Aftermarket sales and services are the main source of value for industrial and automotive companies. As products commoditize and margins tighten, the aftermarket now exhibits the most differentiation to consumers and provides companies with ample opportunity for growth and profitability. For example, in one elevator and escalator company, services presented nearly three-quarters of the margins. Now, in many sectors, aftermarket services are also becoming the main arena of competition—and AI is raising the stakes.
For industrial and automotive sectors, AI is changing the breadth and depth to which customers can be served, what customers expect, what it costs to serve, and ultimately what it means to run a modern service business. Companies that apply AI well will be able to interact with more customers in more meaningful ways, scale expertise, anticipate needs, and deliver outcomes with a level of speed and consistency that was previously difficult to achieve. Those that apply it poorly will struggle to meet customer expectations and will find it harder to staff, deliver, and monetize services. This article outlines how AI is changing the aftermarket and services for industrial and automotive companies. It also shows how companies can use it well to gain a competitive advantage.
How AI is raising the stakes for the aftermarket and services
AI enables a fundamentally different service model. Work that used to rely on distributed human expertise across sales, planning, diagnosis, dispatch, execution, and parts decisions can increasingly be handled by AI. Gen AI can bring expert knowledge to the point of need, from troubleshooting to documentation and customer interactions. Agentic AI can take it a step further by acting across workflows to coordinate decisions across systems, rather than only producing insights. With these tools, service can be delivered at a greater scale, with greater consistency, and with new experiences that were previously too expensive or operationally complex to provide.
Connected assets and Internet of Things (IoT) data accelerate the opportunity. Millions of installed base assets can now stream operational signals across performance, usage, and failure patterns. And by 2030, an estimated 40 billion devices will be connected to IoT data.1 Harnessing this data with AI could offer companies a durable competitive advantage. Importantly, AI can also improve the quality of this data over time by giving structure to unstructured information and reducing manual errors in service processes.
These improvements are already within reach. Leading companies are moving beyond isolated pilots and systematically reshaping how to create service demand from customers, how work is executed, and how value is captured across the end-to-end service life cycle. Companies are using AI to enable intelligent pricing, predict demand for spare parts and wares, automate cash applications, and improve service planning. Done well, companies can convert operational strain into resilient and profitable business models and optimize working capital.
AI adoption is gaining momentum in industrial sectors
Product differentiation has become harder to sustain, making companies rely more on aftermarket sales and services to preserve overall margins. These areas offer more room to deliver customer value, typically come with higher margins, and offer more stable revenue tied to long-term customer relationships. For example, OEMs that focus on selling spare parts, accessories, and upgrades after the original equipment sale, as well as services tied to the installed base, saw margins four times as high as they were for selling new units, and they experienced twice the total shareholder returns.2
As the basis of competition moves into services, customer expectations will rise in parallel. Customers increasingly expect more frequent and more distinctive services. For example, in automotive sectors, customers may want cars that proactively schedule workshop visits, and for B2C companies, customers are looking for suppliers that can help them extend the working life of their assets through predictive maintenance. In short, customers want issues not only fixed but also anticipated. They want predictable costs, faster resolution, and experiences that are easy to access across sites.
AI can affect service profit and loss (P&L) considerably, especially as AI adoption in industrial services moves from experimentation to scaled deployment. Leaders can embed gen AI and agentic AI into daily service decisions, which can improve growth, customer experience, productivity, and the cost to serve.
Early proof points of this impact are emerging across sectors. For example, in the automotive sector, Volvo Trucks rolled out an AI-enabled adaptive maintenance system that uses remote diagnostics to assess necessary services and optimize maintenance intervals.3 Another global automotive manufacturer has embedded AI into its supply chain and can now produce 52-week forecasts instead of 13-week forecasts. In utilities, a leading power services company in the United States used an AI-enabled customer outreach campaign to grow services sales by more than 15 percent. And a leading water treatment company improved service operations with AI-enabled scheduling, increasing technician capacity by 40 percent while reducing overtime by 6 percent.4
The ways AI is reshaping the end-to-end service life cycle
In our earlier work, “Why aftermarket and service are vital to OEMs—and how to excel,” we describe five parts of the aftermarket sales and service journey. Each of these stages and their enablers are already being reshaped by AI to improve the development and deployment of aftermarket offerings (exhibit).
