AI Pilot Scorecard: How SMEs Can Measure Business Value

A useful AI pilot should show whether a defined workflow improves after review, corrections and operating costs are included. A polished demonstration can help a team understand an idea. A decision to keep using it needs evidence from representative work.

For a small or medium-sized business, the first test does not need to cover every department. It needs a clear task, suitable information and a way to compare the new process with how the work is done today.

Begin with a task that can be checked

Choose an activity whose output a competent person can assess: classifying a service enquiry, finding the correct product information or preparing an internal first draft. Define what counts as an acceptable result before running the pilot.

For example, a draft answer might need to address the question, use the current approved product description and clearly identify anything that requires confirmation. A fluent answer that fails one of those tests should not be counted as successful simply because it reads well.

Measure the existing process first

Record a baseline using a representative set of tasks. Include ordinary cases and difficult ones, such as incomplete requests or conflicting source documents. Record the total time required to produce a usable result, not only the initial drafting time.

Keep the test material separate from examples used to tune the workflow where practical. Otherwise, a system may appear effective mainly because the team has already adapted it to the same cases.

A practical scorecard for an SME pilot

The following is an editorial proposal from Swisspresence for structuring a pilot. The measures and acceptance criteria should be adapted to the business; they are not a universal benchmark.

• Useful completion: how many tasks produce an output that the reviewer accepts for its intended use?
• Total effort: how much staff time is spent preparing inputs, checking, correcting and handling exceptions?
• Quality: which errors occur, how serious are they and how often must a result be rewritten?
• Evidence: can factual statements be traced to an approved, current source?
• Cost: what subscriptions, usage charges, integration and ongoing support does the workflow require?
• Operational control: do unclear cases reach the right person, and can the team pause the process?

A simple management measure is cost per accepted task: the pilot’s relevant operating cost divided by the number of usable, accepted outputs. Report setup costs separately as well, so that a cheap demonstration is not confused with a sustainable service. Compare like-for-like work and record the assumptions used to value staff time.

Why source checking belongs in the workflow

NIST is researching evaluation probes that check the factual grounding of agent outputs against trusted documents and record structured evidence. Its project is ongoing research, not a guarantee that automated evaluation can certify an answer. Read the NIST project description.

For a small pilot, the operational lesson we draw is straightforward: keep the source available next to the draft and record meaningful corrections. If a product specification changes, the reviewer should be able to identify which source the answer used. Where evidence is missing or contradictory, the workflow should make that visible.

Illustrative example: drafting replies to customers

The following is an example, not a real customer result. A service business tests AI assistance on a sample of past enquiries with confidential details removed. In the baseline process, a staff member finds the relevant information and drafts the answer. In the assisted process, the AI prepares a draft and the staff member reviews it.

The team records the total handling time and the corrections required in both processes. It also notes whether each answer uses the current approved information and whether any commitment needs a manager’s decision. Nothing is sent automatically during the trial.

If drafting becomes faster but checking takes longer, the total result may show little benefit. If review becomes quicker because sources are easier to locate, that improvement may be valuable even when the model’s wording still needs editing. The decision follows the evidence, including cases in which the pilot should be stopped or redesigned.

Make an explicit decision at the end

Agree in advance who decides whether to continue. That decision should consider the seriousness of errors as well as averages. A fast process that occasionally produces an unacceptable result needs a different response from one that reliably handles a narrow task.

Useful outcomes include continuing within the same scope, revising the source material, narrowing the task, expanding only a tested step or stopping the pilot. Document the reason and set a review date for any workflow that continues.

Connect the pilot to business priorities

The approach complements AI strategy and AI risk management: decide where an improvement would matter, then test whether the process delivers it under realistic conditions.

Through SP AICO, Swisspresence helps leadership teams connect a practical use case with clear ownership, proportionate controls and evidence for the next decision. Discuss a pilot for your organization.