30-Day AI Testing Pilot Scorecard

Compare an AI testing candidate with your current approach using equivalent scenarios, seeded failures, effort records, disqualifiers, and decision gates.

Quick answer

Run an AI testing pilot against a versioned application fixture and a comparable baseline. Use the same workflows, assertions, data, browsers, and seeded defects for both approaches; record human effort and incomplete runs as well as successes. Agree on coverage, reliability, ownership, integration, and portability gates before the first candidate run. At day 30, make an adopt, revise, extend, or stop decision from the evidence packet—not from a demo or a generated-test count.

What this guide produces

This guide combines the pilot plan and its scorecard so the team does not maintain two competing evaluation documents. Copy the tables into your issue tracker, design document, or evaluation repository.

The result is a decision packet containing:

• a frozen baseline and candidate configuration;
• a representative workflow and assertion inventory;
• a known-defect test set;
• weekly execution and operating-effort ledgers;
• coverage and reliability disqualifiers;
• a portability and ownership review; and
• a signed decision record.

The templates below are team-defined evaluation controls. They are not automatic ContextQA API fields or guaranteed product outcomes.

Start with a fair comparison

“AI-generated tests versus our existing suite” is not yet a fair comparison. The candidate and baseline must attempt the same reader-visible job.

Control	Baseline	Candidate
Application	Same commit or immutable build	Same commit or immutable build
Environment	Same service dependencies and network path	Same service dependencies and network path
Data	Same reset procedure and accounts	Same reset procedure and accounts
Workflows	Same critical user journeys	Same critical user journeys
Assertions	Same business outcomes	Same business outcomes
Browsers or devices	Same required matrix	Same required matrix
Failures	Same seeded defects	Same seeded defects
Human work	Creation, review, triage, and maintenance included	Creation, review, triage, and maintenance included

If one approach cannot represent an assertion or environment constraint, record that as a result. Do not silently remove the constraint from both cohorts.

Freeze the evaluation fixture

Record the following before the first run:

Application repository:
Commit or immutable build ID:
Fixture version:
Environment name and revision:
Seed-data revision:
Required browsers/devices:
Baseline framework and version:
Candidate configuration revision:
Evaluation owner:
Decision date:

If the baseline uses Playwright, keep its test plan, fixtures, and assertions under version control. Playwright's official test-agent workflow similarly separates a human-readable plan, a seed test, and generated tests; that structure is a useful comparison boundary even if you do not use its agents. See Playwright Test Agents.

Select representative scenarios

Choose a small set that exposes operational differences. Ten carefully selected workflows are usually more informative than a large set of happy paths.

Include at least:

1. A revenue or activation path.
2. An authenticated flow with role or permission boundaries.
3. A workflow with dynamic UI state.
4. A negative path with a business assertion.
5. A data-dependent or API-backed flow.
6. A maintenance case based on a real, nonfunctional UI change.
7. A failure with diagnostic evidence requirements.
8. A workflow that must run in CI.

For every scenario, complete this contract:

Field	Example format
Scenario ID	PILOT-04
User risk	A viewer must not approve an invoice
Preconditions	Viewer account, one pending invoice
Steps	Login as viewer, attempt approval through the in-scope UI/API paths, inspect invoice state
Required assertion	Viewer approval is denied and invoice state remains unchanged; check the UI and, where in scope, the corresponding API
Evidence	Role, relevant assertion result, observed state, and final run record
Baseline test	Repository path and test name
Candidate test	ContextQA test-case reference
Data reset	Fixture command or named data profile
Reviewer	Named engineering or QA reviewer

Seed failures before measuring reliability

A tool that reports green on an always-green application has not proved much. Introduce controlled defects in a disposable fixture or branch. Never seed a defect in a shared production environment.

Use failures that distinguish false passes, diagnostic quality, and environment handling:

Seed	Expected observation
Change a required confirmation message	The business assertion fails
Remove an authorization check in the fixture	The negative permission test fails
Return a controlled server error	The run fails and preserves useful diagnostics
Delay a dependency beyond the agreed timeout	The run becomes a timeout or incomplete result, not a pass
Rename or reposition a control without changing behavior	The maintenance case records whether intervention was required
Point the runner at the wrong build	The gate detects or exposes the build-identity mismatch

Record the seed commit, the expected failing scenario, the expected reason, and the cleanup commit. If a seeded defect passes, stop expansion until the missing assertion or false-pass behavior is understood.

Interpret control outcomes before scoring

Keep the execution status separate from the business conclusion. For a permission rule, use the following control matrix:

Check	Expected application behavior	Expected test verdict
Allowed editor action	The permitted change succeeds and persists	Pass
Forbidden viewer action	The change is denied and stored data remains unchanged	Pass, because the test asserts denial
Deliberately weakened permission rule in the disposable fixture	The forbidden change becomes possible	Fail for the relevant permission assertion
Authentication or environment failure before the action	The business rule was not exercised	Unknown for that rule

A failed run is not sufficient evidence that a seeded defect was detected. Confirm that the relevant assertion ran and failed for the intended reason. Restore the fixture and rerun the original controls afterward.

