured by existing standards.
The companies that treat AI as special will keep producing expensive demos. The companies that treat it as normal – same code review, same evaluation, same cost accountability, same ownership – will ship things that last.
Discipline over heroics. Same as always.