AI is already inside restoration businesses. The only question is whether leaders are guiding its use or reacting to it. 

I have been using AI for over two years, and my relationship with it has evolved in ways I didn’t expect. Anyone who has heard me speak on the topic knows that I believe AI is the future of work. I often joke that I’m going to get to live in the Jetsons’ world in my lifetime. And like any new tool, it’s not about whether it exists. It’s about whether you learn to lead with it. 

What I did not anticipate was how often others would start pulling AI into our shared work. 

At the time, the colleagues I worked most closely with were using AI, but not at the level I was at. I had a subscription, and my AI was already trained in how we work, who our clients are, and the kinds of problems we solve every day. For certain projects, colleagues began using my AI rather than starting from scratch on their own. 

We experimented together. We used it to think through problems, prepare for meetings, and create presentations. I was using the voice version of ChatGPT, and the voice was female, so I naturally referred to it as “her.” At some point, the group decided we should stop calling ChatGPT “her” and give it a name. We landed on Penelope and the name stuck. As others later got their own subscriptions, many of them named theirs as well. 

What surprised me was not the naming. It was what changed after. 

Once it had a name, it stopped being software. It became a thinking partner. I stopped issuing commands and started having conversations. The questions improved. The answers got used. It became part of how work gets done, not a novelty or a shortcut. 

That’s the shift restoration owners need to understand. 

When you name your AI, something else happens. You stop using it for surface-level tasks. It’s no longer just a better way to write an estimate or a faster version of a Google search. You start using it to think, to challenge your assumptions, and to work through decisions before you make them. That is where AI starts to matter. And that shift is now happening across the restoration industry, not just in my office. 

Our clients span generations—from Gen X and Boomer owners who built their companies from the ground up to Millennials who grew up with technology as a daily tool. What is interesting is that many of them, regardless of age, were using estimating and job-costing software long before AI arrived. That experience quietly built their trust in technology to run core business functions. For some, the leap to AI felt natural. For others it took a nudge from a peer, a family member, or a vendor. Either way, the leap turned out to be shorter than they expected. 

What pushed them over the edge was not a white paper or a webinar. It was a conversation with someone who said, “You need to try this.” That is how most business owners make decisions—through trusted relationships, not top-down mandates. And once they started, the results showed up fast. Productivity gains from AI use are consistent across age groups and technical backgrounds. The barrier was never ability. It was engagement. 

There is also a caution worth naming. Younger employees are using AI every day, often with real anxiety about becoming too dependent on it and losing their ability to think critically. That tension already exists inside many restoration companies. The risk is not that one generation will outperform another. The risk is allowing AI to be used informally and inconsistently without leadership direction.

See also:
The Future of AI in Restoration: How Technology Is Transforming the Industry

What Intentional AI Leadership Looks Like in Restoration

So what does intentional AI leadership in restoration look like? Here are five practices, ranked in the order I would tell everyone to start.

1. Standardize AI use in one function first.

This is the highest-leverage move an owner can make. Telling a team to “go use AI” creates confusion and inconsistency. Instead, leaders should pick one function and be specific. Sales uses AI to prepare for insurance agent meetings and to recap sales calls. Operations uses it to pressure test schedules and subcontractor coordination. Administration uses it to organize job communications and priorities. In restoration, I would start with estimating or job closeout documentation, two areas where consistency directly affects margins.

2. Require AI to explain its thinking.

Output should not be accepted at face value. Leaders should ask, “Why?” What assumptions are being made? What would change the recommendation? If a project manager uses AI to build a labor schedule for a large loss, they should show what questions they asked and what the AI flagged as risks. This keeps critical thinking sharp across the team and reinforces that AI supports judgment; it does not replace it.

3. Use AI before you decide, not after you act.

Most owners use AI to clean things up after the fact. The real leverage comes when it’s used upstream. Before making a hiring decision, restructuring a technician role, changing the pricing structure, or having a difficult conversation with a subcontractor, leaders should run the situation through AI. They should ask what questions are not being asked and what risks may be underestimated. This turns AI into a pre-decision filter instead of a productivity toy.

4. Replace one recurring meeting with AI preparation.

Leaders can start by identifying one recurring weekly or monthly meeting, like the Monday morning job review, the monthly financials meeting, or the weekly dispatch huddle. Before the meeting, AI can be used to help prepare the agenda, surface patterns, and identify decisions that need to be made. Leaders who do this get better conversations and fewer follow-ups because the thinking happens before people sit down.

5. Make your own use visible.

The fastest way AI adoption stalls is when leadership talks about it but never shows it. If AI helped inform a pricing decision or prepare for a difficult conversation with an adjuster, that use should be acknowledged openly. Leaders don’t need to teach AI to their teams. They need to model how it fits into real leadership work. Leadership adoption drives organizational adoption.

It Is Not Too Late to Start Intentionally

For owners who feel behind, the reality is they are not. Those who started early had the advantage of curiosity. Those who start now have the advantage of hearing what actually works. What matters is not when you start. It is whether you start with intention. 

Start with one function. Improve one decision. Run one meeting differently. That is often enough to change how AI shows up in a business. 

Naming my AI did not make it smarter. It made me more intentional. It moved AI from something on the side to a thinking partner in the work. That’s the opportunity in front of every restoration business owner right now. 

The real question isn’t what you name it. It’s whether you’ll lead it. 

Mine still answers to Penelope.