Field Service Medical 2026 brought together service leaders, MedTech innovators, and operations executives for three days to discuss the future of medical device service. Compared to past years, the conversation has shifted. Instead of asking whether AI has a role, sessions focused on real deployments, practical outcomes, and what it takes to make these changes work.
After attending dozens of sessions, five themes stood out to us that should be top of mind for every service leader planning their 2026 AI strategy.
At FSM 2026, knowledge management came up repeatedly as a core capability that remains a challenge for many organizations.
Ziehm Imaging’s Christian Eras, VP of Global Service, outlined the challenge in his session, “MASTERMIND”. Hiring is harder, ramp-up takes longer, experienced technicians are retiring, and devices are getting more complex. His point was simple. Knowledge management needs to function as a readiness system, not just a document repository.
He also reframed ramp-up time as hidden service capacity, a perspective that should resonate with any service leader managing a growing install base with a shrinking talent pipeline.
Bangs Laboratories’ Kathy Kilbride, Sr. Director of Technical Discovery and Customer Success, reinforced this from the customer-facing side. Her session, “From Data to Delight”, showed how Bangs built an integrated support landscape connecting an AI-enabled knowledge base with enterprise content management, serving audiences ranging from end users to field service engineers to distribution partners.
During Cytovale’s presentation, “Predictive Analytics in Remote Field Support,” Jeff Rathmann, Director of Technical Services, showcased his team’s journey from manually importing logs to building a real-time remote monitoring platform.
The results speak for themselves: over 60% of service visits shifted from reactive to planned, a 45% increase in remote resolution rate, 80% improvement in mean time between failures, and meaningful cost reductions per planned visit versus break-fix interventions.
In her presentation, “Leveraging SaaS to Accelerate Revenue through Serviceability,” Shilpa Batra, Director of SaaS & New Product Introduction at Elekta, told a complementary story from the radiation therapy space, where equipment downtime directly delays cancer treatment.
Elekta’s shift to cloud-based SaaS service models with centralized device monitoring, real-time analytics, and AI-based quality assurance reflects a broader industry recognition: predictive maintenance is table stakes for high-acuity medical devices.
Ferno’s session, “For Heroes. For Life,” on their POWER X2 and POWER F2 emergency response systems showcased something that often gets lost in discussions about digital transformation: the physical product itself. With 1,800+ and 1,500+ components respectively, these systems were designed with serviceability as a first-class engineering requirement.
Combined with real-time diagnostics through Ferno Connect and IoT-enabled remote monitoring, Ferno is shifting from a break-fix model to proactive, data-informed service.
This matters because no amount of AI in the service workflow can compensate for a product that was designed without service in mind. The organizations making the fastest progress toward autonomous service are those where engineering, product management, and service leadership share a unified vision for the device lifecycle — and where serviceability is a design constraint, not an afterthought.
Several sessions converged on a theme that will resonate with any service leader managing rapid growth: technology alone doesn’t solve the scaling problem. You need a new operating model.
During the presentation, “Scaling Service Operations to Match Sales Growth,” Jim Larson, Director of Service Operations at Mmic Medical Systems shared lessons on scaling service operations to match sales growth, covering people, process, technology, and the careful calibration of direct, indirect, and hybrid service models.
His framework for competency development and cross-functional alignment offered guidance for organizations navigating high-growth phases where the install base is outpacing the service organization’s capacity.
Kenda Pennington, SVP, Global Patient Experience – Center of Excellence at Novocure, brought the patient perspective into focus in her session titled “Improving Patient Experience.” Her Patient Experience Center of Excellence, launched specifically to improve the support journey for wearable cancer treatment devices, demonstrated that scaling service in MedTech ultimately means building a learning culture, developing quality standards, and ensuring consistency across global operations.
Across the sessions, one pattern was clear. The teams seeing real results from AI were embedding it directly into how decisions get made.
In their presentation, “Protecting Uptime in the Age of Industrial AI”, IFS highlighted how widespread AI adoption already is in service, with 96% of organizations using it in some form, but only a small percentage seeing it fully scaled across operations.
The gap comes down to execution. Many teams have AI, but it remains disconnected from the day-to-day workflow rather than guiding real decisions in the field. Whether it’s troubleshooting, dispatching, or quality review, the value shows up when AI is part of the workflow, not separate from it.
That came through clearly in Circuitry.ai’s case study “Autonomous Service Journeys”, presented by Ashok Kartham, CEO, and Josh Russell, VP, Products, from Circuitry.ai, alongside Brent Lloyd, VP of Service and Technology Operations at Noah Medical.
It stood out as an example of what it looks like when AI is actually used across the service workflow, not just in isolated use cases. That’s still where many teams are today, using AI in pockets but not fully rolling it out across operations.
In Noah Medical’s case, the focus was on bringing AI directly into the service workflow. AI-guided decision support was used to connect fragmented knowledge, support faster troubleshooting, and maintain traceability across service interactions.
Instead of searching across systems, teams could get context-specific answers and next steps within the tools they were already using, improving both speed and consistency.
Field Service Medical 2026 made it clear that the industry is moving past early exploration and into real adoption of AI in service. The conversations focused on what teams are actually doing today, what’s working, and where they’re going next.
Circuitry.ai delivers the enterprise AI-powered Decision Intelligence platform purpose-built for complex service, warranty, and parts workflows. Our Service AI Workers are embedded inside existing systems of record, support explainable decision-making, and drive measurable impact across the service lifecycle.
If you’re exploring how this could apply to your organization, contact us today to get a custom demo.