Measure the value that actually reaches the utility

A fast AI output can be impressive while producing little improvement in the overall process. The reviewer may need to repair it, the next team may repeat the work, or the output may arrive before anyone is ready to use it.

Measure what reaches an accepted outcome. For a filing task, that is a reviewed section ready for inclusion. For a data correction, it is a verified record in the intended system. For a customer interaction, it is a resolved issue with appropriate handling.

Establish a baseline you can compare

Capture a representative sample of the existing workflow before introducing AI. Record volume, active staff time, waiting time, error or rework rates, and the quality of the final result. Segment the work by difficulty where averages would hide important differences.

Compare similar tasks under similar conditions. A pilot during a quiet period should not be compared uncritically with a storm response backlog. When possible, use a concurrent comparison group or alternate between workflows with consistent assignment rules.

Define the acceptance criteria with practitioners. “Looks good” is too vague. A document may need supported citations, reconciled figures, current source versions, and no unresolved material contradictions.

Keep benefit categories distinct

Benefit	What it means	Evidence to collect
Capacity released	Staff time becomes available for other work	Net effort reduction and what the team does with it
Cash savings	An actual expense decreases	Budget or invoice changes attributable to the workflow
Avoided future cost	Planned expenditure is no longer needed	The counterfactual plan and why it changed
Service improvement	Customers or internal teams get better outcomes	Resolution quality, cycle time, and repeat contact
Risk reduction	A harmful outcome becomes less likely or less severe	Validated indicators and an explicit evaluation method

Capacity can be valuable without reducing payroll. Describe it honestly: “The team can clear more applications within existing staffing” is often a stronger claim than a speculative dollar saving.

For infrequent events such as major equipment failures, a short pilot may not establish avoided losses. Report intermediate evidence, such as improved detection or faster investigation, and explain what remains unproven.

An illustrative effort calculation

Suppose a team completes 400 comparable tasks each month. Before the pilot, each accepted task requires 30 minutes of staff effort. With AI, preparation takes 10 minutes and review and corrections take another 8 minutes.

The net difference is 12 minutes per task. At full use across all 400 tasks, that represents 80 hours of monthly capacity. These figures are an example, not an observed result or a forecast for your utility.

Now adjust for reality. If only some tasks are eligible or users adopt the workflow only part of the time, the benefit is smaller. Include escalations, training, and ongoing administration. Do not count time twice when two teams benefit from the same shortened handoff.

Multiplying those hours by a labor rate estimates the value of capacity. It does not establish cash savings. A manager should explain how the time will become backlog reduction, additional output, improved service, or an actual expense change.

Include the full operating cost

Budget for integration, data preparation, security review, software or model usage, storage, support, evaluation, and source maintenance. Include practitioner time during implementation and the ongoing effort of reviewing outputs.

Separate one-time costs from recurring costs. Model a range for volume and usage so the business case remains understandable if adoption grows or a task needs more processing than expected. Confirm whether a lower per-task cost still translates into an affordable total bill.

The source workbook contains directional value estimates intended to explore opportunities. Use that kind of catalog to generate questions. Build the investment case from your own measured baseline and explicit assumptions.

Review a small scorecard regularly

Track completed volume, accepted-output quality, total effort per task, adoption, exceptions, and total operating cost. Add the service or operational measure that motivated the project.

Record what changed during each review period: source coverage, workflow design, staffing, or model version. Otherwise, performance changes can be difficult to explain. Expand when the evidence supports it, and revisit the case when the workflow or cost structure changes.