A platform demo can show that a workflow exists. A pilot should establish whether the workflow works for your accounts, constraints, and team. Those are different levels of evidence, and the scorecard should preserve the distinction.
This is an original evaluation framework from GaaS, an advertising-software publisher. It can be applied to GaaS or another vendor without assuming any product already passes. Adapt the tasks and acceptance conditions to the work you actually intend to delegate.
Define the purchasing decision
Write what the pilot must resolve: reduce reporting work, improve campaign QA, accelerate accurate creative production, support controlled execution, or improve a measured business outcome. Avoid a broad objective such as “see whether AI works.”
Identify the accounts, platforms, task families, users, and time horizon included. Record prerequisites such as working measurement, approved assets, and access. If those are absent, distinguish implementation readiness from product performance.
State the alternative being compared: the current team workflow, another tool, or a narrower automation. A pilot cannot establish value without a clear understanding of what would happen otherwise.
Choose representative tasks before the demo
Include routine work, a meaningful exception, and a case where no change is justified. Examples might be a standard account report, a recommendation constrained by stock, and a recent campaign with insufficient mature data.
Select tasks from actual operating needs rather than vendor-selected success examples alone. Keep the set small enough to review thoroughly while covering the consequential workflow variations.
An AI shadow-mode pilot can provide a first stage when the team wants to evaluate reasoning before granting execution authority. Label simulated and read-only results accurately.
Use evidence levels instead of a vague star rating
| Evidence level | Meaning |
|---|---|
| Documented | A current first-party source describes the capability |
| Demonstrated | The task was shown under recorded conditions |
| Verified | The intended result was checked in the relevant system |
| Measured | The defined operating or business outcome was observed |
| Untested or unresolved | Evidence is missing or insufficient |
A capability can be documented without being demonstrated, and a verified edit can lack a mature business outcome. Keep these distinctions in every row.
If using numeric scores, define the rubric and weights before evaluating vendors. Do not let a large number of minor convenience features outweigh a failed requirement essential to the intended use.
Establish acceptance gates for execution
Define the non-negotiable conditions for the authorized workflow: correct account identity, appropriate permissions, exact approval scope where required, reliable action records, and a way to stop or contain further work.
The NIST AI Risk Management Framework provides voluntary guidance for contextual evaluation and risk management. It is not a certification of a vendor or this scorecard. The practical step is to make your own material operating requirements explicit.
Test those requirements through the actual interface or integration you intend to use. A control demonstrated in one product surface should not be assumed to govern another connected assistant or scheduled process identically.
Record each task as a small case
Keep the prompt or request, input sources, relevant account state, expected acceptance conditions, output, corrections, and verification receipt. Include timing and the product version or configuration where available.
Separate factual errors, unsupported conclusions, scope errors, execution failures, and presentation issues. A typo and a wrong-account action should not become equivalent marks in a generic failure count.
Record skipped and incomplete tasks. Removing them from the denominator can make a difficult workflow look successful simply because only the easy cases finished.
Measure the complete operator workload
Track preparation, tool use, review, correction, coordination, and follow-up. Compare that total with a representative baseline using the current process.
Distinguish active work time from elapsed waiting. A task can become faster for the operator while still depending on a long provider review, or complete quickly while demanding intensive manual correction.
Use the total-cost worksheet to include subscription and integration costs. Reallocated staff time may be valuable capacity, but it is not automatically a reduction in payroll expense.
Design the business-outcome test separately
If the decision depends on acquisition efficiency or revenue, specify the metric, comparison, observation window, and material confounders. Use an appropriate experimental design where feasible, or label observational limitations clearly.
Do not attribute all movement during the pilot to the tool. Offers, seasonality, inventory, tracking, and sales handling can change outcomes at the same time. The attribution-versus-incrementality guide explains the causal boundary.
Some pilots can establish workflow value before they can resolve financial impact. State that result honestly and decide whether the remaining business question warrants a longer or differently designed evaluation.
Evaluate exceptions and recovery
Use authorized, non-destructive tests for missing data, rejected proposals, changed permissions, and partial completion where the environment supports them. Inspect how the workflow reports uncertainty and what the operator must do next.
Avoid creating real customer harm merely to test recovery. A draft or controlled test environment can answer many questions, while any limits of that evidence remain visible in the scorecard.
Ask how data and active work are exported or handed off if the pilot ends. Exit readiness is part of the operating decision, not an afterthought reserved for a failed purchase.
Make the recommendation conditional on evidence
Summarize which tasks passed their acceptance conditions, which failed, which remain untested, and what the total effort showed. State the supported scope for adoption and the conditions required before expanding it.
The conclusion might support a reporting workflow while withholding authority for budget changes, or support controlled launches while leaving business uplift unresolved. That is a useful result when it follows the evidence.
A strong scorecard turns a persuasive demo into a reviewable purchasing decision. It rewards demonstrated work, preserves uncertainty, and gives the team a clear basis for continuing, narrowing, or ending the pilot.