Shayne Hedahl watched his volume at Special Interest Autobody in Everett, Wash., drop from 200 cars a month to 80 over a three- to four-year stretch. Meanwhile, across the border in Canada, Mike Mario at Regina Auto Body had the opposite problem: 108 estimate requests piled up over three months, and many sat unfinished while his customer sales representatives were buried in manual estimate prep.
In our readiness assessment, we talked about identifying your primary bottleneck. These two shops faced completely different problems and implemented completely different AI solutions. Their results show why it’s so important to understand the true bottleneck in your shop and choose smart tools that will make a real impact.
When the Problem Is Getting Cars Through the Door
Special Interest Auto Body is a Toyota-certified shop built on a quality-first philosophy. Hedahl chose to forgo insurance DRP partnerships, betting on the strength of his work and reputation. As both the shop's visionary and day-to-day implementer, he had built the kind of shop that would score high on internal operations.
But none of that mattered if the parking lot was empty. Over three to four years, his monthly volume had fallen from 200 cars to 80, and operational excellence alone wasn't going to reverse that trend. His bottleneck was customer acquisition, not efficiency.
Hedahl partnered with Rodland Toyota dealership to install Tractable's AI-powered LumaScanner in their service lane. The key insight behind this approach is simple math: dealerships see hundreds of cars daily compared to a typical body shop's 10 to 20.
Now, a customer drops their vehicle at the dealership for routine service, and the LumaScanner analyzes it, identifies existing damage, and generates a preliminary estimate. The customer receives a transparent assessment on their own terms, with no pressure. If they're interested, the shop gets a pre-qualified lead.
Why a Dealership Partnership Changed the Equation
Hedahl had strong operations but needed customer access. Since AI amplifies your foundation, this solution targeted exactly that gap.
It created a customer acquisition channel through a high-traffic partnership location. It eliminated the "negotiation" perception that many consumers associate with body shops, because an AI-generated estimate functions as a neutral third party.
Plus, it strengthened Hedahl's Toyota certification partnership by creating mutual value: the dealership offers a better customer experience, and Hedahl gets consistent, qualified leads.
What It Actually Took
This wasn't "install and forget." Hedahl personally spent months pitching dealerships alongside Tractable. He built the relationship with Rodland Toyota through sustained leadership commitment, trust development, and repeated conversations about how the partnership would benefit both businesses. The partnership model was as critical as the technology itself.
Customers obtained through the dealership service department now arrive at Special Interest Auto Body with clear cost and timeline expectations. Trust is established before the first shop conversation ever happens. And the dealership relationship adds referral credibility that cold marketing simply can't replicate.
The results bear that out. Over the last six months, Special Interest Auto Body has averaged 15 additional repair orders per month. The Toyota partnership has been especially productive: in January, 40% of the shop's repair orders were Toyota vehicles, up from 23% two years ago. Hedahl's goal is 50% Toyota by 2027.
For a shop that built its reputation on quality but struggled to get that reputation in front of enough people, the scanner created a pipeline that matched the shop's values: high-quality leads from a trusted source, with no compromises on the customer experience that Hedahl had spent years building.
As Hedahl put it: "The assurity to the customer of knowing what to expect on their own terms. They don't have to feel like they're negotiating with us as a shop."
Applying This to Your Shop
If customer acquisition is your bottleneck, calculate your baseline before exploring solutions:
- Current monthly estimate volume
- Primary customer sources (referrals, walk-ins, dealers, insurance)
- Closing ratio (estimates to completed jobs)
- Days from inquiry to estimate delivery
Then ask the strategic questions. Which local dealerships share your quality philosophy? Which dealer service managers are frustrated with insurance steering? What partnerships could give you access to higher customer traffic? Can you commit to the time it takes to develop great relationships?
Key Takeaway: Technology alone may not be the solution. It’s how you implement it and innovate with it that matters.
When the Problem Is Converting the Demand You Already Have
Regina Auto Body's challenge came into sharp focus over a three-month window in 2025. Under CEO Mario's leadership, the Canadian repair shop had no shortage of work coming through the door.
The shop was processing both insurance and customer-pay jobs, and the demand was there. On an AI readiness assessment, Regina would have shown strong volume and customer flow. But the shop had a process bottleneck preventing it from converting that demand into completed work.
Customer sales representatives were spending their days buried in manual damage assessment and quote generation. The backlog meant customers were waiting longer than they should have been. Regina's bottleneck wasn't customer acquisition; it was internal response time and capacity.
