Where AI Earns Its Keep in the Aftermarket

Two questions keep coming up in my conversations with aftermarket leaders: where is AI proving most useful, and how are other businesses getting value from all the data they now collect?

Those questions take me back to around 2010, when I worked for a global construction equipment manufacturer and we started fitting telematics units to machines. We were suddenly able to collect huge amounts of data about operating hours, machine health, location, and how equipment was being used. For anyone working in parts and service, the potential was exciting.

But translating those signals into useful decisions about parts or service was incredibly difficult. We were literally drowning in data, with no good way to put it to work.

Aftermarket businesses have spent years collecting data that could improve how they operate. They also have experienced people who understand what that data reveals about service needs, parts demand, and customer behaviour. The challenge — even today — is applying that intelligence consistently across a huge parts portfolio, an entire installed base, or a global dealer network.

AI gives us ways to work through complexity that is beyond any individual or team. But where should we put it to work first? There’s so much hype, it’s easy to get caught up in the possibilities. Getting real value from the technology means getting specific about where to apply it, what’s involved, and how to judge whether it’s delivering.

That is the topic of a new paper from IDC, exploring three practical applications of AI in the aftermarket — service demand forecasting, pricing, and warranty claims — and how each contributes to better business outcomes.

From Machine Data to Parts on the Shelf

Accurately predicting parts requirements across a large dealer network involves a huge amount of complexity. You’re dealing with different machines, of different ages, working in different conditions, service requirements that change with usage, and tens of thousands of SKUs held across hundreds of locations.

Take a machine approaching its next service. Its operating hours, read alongside the service plan, give an indication of when the work will be due and which parts it will require. Applying that analysis across the machines a dealer supports gives a clearer picture of upcoming demand, with time to get parts in place.

While working out a service date from operating hours is relatively straightforward, bringing service history and machine health information into that assessment across an entire population, then translating it into demand across the dealer network is a much more complex calculation.

The difficulty is that the information doesn’t arrive ready to feed into a forecast. A relevant detail in a service record has to be interpreted and linked to the right machine and part. When the dealer’s description differs from the manufacturer’s, even making that connection takes work. Repeat that across a global network and you begin to see why so much useful information has remained outside the planning process.

This is where generative AI and predictive modelling work together. Generative AI helps interpret the records and reconcile descriptions, making relevant information available to models that forecast demand. Those forecasts then feed into decisions about replenishing stock across the network.

The dealer can then call the customer and say, “Your machine is coming up for a service. We’ve got the parts — when can we get it booked in?” That gives the customer time to plan for the machine being out of action, with less risk of the job dragging on because a part hasn’t arrived.

Reflecting Commercial Reality in Pricing

A single price list and a fairly simple discount structure make pricing easier to manage across markets, customer groups, and channels. But they also assume those customers have more in common than they actually do. Some receive discounts they don’t need in order to buy, while others are quoted prices that send them to a competitor.

We’re increasingly seeing customers build pricing around how particular parts sell and how customers buy in each market. To do that well, you need to understand which parts are comparable, what alternatives buyers have, and how they respond to price.

Parts in the same category don’t necessarily sell in the same way, while parts in different categories sometimes follow similar demand patterns. AI helps identify those relationships across the portfolio. Analyzing transaction history within those groups gives pricing teams a better understanding of how customers respond to price and where they need to be more competitive.

The IDC study also warns against pricing with outdated models or overlooking changes in the cost to serve. That becomes particularly important with service contracts and extended warranties, where the price you agree today has to cover the work you’ll carry out in the future. A change in parts or labor costs has different implications depending on the equipment, how it is used, and what the contract covers. Across thousands of machines and contracts, those relationships are beyond manual calculation.

Predictive modelling works through what those changes mean for expected service costs. Teams get a clearer picture of the risk in contracts already sold and a better basis for pricing the next quote.

As manufacturers sell more service contracts, understanding those costs and risks becomes increasingly important to making that growth profitable.

Knowing Which Warranty Claims Need a Closer Look

If you’ve worked with dealers, you know how quickly warranty becomes a sore point when they’re waiting to be paid. At the same time, a manufacturer processing large volumes of claims needs to identify payments that shouldn’t be made.

Someone reviewing an individual claim won’t necessarily know that similar versions have already been submitted elsewhere. Small changes to the details make that harder to spot. AI can examine claims together, pick up patterns across submissions, and use those signals to score which claims need attention.

That gives the warranty team a practical way to organize the work. Straightforward claims go through automatically within agreed rules, allowing reviewers to focus on claims with clear warning signs, along with high-value claims that need a closer look before a decision can be made.

For that to be useful, the reviewer needs to understand why a claim has been flagged. There may be a perfectly reasonable explanation, and they need to be able to make that judgment. The IDC study also highlights the importance of human oversight when handling exceptions, particularly where a decision affects a longer-term dealer or customer relationship.

AI also identifies recurring mistakes across a dealer’s claims, helping warranty teams spot where guidance would prevent the same problem happening again. A dealer getting something wrong needs a different response from someone deliberately manipulating a claim. Understanding the difference saves a lot of unnecessary work on both sides.

The net result is fewer fraudulent payments and a smoother process for legitimate claims. The dealer gets reimbursed promptly, while the manufacturer puts its investigative effort where there is good reason to look more closely.

Don’t Try to Boil the Ocean

There are plenty of other places to put AI to work in the aftermarket. Helping a technician find relevant service information or enabling a business user to question data in plain English both have practical value. The right starting point depends on what your business is struggling with and what improving it would be worth.

The forecasting, pricing, and warranty examples give us something concrete to work with. In each case, we understand the problem and the decision that needs to improve. That gives the technology a defined job and the business a way to judge the result.

It also gives you a practical reason to bring the relevant data together and a clear business case for doing the work.

Looking back at those early telematics projects, we knew there was value in the information. Today, there are better ways to extract it and apply it across the operation.

Deploying those capabilities toward a problem with a clear commercial consequence, seeing it through into everyday use, and letting the results make the case for what comes next is where AI earns its keep in the aftermarket.

Read the IDC Spotlight

IDC Spotlight, sponsored by Syncron, AI Demands Practical Action: Delivering Near-Term Aftermarket Value and Impact, #US54915426, September 2026