Pilot Programs Done Right: How to Test AI Without Losing Control
9 min read
By MLNavigator Team
The $40k Pilot That Proved Nothing
In Q2 2024, a Kansas aerospace MRO spent $40,000 on an AI quality inspection pilot. The vendor promised "dramatic improvements in defect detection" and "faster cycle times." After 16 weeks, the vendor's final report concluded: "System is working well. Recommend proceeding to full deployment." But when the shop's CFO asked, "What's the ROI?" the vendor had no answer. No baseline defect rate. No quantified improvement. No time savings data. Just vague claims of "better quality." The shop declined full deployment. $40k and 16 weeks wasted because the pilot lacked structure, baselines, and success metrics. Good pilots provide quantified proof of value—clear numbers showing baseline vs. pilot performance, measurable ROI, and a confident go/no-go decision. This article is your blueprint for pilots that actually prove value.12-Week Pilot Program Timeline
Weeks 1-2
Setup & Baseline
Phase 1
- Hardware deployment (Mac Studio)
- Initial LoRA adapter installation
- Baseline error rate measurement
- Team training (2-hour session)
Deliverable:Baseline metrics report
Weeks 3-6
First Learning Cycle
Phase 2
- Upload first 50-100 drawings
- Engineer feedback on AI suggestions
- First adapter tuning (overnight)
- Weekly progress reviews
Deliverable:First learning cycle complete
Weeks 7-10
Optimization & Scaling
Phase 3
- Scale to 200-500 drawings
- Second adapter tuning
- Process refinement
- Error reduction measurement
Deliverable:Quantified error reduction
Weeks 11-12
Final Report & Decision
Phase 4
- Comprehensive metrics analysis
- ROI calculation
- Team satisfaction survey
- Scale-up proposal (Ops or Ent tier)
Deliverable:Pilot completion report
Expected Pilot Outcomes
20-40%
Error reduction after full deployment
200-500
Drawings processed during pilot
2-3 weeks
Time to measurable improvement
What Good Pilots Deliver
- ✓Clear baseline vs. pilot metrics
- ✓Quantified error reduction (%)
- ✓Time savings per drawing
- ✓ROI projection for full deployment
- ✓Team adoption rate
Red Flags in Pilot Programs
- ✗No baseline measurement
- ✗Vague "it's working" claims
- ✗Cloud-only, no air-gap option
- ✗Requires months of training
- ✗No clear path to scale
Pilot Success Story
A Kansas-based aerospace MRO completed a 12-week MLNavigator pilot in Q1 2025. Starting with 75 drawings and 3 engineers, they measured a 28% reduction in NCRs traced to drawing errors by week 10. The shop scaled to the Ops tier (26 adapters) the following quarter, targeting full production deployment by year-end.
Timeline based on MLNavigator Edge tier pilot framework. Actual results may vary by shop size and drawing volume.
