Field Service East 2025 brought together manufacturers, service leaders, and solution providers to address today’s most pressing operational challenges, from workforce shortages and data fragmentation to the pressure to improve service delivery while lowering cost.
AI was a common thread, but the focus wasn’t hype. It was execution. The most productive conversations centered on practical steps to improve technician performance, speed up time-to-resolution, and build a service organization that can scale intelligently.
Here are four key takeaways from the event.
Across the service industry, workforce shortages have become a daily operational challenge. Skilled technicians are in short supply, even as equipment grows more complex and customer expectations climb.
In Schneider Electric’s presentation, “Transforming Field Services: Leveraging Predictive Maintenance for Profitability,” Doug Beck, Director of Industrial Automation Services at Schneider Electric, highlighted how Schneider is rethinking maintenance strategies to address mounting labor challenges and increasingly complex equipment. As he noted, in 2023, the demand for qualified electrical technicians grew by 7%; however, the technician workforce only grew by less than 1%.
To adapt, Schneider is moving away from traditional, calendar-based maintenance schedules toward AI-driven, condition-based maintenance. By monitoring performance holistically and automating routine checks, organizations can reduce unnecessary site visits, cut operating costs, and ease the pressure on overstretched technician teams.
Schneider’s reported OPEX savings and reductions in on-site interventions show that smarter, AI-guided maintenance is becoming critical for scaling service delivery without overburdening limited staff.
Adoption remains a significant hurdle for many AI projects. During Circuitry.ai’s roundtable discussion, “AI-Driven Decision Intelligence for Optimized Field Service Productivity and Efficiency,” participants emphasized that success depends less on the technology itself and more on where and how it’s introduced.
AI systems that operate outside of existing tools or require technicians to navigate new platforms can often go unused. The most effective implementations are those that integrate directly into field service apps, CRMs, browser extensions, or mobile interfaces, wherever technicians and agents are already working.
The teams seeing the most progress focused less on the technology stack and more on rollout strategy: starting small with a few high-impact use cases, aligning AI to a team’s existing workflows, embedding it in the tools teams already use, and showing how it reduces effort from day one.
Service organizations are moving past basic task automation to improve the quality and consistency of every decision. In Circuitry.ai’s presentation, “Autonomous Service Journey: Service AI that Delivers the Right Fix, Right Part, Right Now,” Ashok Kartham, CEO at Circuitry.ai, introduced autonomous service journeys, the next step in digital service operations, and shared a case study from Takeuchi to illustrate its impact.
Rather than depending on manual effort or static knowledge bases, AI senses issues, interprets context, decides on the best action, and acts, from filing claims to dispatching technicians with the right parts.
Takeuchi’s deployment of Circuitry.ai’s Advisors, Analysts, and Agents showed this in practice. Working together to guide diagnostics, predict issues, and automate routine tasks, these Service AI workers delivered measurable results:
By embedding these capabilities directly into Takeuchi’s existing systems, the company avoided adding extra complexity for its teams and ensured adoption was seamless.
Crucially, Takeuchi’s success wasn’t just about the technology. They identified high-value use cases first (technician support and contact center efficiency), integrated AI where their teams already worked, and expanded from there.
During Honeywell’s session, “The AI Revolution in Field Service: From Hype to Hyper Efficiency,” Yasir Sheikh, Global Head of Applications & Connected Services at Honeywell, showed a clear framework for scalable AI adoption. Rather than deploying enterprise-wide from day one, Honeywell follows a phased approach:
This approach has already yielded significant results: 30% of calls resolved remotely, $11 million in annual productivity gains, and a 13% reduction in miles traveled. By grounding deployment in measurable value, Honeywell ensures AI is aligned to business goals from the start.
The conversations at Field Service East 2025 confirmed what many manufacturers already know: AI is already solving real challenges across the service lifecycle. But adoption isn’t automatic. It takes the right use cases, integrated workflows, and a clear path to value.
If you're evaluating how to move forward, whether that means assisting field technicians, improving parts accuracy, or reducing contact center volume, we’re here to help you start, move fast, and scale when ready.
Check out our ROI calculators to see how AI can impact your use case, or take our field service assessment quiz to get targeted AI strategies for your biggest pain points.
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