AI is a game-changer for field service, the frontline work that maintains and repairs critical assets such as factory equipment and transportation fleets. By streamlining workflows, delivering predictive insights, and providing real-time guidance, AI helps technicians work faster, smarter, and with greater precision. The impact is tangible. Field Service AI by BCG X—which combines digital and AI solutions across the field service value chain—has delivered 10% to 15% productivity gains and 5% to 10% margin expansion.
But companies that see these gains aren’t deploying new tools in isolation. They’re combining cutting-edge technology with robust change management. These organizations know that to realize the benefits of AI, old walls (such as inefficient processes) can’t be replaced by new walls (employee resistance to AI-powered tools and ways of working, for example). Technology—no matter how useful it may be—needs to be usable and used. To seize AI’s potential, these companies are taking a structured approach to engagement, training, and adoption: change management that drives transformation.
What does that change management look like? While the answer will vary across organizations, certain principles—and sequencing—can help drive success.
Catalysts for Change
Perhaps the most important rule of thumb is to start early. Treating change management as an afterthought—tacking it on after unleashing new technology—invariably is too little, too late. Companies that win with AI consider, right from the planning stages, how new tools will impact work and how to develop them (and any relevant processes) to facilitate adoption.
Indeed, change management is so crucial to an AI transformation that it should account for the bulk of an organization’s effort. BCG calls it the 10-20-70 rule. To successfully deploy AI at scale, companies should devote 10% of their effort to specialized machine learning models, 20% to the data and technological backbone, and 70% to people and processes. (See Exhibit 1.)
That 70% covers a lot of ground: training, coaching, culture, and leading from the top. Companies need to inspire employees and set them up for success. They must ensure that new tools really do speed field work and new efficiencies spark a shift to more value-creating work (such as when technicians start spending less time documenting repairs or searching for maintenance manuals and more time tackling highly complex service calls). Getting this right can be a formidable task. But five key principles can smooth the way.
- Activate the leadership. In successful transformations, leaders serve as agents of change. They don’t simply endorse AI initiatives but foster excitement and engagement across the organization. The key is to use clear, compelling messaging to make the case for change and to stress how AI will empower, not displace, frontline teams. We’ve found that an important step is establishing a consistent metric cascade: breaking down high-level KPIs into more specific, actionable metrics across various levels of the organization. This aligns leadership, management, and technicians around key goals.
- Engage service teams. Even the best technology will disappoint if it’s cumbersome or doesn’t target the roadblocks that matter most. Codevelopment—involving frontline technicians in the design of fit-for-purpose tools—helps ensure that AI solutions reflect real-world needs and challenges. It also drives smoother integration of AI tools into workflows. But service team engagement is not a one-and-done process. Continuously seeking feedback, from the design phase through pilots and scaling, lets an organization respond quickly as needs and circumstances evolve.
- Foster executional excellence. Change programs succeed when new technologies, capabilities, and ways of working come together to drive value. To get everything in sync, companies should align service roles and organizational structures with AI-powered service models. They should establish clear ownership for initiatives and implementation. And they should optimize workflows to maximize efficiency. A data-enabled operating rhythm—built around daily standups or weekly reviews—is particularly important because it helps drive executional certainty and cross-functional alignment. Ethical considerations must also remain at the forefront, ensuring transparency and fairness in AI-driven decision-making. To this end, responsible AI, the process of developing and operating AI systems that align with organizational purpose and values, is a proven enabler.
- Create a high-performance culture. One of AI’s key benefits is how it can spark a shift in what people do, enabling them to focus less on repetitive, relatively straightforward tasks and more on higher-priority, value-creating work. Operations managers redirect their efforts from manual scheduling and inventory tracking to strategic decision making and business optimization. Field technicians gain wrench time. Back-office staff spend less time on administrative work and more on customer relationships. (See Exhibit 2.) To ensure that these shifts happen, organizations must create a culture that fuels collaboration, adoption, and always-on transformation. Some best practices can activate this kind of environment. Among them: embracing modern communications tools, such as chat apps, for quick questions and answers; employing gamification and other techniques to incentivize engagement; and using forums such as quarterly town hall meetings to spotlight success stories, foster peer recognition, and introduce competitive dynamics.
- Enable and empower. Helping employees thrive in an AI-enabled world is critical. Accelerated, hands-on training programs ensure that field service teams can adapt quickly to new tools and processes. Another catalyst: designated AI champions. Working within service teams, these individuals help ensure—and amplify—the impact of AI solutions by supporting their peers during the transition. Train-the-trainer initiatives can further embed AI expertise in the workforce, creating a self-sustaining ecosystem of learning and improvement.
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A Phased Approach
Understanding these principles is essential, but the real key is how an organization connects them to facilitate change—and maximize impact—through the three phases of a transformation: design, pilot, and scale and sustain. Here’s how to imbue change management across the journey.
Design. This first phase, when companies are mapping out how to develop and use an AI tool, is all about collaboration. Bringing together leadership and frontline workers helps ensure that companies design the right things in the right way—shaping solutions that not only meet operational needs but also foster adoption. By securing early engagement from key players, organizations can set expectations and align on the highest-priority tools for investment. But codesign also brings something else: a sense of ownership among future users.