By integrating Tractable's AI directly into their estimating workflow, Regina shifted the time-consuming damage assessment and quote generation to the AI — and enabled its people to provide faster, better service to customers.
Where the Hours Went, and What Changed
Regina's problem was response time and completion rate. Manual processes simply couldn't keep pace with demand, and the result was lost business. Not because customers didn't want the work done, but because estimates sat unfinished.
The numbers tell the story. When the shop processed 108 estimate requests through Tractable’s Instant Quote, 64 became completed quotes. That’s a 59% completion rate that represented a significant jump from the manual process.
AI saved 1,320 hours of estimate preparation time over those three months. That represents 16.5 full-time weeks of estimator time that was able to be reallocated to customer interaction, follow-up, and closing.
The bottom-line impact: $22,667 in additional revenue per month, directly traced to faster response times and higher completion rates. Across those three months, the shop converted 17 jobs (12 insurance, 5 customer-pay) that could have been lost under the old process.
It Wasn't Just a Software Install
Regina's team redesigned workflows, retrained staff, and integrated Instant Quote into existing processes. This required trust, both in AI-generated estimates and in staff members' ability to pivot from data entry to customer engagement.
That second part is easy to overlook. Asking experienced team members to change what they do every day is a leadership challenge, not a technology challenge. Mario's team had to believe that the AI would handle the grunt work accurately enough to free them up, and then actually use that freed-up time for customer-facing work instead of finding new administrative tasks to fill it. Results were measured over three months. It wasn't overnight, but clear progress was visible by 90 days.
The 59% completion rate is the number that matters most, because unfinished estimates equal abandoned customers. Under the manual process, the team simply couldn't get to every request, and every unfinished estimate represented a customer who might never come back. The AI closed that gap.
The staff transformation matters just as much. No one was replaced. Customer sales representatives were refocused on high-value activities: building relationships and closing deals instead of entering data.
As Mario put it: "We are now able to save significant time, and our Customer Sales Representatives are turning AI-powered quotes into real jobs. We're very happy with the results and how this technology is helping us serve our customers faster and better."
The critical detail: this wasn't about replacing estimators. It was about removing bottlenecks so talented staff could focus on conversion instead of data entry.
Applying This to Your Shop
If internal efficiency is your bottleneck, calculate your baseline before exploring solutions:
- Total estimate requests per month
- Completion rate (finished estimates vs. abandoned)
- Average hours per estimate
- Staff time allocation (prep work vs. customer interaction)
Define your success metrics now. Regina knew exactly what to measure: hours saved, completion rate, additional revenue. This allowed a clear ROI calculation.
Key Takeaway: Without pre-defined metrics, you can't determine if AI is working — or validate any ROI claims from vendors.
Four Lessons from Two Very Different Shops
1. Match the Solution to Your Actual Issue
Both shops would have scored differently on an AI readiness assessment. Hedahl investing in Regina's solution would have optimized a workflow he didn't have volume for. Regina investing in Hedahl's solution would have generated leads she couldn't respond to.
2. Measure Only What Matters to Your Problem
Hedahl tracks scan volume, conversion rates, and partnership satisfaction. Regina tracks hours saved, completion rates, and monthly revenue. Neither tracks the other's metrics, because they aren't solving the same problem. Define your three to five critical metrics before implementation, based on your specific bottleneck.
3. Leadership Must Drive Implementation
Hedahl spent months personally developing dealership partnerships. He didn't delegate it. Regina led workflow redesign and staff retraining from the top. The timeline reality: both invested 90 or more days before meaningful ROI assessment. Set realistic expectations for full integration.
4. Strong Foundations Amplify AI Results
Both Hedahl and Mario experimented with basic AI tools before investing in specialized solutions. Hedahl maintained operational excellence even as volume declined. Regina had talented customer sales representatives already. AI freed them to use those talents rather than creating them. Technology amplifies what you already have.
Hedahl and Mario didn't know their outcomes when they started. They made educated decisions based on clear understanding of their problems, honest baseline calculations, and realistic timeline expectations. Both followed the same pattern: diagnose the bottleneck, choose the matching tool, measure what matters, and give it time to work.
That's not a technology strategy — it's a business strategy. And it's the same approach any shop can take, whether you're trying to fill an empty parking lot or clear a backlog of unfinished estimates.
Ready to see how Tractable's AI solutions could address your shop's specific bottleneck? Request a demo to explore which tools match your readiness level and operational needs.
