An AI workflow pilot scorecard should evaluate the work from input to an authorized decision, including human review and downstream correction. A faster first draft is useful information, but it is not proof that the whole workflow improved. The pilot asks whether the organization can use the output responsibly at the proposed pace.
This is an illustrative leadership evaluation aid, not a validated AI benchmark, compliance checklist or authorization to introduce a tool. Use only approved systems and permitted data. Work involving consequential or regulated decisions requires the appropriate qualified owners and controls, not a general-purpose scorecard.
Choose one bounded workflow
Select a low-risk, authorized task where outputs can be examined before anyone acts on them. Name the input, drafting step, review, decision and receiving team. Avoid testing the tool on broad confidential records simply because they are available.
A hypothetical example is preparing an internal, non-sensitive meeting brief from approved source notes. The pilot does not automatically send the brief or make decisions. An authorized reviewer checks it before use. The AI review guide explains why that reviewer and standard must be explicit.
Define the comparison before collecting results
Record how comparable work is performed without the proposed change. Agree task complexity, source material, acceptance criteria and the evidence to capture. A difficult manual example and an easy AI-assisted example do not establish a fair improvement.
Include drafting effort, review effort, corrections and the time until usable acceptance. Note tool setup and preparation where relevant. Keep the definitions consistent during the pilot so a favorable conclusion is not created by excluding work that moved elsewhere.
Scorecard field one: quality against an agreed standard
Define what makes the output usable for this task. For a meeting brief, criteria might include faithful representation of the source, clearly identified uncertainties and no invented decision or attribution. Count corrections by type rather than treating every edit as the same problem.
Separate harmless wording changes from errors that would affect a decision. A polished document containing an unsupported commitment is not equivalent to a draft with awkward phrasing. Record whether the reviewer can find and check the source of important claims.
Scorecard field two: total effort and review capacity
Track the time needed to prepare the input, draft, check and correct the result. Identify where the workflow waits. If drafting capacity expands while the same reviewer becomes overloaded, the team may produce a larger queue rather than faster accepted work.
The AI and team effectiveness article connects tools with the way teams coordinate. For the pilot, record how many outputs can receive an appropriate review, not merely how many the tool can generate.
Scorecard field three: downstream work and uncertainty
Examine what happens after acceptance. Did the receiving team need clarification? Did an unsupported assumption require rework? Was the decision recorded accurately? A useful local saving may disappear when another team must reconstruct the evidence.
The NIST AI Risk Management Framework is voluntary guidance for incorporating trustworthiness considerations into AI use and evaluation. This scorecard is not an implementation of that framework. Its narrower purpose is to make a selected workflow's quality, oversight and operating consequences visible.
Agree stop and escalation conditions
Before the test, state what halts use and who is notified. Conditions might include unapproved data exposure, an inability to verify important claims or review capacity that no longer supports the agreed standard. Use established organizational incident and approval procedures when they apply.
Do not quietly relax the criteria because the first drafts look promising. If the workflow changes materially, revisit the authorized scope. The person preparing AI output should not independently decide that the consequence of using it has become acceptable.
Make a decision from the whole record
Summarize the baseline, comparable examples, output quality, total effort, waiting, downstream correction and unresolved risks. Choose to continue the bounded test, change the workflow, expand under explicit conditions or stop. Report a small sample as a small sample; it does not establish a universal productivity return.
If the findings reveal unclear ownership or overloaded review across several teams, discuss the leadership operating system with Shannon. AI can change the pace of drafting. The pilot should show whether the organization can turn that pace into sound, accepted work.
About this resource: Prepared with AI-assisted drafting from published guidance. Examples are illustrative, not reported client results. Cover illustration: AI-generated.