Why Most Pilots Fail
Common reasons pilots don't deliver useful results:1. No Baseline Measurement
Without knowing your starting point, you can't measure improvement. ❌ Bad: "We think quality is better"✅ Good: "NCR rate dropped from 7.2/month to 2.8/month (61% reduction)"
2. Vague Success Criteria
"See if it works" isn't a success criterion. ❌ Bad: "Try it and see how it goes"✅ Good: "Achieve 20%+ error reduction and under 5 second scan time"
3. Too Long or Too Short
- 4 weeks: Not enough data to see patterns
- 6 months: Too long, pilot fatigue sets in, changes in shop operations confound results
4. No Clear Go/No-Go Decision Point
Pilots drift into "well, let's keep trying" mode without committing or canceling. ✅ Good: Week 12 decision meeting with 3 outcomes:- Proceed to full deployment (ROI proven)
- Extend pilot 4 weeks (promising but needs more data)
- Cancel (ROI not there)
5. Vendor Controls All the Data
If the vendor is the only one measuring results, you can't verify claims. ✅ Good: Shop tracks its own metrics independently. Vendor provides system performance data. Both datasets reviewed together.The 12-Week Pilot Framework
MLNavigator's Edge tier pilots follow a structured timeline:12-Week Pilot Program Timeline
Weeks 1-2
Setup & Baseline
Phase 1
- Hardware deployment (Mac Studio)
- Initial LoRA adapter installation
- Baseline error rate measurement
- Team training (2-hour session)
Deliverable:Baseline metrics report
Weeks 3-6
First Learning Cycle
Phase 2
- Upload first 50-100 drawings
- Engineer feedback on AI suggestions
- First adapter tuning (overnight)
- Weekly progress reviews
Deliverable:First learning cycle complete
Weeks 7-10
Optimization & Scaling
Phase 3
- Scale to 200-500 drawings
- Second adapter tuning
- Process refinement
- Error reduction measurement
Deliverable:Quantified error reduction
Weeks 11-12
Final Report & Decision
Phase 4
- Comprehensive metrics analysis
- ROI calculation
- Team satisfaction survey
- Scale-up proposal (Ops or Ent tier)
Deliverable:Pilot completion report
Expected Pilot Outcomes
20-40%
Error reduction after full deployment
200-500
Drawings processed during pilot
2-3 weeks
Time to measurable improvement
What Good Pilots Deliver
- ✓Clear baseline vs. pilot metrics
- ✓Quantified error reduction (%)
- ✓Time savings per drawing
- ✓ROI projection for full deployment
- ✓Team adoption rate
Red Flags in Pilot Programs
- ✗No baseline measurement
- ✗Vague "it's working" claims
- ✗Cloud-only, no air-gap option
- ✗Requires months of training
- ✗No clear path to scale
Pilot Success Story
A Kansas-based aerospace MRO completed a 12-week MLNavigator pilot in Q1 2025. Starting with 75 drawings and 3 engineers, they measured a 28% reduction in NCRs traced to drawing errors by week 10. The shop scaled to the Ops tier (26 adapters) the following quarter, targeting full production deployment by year-end.
Timeline based on MLNavigator Edge tier pilot framework. Actual results may vary by shop size and drawing volume.
Weeks 1-2: Setup & Baseline
Goals:- Deploy hardware (Mac Studio M2 Ultra)
- Install base AI model + initial LoRA adapters
- Establish baseline error rate
- Train team (2-hour session)
- Baseline report: Current NCR rate, error types, cycle times
- Pilot scope: Which engineers, which drawing types, how many drawings
- Success criteria: Quantified targets (e.g., 20% error reduction)
- Provide network access (local only, no internet)
- Assign 3–5 engineers to pilot
- Share past 3–6 months NCR data
Weeks 3-6: First Learning Cycle
Goals:- Upload first 50–100 drawings
- Collect engineer feedback on AI suggestions
- First overnight adapter tuning
- Weekly progress reviews
- Engineers upload drawings
- ADIS flags potential issues
- Engineers accept or reject flags (training the AI)
- Week 4: First adapter update based on week 3 corrections
- Scan time per drawing
- Flag acceptance rate (what % of AI suggestions are correct)
- Issues caught that would have been missed
- Engineer satisfaction (quick survey)
Weeks 7-10: Optimization & Scaling
Goals:- Scale to 200–500 drawings
- Second adapter tuning (incorporates 6 weeks of corrections)
