by Sarah Nicastro, Creator and Editor in Chief, Future of Field Service
On January 14th I listened to a TSIA webinar where Roy Dockery talked through his State of Field Services 2026 report. I won’t give it all away in this article, because it is well worth your time to both watch the recording and read through the report – but let me just say, I took enough notes to fill pages!
Many of you likely know Roy; he’s a military veteran turned service leader who has written a book on leadership and is currently the Sr. Director of Support Services and Field Services Research at TSIA. So, his perspective is well-rounded but grounded in real-world experience. He’s been a guest on the podcast many times, acted as a judge for the 2025 Stand Out 50 Leadership Awards, and most importantly has become a friend.
With such a well-respected voice speaking on the State of Service I expected some words of wisdom – but didn’t anticipate what was a true masterclass for field service leaders in how to consider, position, and articulate not only the role AI will play in the future of service – but how businesses must adapt.
Here I’m sharing a few points that stuck in my mind – not because they were entirely new to me, but because I hadn’t heard them described with such clarity. Again, I urge you to go have a listen to Roy share this perspective firsthand and take your own pages of notes. There’s an urgency to move into this next era of service, and I found Roy’s way of articulating both the opportunities and risks very helpful – I’m sure you will, too.
AI Has the Potential to Restore the Humanity of Service
With much debate about the way AI will impact human jobs, causing ample anxiety, Roy’s explanation about how AI has the potential to restore humanity in service one leaders should be amplifying within their organizations. Roy said, “There’s a lot of noise about AI replacing jobs – in field service, AI will not replace techs; it will augment them. It isn’t about devaluing the human; it’s about making the human more of a hero.”
This perspective is born of a few factors – the challenging talent landscape you’re all too familiar with, the ability for AI to truly transform how knowledge is captured and democratized, but also how AI can lighten the load that digital transformation itself put onto technician’s shoulders.
Roy explains that when field service organizations began embracing digital transformation, it resulted in a multiplication of administrative expectations from technicians. “We need to stop treating our experts like commodities and start treating them like the strategic assets they actually are – technicians are burnt out because we’ve saddled them with all of these administrative duties in addition to demanding their expertise,” says Roy.
(I had an interesting conversation on the podcast with Faisal Hoque on how to unlock AI potential while protecting the human experience if you’re interested in more.)
This issue is compounded by the fact that most service organizations are still focused on transactional business models, forcing them to hyperfocus on utilization in ways that can cause angst for field technicians. And the point is, AI presents a possible solution to this challenge – alleviating low-value tasks and freeing up capacity for high-impact work while paving the way for new customer experiences. Which leads us to the next crucial point: evolving the business model.
Adapt Your Value Proposition, Or Be Left Behind
Roy goes on to discuss why focusing on utilization will fail companies in the AI era. As he says, “This is the fear many of you have – AI destroys your revenue foundation if it is built on truck rolls.” While many may gulp or sigh reading that, it is imperative to digest.
With the potential AI brings to work smarter and the ways customer expectations have evolved based on what they see is possible in consumer experiences every day, it’s (past) time to adapt. Today and into the future, more money will not come from more billable hours – it will come from more value delivered.
“As organizations, many of us have focused on time and material or cost-plus-margin; it’s been difficult to get to outcomes,” says Roy. “But in the way expectations are evolving, and with what AI makes possible, we have to adjust or be left behind. If your money only comes from billable time, and now we only roll a truck for the most complex problems, you have an issue.”
The AI era brings with it more opportunity for remote resolution and self-service, as well as insights that ensure when an on-site visit is required the technician arrives knowing what needs to be done and is prepared. All capabilities that allow for smarter work and no excuse not to offer measurable value, deeming transactional business models far less appealing, if not irrelevant altogether.
“We have to shift from just trying to keep people busy (utilization) to absorption – value delivered relative to the cost of that technician,” urges Roy. To his point, this can include on-site work when necessary or done preventatively, but it also encompasses remote work, training, providing insights, customer engagement, and more. The focus shifts from keeping employees busy to empowering them to deliver value customers feel warrants more revenue than the time and materials model.
(You can listen to an example of how ABB is approaching this in a podcast on its use of AR and AI to modernize field service and evolve the customer experience.)
We’ve talked about the shift of service from a cost center to profit center, but Roy rightfully points out how that framing is too narrow – we shouldn’t settle for delivering profits but shift to viewing service as a strategic revenue driver.
Think Bigger Than Operational Excellence
What will keep companies from leveraging AI in ways that make their field technicians heroes and positions service as a strategic revenue driver? Falling into the default thinking that technology is (only) an enabler of operational excellence.
AI is far more than an enabler; it’s an amplifier. One IFS customer I interviewed recently about their use of agentic AI described it as their “force multiplier” – and that is the view more organizations need to take. Less “how can we improve efficiency” and more “how do we reimagine what’s possible?”
In his webinar, Roy pointed to numerous examples – one of the most significant is around knowledge transfer. “We need to use AI to digitize the brain of senior and retiring techs – to productize the expertise that already exists within our teams in a way that allows a new hire to be effective in weeks or months versus months or years,” he says. “It isn’t just about making troubleshooting less complex – it’s about equipping a new talent pool to show up with the confidence of 20+ years of experience.”
(Makino shares a great example of how they’re approaching this in this article.)
Businesses need to look for the opportunities to eliminate friction and remove any administrative, repetitive, low-value tasks possible. “We’re too short on talent to have techs dealing with friction. Agentic AI should handle the tirage; humans should handle complex issues that require empathy – this restores the dignity of the role,” says Roy. “We can strip away data entry, robotic tasks, techs managing schedules, and mindless fixes and liberate our technicians to focus on how they engage with customers.”
Not only should AI bear the burden of tasks like looking up documentation, recording time, after call notes and summaries, etc. – but it helps to enhance decision making and empower technicians that may be skilled but not as experienced.
Further, it ushers in far more expansive capabilities to provide valuable insights to customers – contributing the point above about expanded value propositions. With data at their fingertips, field technicians are able to provide coaching, as well as offer suggestions on better use of assets, further capabilities, and even more easily and naturally partake in lead generation.