Learning Objectives

After completing this lecture, you will be able to:

• Understand the key concepts of ai pilot program design in a government context
• Participate in structured workshop activities with real-world scenarios
• Connect ai pilot program design to your agency's AI initiatives
• Identify next steps for applying these concepts in your role

Key Topics Covered

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Structured evaluation before full commitment

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Pilot success criteria

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Go/no-go decision framework

Why This Matters for Government

Overview

Government agencies face unique challenges when it comes to AI adoption. This lecture addresses these challenges head-on by providing senior managers, procurement officers, program directors with the knowledge and frameworks needed to navigate AI in the public sector responsibly and effectively.

As part of the L3 (AI Strategist) curriculum, this lecture builds on the foundational principle that every AI system in government ultimately serves citizens. Whether you are working with AI tools daily or setting strategy for your agency, understanding ai pilot program design is essential for responsible, effective government AI adoption.

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TRANSCRIPT: AI Pilot Program Design

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What you will learn: Designing effective pilot programs. Defining success criteria. Go/no-go decision frameworks. Risk management in pilots.

Welcome to "AI Pilot Program Design," where procurement theory meets operational practice. A pilot is your chance to validate vendor claims on your data, your infrastructure, with your success metrics--before full deployment.

A well-designed pilot prevents expensive mistakes. A poorly-designed pilot wastes time and delays decision-making. This lecture teaches you how to structure pilots that answer critical questions: Does this system actually work in our environment? Are vendor claims accurate? What problems emerge that we need to fix before full deployment?

PURPOSE AND CONTEXT

Pilots serve three critical purposes. First, they validate technical claims (does accuracy match vendor promises on YOUR data?). Second, they surface implementation challenges (does the system integrate with your infrastructure? How long does integration actually take?). Third, they enable go/no-go decisions (do we deploy this system or look for alternatives?).

Without pilots, agencies make deployment decisions based on vendor demos and marketing claims. With pilots, agencies make decisions based on evidence from their own operational environment.

WHY THIS MATTERS FOR GOVERNMENT

Government AI acquisitions are high-stakes. Deploy the wrong system and you damage public trust. Failure to deploy a good system means missed opportunity to improve service. Pilots reduce both risks.

Additionally, government procurement is transparent. You may need to defend pilot design and results to IG, GAO, or Congress. A pilot designed rigorously is defensible; a pilot designed hastily is not.

CORE CONCEPTS

• PILOT OBJECTIVES AND SCOPE

What are you trying to learn in the pilot?

TYPICAL PILOT OBJECTIVES

• Technical validation: Does accuracy match claims? Is latency acceptable?
• Integration: Can the system integrate with your data infrastructure?
• Fairness: Does system perform equally across demographic groups?
• Operational: How does the system work in practice? What training do staff need?
• Economic: What's the real cost (not just license fee)?
• Organizational: Will staff accept the system? What change management is needed?

SCOPE CONSIDERATIONS

• Duration: 4-12 weeks typical (long enough to test real usage patterns)
• Data volume: Representative sample of your actual workload
• Geographic coverage: Single location or multiple? (affects complexity and learning)
• Staffing: Which teams participate? How much of their time?
• Budget: Vendor support during pilot? Third-party evaluation? Staff time?
• SUCCESS CRITERIA AND METRICS

Define what "success" means before the pilot starts. This prevents argument later about whether objectives were met.

HARD SUCCESS CRITERIA (Go/No-Go factors)

These are objective, measurable. If not met, system fails pilot.

• Accuracy: Must achieve X% (specify on YOUR data, not vendor test set)
• Fairness: Accuracy variance across demographic groups must not exceed Y%
• Latency: 95% of decisions made within Z seconds
• Integration: System successfully processes 100% of data without errors
• Compliance: System demonstrates compliance with security/privacy requirements
• Cost: Actual cost during pilot doesn't exceed budget estimate by >10%

SOFT SUCCESS CRITERIA (Qualification factors)

These are qualitative. Influence decision but aren't automatic go/no-go.

• User acceptance: Staff feel confident using system
• Training effort: Training team to use system takes <10 hours per person
• Error handling: System handles edge cases gracefully
• Support quality: Vendor support responsive and helpful
• Monitoring capability: Can you see what the system is doing?

METRIC MEASUREMENT

For each metric, define how you'll measure it:

• Who measures (internal team, vendor, independent evaluator)?
• Measurement frequency (daily, weekly, upon completion)?
• Sample size (must be statistically significant)?
• Baseline comparison (compared to what? manual process? previous system?)
• PILOT DESIGN FRAMEWORK

PHASE 1: PREPARATION (Weeks 1-2)

• Define success criteria and metrics
• Prepare test environment (representative of production)
• Prepare test data (representative of actual workload)
• Brief staff on pilot; recruit participants
• Establish governance (who makes daily decisions? How escalate issues?)

