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15–22 minutes

The Mit List | Episode 1: Paul on AI in Restoration

On the show floor at RIA 2026 in Savannah and other recent shows, every other conversation was about the same thing: AI.

What’s real, what’s hype, who’s innovating and who’s slapping “AI-powered” on a brochure? That’s what restorers wanted to know from our CEO, Paul Donald. So we sat him down for a 15-minute recap of what he heard, what he pushed back on and where contractors should actually put their energy over the next 12 to 18 months.

The short version: AI looks like an easy button, but the value is real only when the data behind it is real. And the data only comes from one place—the field.

Paul Donald

Chief Executive Officer

Encircle

Leah Vusich

Director, Product and Content Marketing

Encircle

Join us for the next webinar

Rethinking Scoping & Estimating: A Better Way to Understand Loss

Blurring scoping and estimating leads to missed details, overlooked costs, and difficulty justifying past and future work. Let’s reframe it together.

We’ll talk about what a complete scope actually looks like, why there’s no such thing as too much detail, and how small gaps can turn into big problems.

There’s no shortcut around field documentation

Restorers asked Paul if AI can shortcut the documentation step. Skip the photos. Skip the readings. Just talk into an app and get a scope.

The answer: No. But documentation can shortcut your use of AI.

Two reasons:

AI isn’t free. Every LLM call costs tokens, and tokens add up—especially when you’re feeding it unstructured data and asking it to figure out what matters. Good documentation upfront means less to process and a cleaner output on the back end.

Standard LLMs don’t know what’s missing. A generic AI tool will happily produce a scope from whatever you give it. It won’t tell you, “Hey, you didn’t capture a moisture reading on day three — go back and grab one before the carrier asks.” A purpose-built system can.

The margin difference: 10 to 25% on every job

Paul has been saying this for years and the data keeps backing it up. Contractors with documentation and scope dialed in capture 10 to 25% more profitability on every job. Once you layer in equipment revenue and aligned day-one, day-three and day-five estimates, it compounds.

The math is hard to argue with. Thousands of dollars per job. On every job. Forever.

And the side benefit — the one a lot of shops don’t think about — is internal. If your PMs are pushing their personal assessment through a generic LLM, you’ve got no way to audit the file. There’s no record of what they saw or didn’t see. The facts have to come from the field, with timestamps, geolocates and the name of who captured them. Otherwise, you’re exposed.

What’s coming next: field data drives everything

Paul’s prediction for RIA 2027 — the conversation moves past scoping. In the next 12 to 18 months, AI tools are going to start eating the administrative overhead on top of restoration. Job scheduling. Work orders. CRM updates. Job management. All of it driven by the field data you’ve already captured.

If you’ve got the data, you can put it to work. If you don’t, you’re back to square one — paying for AI that doesn’t have enough to chew on.

Paul Donald

Where restoration contractors should invest in AI right now

If you’re a contractor serious about AI, here’s the priority order Paul gave at RIA:

Fix your field documentation processes first. Photos, videos, notes, moisture maps, video notes from the tech explaining the conditions. This is the foundation. Without it, AI has nothing useful to work with.

Build a scope every day. Day one, day three, day five. However long mit runs. Daily scopes catch what’s missing before the carrier does.

Pick AI tools that are restoration-specific. Generic LLMs are great for office work. They’re not great for files that have to survive a carrier review. Look for tools built on IICRC standards, with restoration-specific logic baked in.

Don’t trade documentation for speed. A few extra minutes in the field is worth thousands of dollars on the invoice.

Frequently Asked Questions

No. AI can’t stand in a flooded basement at 3am, run containment, set drying equipment or read a moisture map on a real wall. What AI can do is take administrative work off the plate — generating scope, building reports, drafting work orders — based on the field data the crew captures. The contractors who fix their documentation first are the ones who benefit most from AI.

Yes. Carriers are investing heavily in AI to reduce cycle time, control claim costs and cut overhead. That includes using AI to write fact-check estimates against the contractor’s submission. The only defense is documentation — verifiable, time-stamped, photo-backed evidence that supports every line item.

They can produce something that looks like an estimate, but it won’t be defensible. Generic LLMs aren’t trained on IICRC standards, OSHA requirements or building codes. They’ll miss billable line items, can’t flag what’s missing from the documentation and have no way to verify the field conditions. For restoration work, you need an AI system built on restoration-specific logic and your own field data — like Encircle AI.

A rational estimate reads well and looks complete. A defensible estimate is backed by verifiable evidence — photos, moisture readings, time stamps, geolocated notes, IICRC citations. When a carrier or engineering company reviews your file, only the defensible version holds up. Rational alone gets compressed or cut.

Encircle customers with documentation and scope dialed in report 10 to 25% higher profitability on every job. Once equipment revenue and aligned daily estimates layer in, the lift compounds. Hydro users specifically see around 10% more equipment revenue and 20%+ increase in claim profitability.

Start with field documentation. Photos, video, notes, moisture readings, floor plans — captured in one app, structured for restoration, available to your office in real time. That’s the foundation every useful AI feature builds on. Without it, AI has nothing to work with. With it, you unlock scoping, reporting and (soon) scheduling and job management.