McKinsey analysis of the performance of more than 50 industrial organizations over a 15-year period found that those with a high service focus generated 1.7 times the total shareholder returns (TSR) of those that focused mainly on products. In the aftermarket and field services context, services cover a wide range of activities. They range from the traditional—such as equipment installation and commissioning, or repair and aftermarket supply of components and consumables—to newer models including monetizing data via new digital solutions, long-term asset rental and support agreements, and rental or “as-a-service” business models.
Service value chains are prime candidates for accelerated digitalization and the integration of AI or gen AI technologies. Many service-oriented companies possess vast stores of customer-relevant data, such as asset history, Internet of Things (IoT) data, technical publications, maintenance manuals, and service requests, which present a rich resource for data mining and deriving actionable insights. The inherent variability and complexity of services make them especially suitable for the application of AI techniques, which can predict outcomes with a precision that surpasses human capabilities.
Service-focused companies are aware of this potential, and most are already experimenting with gen AI. McKinsey’s digital strategy survey found that 70 percent of top-performing companies are utilizing advanced analytics to develop proprietary insights, and half are deploying AI to enhance and expedite decision-making processes.1
Over the past year, we have seen many organizations demonstrate the potential of gen AI solutions in pilot use cases. Few have been able to translate that potential into measurable profit-and-loss (P&L) impact. As in previous waves of tech-driven innovation, it seems that companies are falling into the “pilot purgatory” trap, unable to scale their digital or gen AI strategies beyond initial experiments.
The hazards that trip up digital rollouts include fragmented data, outdated systems, inadequate technology, lack of specialized expertise, change management challenges, weak business cases, and poor strategic leadership. We believe aftermarket and field services companies may be equally or more vulnerable to these challenges in scaling their digital and AI initiatives, presenting a significant obstacle to capturing the potential value of gen AI. Here are five key actions that can help organizations navigate a smoother path from pilot to profit.
Transform the service domain
The first step in generating P&L impact is reimagining how services could be delivered. Rather than adding digital point solutions onto existing service processes, leading companies start by mapping the service journey of the future. That helps them understand how value will be created for various stakeholders, including customers, service providers/partners, the supply chain, technicians (field and remote), sales teams, and back-office personnel. It also shows them where these stakeholders will need to transform their ways of working.
Implications for core processes, governance, KPIs, and talent requirements form the basis of this new service blueprint. For example, when one industrial company went through this process, it realized that its vision of an AI-powered service organization would imply the elimination of some roles and the transformation of others. Moreover, full realization of the potential from AI required the company to build deeper links between its service, sales, contracting, supply chain, and finance functions. These changes were needed to ensure that contract language with vendors reflected the adjusted risk the company would take with customers, which in turn linked to scheduling, dispatch, debriefing, and invoicing.
Pursue the most promising use cases
While there are many AI and gen AI use cases, a few have emerged as early leaders, driving significant impact in service value chains. We provide four such examples below.
Sales lead generation and tender response
Several leading companies have seen significant growth in services enabled by AI lead generation and gen-AI-enabled lead qualification and RFP response. A leading water technologies OEM enhanced its operations by deploying an aftermarket analytics solution to refine sales and operations data. The solution helped map the company’s installed base, generating more than $350 million in leads for aftermarket parts and services, covering 45,000 customers, and including 8,000 white-space opportunities. The company used gen AI to reach out to customers and qualify these leads in a near-automated fashion, enabling it to accelerate revenue generation.
Some organizations are now fully integrating gen AI “virtual sales agents” into their customer relationship management (CRM) systems. These agents can automatically prepare and send personalized communications to hundreds or even thousands of customers across multiple channels including email and text messaging.
Troubleshooting and customer self-service
Companies are increasing remote resolution and first-time fix rates with AI-enabled troubleshooting tools. Successful approaches involve the use of an AI copilot to more accurately identify the issue and suggest a resolution. That improves customer experience, builds agent capability, increases contact center efficiency, and minimizes technician site visits through remote resolution and customer self-service. For example, global provider of machinery and equipment Ascendum deployed a gen AI solution that crunches data from over 13,000 documents to help the technical support team resolve issues in vehicles. The new system has increased first-contact resolution rates by 50 percent and reduced typical troubleshooting time from 30 minutes to under a minute.
