State of Field Service: The Road to Revenue in the Agentic Era

These goals aren’t new. But organizations are betting on AI as the way to achieve them. Ninety-five percent now say they have some form of AI, and 85% are increasing their investments in the next 1–2 years.

Field service leaders are putting AI to work on the priorities themselves. Over half now use AI-driven tools for customer communication (54%) and to assist mobile workers in the field (51%) — the same areas they rank highest in importance for the year ahead.

And where organizations measure the ROI of these investments, the returns follow suit: 40% report improved customer satisfaction, 43% higher mobile worker productivity, and 34% fewer safety incidents.

The data reflects alignment: Business goals centered on customer satisfaction and mobile worker productivity are being realized where organizations measure ROI.

Those who have made the leap to AI-powered scheduling and dispatch report a different picture: 57% see higher revenue per job, alongside higher mobile worker productivity (57%), reduced emissions (52%), and lower labor costs (49%).

Once onsite, the revenue opportunity lands in a mobile worker’s hands, and organizations are equipping them accordingly. Seventy-nine percent now track revenue generated by mobile workers, and nearly three-quarters (74%) provide real-time upsell recommendations to field teams.

Yet recommendations often arrive without context. Sixty-one percent of organizations say mobile workers have limited access to the relevant customer data they need. Mobile workers may have an opportunity in front of them, but not the information needed to close the deal.

Data access is the dominant blocker to mobile worker revenue generation. Sixty-one percent cite limited access to customer information. Lack of clear sales process (49%), limited ability to quote (44%), and payment acceptance struggles (38%) trail significantly behind.

The revenue opportunity is in mobile workers’ hands, but for many organizations, the data and processes to capture it aren’t yet in place.

Asset data is fragmented. Sixty-three percent use mobile apps and inventory management systems. Fifty-eight percent have connected sensors. But 52% still use spreadsheets. And 43% still rely on paper logs to manage assets in the field. Organizations are managing assets across multiple disconnected systems simultaneously.

Disconnected systems are only part of the problem. There’s also a crisis emerging with the people expected to use the systems. Two-thirds of field service leaders say mobile worker turnover has increased over the past two years.

When asked to point to the factors that are driving the increased turnover, leaders pointed to one issue above all others: insufficient training or support when new technology is introduced.

Without the proper training and management in place, organizations are exacerbating the skilled labor shortage. The very problem that AI can help is being worsened with an improper approach.

When equipment fails without warning, the ripple effects extend well beyond the repair itself: rushed emergency dispatches, missed service windows, and disruptions for whoever was counting on that asset being up and running. Connected operations can prevent these disruptions through intelligent asset management.

Fifty-four percent of organizations now have real-time asset health monitoring, and 60% say they have automatic maintenance triggers in place. These capabilities give teams a head start: spotting equipment nearing end-of-life before it fails, avoiding costly emergency repairs, and extending the useful life of existing assets.

Automated maintenance triggers are one thing. AI agents that intelligently assess asset health and decide when to trigger maintenance? That’s another. For more than half of organizations using AI agents, asset monitoring and autonomous maintenance decisions rank as the top use case.

Agent adoption spans a wide range of other use cases too — customer communication, work order generation, dispatch decisions, and parts ordering among them — and agents are showing up directly in the field, assisting mobile workers with tasks like troubleshooting and job context.

Broad AI adoption doesn’t mean unconditional trust. Organizations are deploying AI everywhere, but 40% say they struggle to measure whether it’s working. That gap between deployment and proof carries into how organizations choose who to work with.

When selecting a partner for AI agents, organizations weigh a range of factors. Transparency into how AI makes decisions edges out other considerations, including cost. Data security, ongoing support, and external validation all follow closely behind.