Simply Having AI Won’t Be Enough. Implementation Will Matter.

This is a small signal. But I think it is worth mentioning because it points toward what may become a much larger issue in the race: how AI is actually implemented.

Clariti AI Studio, a new program from the company that provides community-development and permitting software for governments, is offering free workshops for local governments. Government Technology reports that the program uses a city or county’s actual permitting process to examine where AI might help.

That matters.

Permitting is not a simple workflow. It can involve multiple departments, reviews, approvals, handoffs, records, legal requirements, staff judgments, and timelines. Permits also vary widely. A routine residential permit is not the same as a major commercial project, a housing development, or an emergency repair after a disaster.

Current tracking systems may work reasonably well and still leave the overall process slow and cumbersome. That is not because city staff need magic. It is because permitting is a complicated public process, and it matters a lot. When permitting works better, cities can support housing, small business activity, infrastructure repair, emergency response, and economic development more effectively.

What makes the Clariti example interesting is not simply that it is offering AI. It is that the workshop starts with the buyer’s actual process. That is different from asking a city to imagine, in the abstract, where AI might fit.

The useful question is not, “Would AI be good for permitting?”

The better question is:

Where, specifically, could AI help in this process?

That means looking at the entire system: what humans do, what records are involved, where those records live, how they are accessed, what laws and codes need to be reviewed, where handoffs slow things down, what applicants need to know, and where staff judgment must remain central.

That is implementation capacity.

Clariti’s offer is obviously also a sales pitch. That should be kept in mind. Vendor-provided guidance can help smaller communities move faster, but it can also shape how those communities understand the problem and what solutions they consider.

So the jury is still out on whether this kind of AI adoption will ultimately push toward concentration or dispersion. If implementation knowledge lives mostly inside vendors and platforms, dependency deepens. If smaller communities gain practical capacity to understand, evaluate, and improve their own processes, AI could spread capability much more quickly.

That is why this small signal matters.

The race will not be decided only by who has access to AI. It will also be decided by who knows how to use it well.