Unfiltered with Neil Singh: When Parts Identification Goes Wrong: How to Make Aftermarket Expertise Go Further

Having spent many years in the automotive aftermarket, one thing I’ve always valued is the level of knowledge that sits behind a parts counter.

An experienced parts person can often start with very little information, ask two or three very specific questions and somehow arrive at exactly the right part. It can look effortless, but it isn’t. That ability comes from years of experience: understanding vehicles, recognising terminology, knowing where identification commonly goes wrong and, perhaps most importantly, knowing what question to ask next.

That expertise is incredibly valuable. The challenge for the aftermarket is how we make more of it available across the business.

Getting a part wrong costs more than the return

Parts identification has always been important, but the cost of getting it wrong goes well beyond the value of the part itself.

A wrong part can mean:

• a return or credit to process,
• the enquiry being handled again,
• the correct part being identified,
• stock being handled for a second time,
• potentially another delivery,
• another conversation with the customer.

One incorrect part might not sound significant. Multiply that across a busy parts operation and the operational cost quickly adds up.

There’s also the customer experience to consider. When somebody calls or walks into a branch, they expect the person serving them to help identify what they need quickly and confidently. If the process takes too long, or they have to come back because the part is wrong, that confidence can quickly disappear.

Expertise is difficult to scale

The best parts people don’t simply know part numbers, they understand the conversation.

They recognise when something doesn’t sound quite right, know when another piece of information is needed and understand the terminology customers use. They also know that the first question isn’t always enough to get to the right answer.

This kind of knowledge takes years to build, which creates an interesting challenge for businesses. How do you help a newer member of the team benefit from the knowledge an experienced colleague has accumulated over a career? And how do you make that expertise more readily available across branches and teams?

For me, this is where AI starts to become genuinely useful.

AI should support expertise, not replace it

I don’t see AI replacing the experienced people behind the counter. Their knowledge, judgement and understanding of the customer remain incredibly important.

What AI can do is help make some of that knowledge easier for other people to access.

If somebody asks for brake pads, for example, there may be several questions an experienced salesperson instinctively knows to ask before identifying the correct part. AI can help guide that conversation, recognise when important information is missing and help the person behind the counter get closer to the right answer.

This gives a newer employee more support while helping an experienced salesperson get to information faster.

For me, the most valuable technology in our industry will be technology that helps knowledgeable people do their jobs even better.

Better identification can support better selling

There’s another part of this that I think is sometimes overlooked.

Good parts identification isn’t only about finding the individual item a customer asks for. An experienced salesperson understands the wider job.

If somebody is replacing brake pads, they may need discs. If they’re changing a component, there may be associated products required to complete the repair.

That’s not simply upselling. Done properly, it’s good customer service – helping the customer make sure they have everything they need to complete the job.

AI has the potential to support those conversations too, helping prompt relevant questions or associated products that might otherwise be missed. Each individual opportunity may be relatively small, but across hundreds of customer conversations, those improvements can add up to a much more significant commercial impact.

Bringing AI into the existing workflow

This is exactly the thinking behind the AI Sales Assistant we’ve built into AutoCat.

One thing that was particularly important to us was where the technology should sit. Rather than asking somebody to leave their existing workflow, open a separate AI tool and start another conversation, we’ve brought the capability directly alongside the catalogue and the identification process.

That means AI can support the job the salesperson is already doing: helping them ask the right questions, work through an enquiry and identify the right product with greater confidence.

It isn’t there because AI is the latest technology everyone is talking about. It’s there because there’s a practical job it can help people do.

Make your expertise go further

The automotive aftermarket has an enormous amount of knowledge within it. I don’t think the goal of AI should be to replace that expertise.

The opportunity is to make it easier to share and apply.

If technology can help a new starter become confident sooner, give an experienced salesperson a quicker route to an answer, reduce incorrect parts and support better customer conversations, that feels like a very practical use of AI to me.

Ultimately, the opportunity is to take the knowledge already inside aftermarket businesses and make it available to more people, at the point they need it.

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