The special sauce is identifying champions who can spotlight key needs and suggest tweaks to the AI tool, increasing effectiveness and reducing potential resistance.
The special sauce is identifying—and including—champion technicians, operations managers, and support staff. By participating in this early phase, these individuals can spotlight key needs and challenges, suggest tweaks that improve usability, and help refine the AI tool’s value proposition. This increases the effectiveness of the AI solution and, at the same time, reduces the potential for resistance.
By the end of this phase, organizations should have a clearly articulated roadmap for AI deployment and an engaged network of champions (whose investment in the solution is as important as the financial outlay). And savvy leaders will have developed an effective strategy for communicating the benefits of the AI transformation.
A leading manufacturer of food-processing machinery embraced codesign in developing an AI-enabled solution for the aftermarket. In weekly sessions, the company engaged with end customers to uncover pain points, test early prototypes, and iterate. This approach—initiated at the outset of the design phase—led to a solution that reflected the day-to-day realities of end users and fit seamlessly into workflows, strengthening adoption and enabling early wins. These first successes generated momentum and laid a foundation for sustained success, with the company on its path to unlocking more than $100 million in recurring revenue and seeing a two- to five-fold premium on its service contracts.
Pilot. The next phase is employing a structured pilot program to adopt AI in operations. Rollouts in select locations allow an organization to test solutions in real-world conditions with minimal disruption to the business. They also let a company provide the hands-on experience that activates their champion network. That’s important because champions serve a dual role in this phase: supporting their peers and providing valuable feedback for refining the solution.
Feedback shouldn’t stop with champions, however. Prioritizing improvements based on user input ensures that enhancements—both to tools and processes—align with frontline needs. So companies should create feedback loops that make it easy for anyone using the tool to weigh in. Implementing practical suggestions also should be a team effort, with technicians, service managers, and engineers collaborating to address functionality gaps and refine AI features.
Pilots with lower-than-expected adoption rates can be a signal to reengage with users to understand what’s working—and what’s not.
As a pilot progresses, the organization should continuously evaluate adoption rates and end-user engagement. Lower-than-expected adoption rates can be a signal to reengage with users to understand what’s working and what’s not and to explore enhancements for a more seamless integration of tools and processes.
We’ve seen this combination of pilots, feedback, and collaboration pay dividends in launching an effective, efficient AI solution. Consider the case of a leading heating, ventilation, and air-conditioning equipment manufacturer. An early adopter of BCG X’s Field Service AI solution, the company employed a phased rollout strategy, starting with pilots in select locations. By employing a train-the-trainer program, it steadily expanded the solution to more and more locations. This strategy enabled frontline teams to become advocates for the transformation and sustain adoption across the service network.
Collaboration between the manufacturer’s transformation and service teams—with technicians, service managers, parts managers, and branch and regional leadership working together—helped define the operational metrics and cadence that would best drive value. Crucially, this hands-on engagement also built trust in the solution, which now checked all the boxes: useful, usable, and used. After the transformation, the company saw its revenue growth jump from 5% annually to more than 15% and its EBIT double within 12 months.
Scale and Sustain. After pilots have validated an AI solution, the focus shifts to scaling across the organization. By integrating lessons from the pilots and employing pull-forward mechanisms—such as demonstrations, success stories shared by early users, and other value proofs—companies can accelerate adoption in new locations. Champions are especially powerful enablers in this phase, serving as coaches, trainers, and mentors to their peers (and—no small thing—as proof that the tool works and can be mastered).
Another best practice is to continue roadmap discussions, scheduling these on a regular basis and including field technicians, solution owners, and engineering teams. This helps ensure the continuous optimization of AI solutions. Long-term mechanisms for ongoing training (typically a combination of onsite and virtual sessions) and feedback collection and sharing are also crucial. Keeping teams engaged and informed keeps the momentum going—for both adoption and enhancements.
The most successful companies develop clear processes for continuous improvement and future scalability. Take, for instance, a major trucking player that was the first in its industry to build a tool to help its customers improve fleet performance. In their feedback, pilot customers were impressed with the depth of data and insights the solution generated. But to get even more out of the product, customers wanted support in connecting insights to performance improvements that could deliver sustained ROI.
The trucking company realized that no matter how robust the technology, its solution would not deliver on its promise if customers couldn’t see that link from insights to action—and potential returns. So, it strengthened collaboration between sales and customer success teams, upskilled team members on ROI quantification, and developed a playbook for helping customers leverage the solution’s insights. Testing this approach with an initial set of customers, the company saw its customer conversion rate increase by more than 40%. The strategy also smoothed the path to higher customer stickiness, new subscription revenue, and expanding its share of wallet.
Field service is critical to keeping operations running, and AI can transform this work. But organizations will unlock AI’s full potential only when people are equipped and motivated to use it well. Great technology must be paired with robust change management. Done right, change management does more than drive adoption. It fuels innovation, strengthens resilience, and accelerates the outcomes that spark—and sustain—success.
The authors would like to thank Julia Dhar for her contributions to this article.
