The use case trap, defined honestly
A use case is the right starting unit for exploration. It’s a clearly scoped scenario, a clinician interacting with a system, a payer adjudicating a claim, a pharmacist verifying a prescription, that tests whether AI can add value in a specific context. That’s useful. The trap isn’t in running use cases. It’s in treating them as the finish line.
Healthcare delivery is process-driven by design. Clinical workflows cross systems. Administrative decisions change patient outcomes. SOPs enforce safety and auditability across every step. Prior authorization isn’t a task; it’s an end-to-end process moving from provider order through payer decision to patient scheduling, each stage touching different teams, different data sources, and different compliance requirements. An AI tool that accelerates the decisioning step while leaving the rest of the process untouched will show a narrow metric improvement and a flat ROI line.
According to HFS Research in 2025, one in two enterprises finds that AI solutions stall at the POC or pilot stage. Operational efficiency gains, once celebrated as breakthrough value, are fast becoming table stakes. The organizations pulling ahead aren’t running more pilots. They’re redesigning the processes those pilots were trying to improve.
What leading organizations do differently
The shift isn’t subtle. Organizations demonstrating durable AI ROI don’t open the conversation with ‘which use case should we apply AI to?’ They start with a service delivery question: where is specialty care too slow, too expensive, or too inconsistent? From there, they identify the processes that create that problem, map the SOPs and decision points within those processes, and then, only then, determine where AI, automation, human-in-the-loop design, or agentic systems add the most leverage.
For a payer, that means embedding AI across the full utilization management process and measuring outcomes through authorization turnaround time, administrative cost per member, and CMS Star Rating movement. For a provider, it means integrating AI into revenue cycle and care coordination and tracking denial rates, cash realization speed, and clinician administrative burden. For a life sciences organization, it means wiring AI into clinical trial execution and watching site activation timelines, patient retention curves, and time-to-submission compress.
In each case, the use case is still present. But it’s nested inside a process, governed by an SOP, and pointed at a service delivery outcome. That nesting is the difference between a metric and a result.
The right unit of measure
Process is the right unit of measure for AI ROI in healthcare and life sciences. Not because use cases don’t matter, they’re essential for experimentation and early-stage validation. But because AI ROI compounds when processes change, not when tools get added.
The layering works like this: use cases handle execution and experimentation. SOPs provide control and compliance, governing exactly how prior authorization decisions get made, how coding reviews proceed, where human accountability sits. Processes drive value realization and scale across the full prior authorization workflow, the revenue cycle, the clinical trial process. And service delivery, specialty care, chronic disease management, trial execution, is where the ultimate proof of ROI lives.
Think of it as a measurement stack. Every layer is necessary, but only one layer is sufficient as the primary ROI yardstick. Organizations that measure at the use case layer will always find their numbers too small and their stakeholders too skeptical. Those that measure at the process and service delivery layer will find the conversation changes entirely, from ‘did the tool work?’ to ‘did the care delivery model improve?’
