An AI business case starts with the review work

A useful AI business case starts with one workflow and a question: how much better will the work be after someone checks the output?

Generating a draft quickly is only part of the process. Someone may still need to verify facts, correct mistakes, move information between systems, and approve the result. Those steps belong in the estimate from the beginning.

We built a free AI opportunity calculator to make that conversation more concrete. It runs in your browser, needs no email address, and lets you download a project brief. The numbers are scenarios to test, not promises about what AI will deliver.

Start with a task you can measure

“Improve productivity” is too broad for a first pilot. “Prepare the weekly operations report from three existing sources” gives you something to evaluate.

Choose a process with a named owner, a clear output, and work you can sample. Write down what a satisfactory result looks like. Include the checks that currently happen, the mistakes that matter, and the person who has authority to approve the final output.

Then measure the current process on representative examples. Use that baseline to estimate the change. Keep the pilot narrow enough that an unsuccessful experiment is affordable to stop.

Count adoption and additional checking

Here is an illustrative scenario, not a client result:

• Five people each spend five hours a week on email and administrative work.
• AI could reduce that task time by 50% before additional checking.
• Three quarters of the team use the new process consistently.
• Each active user spends an additional half-hour a week reviewing and correcting AI output.
• The working assumption for a fully loaded hour is €35.
• Total running costs are €150 per month, with €2,500 for initial implementation.

The model uses 52 weeks divided by 12 months. Those assumptions produce 32.5 hours of recovered capacity per month. At €35 per hour, that capacity is valued at €1,137.50. After the monthly running cost, the modeled net capacity value is €987.50 per month.

Subtracting the initial implementation cost from twelve months of that net value gives €9,350 in first-year modeled value. Modeled payback is about 2.5 months.

Those figures assume stable adoption from the first month. If onboarding takes longer, review is more demanding, or integration costs more, the result changes. The calculator makes those assumptions editable so the discussion can focus on what you actually know.

Recovered time is not automatically cash saved

A team can gain useful capacity while payroll remains exactly the same. That capacity might mean faster turnaround, less overtime, more time with customers, or simply a more manageable workload.

A monetary estimate of that time is a planning tool. It becomes cash savings only when an expense actually falls. It becomes revenue only when additional work produces a sale. Avoid counting all three as separate benefits of the same recovered hour.

This distinction matters when a proposal moves from a team discussion to a budget decision. Label the benefit precisely, identify who owns the outcome, and decide how it will be measured.

Include the work around the model

For a pilot budget, consider more than the AI subscription. Depending on the workflow, the scope may include:

• Connecting systems and preparing the data the workflow needs.
• Defining permissions and human approval points.
• Testing output quality on representative tasks, including difficult cases.
• Training the people who will use the new process.
• Supporting and maintaining the integration after launch.

The calculator provides separate setup and monthly operating-cost fields. It does not discover these costs for you. Use the fields to make them visible and discuss the uncertain items before deciding to build.

Make “stop” an acceptable pilot result

A good pilot produces evidence, including evidence that the idea is not worthwhile yet. Agree a small set of success measures in advance: time per completed task, review effort, acceptable output quality, and the conditions under which a human must intervene.

Compare the measured result with the original assumptions. If checking takes longer than the time the model saves, the workflow may add work. If operating costs exceed the value of recovered capacity, a positive payback cannot be inferred from this model.

You might choose a narrower scope, a different workflow, or no implementation. That is a useful decision to reach before making a larger commitment.

Run your own scenario and download the brief. If you want help assessing feasibility, an AI Opportunity Sprint can turn the assumptions into a pilot plan with an agreed scope and fee.