Choose an AI use case that addresses a recurring service problem, fits existing workflows, and delivers a measurable improvement without introducing unacceptable quality or patient risk. For service transformation leaders, dispatch managers, and IT teams, a focused troubleshooting or parts-identification pilot can be a more practical starting point than attempting to automate the entire service lifecycle.

At the 2026 Service Next Medical event, Jason Kim, General Manager at Wontech USA, described an adoption journey that began with dispatch troubleshooting and expanded as field engineers gained access to service procedures and parts information. He emphasized ease of use "to make sure it's not going to be difficult to use by our field service engineers" alongside affordability and CRM integration. His experience points to a selection principle: prioritize a task employees already need to complete, then make that task easier.

Another 2026 event conversation illustrated why use case selection must account for adoption. An initial AI rollout struggled when leaders rushed implementation and mandated use. Adoption improved after experienced technicians experienced a concrete benefit: reducing the time they spent answering newer colleagues' questions.

Start by comparing a few candidate workflows against task frequency, information quality, integration effort, user effort, and the consequences of an incorrect answer. Involve dispatchers, technicians, IT, and quality colleagues before choosing one bounded pilot, and define success through task completion time, answer accuracy, adoption, and service outcomes. Include human review and escalation rules so faster execution does not come at the expense of reliable decisions.

Join Service Next Medical to compare how medical device service leaders select, test, and scale AI applications that support frontline performance.