CAse Study

The challenge

A busy automotive workshop was consistently booked out, but the owner felt like the team was working harder than necessary. The technicians and front desk were busy and customers were largely looked after but the days felt chaotic. Jobs were consistently being moved around, staff were always chasing information and parts weren’t always ready when vehicles arrived. Customers were constantly calling for updates and the team were trying to find out what was happening to their vehicles. The owner was considering hiring another person but had done that the year before and noticed it didn’t make much of a difference to flow of work or the chaos of the days.

So, instead we looked at where is time and effort being lost?

Our approach

We started with looking at the actual flow of work rather than looking for technology that might be helpful in the workshop.

We then mapped the journey of a typical job:

Booking → vehicle arrival → diagnosis → parts → repair → quality check → customer → payment

We spoke to the people doing the work and followed the process from beginning to end, focusing not on how thing should work but rather how they were actually working.

We found

Too much information in too many places

Booking information was recorded in one place, job notes somewhere else, and important updates were sometimes sitting in emails or messages.

Technicians were being interrupted

The front desk regularly had to stop technicians to clarify job details, check progress or ask questions about additional work.

Parts availability wasn’t always known early enough

Some jobs couldn’t progress because parts hadn’t been confirmed before the vehicle arrived.

Customers were chasing updates

The team was spending time answering “How’s my car going?” calls because there wasn’t a consistent process for proactively updating customers.

The schedule wasn’t reflecting reality

Jobs were being booked based on estimated time, but there wasn’t a consistent approach to understanding actual job duration and capacity.

None of these problems look like major issues one by one. There wasn’t one part of the business that needed fixing, but instead there were a lot of small points of friction that were adding up, causing a huge amount of lost productivity. The technician waiting for clarity, the front desk running around trying to find updates, someone needing to make a call or update a spreadsheet, individually, these things were easy to ignore but collectively they were impacting productivity, customer experience and ultimately profitability.

Our recommendation

We didn’t recommend replacing the workshop’s systems, buying a complicated new platform or just putting in AI straight away. Instead, we focused on a handful of practical changes.

1. Improve the booking process

We introduced a simple pre-booking checklist so the team captured the information needed before the vehicle arrived.

This reduced avoidable back-and-forth and gave the workshop better information upfront.

2. Confirm parts earlier

For jobs where parts were predictable, parts availability was checked before the vehicle entered the workshop.

This meant fewer vehicles sitting around waiting.

3. Create a clearer job-status process

Rather than relying on people asking each other for updates, jobs moved through a small number of clearly defined stages.

Everyone could see what was happening.

4. Reduce interruptions

Common questions and information requests were built into the process so technicians didn’t need to repeatedly stop what they were doing.

5. Automate customer updates

Where appropriate, routine customer communications were automated.

The team still controlled the important conversations, but they didn’t need to manually send every routine update.

Then we looked at AI

Only after understanding the process did we identify opportunities where technology could help. One example was inventory, the workshop was regularly ordering common consumables based largely on experience and “what looks low.” We explored whether historical usage, upcoming bookings, vehicle/job information, current stock levels and supplier lead times could be combined to provide a recommended order quantity.‍ ‍The aim wasn’t to let AI run the inventory but rather give the person ordering stock a better starting point, removing guesswork and repetitive checking.

The result

After implementing the changes, the workshop had:

Less chasing

Information was easier to find and people spent less time asking where things were up to.

Fewer avoidable interruptions

Technicians could spend more time working on vehicles rather than answering routine questions.

Better visibility

The front desk had a clearer picture of what was happening across the workshop.

More proactive customer communication

Customers received updates without needing to call for them.

Better use of workshop capacity

The team had greater visibility of where time was being lost and where additional capacity could be created. Most importantly, the owner had a much clearer understanding of the business before making the decision to hire another person or invest in new technology.

The takeaway

The biggest improvement didn’t come from AI, but instead from understanding the work.

Once the friction was visible, the solutions became much clearer.

Some were process changes.

Some were changes to the way information was managed.

Some could be automated.

And some could potentially benefit from AI.

That’s how we approach improvement.

We don’t start with technology.

We start with the problem.