PHASE 2: CONTROLLED TESTING (Weeks 3-4)

• System operates on historical data; no live decisions made
• Allows testing system behavior without stakes
• Measure accuracy, fairness, reliability
• Discover integration issues in safe environment
• Refine system configuration based on early findings

PHASE 3: PARALLEL OPERATION (Weeks 5-8)

• System operates alongside existing process (manual or old system)
• AI system makes recommendations; humans make actual decisions
• Captures real usage patterns, edge cases
• Compare AI recommendations to human decisions
• Measure accuracy, fairness, override rate

PHASE 4: EVALUATION AND GO/NO-GO (Weeks 9-12)

• Complete analysis of all metrics
• Structured decision: Go (deploy), No-Go (don't deploy), or Conditional-Go (deploy with modifications)
• Document findings and rationale
• Plan next steps
• GO/NO-GO DECISION FRAMEWORK

GO DECISION

All hard success criteria met, no major issues, soft criteria generally positive.

• Accuracy meets or exceeds target on your data
• Fairness metrics acceptable (no demographic group significantly disadvantaged)
• Integration successful; system processes all data without errors
• Cost within budget
• Staff confidence high; training effective

Next step: Plan full deployment; establish monitoring metrics

NO-GO DECISION

Hard success criteria not met; fixing would require substantial work.

• Accuracy significantly below target even after optimization
• Fairness issues can't be resolved; system inherently discriminates
• Integration impossible without major rework of infrastructure
• Cost substantially exceeds budget
• Staff can't be trained to use system effectively

Next step: Evaluate alternatives; consider different vendor or different approach

CONDITIONAL-GO DECISION

Hard criteria mostly met but some issues require fix before full deployment.

• Accuracy acceptable but below target; vendor commits to improvement with specific timeline
• Integration works for 95% of data; 5% requires workaround; acceptable for Phase 1
• Fairness acceptable for primary use case but concerning for secondary use case; limit to primary use case initially
• Cost slightly above budget but savings in other areas offset

Next step: Define specific conditions that must be met before Phase 2 deployment; establish monitoring to verify conditions maintained

• RISK MANAGEMENT IN PILOTS

SCHEDULING RISK

Pilot takes longer than expected; delays full deployment decision.

Mitigation: Tight project management; weekly status reviews; clear critical path; identify delays early

SCOPE CREEP

Pilot expands beyond initial scope; adds time and cost without adding learning.

Mitigation: Written scope document; change control process; any expansion requires approval

DATA QUALITY ISSUES

Pilot data isn't representative of actual operations; learning doesn't transfer.

Mitigation: Compare pilot data distribution to production data; validate representativeness upfront

VENDOR SUPPORT ISSUES

Vendor doesn't support pilot adequately; problems can't be resolved.

Mitigation: Establish SLA for vendor support during pilot; include penalty clauses if support insufficient

STAFF BURNOUT

Pilot requires staff to do double work (old process + new system); staff exhausted.

Mitigation: Reduce other responsibilities for pilot participants; limit pilot duration; recognize extra effort

ANTI-PATTERNS TO AVOID

ANTI-PATTERN 1

Risk: Pilot is theater to justify predetermined decision; doesn't actually inform decision

Why: Procurement timeline pressure creates incentive to decide early

What Goes Wrong: Pilot finds major issues but agency deploys anyway; system underperforms

How to Avoid: Make go/no-go decision contingent on pilot results. Don't decide before pilot data exists.

ANTI-PATTERN 2

Risk: Pilot too short to surface real issues; edge cases and long-term problems not discovered

Why: Impatience; desire to deploy quickly

What Goes Wrong: Full deployment reveals problems pilot didn't catch; expensive remediation

How to Avoid: Minimum 8-12 weeks. Long enough to see full cycle of work patterns.

ANTI-PATTERN 3

Risk: Pilot data is "clean" and curated; production data is messier; system fails

Why: Vendors provide their best data for pilots

What Goes Wrong: System works perfectly in pilot; fails in production on real-world data

How to Avoid: Pilot must use your actual data (or realistic simulation). Resist vendor suggestion to use clean data.

ANTI-PATTERN 4

Risk: Unclear what "success" means; argument later about whether pilot was successful

Why: Hard to define precise criteria; easier to be vague

What Goes Wrong: Vendor claims pilot was successful; government says it wasn't; can't resolve disagreement

How to Avoid: Define specific, measurable success criteria before pilot starts. Document in writing.

