Software-Defined Aftersales: Where OEMs Lose Value Between Engineering and the Workshop

How connected vehicle data, AI-guided diagnostics and smarter workflows can unlock aftersales value, improve workshop efficiency and strengthen customer retention.

The vehicle already knows what is wrong with it. The real question is whether the service network is listening.

KPIT’s value assessments with automakers show that a large OEM’s aftersales network can hold more than $150 million in untapped annual value. With over 20 years in vehicle software and diagnostics, from engineering to the workshop floor, we have seen how this value is lost in the gap between how vehicles are engineered and how they are serviced.

Why aftersales matters more than ever

Aftersales earns three to five times the operating margin of new vehicle sales. Yet nearly one in two drivers could switch brands after a frustrating service experience. Service quality now has a direct bearing on both customer retention and business performance.

Customer expectations raise the stakes further. People are used to speed, convenience and transparency in e-commerce, food delivery and digital banking, and they now expect the same from vehicle servicing: quicker diagnosis, clear communication about repairs, visible timelines and fewer unexpected costs. Traditional aftersales processes often struggle to keep up.

What is software-defined aftersales?

Software-defined aftersales uses connected vehicle data, diagnostics and AI-enabled workflows to support service decisions. The challenge is not a lack of information. Modern vehicles generate extensive data through dozens of electronic control units, but much of it never reaches technicians in a usable form when they need it. Software bridges that gap, helping service teams make better decisions, anticipate maintenance needs and deliver a more seamless experience.

How does connected data change the service visit?

The service visit begins well before the vehicle arrives. When connected data flags an emerging fault early, the right part and the right technician can be ready in advance, and some issues can be resolved remotely. Even for unplanned visits, capturing the vehicle’s data and the customer’s symptoms accurately at intake shapes everything that follows, from bay allocation to parts planning.

In our experience, predictive maintenance can reduce unplanned downtime by 15–20%. For commercial vehicle and off-highway operators, where every idle day means lost revenue or a stalled site, that difference matters most.

How do AI-guided diagnostics reduce repeat repairs?

Many repeat repairs trace back to the first diagnosis. Today’s faults often sit at the intersection of hardware, software and connectivity, and conventional troubleshooting is not built for that. AI-guided diagnostics, drawing on field data, repair history and engineering knowledge, can reduce repeat repairs by up to 30%. As fewer vehicles return for the same issue, technician productivity improves by 25–30%.

For passenger car owners, getting it right the first time is often what decides whether they return to the authorised network once the warranty ends.

Why is warranty a diagnostic problem?

When parts are replaced on suspicion rather than evidence, costs rise, parts come back with no fault found and the real root cause stays hidden. Precise diagnosis improves claim accuracy. The data flowing back also helps engineering and quality teams spot emerging patterns earlier, improving the next generation of vehicles.

Why the value compounds

None of these improvements sits in isolation. Better intake leads to more accurate diagnosis. Accurate diagnosis leads to the right part, a cleaner warranty claim and a vehicle back on the road sooner. The gains come from improving the connections between stages, rather than treating each problem on its own.

These improvements reach across the automotive value chain.

How should OEMs measure progress?

Capturing these gains means measuring performance across the whole service journey, not function by function. Tracking a few indicators shows where improvements are holding and where inefficiencies persist, giving service leaders a clearer basis for deciding where technology and process changes will deliver most.

The India opportunity

India offers a unique chance to apply these capabilities at scale. With a fast-growing vehicle population and rising use of connected cars, the opportunity for data-driven servicing is much larger. Yet access to diagnostic expertise varies widely across service networks, from city dealerships to highway workshops.

Software can make vehicle information and diagnostic guidance available exactly where it is needed, so technicians can fix issues accurately wherever a vehicle is serviced. The real test is how well automakers build software into everyday workshop routines, so the benefits become practical, on-the-ground improvements.