Want to implement AI but not sure if it’ll work? Run a pilot.

Pilots prove value before full commitment. But most pilots are run poorly and prove nothing.

Here’s how to run AI pilots that actually inform decisions.

What Pilots Are For

A pilot is a controlled test. It answers:

• Does this work for our situation?
• What results can we realistically expect?
• What implementation challenges exist?
• Is this worth full investment?

Pilots de-risk decisions. They convert uncertainty into evidence.

Why Most Pilots Fail

The Demo Disguised as Pilot

“Try our tool for 30 days!” isn’t a pilot. It’s an extended demo.

Real pilots have:

• Defined scope
• Clear success criteria
• Actual users
• Real data
• Rigorous measurement

The Best-Case Pilot

Running a pilot in ideal conditions proves nothing about real conditions.

If you pick your best users, simplest data, and most favorable circumstances, you’ll get favorable results that don’t replicate at scale.

The Abandoned Pilot

Starting a pilot but not finishing. No measurement. No conclusions. Just… fading away.

If you can’t commit to completing a pilot, don’t start it.

The Goalless Pilot

“Let’s try it and see what happens” isn’t a pilot. It’s experimentation without learning.

Without defined success criteria, you can’t determine success.

Designing Effective Pilots

Step 1: Define the Question

What are you trying to learn?

Good questions:

• “Can AI invoice processing achieve 85% accuracy on our invoices?”
• “Will AI email drafting save at least 5 hours per week for our sales team?”
• “Can AI chatbot handle 40% of customer inquiries without human intervention?”

These are specific, measurable, and answerable.

Step 2: Set Success Criteria

Before starting, define what success looks like.

Be specific:

• Accuracy threshold
• Time savings target
• Cost reduction goal
• User satisfaction minimum

Write these down. Agree on them with stakeholders. Don’t move the goalposts later.

Step 3: Scope Appropriately

Pilots need enough scope to be meaningful, but not so much they’re unmanageable.

Too small: Results don’t represent reality.

Too large: Complexity overwhelms. Takes too long. Risks too much.

Right-sized: Representative subset that can be completed in 4-8 weeks.

Step 4: Select Realistic Conditions

Resist the temptation to stack the deck:

Users: Include average performers, not just your best.

Data: Use typical data, including messy cases.

Circumstances: Real workload, real timeline, real conditions.

If pilot conditions don’t match deployment conditions, pilot results won’t match deployment results.

Step 5: Plan Measurement

Define what you’ll measure and how:

Baseline: What’s the current state? (Measure before pilot starts.)

Pilot metrics: What will you track during pilot?

Comparison: How will you compare pilot to baseline?

Collection method: How will data be gathered?

If you can’t measure it, you can’t evaluate it.

Step 6: Document Everything

Record:

• What you set up
• How users responded
• What problems occurred
• What workarounds were needed
• What worked well
• What didn’t

This learning informs full implementation.

Running the Pilot

Week 1: Setup and Training

• Configure the AI tool
• Train pilot participants
• Establish measurement processes
• Set expectations

Don’t skimp on training. Pilot failure due to inadequate training teaches you nothing about the tool.

Weeks 2-5: Active Pilot

• Users work with AI tool
• Collect ongoing metrics
• Gather regular feedback
• Address issues as they arise
• Document everything

Active management keeps the pilot on track.

Week 6: Evaluation

• Compile all data
• Compare to success criteria
• Gather final feedback
• Assess implementation challenges
• Draft conclusions

Week 7+: Decision and Communication

• Make go/no-go decision
• Communicate findings
• Plan next steps
• Archive learnings

Common Pilot Patterns

Pattern 1: AI Document Processing

Typical scope: 200-500 documents over 4 weeks.

Success criteria: Accuracy rate, processing time, exception rate.

Measurement: Compare AI extraction to manual verification.

Key learnings: Accuracy by document type, exception patterns, training needs.

Pattern 2: AI Customer Service

Typical scope: Subset of inquiry types for 4-6 weeks.

Success criteria: Resolution rate, customer satisfaction, escalation rate.

Measurement: Track outcomes, survey customers, compare to baseline.

Key learnings: Query types that work, failure patterns, handoff friction.

Pattern 3: AI Content Creation

Typical scope: Specific content types for 4 weeks.

Success criteria: Time savings, quality assessment, adoption rate.

Measurement: Track production time, quality review results, user feedback.

Key learnings: Effective use cases, editing burden, prompt patterns.

Pattern 4: AI Sales Support

Typical scope: Sales team subset for 6-8 weeks.

Success criteria: Time savings, lead quality impact, adoption.

Measurement: Activity tracking, conversion comparison, user feedback.

Key learnings: Valuable features, workflow fit, resistance points.

Evaluating Pilot Results

Did You Achieve Success Criteria?

The simplest question. Compare results to pre-defined criteria.

If yes: strong signal to proceed. If no: understand why before deciding.

Were Conditions Representative?

Did the pilot reflect reality? If not, results may not replicate.

What Challenges Emerged?

Every pilot reveals implementation challenges. Document them. Plan for them.

What Would Full Implementation Require?

Based on pilot experience:

• Training needs
• Configuration work
• Process changes
• Integration requirements
• Ongoing maintenance

Is Full Investment Justified?

Given results, challenges, and requirements: is this worth it?

Sometimes the answer is no. That’s valuable learning.

Making the Decision

Go

Pilot met criteria. Challenges are manageable. Investment is justified.

Next steps: Plan full implementation based on pilot learnings.

No-Go

Pilot didn’t meet criteria or revealed deal-breaking issues.

Next steps: Document learnings. Consider alternatives. Revisit if conditions change.

Modify and Re-Pilot

Pilot revealed issues but potential remains.

Next steps: Address issues. Run another pilot with adjustments.

Delay

Not the right time due to resource constraints, competing priorities, or external factors.

Next steps: Schedule future reconsideration. Preserve pilot learnings.

Getting External Support

Pilots benefit from experienced guidance:

AI consultants Brisbane and similar specialists can:

• Help design effective pilots
• Provide implementation support
• Offer objective evaluation
• Accelerate learning

Their experience across many pilots prevents common mistakes.

Common Pilot Mistakes to Avoid

Starting without success criteria. Define success before starting.

Cherry-picking conditions. Represent reality, not best case.

Skipping measurement. If you don’t measure, you don’t know.

Abandoning early. See pilots through to conclusion.

Ignoring negative results. Honest assessment beats wishful thinking.

Over-scoping. Keep pilots manageable.

Under-training. Inadequate training ruins valid pilots.

Building Pilot Capability

Over time, develop internal pilot capability:

• Standard pilot frameworks
• Measurement templates
• Decision criteria
• Documentation practices

This enables faster, more consistent pilot execution.

Team400 and similar advisors can help build this capability systematically.

The Pilot Portfolio

As AI matures in your organization, maintain a portfolio:

Completed pilots: What you’ve learned.

Active pilots: What you’re testing now.

Planned pilots: What’s coming next.

Archived ideas: Pilots considered but not pursued.

This creates organizational learning about AI.

The Bottom Line

Pilots convert AI uncertainty into evidence.

Run them properly:

• Define success criteria upfront
• Use realistic conditions
• Measure rigorously
• Complete fully
• Decide honestly

That’s how you make AI decisions based on evidence, not hope.

Good pilots prevent expensive mistakes. They also reveal opportunities you might have missed.

Invest in pilots. They pay for themselves in better decisions.