AI Changed Our Company. It Didn’t Replace Our Expertise.

Bret C. Keast, AICP, is the principal-in-charge of enCodePlus’ software systems and CEO of its parent company, Kendig Keast Collaborative. Photo courtesy of Bret C. Keast.

We had to start asking different questions. It was no longer enough to put plans, zoning rules, and maps online and make them easily searchable. They also had to be organized so residents, staff, applicants, software, and AI tools could all understand them the same way. We also had to think about how zoning standards connect to the steps people actually follow when they apply for approval, review a project, check infrastructure requirements, or decide whether development can move forward.

That realization changed our product priorities. We needed to build tools that showed how planning information works together, and how it can be made usable across the full planning and development process. That meant bringing data scientists and software developers into the same conversations as our planners and zoning specialists, so the platform could better construct and support that municipal regulatory infrastructure. 

A zoning standard does not stand alone. It relates to a plan policy, a map, an application requirement, a review procedure, an infrastructure standard, and eventually a decision. The more clearly those relationships are organized, the easier it becomes for staff, applicants, residents, software, and AI tools to reach the same understanding and for a community to carry its policies into day-to-day decisions.

From static documents to regulatory infrastructure

Looking ahead, we know that access to information is only part of the challenge. To be useful, planning and zoning information must be organized in ways that allow it to be connected, understood, and applied in real-world decisions.

More than ever, planners are needed to weigh competing priorities, understand local context, interpret community values, and build the public trust that effective implementation depends on.

The difference may seem subtle, but it changes everything. While documents remain important, the real opportunity lies in building a single ecosystem that connects all the component pieces. AI has accelerated this shift but did not create it. Planners have always known that the most difficult part of the work is not producing a plan or adopting a zoning ordinance. It is making those policies work in practice: turning community goals into standards, standards into procedures, procedures into decisions, and decisions into outcomes that reflect local priorities.

Much of the conversation about AI in local government focuses on which planning tasks it might replace, but our experience suggests the opposite: AI increases the importance of human judgment. More than ever, planners are needed to weigh competing priorities, understand local context, interpret community values, and build the public trust that effective implementation depends on.

An AI system cannot determine what a community values. It cannot balance competing priorities among residents, property owners, elected officials, developers, and staff. It cannot decide whether a standard is politically acceptable, legally defensible, administratively workable, or appropriate to the local context.

As a company that has worked with hundreds of local communities to prepare zoning and development codes, we remind ourselves often: AI can draft language, but it cannot replace the planning judgment behind the code. Every planner knows that a zoning code reflects years of public discussion, political negotiation, legal review, implementation experience, and community priorities. The deeper we went with AI, the more apparent that became. Generating regulatory language was easy. Capturing the judgment, context, tradeoffs, and institutional knowledge behind that language wasn’t.

As planning technology advances, the communities that benefit most will not be those that simply adopt new AI tools. They will be those who use this moment to modernize how information is structured, connected, governed, and applied.

If AI is used in planning, it must be transparent about sources, distinguish adopted requirements from guidance, and support professional judgment rather than obscure it. The future of AI in planning depends on trust, traceability, transparency, and governance — not novelty.

The opportunity for planners

Today, communities are asking broader questions than in the past — about how information flows between departments; the relationship of adopted policy standards, requirements, and final decisions; and how deep staff knowledge and experience can be preserved and shared over time.

Those questions reveal something planners have long understood: Plans, regulations, procedures, infrastructure standards, and development review workflows are not separate pieces of work. They are part of the same implementation system. What may first look like a technology problem often turns out to be a larger challenge of coordination, consistency, and institutional knowledge across departments.

The planner’s role is shifting from finding information to ensuring information is coherent, defensible, and connected.

As planning technology advances, the communities that benefit most will not be those that simply adopt new AI tools. They will be those who use this moment to modernize how information is structured, connected, governed, and applied. AI will continue to change planning technology. The enduring work of planning remains human: setting priorities, balancing interests, building trust, interpreting local context, and translating community values into decisions that shape places over time.