Build the Infrastructure Before You Deploy

What the Agencies That Didn't Stall Did Differently

Minnesota's Pollution Control Agency didn't start with a tool. They started with a problem: complex air permits took a median of nearly 600 days to process, almost four times the agency's stated goal. In early 2025, the MPCA secured $780,000 from Minnesota's Technology Modernization Fund for AI applied to three specific workflows: emissions modeling data preparation, public comment analysis, and permit risk determination. The dollar amount and the technology are the least interesting parts. Governance requirements were written into the grant application before a single vendor was contacted. The agency had to answer how the tool would be overseen, who would be accountable for its outputs, and what would happen when it was wrong. All before the money was approved.

Governor Walz followed up in 2026 with an executive order directing the MPCA to pilot AI tools, upgrade its permit application tracker for public visibility by April 2026, and submit a joint efficiency report with the Department of Employment and Economic Development by December 2026. The oversight arrived packaged with the technology. That ordering matters.

In the first post, I described what we've been calling the Abundance Paradox: 320 permitting technology tools, most of them working, most with APIs, almost none connected. The reason is always the same. No single entity has the authority to require interoperability. AI will follow the same pattern unless agencies make different decisions before deployment. The agencies that stall are the ones that treated deployment as a technology decision; their tools were usually fine. The tool arrives, no one has been designated to oversee its outputs, no one knows what to do when it's wrong, and it runs for a few months before being quietly q.

Five governance decisions separate the agencies that succeed from the ones that stall. None requires new technology. All require authority. Each one is a locked widget, to use the term from the last post: a governance decision sitting unresolved, invisible to the org chart, waiting for someone to own it.

Start with documented decision rules before deployment. What decisions will AI inform? What decisions will it not inform? Written down, reviewed by legal, and signed by a named official. If the AI flags an application as incomplete, what happens next? Who reviews the flag, and what criteria do they apply? This document makes an AI pilot defensible. Without it, the tool runs on informal consensus, which works until someone disagrees.

Then: a named accountable official for every AI-informed decision. A person who reviewed the AI's recommendation, applied professional judgment, and can explain what they decided and why. This is what makes an administrative record hold up. It also gives staff confidence that they're using the tool rather than being used by it. As the NAEP code of professional ethics states, environmental professionals are personally responsible for the validity of analyses performed under their direction. AI doesn't change that obligation. It makes it more visible.

A single-pilot use case, run end-to-end before scaling. Agencies that succeed don't deploy AI across their workflow at once. They pick one task and run it through a real project. They validate accuracy against the outcome they would have reached without the tool. They document what happened when the AI was wrong. Only then do they expand.

The fourth is easy to defer and expensive to reconstruct later: audit logs that outlast the project. What did the AI recommend? What did the reviewer decide? When? This documentation is legal protection, yes. It's also how an agency learns whether the tool is actually working over time. Agencies that don't build this in from the start tend to lose the data they need to make the case for continued investment during budget season.

And public reporting. If AI is being used in decisions that affect public interest, say so. Where in the process is it used? What oversight applies? A one-paragraph note in the administrative record is enough. The counterintuitive finding from agencies that have been transparent about AI use is that they tend to face fewer legal challenges, not more. Transparency turns AI from a liability into a documented choice.

The federal government has done part of this work. The CEQ Permitting Technology Action Plan directed agencies to begin adopting NEPA data and technology standards.[1] OMB's 2025 memoranda require federal agencies to establish AI governance and procurement frameworks.[2] Federal mandates create conditions, though. They don't create authority inside each agency. That authority has to be built from within, one decision at a time.

If you've read this far, you've seen the whole arc. AI can handle the administrative work that consumes reviewers' time using tools that exist today. The line between where AI helps and where it doesn't is knowable. The right vendor questions protect agencies before they sign. The governance infrastructure that makes it all stick costs less than the tool you're about to buy.

Here is the explicit ask: before your agency deploys anything labeled with AI, designate a human official accountable for AI-informed permitting decisions. Write down the specific decisions AI will inform and the decisions it won't. Run one use case end-to-end before you scale. Those are three decisions. They take an afternoon to make and months to undo if you skip them.

The tools exist. Most of them work. The sequence is always the same: data standards before interoperability, interoperability before automation, governance before all of it. Whether your agency builds the authority structure that lets any of it matter is the question these four posts have been circling.

Council on Environmental Quality. Permitting Technology Action Plan. https://www.whitehouse.gov/ceq/

OMB Memoranda M-25-21 and M-25-22. Office of Management and Budget, April 2025. https://www.whitehouse.gov/omb/information-regulatory-affairs/artificial-intelligence/