A repeated pass can still check the wrong outcome. A variable result can come from the application, test, environment, data or dependency. Preserve first-attempt outcomes and report retries separately. Label the cause unresolved until evidence distinguishes a real intermittent product failure from suspected test flakiness.

Use Pass when the agreed condition is observed, Fail when evidence contradicts it, and Unknown when the condition was not exercised or evidence cannot support a conclusion. An unknown mandatory gate holds the adoption decision; it is not a conditional pass and cannot be offset by a weighted score.

Define disqualifiers and remediation windows

Write disqualifiers before candidate testing starts.

Disqualifier	Immediate response	Remediation window
A critical workflow has no enforceable assertion	Pause that workflow	Owner and date required before resuming
A seeded security or permission defect passes	Stop the pilot expansion	Root cause and rerun required
The executed build cannot be identified	Mark the result Unknown	Fix identity propagation before gating
A timed-out or canceled run is reported as passed	Keep all gates advisory	Correct state mapping and rerun the acceptance matrix
Required evidence is absent or inaccessible	Do not count the run as verified	Restore evidence or document an approved substitute
Export or ownership claims cannot be demonstrated	Record as a contract dependency	Complete the portability test before purchase

Do not average a disqualifier away. A pilot can be fast and inexpensive while still being unsafe for its intended release decision.

Use a four-week operating plan

Week 1: baseline and configuration

• Freeze the fixture, scenario contracts, and seeded defects.
• Run the current approach three times under normal conditions.
• Configure the candidate without changing the acceptance criteria.
• Record initial setup and review time by role.

Week 2: creation and first execution

• Create or migrate the candidate tests.
• Review every required assertion before running.
• Execute the normal and seeded-failure cohorts.
• Classify missing coverage, false passes, false failures, and incomplete runs.

Week 3: maintenance and integration

• Apply the planned nonfunctional UI change.
• Measure the intervention needed in each approach.
• Exercise the CI path in advisory mode.
• Test failure, timeout, cancellation, retry, and wrong-build behavior with the CI acceptance runbook.

Week 4: repeated runs and decision

• Repeat the comparable suite under normal team conditions.
• Complete the ownership and portability checks.
• Review the evidence with engineering, QA, and security stakeholders.
• Decide: adopt, revise, extend for one named unknown, or stop.

Record every run

Use one row per attempt, including attempts that never reached a terminal test result.

Field	Value
Run date and cohort
Application commit/build
Environment and data revision
Baseline or candidate
Scenario IDs
Runner/browser/device
Start and finish time
Terminal state	Pass / Fail / Incomplete / Canceled / Timed out / Unknown
Expected seeded failures
Observed failures
False pass or false failure
Evidence reference
Human triage minutes
Missing or inaccessible evidence
Reviewer and disposition

For ContextQA runs, use the saved plan and test results rather than reconstructing results from CI console text.

Measure operating effort

Separate elapsed machine time from human time. Both matter, but they answer different questions.

Activity	Role	Baseline minutes	Candidate minutes	Notes/evidence
Initial environment setup
Test creation or migration
Assertion review
Routine execution supervision
Failure triage
Test repair or maintenance
CI integration upkeep
Evidence packaging
Security and access review

Avoid turning a one-month observation into an annual savings claim without stating the model. If you estimate annual impact, show the run frequency, labor-rate range, adoption ramp, and uncertainty separately.

Calculate cost per usable verdict

“Cost per test” rewards cheap output even when the test is not reviewed, cannot fail, or never reaches a usable result. Use a denominator that reflects the decision your team consumes.

Define a usable verdict as a result that:

• ran the reviewed scenario and assertions;
• is tied to the intended application build, environment, and test revision;
• reached an accepted terminal state;
• includes the required evidence;
• was reviewed under the pilot protocol; and
• is not invalidated by missing coverage or an unresolved setup problem.

cost per usable verdict =
  (platform + model + infrastructure + human review + triage + maintenance cost)
  / accepted usable verdicts

Record the calculation inputs:

Field	Value
Actual usage period and timezone
Task/scenario cohort and sample size
Total attempts
Passing usable verdicts
Failing usable verdicts
Incomplete, canceled, timed-out, or invalid attempts
Platform cost allocated to cohort
Model input/output/cache/request cost, if separately observable
Browser/device/infrastructure cost
Human creation, review, triage, and maintenance time
Labor-rate range and source
Quality threshold and reviewer
Missing costs/unknowns

Include failed and retried attempts in the numerator. Do not count the same avoided labor in two savings categories. Keep machine elapsed time and human time separate.

Compare model or configuration variants fairly

If the workflow allows a meaningful model or agent configuration comparison, use the same:

• tasks and application build;
• tools and permission boundary;
• context pack and prompt contract;
• expected artifacts and quality rubric;
• retry/stop policy;
• reviewer protocol; and
• minimum repeated-trial count.

