AI and new ways of working are recasting value creation in aftermarket services and customer care. These areas of business are experiencing technological and structural disruption as customer expectations rise and peers (and, indeed, customers) begin to embed agentic AI into their processes. However, despite widespread investment, only a few have seen real impact. We learned more from discussions at McKinsey’s North America Customer Care roundtable and our Aftermarket and Field Services Summit, both held in New York recently.
Routes to agentic futures
Two-thirds of our aftermarket summit attendees are investing in agentic AI to improve their operations and customer satisfaction, including commitments to troubleshooting applications, smart search, customer inquiry triage, customer service support, parts scoping, and both dispatch and debrief assistants. Most summit attendees said they are working in partnership with third parties to achieve this. Around a third are building solutions in-house, and a smaller but significant group are pursuing M&A to acquire the technologies and expertise they need.
Customer expectations outpace delivery
Despite these efforts, a survey of 250 aftermarket customers from the United States and Europe shows that customer expectations are outpacing delivery, with some 70 percent of customers saying they are as satisfied, or less so, with the service they are receiving than they were a decade ago.
Aftermarket and field services customers now expect more uptime and predictability from machinery, and a customer experience more akin to traditional B2C relationships. They want consistency of aftercare management, including, for example, extending machinery life cycles and supporting productivity growth. Some 35 percent of the survey group say they would be willing to pay more for access to more talented field agents, while 70 percent say they would pay more for faster resolution of issues.
Change is bigger than automation
Changes in customer expectations and demand necessitate a shift to remote-first and agentic-first models for successful care and aftercare-focused businesses. Organizations that play big in these areas need to move beyond automating specific tasks and rewire entire workflows. Many also have an urgent need to move beyond efficiency-only KPIs to capture the full impact of their services. This means understanding more intimately what the customer is trying to achieve and adopting KPIs that include measurements of quality, trust, outcomes, and sustained loyalty.
Such an approach means redefining service value way beyond contract compliance. McKinsey Partner Oana Cheta advises that the traditional fixes of addressing problems directly by deploying talent and resources can in fact be counterproductive. “We always talk about technical debt in software, but to bring on the power of AI, we need to talk about and tackle what we might call ‘operational debt.’ Too many organizations tend to throw people at operational problems rather than addressing the problem at its foundations.”
Resistance and the human factor
Overcoming roadblocks rooted in mistrust, culture, skills, knowledge gaps, risk aversion, and entrenched behaviors is an essential part of the journey. Leaders who are succeeding are learning to address legacy systems and vendor dependencies, to clarify governance so AI capabilities don’t stall at the pilot stage, and to oversee the centralizing data and retraining of staff so every customer interaction feels continuous and connected, building positive customer experiences.
Learnings from a recent McKinsey customer care survey highlight the importance of co-design to drive adoption, higher levels of trust, a shared vision, and a route to overcoming the widespread challenges of integration complexity.1 Other areas of interest include speed and customer experience, tech readiness, risk mitigation, and quality metrics relating to AI performance. Organizations that have made progress in these areas are seeing significant customer experience and efficiency gains, with 50 percent showing revenue growth, compared to just 8 percent of those lagging the field.
Frontline examples of emerging challenges include how to retain the highly educated and skilled people needed to manage software, for example, or protect the organization from cyberattacks. Another common challenge is navigating the inevitable complexity that results from crossing so many organizational lines on the journey to agentic implementation.
Leaders face strategic trade-offs
In the AI and agentic AI era, no care-oriented business will escape the pressures and forces of change. Organizations that are coordinated and focused when leveraging technology to meet customer needs for speed, quality, and cost will see meaningful results. Those who are more scattergun or inconsistent in their approach risk investing without realizing real value. Leaders need to be mindful of the trade-offs and decisions required to get there, balancing speed with trust, and efficiency with empathy.
A defined strategy, an ongoing conversation, and a clear articulation of the value being delivered will help companies succeed as technological capabilities develop and customers’ expectations continue to change at pace.
1 McKinsey State of Customer Care Survey 2025, n = 440.