Measure AI by the work your team can use

Choose one unit of work that a business owner recognizes. Then define what makes it ready for its next step.

For a credit review packet, that might mean the required documents are present, material statements have supporting references, gaps are clearly marked, and an accountable reviewer can act on the result. The packet supports the analyst’s judgment.

Agree on that standard before comparing the existing process with an AI-assisted one. Apply it consistently to a representative mix of routine and difficult cases.

This gives everyone a shared definition of success. The business owner knows what they’re buying. The delivery team knows what to build. Reviewers know what they’re accepting.

At Firebrand, we build and operate enterprise AI applications around the customer’s data, systems, and business rules. FireFoundry, our enterprise AI orchestration platform, provides a shared foundation for coordinating workflows, managing human approvals, and monitoring performance within the customer’s cloud environment. Our engineers work alongside the customer’s team through deployment and ongoing operation.

That combination matters because useful work depends on how the application fits into the business. The right information needs to reach the right person, with enough context to act.

Our work with GHR Healthcare illustrates the economic opportunity: the published results include 92% less recruiting labor per job posting.

ReviewIQ, built for Harvard’s Institute for Quantitative Social Science, illustrates how to preserve expert oversight. It screens Dataverse submissions and recommends pass, expert review, or block, with plain-language reasoning. Curators retain oversight inside their existing workflow. In pre-production testing across 200 historical submissions, ReviewIQ achieved 97% agreement with expert review.

Each example measures a different part of the outcome. Together, they show why the workflow deserves as much attention as the generated output.

A useful operating measure is:

Cost per accepted unit = total workflow operating cost ÷ accepted units of work.

Use the same period and scope for both. Include all attempts, including rejected results, corrections, and exception handling. Count each accepted unit once.

The operating cost should cover input preparation, model usage, infrastructure, human review, support, and monitoring. Track initial implementation investment separately.

Here’s an illustrative example:

A workflow costs $20,000 to process 1,000 cases in a month. Of those, 800 meet the acceptance standard. Its operating cost is $25 per accepted case. The $20,000 includes the work spent on all 1,000 cases.

Run the same calculation for the existing process, using comparable work and the same acceptance standard. Now you have a meaningful basis for evaluating efficiency.

The CreditIQ results establish gains in review time and capacity. Calculating cost per accepted review also requires the workflow’s operating costs.

Hours freed create capacity. That capacity can help a team handle more demand, improve service, or spend more time on work that needs judgment.

Call it cash savings when spending actually falls. When the benefit is additional capacity, show how the business uses it.

For a cost-based comparison, calculate the operating cost avoided over a stated period: the baseline cost of delivering the same accepted volume, minus the AI-assisted operating cost.

Then calculate:

Net return on implementation investment = (operating cost avoided − implementation investment) ÷ implementation investment.

Keep implementation costs separate so they’re counted once. Label estimates clearly, and avoid counting the same benefit twice. Hours released and additional volume handled may describe two effects of the same improvement.

This gives leaders a more useful investment case: what improved, how it translated into business value, and what it cost to achieve.

Cost is one part of the picture. Track these measures alongside it:

These measures also point to the next improvement. A clearer input requirement might prevent repeated corrections. A better review screen might reduce checking time. A defined escalation route might clear a persistent queue.

Review disagreements are useful evidence, too. If people interpret the acceptance standard differently, clarify it before relying on the acceptance rate.

Results that hold across representative cases give you evidence to support expansion, alongside security and operational readiness.

Ask any vendor, consulting firm, or internal team these questions. Ask us the same ones.

1. What counts as one unit of work, and who decides it’s acceptable?
2. What share of attempts meets that standard on the first pass, and on which mix of cases?
3. How much human effort does each accepted unit require, including corrections?
4. What happens to exceptions, and who has the authority to resolve them?
5. What does the accepted work cost compared with the existing process, including implementation over a stated period?

My recommendation: start with one workflow, one owner, and a clear definition of accepted work. Establish the baseline, measure the improvement, and expand where the results justify it.

If you’re evaluating an enterprise AI investment, bring us the workflow and the outcome you want to improve. We’ll help define what success should look like and how to measure it.