A Note from Our Founder | When AI Does the Work That's Been Stuck for Months
I want to share something I've been working on outside of netMethods — though the more I think about it, the harder it is to separate the two.
For over seven years, I've volunteered with the USDA Forest Service Trabuco Ranger District doing trail crew work in the Santa Ana Mountains. It's the kind of work that keeps your hands busy and your head clear. What I didn't expect was that it would become one of the most compelling AI use cases I've seen this year.
Here's the situation. The Trabuco Ranger District, like a lot of federal land management offices right now, is short-staffed. When the district ranger reached out last month and asked if I could help move a NEPA categorical exclusion process forward for some of our historical legacy trails — the Luge, Upper Holy Jim, Bell Ridge — I said yes immediately. These are trails with deep use histories that need formal federal authorization before any organized restoration work can happen on them.
What I learned quickly is that local nonprofits had already been trying to get this done for months. Smart, dedicated people who care about these trails. They hit the same wall everyone hits: the research required to qualify for a CE is substantial, the regulatory framework is complex, and the district doesn't have the bandwidth to hand-hold the process when they're already stretched thin. So it stalled.
A categorical exclusion — for those unfamiliar — is a NEPA determination that a proposed action doesn't require a full Environmental Assessment or Impact Statement because it won't have significant environmental effects. It's the faster path to getting restoration work authorized. But "faster" still requires doing the work: cross-referencing the applicable CFR sections, working through the extraordinary circumstances checklist, documenting site-specific conditions for each trail, and producing a Decision Memo that meets federal requirements under 36 CFR 220. Shortcut the research, and you don't get the exclusion.
So I built a structured AI research workflow to do it right.
I designed a prompt that walked AI through the CE eligibility questions specific to legacy trail restoration — which exclusion categories apply to established historic routes, what extraordinary circumstances look like for trails with long documented use histories, how to cross-reference land management plan consistency requirements, and how to structure the Decision Memo documentation. AI handled the regulatory synthesis. I reviewed, verified, and made the calls. The research that had been stalled for months moved forward in a fraction of the time.
The part I'm most proud of isn't the speed. It's that the prompt is repeatable. I built it to be approachable — no legal background required, no prior NEPA experience assumed. Any volunteer coordinator, conservancy, or small nonprofit working with a Forest Service district on similar legacy trail projects can run the same workflow and get to the same quality of output. The goal was to remove the research bottleneck entirely, not just solve it once for us.
That's what practical AI looks like to me. Not a product demo. Not a chatbot answering FAQs. An AI that can hold the complexity of a federal regulatory framework so that an underfunded district office and the volunteers who support it can actually move the work forward.
The Luge, Upper Holy Jim, and Bell Ridge deserve to stay open. So do the hundreds of other legacy trails sitting in similar limbo across national forests because the paperwork is too heavy and the staff is too thin.
If you're working with a federal agency on any kind of permitting or compliance research and hitting the same wall — reach out. This is exactly the kind of problem practical AI was built for.
— Jason Bennett Founder & President, netMethods