1. Innovation-to-market. The innovation-to-market stage, which includes product development and launch, is being transformed by digital twins and advanced machine-learning models that shorten the design process for spare and ware parts and help develop new aftermarket offerings. One industrial company, for example, uses digital twins to accelerate prototyping, reducing R&D efforts by up to 5 percent and material costs by 1 to 2 percent.
2. Market-to-order. The market-to-order stage, which involves business development and order fulfillment, is benefiting from smart pricing engines that recommend discounts based on actual demand elasticity and competitor signals. AI-assisted sales copilots can work within seller workflows to determine which accounts are more likely to buy spare parts or service contracts, make purchasing decisions, draft proposals, and recommend next-best actions. For example, in one industrial OEM, AI-driven lead generation and prioritization led to twice the new service opportunities compared with a standard workflow and to a ten-percentage-point increase in its factored pipeline.
3. Order-to-delivery. This stage includes procurement, manufacturing, and product delivery and is improving efficiency with optimized routing solutions that balance service-level agreements, technician skills and capabilities, and parts availability to raise first-time-fix rates. AI can be incorporated into remote diagnostics to assess failure modes and then stage the required parts and schedule the right technician when the equipment comes in for repair. For example, an engine OEM applied AI to predict issues and necessary parts, improving quote accuracy and first-time-fix rates. The OEM reduced labor hours per job by 15 percent and the number of parts needed per job by 18 percent.
4. Delivery-to-cash. In this stage, which involves managing payments from customers, AI can be used to automate billing, create service orders, correct invoices at the source, and optimize collection management. For example, in one company, automated invoicing cut hours spent on manual billing work by 80 percent, freeing capacity for higher-value work and reducing error rates.
5. Enablers. Enabling factors are also being actively automated. Frontline copilots can help technicians, aftermarket sales personnel, and planners. AI can also unify product and asset graphs by linking design, installed base, service, and finance data. AI technology also offers employees a chance to learn new skills in real time that can be used for various use cases, such as prompting and model stewardship.
In addition to improving functions across the aftermarket and services journey, AI capabilities open new revenue streams, including outcome-based uptime contracts (that provide compensation based on equipment availability rather than activity), remote monitoring and analytics subscriptions, and AI-enabled retrofit kits for installed equipment. At the same time, automating proposal drafts, compliant scopes, and pricing options helps compress quote-to-order timelines.
What AI at scale looks like in service operations
The five stages above describe where AI is reshaping the aftermarket service life cycle. But what does AI at scale actually look like inside a sector?
Evidence from field service deployments suggests that compared with companies that use more conventional approaches, top AI adopters are already seeing materially better performance on the metrics that matter, such as resolution time, first-time-fix rates, and technician productivity. Agentic AI can be embedded inside the core systems teams already use, which reduces the friction to adopt.
Leading companies concentrate their at-scale investments across priority use cases, such as the following:
- Vision-based condition assessments at the edge. Camera systems and foundation models detect wear and tear on operating assets, with up to 90 percent higher accuracy compared with human inspection, enabling preventative condition-based replacement cycles.5
- Field service support copilots. These copilots give technicians faster access to technical information and guided troubleshooting, leading to a 10 percent increase in first-time-fix rates and reducing equipment downtime. Customers can save between $5,000 and $12,000 per hour through these efficiencies by avoiding shutdowns or lengthy repairs.
- Quote and proposal automation. Large language models can assemble share-of-wallet assessments and formulate a best-pricing model with faster, tighter guardrails. Employee productivity for B2B companies can improve by as much as 75 percent through this optimization.
- Knowledge capture and retrieval. AI can consolidate manuals, bulletins, and shift logs into a single source of truth. AI can also auto-document maintenance visits, accelerating equipment ramp-up and improving efficiency. These tools can reduce nonproductive technician time by up to 25 percent.
A further way leaders scale AI is by linking use cases into a reinforcing system so that each service interaction improves data quality and lifts performance across the workflow. For example, one industrial company did this through four connected capabilities.
