Practical AI for Public Institutions: Where to Start in 2026

Every few months a client asks me some version of the same question: "Should we be doing something with AI?" Usually they've just come from a conference where a vendor demo'd something impressive, and now they're worried they're falling behind. My answer is almost always the same: probably, but not where you think.

The pilots that actually work start boring. Document classification in a permitting office. Transcript routing in a registrar. The kind of high-volume, structured tasks where you can measure accuracy, where mistakes are cheap to catch, and where the staff doing the work today can tell you exactly what "good" looks like. I've watched agencies try to jump straight to chatbots or predictive analytics and stall out because nobody agreed on what success meant. Start with something you can measure on day one.

The other thing I always bring up — and it never comes up in vendor demos — is lock-in. Every AI platform wants to be your everything. But if your data lives inside their system in a proprietary format, switching costs become your biggest budget line item in three years. We push clients toward modular setups: keep your existing systems, layer the AI on top through APIs, and make sure you have exit clauses in the contract. The market is moving fast enough that whatever you pick today probably won't be what you're running in five years.

The hardest part isn't the technology. It's getting your team on board. People hear "AI" and think their job is about to disappear, and if you don't address that head-on you'll get passive resistance that kills the pilot quietly. Be specific about what the tool does and doesn't replace. Train people until they're actually comfortable, not just until you can check a box. And create a real feedback channel — if the people using the tool every day can't flag problems without feeling like they're being difficult, you'll never find out what's broken until it's too late.