How AI is transforming aftermarket as service becomes core profit driver

Artificial intelligence is rapidly transforming the automotive aftermarket, shifting how service businesses operate, compete and generate value as margins increasingly move beyond vehicle sales, according to a new report from McKinsey.

The consulting firm said aftermarket parts and services already represent the primary source of profit for many industrial and automotive companies, often delivering significantly higher margins than new product sales. As a result, AI is raising the stakes in what has become the industry’s main arena of competition.

AI is enabling a fundamentally different service model, one where tasks historically dependent on technician experience and manual coordination — from diagnosis and parts planning to customer interaction and invoicing — can be handled or augmented by digital tools, McKinsey argued. Generative AI can bring expert knowledge directly to the point of need, while more advanced “agentic” AI systems can coordinate decisions across workflows, increasing speed and consistency.

For aftermarket professionals, the impact is already visible across the service lifecycle. AI is being used to predict failures before they occur, optimize parts inventory, improve technician scheduling and automate administrative work such as quotes and billing. In one example cited, an engine OEM reduced labour hours per job by 15 per cent and parts usage by 18 per cent through AI‑driven diagnostics and planning. Another case showed technician capacity rising 40 per cent with improved scheduling, while overtime declined.

Predictive maintenance is a key shift for the sector, according to the report. Connected vehicles and equipment generate large volumes of operational data, allowing AI systems to anticipate service needs rather than only respond to breakdowns. McKinsey notes that by 2030, tens of billions of connected devices will be feeding real‑time data into service ecosystems, creating new opportunities for aftermarket businesses to offer proactive maintenance and uptime‑based service contracts.

This shift is also changing customer expectations. Rather than reactive repairs, customers increasingly expect vehicles to schedule service, deliver predictable costs and minimize downtime proactively. For aftermarket operators, that means moving toward service models built on convenience, speed and reliability rather than purely transactional repairs.

AI is also creating new revenue streams. McKinsey points to opportunities in subscription‑based monitoring, outcome‑based service agreements and AI‑enabled retrofit packages for existing vehicles and equipment. These offerings can deepen customer relationships and generate recurring revenue, particularly for businesses managing large installed vehicle populations.

At the same time, the report identified clear productivity gains. AI‑enabled tools can improve first‑time‑fix rates, reduce diagnostic time and cut non‑productive work such as documentation. Technician support copilots, for example, can increase first‑time‑fix rates by about 10 per cent while reducing downtime costs for customers. Knowledge‑management tools can cut non‑productive technician time by as much as 25 per cent by consolidating technical information and automating service documentation.

Challenges

However, McKinsey cautioned that many companies are struggling to capture the full value of AI. While adoption is increasing, only a small share of potential financial impact has been realized. The report identifies several common challenges that are particularly relevant for aftermarket businesses.

One major obstacle is scaling. Many companies have successfully piloted AI tools but have not extended them across their full network of locations, assets or customers. As a result, benefits remain localized and fail to deliver system‑wide improvements.

Data quality is another critical issue. AI systems rely on accurate, standardized data, but many organizations still operate with fragmented or inconsistent information across parts systems, service histories and customer records. Without strong data governance, AI outputs can be unreliable.

Adoption at the shop‑floor level also presents challenges. If tools are difficult to use, poorly integrated into workflows or perceived as “black box” systems, technicians and service advisors may resist them. McKinsey noted that successful implementations embed AI directly into existing systems, minimizing disruption and improving usability.

Organizational structure can also limit results. Aftermarket operations often span multiple functions — parts, service, logistics and finance — but lack clear ownership across the full service lifecycle. Without end‑to‑end accountability, AI initiatives can stall or fail to deliver measurable outcomes.

Positioning

For aftermarket professionals, one of the most important insights is that AI is not just about efficiency — it is reshaping competitive positioning. Companies that effectively integrate AI can scale expertise, reduce costs, improve service quality and unlock new revenue streams. Those that lag risk falling behind as customer expectations rise.

McKinsey emphasizes that the most successful companies are focusing on a small number of high‑impact use cases, such as predictive maintenance, parts forecasting, service scheduling and technician support, and then linking them into a connected system. In these environments, each service interaction improves data quality, creating a feedback loop that enhances performance over time.

The report further noted that AI is becoming an operating system for aftermarket businesses rather than a standalone tool. For companies that execute well, the result could be higher uptime, lower cost to serve, improved profitability and stronger customer retention, all critical advantages in an increasingly competitive aftermarket landscape.

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