Planning and scheduling
AI tools can improve forecasting of demand through continual analysis of relevant factors, allowing for dynamic rescheduling of the workforce and clearer communication with customers. This leads to better customer outcomes and improved workforce utilization. A leading water treatment company enhanced its operations by adopting a digital scheduling solution, resulting in a 40 percent increase in technician capacity, while also reducing overtime by 6 percent.
Contract analysis
AI tools can help companies identify opportunities within existing contracts to offer new services, or to offer additional products to existing customers based on analysis of their preferences and behaviors. A global truck OEM was facing major manual work with over 1,000 nonstandard contracts being set up each year. The company implemented a “contract agent” for the procurement department that could compare new contracts against historic ones to identify differences and make suggestions for adaption. A “chat with your contract” feature helped teams pressure-test contracts and check specific priority items, while a contract dashboard used traffic-light logic for terms including warranty, liability, and duration. The increased efficiency in contract handling enabled by the new tool saved the company more than 5 million euros per year.
Connect use cases for more impact
Linking multiple AI use cases and data together can generate more value. One industrial company linked five use cases into one program that changed the way it maintained its equipment. A gen-AI-driven technical documentation search function along with root cause analysis helped technicians determine the best corrective actions. This tool also helped technical support resolve many issues remotely or through customer self-service. An AI-driven scheduling algorithm helped minimize the need to visit the same customer on multiple occasions, reducing travel time. Another AI assistant kept track of the tasks performed and automated invoicing and the recovery of costs from vendors where needed. Finally, the company minimized repeat visits through a parts-scoping assistant that predicted the necessary parts for each job and ensured availability prior to dispatch.
Build the right data foundations
AI won’t be much good with bad or limited data. Integrating gen AI into existing systems has significant implications for the technology stack, which needs to be integrated, scalable, and secure. This may involve upgrading existing systems to ensure that legacy systems can support gen AI capabilities, and building new infrastructure that can meet the data requirements of advanced gen AI use cases. These requirements involve a broad set of data sources and data types, including:
- Process data: documentation for maintenance, troubleshooting, key failure modes, and failure mode and effects analysis (FMEA), along with standard operating procedures and work instructions
- IoT data: sufficient data from sensors on equipment to monitor key parameters and predict abnormal conditions
- Equipment history: data on equipment configuration and configuration changes, past failures, and maintenance actions
- Supply chain data: including parts availability, inventory locations, shipping times, and vendor lead times
- Personnel data: covering the skills, availability, and location of field services teams and support staff
Organizations need to ensure that this data is available, accurate, and accessible, while also protecting sensitive information, intellectual property, and AI models to prevent data theft or illegitimate use. That calls for effective data governance with strictly enforced standards for data collection, clear labeling, and robust cybersecurity measures.
Challenge yourself
Gen AI could allow aftermarket and field services players to capture substantial value. McKinsey anticipates that, over the next 12 to 24 months, this technology could slash content creation costs by 80 percent, boost operational efficiency by 30 percent, and automate a quarter of customer interactions. It could increase revenues by 10 to 30 percent, enhance customer satisfaction by 10 to 30 percent, and boost overall services productivity by the same amount. Any services firm that manages to implement these improvements before their peers will have a huge competitive advantage in both cost and quality.
Achieving impact with gen AI requires creativity, risk-taking, and the willingness to look across entire journeys or processes to identify the most valuable opportunities. We asked if gen AI could write technical debriefs? It did! We asked if gen AI could debate with technical teams to find quicker solutions to complex problems. It did! We asked if AI and advanced analytics could help schedule complex technical tasks within engineering tolerances and risk guardrails. They did! Given the inherent risks for these tasks, none of these things happen without a human in the loop, but we must step back and consider the opportunities with an open mind to unlock this impact.
Gen AI holds significant potential for aftermarket and field services, but before implementing it in your organization, consider these key questions:
- What are your service goals? Clearly define what you want gen AI to achieve for your business, whether it’s increasing revenue or improving efficiency. Decide which is more critical for your organization.
- Where can gen AI have the most impact? Look at the broader picture and identify areas along the entire journey where AI can add value. A series of interconnected AI use cases is more likely to create substantial change and help meet your strategic objectives.
- Does your organization have the necessary digital foundations in place? They include robust infrastructure, reliable data, and effective governance.
Addressing the questions up front will help your organization build a solid foundation for next-generation aftermarket and field services. Given the financial and time investments involved, ensure you’re positioned to capture value that benefits customers, employees, and shareholders.
