Where AI Fits, and Where It Doesn't
Drawing the Line Between Administrative Support and Professional Judgment
At a 2025 PNNL workshop on AI in environmental review, Paces CEO James McWalter demonstrated a tool that compresses two to twelve weeks of environmental report drafting into under twenty minutes for a large power project. An audience member responded directly: "I'm concerned about the commoditization of permits to just become checkboxes." McWalter's stated goal was full automation. The audience member's concern was that full automation guts the purpose of public consultation.
Both of them were right about something. This post is about figuring out where that line sits, and why it matters that agencies draw it before vendors draw it for them.
AI is reliable for tasks with clear structure and correctable outputs. Comment clustering, where natural language processing categorizes thousands of submissions by topic and flags duplicates, lets a reviewer start with a map instead of a pile. Document comparison flags changes between EIS drafts, identifies unresolved citations, and surfaces sections where data points conflict. Completeness checking screens applications against known requirements before a human opens the file. In Cook County, Illinois, Esri's aerial imagery comparison overlays new satellite photos against permit databases to detect unpermitted structures. Detection is automated. The enforcement decision requires a human.
These are all cases where being wrong is correctable before a decision becomes final. The reviewer still reads, still decides, still signs. AI reorganized the work. It didn't replace the judgment.
The cases where AI shouldn't have the last word are fundamentally different. Final permit determinations that weigh public interest against project benefits require a type of reasoning that resists pattern-matching: interpreting ambiguous regulatory language where precedent is contested, weighing community input that is itself part of the analysis rather than data to be processed, producing outputs that anchor an administrative record subject to judicial review. The consequences of being wrong in these situations compound rather than self-correct.
Lauren Schramm, a consultant at ESA, presented a session at NAEP 2026 called "Beyond the Hype: The Dark Side of AI." It was the sharpest talk I attended. Her target was carelessness, not AI. Environmental review, she said, is not a pattern-recognition problem. AI assumes transferable patterns and generalizable logic. NEPA requires site-specific analysis, procedural rigor, and legal defensibility. The mismatch produces what Schramm calls "AI Slop": text that reads as complete but contains no actual analysis. Her example was a generic wetlands paragraph that referenced the importance of wetlands four times in different phrasings and included a mitigation sentence with no quantification, resource type, impact category, or proposed measure. The recursive phrasing was the obvious tell. The empty mitigation sentence was the functional one. Agencies are already receiving documents like this in their review pipelines, she reported, with fabricated citations and incorrect regulatory references.
Schramm offered a triage framework organized around four questions: Is it necessary? Is it proportional? Is it transparent? Is it reviewable? Meaningful AI use (formatting, summarizing large datasets with human review, data exploration, and efficiency on low-risk tasks) passes all four. Frivolous use (drafting full NEPA sections, generic impact analysis, automating conclusions) fails on reviewability and, more often, on proportionality. The decision threshold: if you cannot explain how the output was generated, verify the sources independently, or defend it in a public record, do not use it.
That threshold isn't arbitrary. Schramm grounded it in the NAEP code of professional ethics, which states that environmental professionals will be personally responsible for the validity of all data collected, analyses performed, or plans developed under their direction. The accountability doesn't transfer to the tool. Most practitioners are already bound by professional ethics that give them the decision rule they need. Triage is the operational manifestation of that commitment.
There's a role shift here that I think the field hasn't fully reckoned with. When AI handles the drafting, the practitioner's job changes. You stop producing narrative and start validating assumptions and stress-testing analysis. Agencies and consulting firms that recognize this shift early will train their staff differently from those that don't. The entry-level question I raised in the previous post gets sharper here: the professional judgment that distinguishes a competent environmental reviewer from a competent AI prompt operator develops through years of doing the analytical work that AI is now absorbing. How you build that judgment when the drafting is automated is a question the field hasn't answered yet.
Beveridge and Diamond published a useful framing in late 2025: project opponents will exploit AI use in litigation if it isn't well-documented.[1] Courts have not yet established how they'll give deference to AI-assisted agency decisions. Early adopters who blur the line between administrative support and professional judgment will make it harder for every agency that follows.
For any AI application you're evaluating: AI holds up when the task is pattern recognition on large, structured datasets, and a human reviews the output before it matters. It doesn't hold up when the task requires judgment about competing public interests, cumulative impacts, or contested regulatory language. If you can place your use case clearly on one side of that line, you have a defensible position. If you can't, that's a locked widget: a governance decision that hasn't been made yet, and no amount of software will make it for you.
The next post covers what to ask a vendor before you sign anything.
Beveridge & Diamond. "AI in Environmental Permitting: Anticipating the Next Wave of Legal Challenges." 2025. https://www.bdlaw.com/publications/
