Choose a bounded, recurring service problem with usable data, frontline support, and a measurable baseline. Prioritize a use case you can test and refine before expanding its scope. For service operations and IT leaders, the strongest starting point is a task AI can improve within an existing workflow, such as retrieving troubleshooting knowledge or documenting a case.

A live conversation at the 2026 Field Service Next East conference showed why workflow fit deserves attention during selection. Neeharika Sinha, PhD, AI Product Owner at Cytiva, described the organization's approach as "not a replacement but an augmentation." Cytiva embedded its AI application in the dashboard field service engineers already used and gathered feedback from early adopters. The example highlights the value of choosing a use case that supports familiar work rather than requiring an immediate process overhaul.

Service Next Insights' The State of Field Service in 2026 found that 40% of the 100 senior leaders surveyed identified inadequate technology adoption processes as a barrier to realizing value from technology investments. The finding supports considering implementation and user adoption alongside a use case's potential benefits.

Build a shortlist with input from frontline employees, then compare each candidate's frequency, data readiness, workflow fit, and expected benefit. Select one with a clear owner and a manageable test scope. Record current performance, agree on success criteria, and involve users in reviewing results before committing to a broader rollout.

Join service operations and IT leaders at Service Next West to explore how to select an AI use case that fits frontline work and delivers measurable value.