Variant	Exact model/client/tool versions	Date	Attempts	Usable verdicts	Fail/incomplete	Median human review	Direct cost	Known limitation

Do not publish a universal “best model” conclusion from one task set. Report the tested scope, versions, date, sample size, failure samples, and uncertainty. Do not imply that ContextQA supports model switching unless that control is actually exposed and verified in the evaluated workflow.

Score with gates, then weights

First evaluate the non-negotiable gates.

Gate	Pass condition	Result	Evidence
Coverage integrity	Critical assertions are present and reviewed
Failure fidelity	Seeded defects fail for the intended reason
Execution identity	Commit/build, environment, and test version are traceable
State integrity	Incomplete states cannot become a passing gate
Security	Secrets and permissions meet the pilot policy
Portability	Export, dependencies, and ownership are demonstrated

Only after the gates pass, use the weighted scorecard. Choose weights before scoring.

Criterion	Weight	Baseline 1–5	Candidate 1–5	Evidence
Coverage and assertion quality	25%
Reliability and failure fidelity	20%
Diagnostic evidence	15%
Human operating effort	15%
CI and workflow fit	10%
Security and administration	10%
Portability and ownership	5%

Define 1, 3, and 5 for every criterion so reviewers do not score from intuition. For example, a 5 for diagnostic evidence might require a failed step, expected-versus-observed result, screenshot, execution log, and stable run reference; a 3 might require manual correlation; a 1 might be a generic failure message without reproducible evidence.

Clarify ownership before rollout

Responsibility	ContextQA	Your team	Another system	Named owner
Scenario and assertion approval
Test data reset
Environment availability
Browser/device coverage
CI trigger and required-check policy
Failure triage
Test maintenance approval
Evidence retention
Export and exit test

This table exposes the work that demos often hide. “Managed” does not mean “ownerless.”

Write the final decision record

Decision: Adopt / Revise / Extend / Stop
Decision owner:
Date:
Approved scope:
Evidence packet:

Gates passed:
Gates failed:
Weighted result, if used:

Observed advantages:
Observed limitations:
Unresolved unknowns:
Required remediation and owner:

Rollout boundary:
Rollback trigger:
Next review date:
Approvers:

Choose Extend only for a named unknown with a bounded test and deadline. It should not become an automatic extra month.

Run the weekly quality review after the pilot

Once the workflow is in use, hold a 30-minute weekly review that turns evidence into one owned improvement. Do not build a dashboard until the team agrees on definitions and data sources.

Agenda

1. Prior action: Was last week’s improvement completed, and did the acceptance check pass?
2. Important changes: Which releases or pull requests carried the highest user risk?
3. Evidence state: Which decisions were Pass, Fail, or Unknown, and why?
4. Coverage gaps: Which critical requirements lack reviewed assertions?
5. Unreliable tests: Which tests were retried, skipped, flaky, or excluded, and what coverage did that remove?
6. Operating burden: Where did creation, review, queueing, triage, or maintenance time accumulate?
7. One decision: Select one bottleneck, owner, due date, and acceptance check for the next week.

Copyable scorecard

Metric	Numerator	Denominator	Current week	Prior week	Evidence source	Owner
PRs with a usable verdict	PRs meeting the verdict definition	In-scope PRs
Critical requirement assertion coverage	Critical criteria with reviewed assertions	In-scope critical criteria
First-attempt complete runs	Complete, non-retried first attempts	Started attempts
Unknown decision rate	Unknown verdicts	In-scope decisions
Retry/skip affected coverage	Required cases retried or skipped	Required cases
Median PR-to-verdict time	—	Usable-verdict PRs
Median human intervention	—	Usable verdicts
Open quality actions past due	Past-due actions	Open actions

If a denominator or source is unavailable, write Not collected. A synthetic example can teach the format, but only reviewed production data should drive a leadership claim.

Action record

Week ending:
Highest unresolved risk:
Evidence:
Decision:
Owner:
Due date:
Acceptance check:
Coverage affected while open:
Escalation trigger:

The review should end with one or two decisions, not a list of unowned observations.

Common questions

Should the candidate create exactly the same implementation as the baseline?

No. Compare the same user risk, workflow, assertion, environment, and evidence requirement. The implementation may differ; silently weakening the outcome may not.

Can generated-test count be a success metric?

It can describe throughput, but not decision quality. Count reviewed assertions, seeded-defect detection, complete runs, diagnostic value, and human effort alongside generated tests.

What if a product capability is unavailable in our workspace?

Record it as unavailable for the tested configuration. Do not score a roadmap statement as observed behavior. If it is essential, make enablement and a rerun a documented prerequisite.

Related documentation

• Evaluation and governance
• Running tests
• Test results
• Interpret a bug reproduction against the reported behavior
• Migrate an existing suite
• Prepare a CI/CD quality gate

Ready to test the scorecard on one real release path? Book a pilot-design session → — We will help define the baseline, seeded failures, and stop conditions before the first candidate run.