A practical framework for evaluating AI use cases in operations

Almost every operations leader we talk to has an AI pilot underway or recently completed. Far fewer have one running in production a year later, and the reason is rarely that the model didn't work. It's that the use case was chosen before anyone rigorously assessed whether the underlying data and process could actually support it.

Start with the failure mode, not the opportunity

Most AI use case evaluations start by asking "where could AI add value?" — which tends to surface the most exciting possibilities, not the most viable ones. A more useful starting question is "where does a wrong answer cost us the least?" Use cases where errors are cheap and recoverable are dramatically easier to get into production than ones where a mistake is expensive or hard to detect, even if the exciting use case would technically deliver more value if it worked perfectly.

The data readiness check most teams skip

Before scoping a model, we run a deliberately unglamorous exercise: pulling a sample of the actual historical data the model would train on and asking whether a skilled human, given only that data, could reliably make the same decision the model is meant to automate. If a human with full access to the data still can't do it consistently, no model will either — the problem is data or process, not modeling.

• Is the outcome you want to predict actually recorded anywhere, consistently, historically?
• Is the data that would inform a good decision captured before the decision is made, or only after?
• How much of the historical data reflects a process that has since changed?
• Would a subject matter expert, given the same inputs, actually agree on what the 'right' answer looks like?

Sequencing matters more than ambition

The organizations that get real production value from AI tend to sequence use cases from lowest-risk to highest-value, building institutional trust and MLOps muscle on the easy cases before tackling the ambitious ones. The organizations that stall tend to start with the most ambitious use case because it has the most executive attention, then lose momentum when it turns out the data wasn't ready and there's no smaller win to fall back on.

This isn't an argument for being unambitious about AI. It's an argument for being honest about sequencing — building the muscle memory and data infrastructure on problems you can actually solve this quarter, so the ambitious use case has a foundation to stand on when its turn comes.