The restoration industry is currently navigating a wave of “AI noise.” Every time you open LinkedIn or your email, you’re likely bombarded by promises of new software that will solve all your problems or, conversely, dire warnings about the risks of the technology.
To cut through this static, we recently hosted the Practical AI for Restoration bootcamp, a three-hour session designed to turn down the noise and stick to tangible facts. This article summarizes the core takeaways from that event, which featured seven restoration voices sharing their expertise on how to adopt technology in ways that strengthen day-to-day execution. From organizational leaders and technology architects to marketing experts, these speakers provide a fact-based framework for navigating the new frontier of AI.
Clarity Before Capability
The most significant lesson we’ve learned is that AI does not create clarity; it exposes the lack of it. As Taylor Carmichael, Director of Systems for Southeast Restoration, pointed out, if your internal processes aren’t defined, AI will fill that gap with assumptions—and it will likely guess wrong.
Before you layer AI onto a workflow, you must slow down and ask these critical questions :
- Is the process documented? If it only lives in someone’s head, AI will guess wrong.
- Is ownership clear? AI won’t fix a situation where no one owns the input or the outcome.
- Is the data trusted? AI will confidently amplify whatever you give it, good or bad.
- Would you defend the output in a courtroom? If you wouldn’t stand behind it with a carrier, attorney, or regulator, AI has no business touching it.
AI is not your starting point; it is the reward for doing the foundational work first.
The Restorer as “Ground Truth”
One of the biggest misconceptions is that Large Language Models (LLMs) are “thinking” or “reasoning” the way humans do. In reality, an LLM is a professional writer, not a restoration professional.
While AI can draft a professional-sounding email in seconds, it has no concept of what is actually happening on a job site. You—the restorer—are the ground truth . If you rely on AI to write a narrative without grounding it in your field evidence, you risk sending a perfectly written document to an adjuster that is factually unsupported. This doesn’t just delay payment; it damages your reputation.
Managing the Risks of “DIY Tech”
The experts highlighted the dangers of experimenting with free AI tools. This “DIY” approach carries significant risks:
- Data Leakage: If you input a customer’s personal information into a free, public LLM, that data can surface in other people’s search results later.
- The Authoritative Fallacy: Because AI writes so well, it’s easy to fall into “blind trust,” assuming that polished output equals correct facts.
- Scalability Issues: A prompt that works for one person doesn’t necessarily work for a team. True transformation requires purpose-built architecture where AI is integrated into secure, validated workflows.
Evaluating Your Partners
Nick Hindle, CIO for PuroClean, shared that when evaluating AI vendors, you should look for partners, not just vendors. A vendor sells you a tool and leaves you on your own; a partner helps you overcome the hurdles of the first several months.
At a minimum, ensure your partners are SOC 2 certified, demonstrating a rigorous commitment to data security. Prioritize solutions that are restoration-focused and trained on industry standards, rather than general-purpose tools.
It is no longer enough for a vendor to claim their AI is “accurate” or “fast”. Dianna Lee, Senior Platform Product Manager at Encircle, argues that you must question your AI vendors on their specific evaluation and validation protocols before trusting them with your client files:
- The “Messy Data” Test: Ask vendors what kind of dataset they used to test their models. If they only validated using “clean,” perfect job files, the AI will likely fail when it encounters incomplete field notes or complex real-world claims.
- Systematic Benchmarking: Inquire how they measure performance and reliability over time. Reliable partners use systematic processes—like Encircle’s nine-step validation framework—including “smoke testing” to catch potential errors or “hallucinations” before the output ever reaches your team.
- Business Risk Alignment: A true partner can explain how their AI handles assumptions and what safeguards are in place when data is conflicting or missing.
By demanding transparency in how these tools are built and tested, you ensure that AI remains a defensible asset to your business rather than an unverified liability.
The Path Forward
AI is already driving real results—from voice dictation that converts field notes into structured documentation instantly to AI-powered intake that can capture over $500,000 in previously missed leads per year.
The future of restoration isn’t about AI replacing people; it’s about AI removing the manual bottlenecks that slow us down. By focusing on clarity first, we can use this technology to move jobs faster, get paid sooner, and ultimately provide a better experience for the homeowners we serve.
Next Steps
For a deeper dive into these topics, you can view the full 3-hour “Practical AI for Restoration” webinar recording and earn 3 IICRC continuing education credits at getencircle.com/webinars/practical-ai-for-restoration.
Paul Donald
Paul is an accomplished entrepreneur and technology executive with over 25 years of experience solving productivity issues with innovative technology. Prior to co-founding Encircle, he was the COO at PushLife which was acquired by Google in May 2011. Paul spent the prior decade on the senior executive team at BlackBerry and was also the Founder and CEO at PeerDirect, which he sold to Progress Software in 1999. Paul is an active mentor, coach, and angel investor in Canada’s Technology Triangle.
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