First is troubleshooting. An AI solution uses historical data to recommend remote fixes or scopes the right in-person activities. This tool has a troubleshooting accuracy of nearly 90 percent, and more than half of fixes are done remotely. The solution also cut technician time spent troubleshooting and allowed less-experienced technicians to perform as well as experts by offering them knowledge assistance quickly, enabling them to do jobs efficiently and correctly. Additionally, the tool enables better data quality over time by capturing debrief data after each troubleshoot and creates feedback loops to ensure better prediction over time. The accuracy of prediction depends on underlying data quality, so improving data constantly leads to better outcomes moving forward.
Second is parts scoping. An analytics solution automatically scopes the right replacement parts and materials from machinery and maintenance contexts. This process was previously executed manually through an enterprise resource planning system and several spreadsheets maintained in the regions. With the new system, the company could autoscope about 90 percent of parts. Now, the company is aiming to automate the parts-ordering process using agentic AI.
Third is dispatch. A dispatch solution can optimize both planned and unplanned maintenance by determining the minimum number of trips needed based on field events while ensuring the right technician arrives with the right parts and tools. Balancing service priorities, parts availability, and travel time ultimately helps schedulers improve first-time-fix performance. By moving schedulers from detailed spreadsheets to the system, they could improve data capture and quality to incorporate agents into the dispatch process.
Fourth is debrief. An AI solution creates high-quality service interaction debriefs based on the proposed scope and unstructured notes from the field. It can prompt technicians to update critical missing data and strengthen the debrief with photos, particularly where vendor parts are involved. This tool improves technician experience, supports more accurate invoicing, and strengthens vendor recovery management. Most important, these debriefs create quality data that can be used for better troubleshooting and parts scoping, linking these use cases.
Capturing value from AI intentionally
Despite many companies racing to start deploying AI, McKinsey research found that only about 5 percent of an organization’s EBIT can be attributed to its deployment of gen AI.6 In the aftermarket and services, value most often stalls for five reasons.
First, companies find success in pilots but don’t have a clear path to scale their solutions. As such, what works in a controlled proof of concept is not scaled across sites, asset populations, and customer segments, so impact stays local or fades. Second, companies often have insufficient data management. Data lineage, hierarchy, and standardization is not accurately preserved. Coupled with a lack of dedicated ownership and strict guidance, the impact of AI models is limited, and models may perform unreliably once they are embedded into real operations.
Third, when AI solutions feel like a black box, impractical, or misaligned with workflows, frontline technicians and planners avoid adoption, reducing the probability of success. Fourth, companies often lack end-to-end ownership for outcomes. AI cuts across dispatch, parts, field service, and the back office. When ownership is obscure and fragmented, initiatives stall in handoffs and impact is hard to sustain.
Last, AI initiatives often fail when solutions do not integrate cleanly into core workflows and systems or when the cost to run, maintain, and govern AI solutions outweighs the benefits. If adoption, cost to serve, and realized impact are not coordinated, value will be suboptimal and solutions will be hard to scale.
The opportunity is clear, but the path forward is not one-size-fits-all. Leaders can reflect on a small set of questions before they embark on their AI transformation journey:
- Where is the value? Which two or three moments in the service journey matter most for margin, working capital, and customer experience?
- Where should we apply AI? In those moments, what decisions should AI automate, and what decisions should it support? Why?
- What does it take to scale? What data products, platform integrations, and controls are required to achieve reliable day-to-day performance improvements across sites and asset populations?
- Who owns outcomes? When value crosses functions, who is accountable end to end for results, including funding, adoption, and sustained impact?
- How do we drive adoption and measure impact? How will frontline adoption be designed into the workflow, and how will performance be measured through a set of input and output metrics?
The opportunity for AI in the aftermarket and services is clear, but the path forward must be customized to the company. For industrial and automotive OEMs that thoughtfully steer this transformation, AI stops being a program and instead becomes an integral part of the operating system of the business. Doing this well could result in higher uptimes, lower costs to serve, improved profits, and greater customer satisfaction and retention.