AI adoption in the commercial-vehicle aftermarket is accelerating rapidly, but value creation is not. BCG recently partnered with MEMA Aftermarket Suppliers to survey and analyze commercial-vehicle aftermarket companies—including parts suppliers, distributors, dealers, independent service providers, and retailers. We then supplemented the survey with interviews of leaders of these organizations. The results should be a wake-up call. Although 70% of the respondents have some level of AI in place, only 14% are generating business value.
Given the technology’s potential, the companies that win will treat AI not as a technology project but as a core operational capability embedded across pricing, supply chain, and service operations. To capitalize on this potential, companies should prioritize a small number of AI use cases and focus on changing daily behaviors and ways of working, rather than just investing in technology. The upside? Increasing the industry’s profitability and boosting first movers’ profits by up to 9%.
Stay ahead with BCG insights on the automotive industry
Three Ways to Create Rapid Value with AI
Many companies in the $50 billion commercial-vehicle aftermarket still rely on fragmented data, manual processes, and legacy systems. In the past, those issues often created inefficiencies and limited scale. In the current, more volatile environment, however, they can be crippling.
Our analysis of the survey results—along with our direct experience working with clients—shows that players in the industry face three main imperatives:
- Controlling Costs and Margin Pressure. Among suppliers, 80% cited navigating trade and tariff shifts as a top business challenge, and 56% cited rising material, energy, or logistics costs.
- Improving Forecasting and Predictability. Eighty percent of suppliers pointed to demand volatility or forecasting as a significant business challenge, and 53% of distributors, dealers, and independent service providers said that they face difficulty sourcing parts or dealing with suppliers’ long lead times.
- Increasing Service Quality and Reliability. Forty-four percent of distributors, dealers, and service providers said that customers have rising expectations for faster delivery or response times, and 38% noted that they face skill gaps among counter staff, sales teams, or technicians.
AI can help companies manage these challenges and unlock value. Specifically, respondents identified three AI use cases as having the most potential value.
Smarter Pricing and Cost Modeling. The first key use case for AI is smarter pricing and cost modeling. Currently, many companies lack sophisticated pricing capabilities for the thousands of SKUs that they sell. AI-based pricing tools can analyze historical sales data, elasticity impacts, competitor pricing, cost changes, and regional demand—all in real time—to set target pricing for individual parts. That can help companies respond more effectively to market shifts.
AI pricing solutions also can identify the specific products and categories with the highest margin leakage and model the potential impact of pricing shifts, such as how they could affect demand. Such tools enable leaders to make informed, data-driven decisions before rolling out a pricing change. Particularly for suppliers, distributors, and service providers, comprehensive AI-enabled pricing can lead to revenue increases of up to 6%.
Demand Forecasting and Inventory Optimization. Many companies struggle with forecasting demand across thousands of SKUs, leading to bloated inventories, expedited freight charges, and stockouts. Changes in customers’ ordering patterns and long lead times for raw materials compound the challenge—especially given the rapidly changing geopolitical environment.
A second use case, then, is using AI to aggregate data from enterprise-resource-planning, point-of-sale, and telematics systems, as well as by gathering data that reflects seasonal patterns, macro industry trends, and other factors. AI can then generate forecasts for individual SKUs, regions, distributors, and customers. The forecasts can include order timing, optimal reserve stock levels, and replenishment triggers, leading to a 30% reduction in spare parts inventory across the value chain based on BCG experience, while maintaining or even improving service levels. Individual companies can unlock significant cash savings.
Automated Parts Cross-Referencing. A third use case is to use AI for cross-referencing parts (cited by 56% of respondents). This use case is particularly relevant for distributors and service providers because they routinely face challenges in terms of parts compatibility, often due to fragmented or inconsistent product data from suppliers. Many companies have immense and complex parts catalogs, often handled manually by small teams or even just one person.
AI can manage this complexity by synthesizing information from catalogs, spec sheets, original equipment numbers, aftermarket numbers, and other sources. An AI engine can then match products to customers’ needs. Solutions include automated comparability tables and cross-reference mapping, and they can flag data inconsistencies and suggest corrections. As a result, staff and technicians can look up parts more quickly, improving productivity and reducing the number of incorrect orders and downtime for customers.
A key success factor for this use case is connecting data streams across the value chain, meaning direct coordination between suppliers, distributors, and service providers.
The potential gain for parts cross-referencing solutions is a reduction in the cost of goods sold of up to 2% and a revenue boost of nearly $10 million for a company with $500 million in annual sales.
Four Implementation Priorities to Get More from AI Efforts
Identifying AI use cases is only part of the process. Companies also need a disciplined, results-driven implementation approach:
- Define clear business outcomes to align AI investments with the biggest business challenges and value drivers in the company.
- Prioritize AI use cases that directly address these outcomes. Go deep in one function or business unit, with a focus on cleaning data, engaging people, and redesigning processes. Design for substance over style, and aim for a high ROI over flashy solutions.
- Start with a pilot project to build capabilities and organizational confidence. For example, focus on a single business unit, region, or product category, and look to demonstrate value quickly (in weeks or months, rather than years). Document the lessons learned—both positive and negative—and adjust the model before scaling.
- Scale up by focusing on people, not just technology. In BCG’s experience, just 10% of the value of AI initiatives comes from algorithms, and 20% comes from technology. The remaining 70% stems from people and processes—changes to daily behaviors and ways of working. Companies need a strong change-management effort to ensure that employees understand the technology, willingly adopt it, and have the skills needed to capitalize on it.
For leaders in the commercial-vehicle aftermarket, the question is no longer whether to invest in AI—it is how quickly they can translate it into operational advantage. Companies that move now, focusing on a few high-impact use cases and embedding them in day-to-day operations, can capture a meaningful share of the industry’s next wave of profitability.
MEMA, the Vehicle Suppliers Association, is the trade association in North America for vehicle suppliers, parts manufacturers and remanufacturers. MEMA Aftermarket Suppliers exclusively serves manufacturers of the parts, chemicals, tools, diagnostics, and technologies that keep vehicles running safely and affordably across their lifetime.
