Pilot Plan & Acceptance Criteria Template

Purpose and quick orientation

This template helps teams design pilots that produce clear, defensible evidence for two possible outcomes: scale the solution into production, or stop and learn. Use this as a practical checklist and a lightweight specification you can share with vendors, stakeholders, and implementers. Keep the pilot timeboxed, measurable, and focused on learning one major question at a time.

Pilot plan template (fill-in sections)

1. Objective & Hypothesis

What specific business outcome or capability are you testing? State a single, testable hypothesis that links the solution to a measurable outcome.

• Objective: (e.g., Reduce manual invoice processing time)
• Hypothesis: (e.g., If the automated OCR + routing reduces data entry errors, processing time per invoice will fall by at least 40%.)
• Primary decision gate: (e.g., Approve pilot to scale if primary metric meets acceptance criteria and no critical compliance issues.)

2. Scope & Boundaries

Define what is and isn’t included so results are interpretable.

• Business units, services, user groups, geographies
• Data sources and integration points
• Excluded functionality or edge cases

3. Success metrics & SLA (Acceptance Criteria Template)

List one primary metric and up to three supporting metrics. For each metric provide baseline, target, measurement method, and the acceptance rule.

Acceptance criteria row format: Metric name | Baseline | Target (threshold) | Measurement window | Data source | Owner | Pass = ?

• Primary metric: (e.g., Median processing time per invoice — Baseline: 10 days — Target: ≤6 days over a 4-week window)
• Secondary metrics: (e.g., error rate, user satisfaction, cost per transaction, throughput, FCR for support bots)
• SLA targets: Availability, response-time SLOs for the pilot environment, expected support response times from vendor or team.

4. Dataset & Sampling Plan

Describe the data you'll use, how you'll sample, and any privacy or consent considerations.

• Data sources and schemas
• Sample selection method (random, stratified, targeted)
• Expected sample sizes and why (see suggested ranges below)
• Data quality checks and required labeling
• Privacy, compliance, and data retention rules

5. Test plan & experiment design

Describe activities, who performs them, and how results will be recorded.

• Test cases and acceptance scenarios (happy path and representative edge cases)
• Control vs treatment design (if applicable)
• Instrumentation and logging requirements
• Who validates data integrity and how often

6. User acceptance criteria & qualitative feedback

Quantitative metrics are necessary but not sufficient. Capture end-user feedback and operational observations.

• Who will sign off (roles, not necessarily names)
• Qualitative acceptance checklist (ease of use, task completion, training needs)
• User testing plan: number of sessions, script, and feedback capture method

7. Monitoring & Observability

Define what you will monitor in real time and how alerts will be handled.

• Dashboards and key charts (primary metric trend, errors, latency, throughput, data drift)
• Alert thresholds and on-call responsibilities
• Logging and audit trails for troubleshooting and compliance

8. Risk, compliance & ethical checks

List known risks and mitigation steps.

• Data privacy and consent
• Bias/fairness checks, adversarial and safety concerns
• Regulatory constraints and required approvals

9. Rollback & stop criteria

Define explicit triggers for pausing or stopping the pilot and the steps to revert.

• Technical triggers (e.g., error rate above X% for Y minutes)
• Business triggers (e.g., customer complaints above threshold, costs exceed budget)
• Rollback steps and contacts (restore data, disable integrations, notify stakeholders)

10. Decision gate, handoff & scaling checklist

What must be true to move from pilot to scaled production? Use this checklist to guide acceptance and contracts.

• Primary & secondary metrics meet acceptance criteria over the agreed measurement window
• Performance and load testing passed for expected production volumes
• Operational runbooks, monitoring, and alerts are in place
• Vendor responsibilities, SLAs, and support model documented in contract
• Data ops: pipelines, backups, retention, and security controls validated
• Training and knowledge transfer completed for operations and support teams
• Cost model and ongoing license/hosting expenses reviewed and approved

11. Timeline, resources & responsibilities

High-level schedule, milestones, and who owns each deliverable.

• Kickoff date, mid-pilot review, end-of-pilot review
• Named owners for data, engineering, product, compliance, and vendor liaison
• Estimated effort and budget

12. Vendor evaluation checklist (if using an external product)

Points to include in vendor selection and contracting.

• Deliverables and measurable acceptance criteria in the statement of work
• Data ownership and IP clauses
• Support levels and response times
• Exit and handover terms (data export format, runbooks, code access if appropriate)
• Security and compliance attestations

Suggested experiment durations & sample-size guidance (practical ranges)

Use these as pragmatic starting points; adapt based on risk, traffic, and business impact. For outcomes that depend on human behavior, include both quantitative and qualitative checks.

• Process automation (back-office workflows): 2–8 weeks; sample: 200–1,000 transactions depending on variability and cycle time.
• Customer-facing chatbots / support automation: 2–6 weeks; sample: 500–2,000 conversations to measure deflection and FCR reliably.
• Recommendation/personalization: 6–12 weeks; sample: thousands to tens of thousands of events depending on traffic—measure CTR, conversion, and revenue lift.
• Anomaly detection / predictive maintenance: 4–12 weeks; sample: a representative period capturing normal and anomalous events. Labeling of important events is critical.
• Small usability or workflow changes: 1–4 weeks; 5–15 qualitative user sessions plus operational tracking.

Note: These ranges are heuristics. Consult analytics or statistics experts for precise power/sample-size calculations when outcomes must be estimated with high confidence.

Example acceptance-criteria (concrete)

Primary metric: Mean handling time (MHT)

• Baseline: 12 minutes per case (30 days pre-pilot)
• Target: ≤8 minutes per case sustained for 4 consecutive weeks
• Measurement: daily aggregated MHT from production logs, validated by manual spot checks
• Owner: Product Manager
• Pass rule: If target achieved and error rate <= 1% for same window, approve scaling

How to use and share the template

1. Fill in the template jointly with the vendor or implementation team before work begins.
2. Keep the scope narrow enough to answer the hypothesis within the pilot window.
3. Run a short mid-pilot review to check instrumentation and adjust the sampling plan if needed.
4. At pilot close, hold a decision review that documents results, lessons learned, and the explicit decision to scale, iterate, or stop.

Quick checklist (one-page handoff)

• Objective & hypothesis documented
• Primary metric and acceptance thresholds set
• Sampling plan and data sources agreed
• Monitoring dashboards and alerts in place
• Rollback and stop criteria defined
• Handoff requirements and runbooks drafted
• Contract and vendor SLAs reflect pilot acceptance and exit terms

Related artifacts to attach or produce

• Data dictionary and sample records
• Test scripts and labeled examples
• Dashboard snapshots and raw measurement queries
• Signed acceptance form and decision log

Final note

Good pilots are concise experiments designed to answer a clear question. The goal is to create defensible evidence that reduces uncertainty and supports a confident decision. Use the template to eliminate ambiguity, protect operations, and create a repeatable vendor-to-production handoff.