ANTI-PATTERN 5

Risk: Pilot ends; unclear what happens next; indefinite delay while deciding

Why: Hard to make go/no-go decision; temptation to defer

What Goes Wrong: Pilot results gathered but organization can't decide whether to deploy; valuable learning opportunity lost

How to Avoid: Define decision framework upfront. Make decision based on pre-defined criteria, not post-hoc debate.

PRACTICE PROMPTS

EXERCISE 1

Design a complete pilot for an AI system you're considering:

• What are the 3-4 most critical objectives to validate?
• What success criteria would prove the system works?
• How long would the pilot take?
• What risks would you monitor?
• What's your go/no-go decision framework?

EXERCISE 2

For an AI benefits determination system, define:

• Accuracy metric (how measured? on what data?)
• Fairness metric (what demographic groups? acceptable variance?)
• Integration metric (% of applications processed successfully?)
• Cost metric (actual cost vs. budget?)
• User acceptance metric (how measured? threshold for success?)

EXERCISE 3

Your AI hiring system pilot is complete. Results:

• Accuracy 88% vs. 92% target: 4% below target
• Fairness: Women 85%, men 90%: 5% disparity (was 6% in historical hiring)
• Integration: Works for 98% of applications
• Cost: 12% above estimate
• User acceptance: Staff confident using system

Is this Go, No-Go, or Conditional-Go? What's your rationale? What would conditions be if Conditional-Go?

EXERCISE 4

Identify potential risks in your pilot program:

• List 5-6 risks that could occur
• For each: probability (low/medium/high), impact (low/medium/high)
• For each: mitigation strategy
• How would you monitor for these risks during pilot?

EXERCISE 5

Plan stakeholder involvement in pilot:

• Who needs to participate (which teams)?
• What's their time commitment?
• How will you communicate pilot progress?
• How will you gather feedback?
• How will you address concerns that emerge?

KEY TAKEAWAYS

• PILOTS VALIDATE VENDOR CLAIMS USING YOUR DATA

Vendors' claims may be accurate for their test conditions. Your pilot proves they're accurate for your conditions.

• SUCCESS CRITERIA MUST BE DEFINED BEFORE PILOT STARTS

Vague criteria allow post-hoc argument. Specific, measurable criteria enable clear decisions.

• GO/NO-GO FRAMEWORK MUST EXIST BEFORE PILOT RESULTS ARRIVE

Predetermined decision framework prevents post-hoc rationalization.

• REPRESENTATIVE DATA IS ESSENTIAL

Pilot data must reflect actual workload, including edge cases and messy real-world characteristics.

• PARALLEL OPERATION IS MOST INFORMATIVE

Comparing AI recommendations to human decisions surfaces errors and edge cases better than testing in isolation.

• MINIMUM DURATION IS 8-12 WEEKS

Shorter pilots miss seasonal variations, long-tail problems, and real organizational friction.

• RISKS MUST BE ACTIVELY MANAGED

Pilots have schedule pressure, scope creep, data quality issues. Active management prevents these from derailing the pilot.

GLOSSARY

GO/NO-GO DECISION: Point decision whether to proceed to full deployment based on pilot results.

PARALLEL OPERATION: Running new system alongside existing process; comparing outputs without replacing actual decisions.

SOFT SUCCESS CRITERIA: Qualitative criteria that influence decision but aren't automatic go/no-go (e.g., "staff accept the system").

HARD SUCCESS CRITERIA: Objective, measurable criteria; if not met, system fails pilot (e.g., "accuracy 92%").

REPRESENTATIVENESS: Extent to which pilot data/environment matches actual production conditions.

Well-designed pilots answer the critical question: Should we deploy this system? They do this by creating a safe environment to test real-world performance, surface problems early, and make go/no-go decisions based on evidence.

Your approach: Define objectives and metrics upfront, use representative data, run for sufficient duration, manage risks actively, and use predetermined decision framework to decide whether to deploy.

For an AI acquisition you're planning:

• What would be the critical success criteria?
• How would you design the pilot to test them?
• What would make you say "no-go"?
• What risks would you actively monitor?
• How would you ensure pilot data is representative?

Pilots prevent expensive deployment mistakes. Invest time in designing them well. The cost of a thorough pilot is trivial compared to the cost of deploying a system that doesn't work.

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Level 3: AI Practitioner | Federal Acquisition of AI | Lecture 3.3.9

A GOVT.CLUB initiative.

<- 3.3.8 Algorithmic Impact Assessments
3.3.10 Managing AI Vendor Performance ->

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