The Work Before the Work

What AI Actually Does for Permitting

“Two thousand six hundred public comments on a single federal project. The standard process: four people, four weeks. The first week wasn't reading comments. It was proposing category names before anyone had substantively reviewed a single submission. With AI support, the same task took four hours of data management and another hour or two to run the system. Same output. The weeks vanished.”

That example comes from Jacobs Engineering's AI-Engage platform, presented at a PNNL workshop on AI tools for environmental review. Most conversations about AI in permitting get the bottleneck wrong. The delay is rarely the environmental analysis itself. It includes completeness checks, comment triage, citation cross-referencing, document version tracking, and drafting project descriptions. Administrative work consumes the people who are supposed to do the analysis. By the time a reviewer reaches the substance of a permit application, they've already burned their capacity on paperwork.

I spent a week at the National Association of Environmental Professionals conference in Anchorage, where practitioners gathered to talk about where the field is headed. Presenters from different agencies and firms, in separate sessions on separate days, arrived independently at the same observation. Eric Beightel, an ESA consultant who works with state DOTs on categorical exclusions, put it plainly: there are no standardized systems to track CEs across agencies, workflows vary by office, and definitions that should be shared aren't. A governance failure made worse by the absence of basic workflow tools. Wes Furlong from the Native American Rights Fund, speaking in a completely different room, described the same structural pattern from the tribal consultation side: agencies that lack basic infrastructure to track who needs to be consulted, when, and about what. Between sessions, practitioners put it in more personal terms. They spend their days on logistics that shouldn't require a professional degree. With federal staffing reduced, AI has become a practical necessity for many teams, not a luxury.

The tools that address this aren't experimental. They're deployed.

At Idaho National Laboratory, a developer built a tool called AI Scope Assist in about 1.5 weeks. It drafts project descriptions for categorical exclusion documents. The CX process at INL wasn't slow because the environmental determination was complicated. It was slow because writing the project description was the most time-consuming step, and the task followed predictable patterns. Scope Assist plugs into the existing workflow. No system-switching. No new platform to learn.

In Los Angeles County, Archistar's eCheck platform uses generative AI and computer vision to review building plans for code compliance.[1] The pilot launched in July 2025 as part of the wildfire rebuild effort, after the LA fires turned the permitting backlog into a public emergency. Municipalities using eCheck report a 55% reduction in permit review cycles and 81% fewer resubmissions. The platform checks plans against local codes before a human reviewer opens the file. When the plan passes, it passes faster. When it doesn't, the reviewer starts with a specific list of issues instead of reading the full submission cold.

None of these tools requires professional environmental judgment. Comment triage doesn't involve deciding whether a comment has merit. Completeness checking doesn't involve evaluating the substance of an application. Drafting the project description doesn't involve making a determination. These are administrative tasks that consume analytical professionals' time. AI works here because the tasks are structured, repetitive, and can be corrected before a decision becomes final.

EPIC has tracked 320 permitting technology tools across every stage of the environmental review process.[2] They solve individual tasks well. Most have APIs. Almost none connect to each other. We've started calling this the Abundance Paradox: the tools exist, but the governance decisions that would make them useful don't. Getting from isolated tools to connected systems requires a sequence that most agencies skip: data standards before interoperability, interoperability before automation. That governance problem is the subject of the rest of this series. It starts here, though, with the recognition that the work consuming most reviewers' time isn't the work that requires their expertise.

There's an uncomfortable question here. At the PNNL workshop, someone asked what happens to entry-level staff when AI handles the tasks they used to cut their teeth on. The answer was that they're learning to use AI tools interactively, developing a different kind of fluency with the work. That's a real tradeoff, and it mirrors every previous wave of workflow tools, from word processors to GIS. I'll come back to this in the next post, because the workforce question gets sharper once you see where AI stops being useful.

If you recognize your own workflow in any of this, here's the practical step: identify the single task that consumes the most time before you get to the actual analysis. That's your pilot candidate: the most annoying task, even if it isn't the most impressive to automate. Share this post with whoever controls your software budget. Ask them to read the next three.

The next post gets into the harder question. Knowing where AI saves time is easy. Knowing where to trust it is where agencies need a framework.

Archistar eCheck platform. https://archistar.ai

EPIC. "2026 Permitting Technology Landscape Report." https://policyinnovation.org/insights/the-2026-permitting-technology-landscape-report