AI in Field Service: Building a Business Case That Encompasses Both ROI and ROE

That was the central theme of Mike Hughes’ talk at the Aftermarket Power of 50 event. As Group Service Director at Peak Scientific, he spent the past year building a compelling business case for AI adoption, one that addresses both the traditional concept of ROI and the increasingly vital metric of ROE: Return on Employee.

Hughes’ year-long initiative produced a practical blueprint for service leaders seeking to drive operational impact while also improving the everyday experience of their engineers.

Why ROE Matters More Than Ever

Field service engineers are, in Mike’s words, “our greatest asset by far.” But attracting and retaining them has become more difficult, especially as expectations from employers continue to rise. That’s where ROE takes on a crucial role. It reflects the indirect yet vital benefits of enhancing technician experience, including better onboarding, higher job satisfaction, reduced admin time, and improved retention.

Few service organisations, however, have clear metrics for these outcomes. When Mike asked the audience who measured onboarding or time-to-effectiveness, not a single hand went up. Yet more responded when asked about employee Net Promoter Scores, suggesting the industry is starting to shift towards people-focused KPIs.

ROI vs ROE: A Clear Distinction

To build a solid business case, Mike began by defining ROI and ROE in simple terms. ROI, in the traditional sense, refers to measurable financial improvements such as higher first-time fix rates, fewer repeat visits, lower cost per repair and reduced parts usage.

ROE, by contrast, focuses on the less tangible benefits. These include onboarding efficiency, employee satisfaction, retention, and the ability to attract top talent. Although these metrics are harder to quantify, they can have significant long-term benefits.

One clear contradiction Mike highlighted is the pressure engineers face to be more productive while still spending large amounts of time searching for information. According to a recent survey he cited, 41% of engineers identified looking for information as a major point of friction, while 70% reported relying on a colleague to provide the answers they need.

Listening to the Front Line

Mike’s journey began with a strong mandate from his COO: “Our roadmap and objectives must be centred on the experience of the FSE.” That directive led him to dig into the daily pain points of technicians. Among the top frustrations were the time spent searching for information and the pressure to increase productivity, which are two problems that often occur simultaneously.

An internal mapping exercise revealed that critical information was stored in fragmented systems. Technical bulletins were separate from ERP data, SOPs were elsewhere, and asset history was held in yet another location. To demonstrate the issue, Mike arranged for members of Peak Scientific Instruments’ executive committee to shadow technical support staff. Their reactions, he said, included a few four-letter words.

This exercise made the opportunity for AI even clearer.

Discovering the Right Use Cases for AI

Mike attended a conference in the United States, intending to explore IoT solutions, but the dominant topic was AI. As he watched technology demonstrations, several use cases immediately stood out.

First was knowledge search and parts identification, directly tied to the friction technicians face when looking for the correct information. Second was guided troubleshooting, which engineers had regularly asked for. Third was predictive insights, a step toward moving from reactive to proactive service.

Rather than attempting to implement everything at once, Mike focused on just these three. His advice to others was to be intentional, pick a few use cases and connect them to specific business outcomes.

Change Management Begins Early

For Mike, change management did not begin after board approval. It started the moment the opportunity became real. He used the ADKAR model to guide his internal engagement strategy, emphasising the importance of building awareness and desire early.

By sharing the vision with technicians first and involving them in the conversation, Mike created in-house ambassadors. These technicians began advocating for the AI platform themselves, helping to influence middle managers and regional leaders.

Stakeholder Buy-In: Know Your Audience

Even though Mike sits on the executive committee, he still needed to win the support of up to 12 different stakeholders. His advice was simple: “If you’re not best friends with your CFO and your CIO, change that.”

Each stakeholder had a different perspective. The CFO wanted measurable financial outcomes within 12 months. The CIO’s team, meanwhile, believed they could build an internal AI tool in-house. Mike challenged this assumption by giving them three real-life diagnostic questions. Their tool failed all three.

In a decentralised organisation like Peak, buy-in from regional general managers was also essential. The support from technicians helped tip the scales, creating a bottom-up push that complemented the top-down mandate.

Building the Business Case

The final business case was designed with two layers. The first was direct ROI: improvements in first-time fix, fewer repeat visits, reduced cost per job and fewer unnecessary parts used. Mike explained that engineers sometimes replace multiple parts because they lack full confidence in their diagnosis, a practice known as “parts shotgunning.” AI could significantly reduce this behaviour by improving diagnostic accuracy.

These direct outcomes were sufficient to justify the investment on their own, satisfying the CFO’s requirement for impact on the profit and loss statement within 12 months.

The second layer was ROE, with better retention, faster onboarding, higher satisfaction and stronger recruitment appeal. While harder to quantify, these factors were crucial for long-term service stability and won strong support from the Chief People Officer.

Tell the Story

Mike’s final message was that it is not enough to present data and metrics. You must tell a story that connects the current pain points with the proposed solution, showing why AI is the right tool for the job.

Define your purpose clearly. Choose intentional use cases. Begin change management from day one. Map your stakeholders and speak their language. Most importantly, make sure the business case stands on its own in terms of ROI, even if the ROE benefits are ultimately what will transform the organisation.

Put People First

AI presents a huge opportunity for service leaders, but it’s not just about technology. Its success hinges on managing change and on the people who make it work.

For Mike Hughes and Peak Scientific Instruments, technicians are more than users of the tool; they’re the reason for building it. Get them on board, tell their story, and you’ll find that AI is not just a wise investment but a necessary one.