- Measure error rate reduction
- Refine processes
- More engineers join pilot
- Adapter gets smarter (week 8 tuning)
- NCR rate vs. baseline tracked
- Edge cases identified and addressed
- NCR rate (pilot drawings vs. non-pilot control group)
- Time savings per drawing
- False positive rate (AI flags incorrect issues)
- False negative rate (AI misses real issues)
Weeks 11-12: Final Analysis & Decision
Goals:- Comprehensive metrics analysis
- ROI calculation
- Team satisfaction survey
- Go/no-go decision
- Pilot Completion Report including:
- Baseline vs. pilot NCR rates
- Error reduction percentage
- Time savings per drawing
- Cost avoidance (NCRs prevented × avg cost per NCR)
- ROI projection for full deployment
- Team feedback and adoption rate
- Recommendation (proceed, extend, or cancel)
- Proceed to Ops Tier (26 adapters, more engineers)
- Extend pilot 4 weeks (need more data)
- Cancel (ROI not there)
What Good Pilots Deliver
A well-run pilot provides:1. Quantified Baseline vs. Results
| Metric | Baseline | Pilot | Improvement | |---|---|---|---| | NCR rate | 7.2/month | 2.8/month | 61% reduction | | Avg scan time | 25 min/drawing | 3 min/drawing | 88% faster | | Repeat errors | 18/quarter | 6/quarter | 67% reduction | | Engineer satisfaction | N/A | 4.2/5 | 84% positive |2. ROI Calculation
- Avoided NCR costs: 4.4 NCRs/month × $12k/NCR × 12 months = $633k/year
- Time savings: 22 min/drawing × 200 drawings/month × $80/hr = $52k/year
- Total value: $685k/year
- System cost: $25k (Edge tier) + $5k/year maintenance
- ROI: 27× in Year 1
3. Clear Next Steps
If ROI is proven:- Scale to Ops tier (26 adapters, 10-50 engineers)
- Target full production deployment by [date]
- Budget for hardware expansion
- Identify limiting factors (too few drawings? Wrong types?)
- Extend pilot with adjusted scope
- Re-evaluate in 4 weeks
- Cancel pilot
- Document lessons learned
- No hard feelings—better to know now than after $200k full deployment
Red Flags in Pilot Programs
Avoid pilots with these warning signs:❌ No Baseline Measurement
Vendor says: "We'll show you the improvement as we go"Problem: Can't measure improvement without knowing starting point
❌ Vague "It's Working" Claims
Vendor says: "Users love it" or "Quality is better"Problem: No numbers, no way to verify, no basis for decision
❌ Cloud-Only, No Air-Gap Option
Vendor says: "Just upload your drawings to our portal"Problem: ITAR violation, CMMC non-compliance, audit finding waiting to happen
❌ Requires Months of Training
Vendor says: "After 6 months of use, you'll see results"Problem: Pilot fatigue, too long to maintain focus, changes in shop confound results
❌ No Clear Path to Scale
Vendor says: "Let's just try it and see"Problem: If it works, how do you expand? If it fails, what did you learn? No plan either way.
❌ Vendor-Only Metrics
Vendor says: "We'll measure everything and report back"Problem: Fox guarding the henhouse. You need independent verification.
What to Demand From Vendors
Before starting a pilot, insist on:1. Written Pilot Agreement
- Duration (12 weeks recommended)
- Scope (which engineers, how many drawings, which types)
- Success criteria (quantified targets)
- Baseline measurement process
- Decision framework (what happens at week 12)
2. Access to Raw Data
- Your shop tracks its own NCRs, cycle times, error rates
- Vendor provides system logs, scan times, flag accuracy
- Both datasets reviewed together
3. Training and Support
- Initial training (2-4 hours)
- Weekly check-ins
- On-call support for issues
- Documentation for processes
4. Exit Plan
- If pilot fails, how do you cleanly exit?
- If pilot succeeds, what's the cost to scale?
- No surprise fees, no vendor lock-in
5. Proof of Compliance
- Air-gapped option (no cloud dependency)
- CMMC-aligned logging and access control
- ITAR-safe (data stays on-premises)
- Audit-ready documentation
Pilot Success Story: Kansas MRO
A mid-sized Kansas aerospace MRO completed a 12-week MLNavigator Edge tier pilot in Q1 2025:Baseline (Weeks 1-2)
- 75 drawings processed
- Three engineers assigned
- NCR rate: 6.8/month (drawing-related)
- Manual review time: 22 min/drawing
Pilot Results (Weeks 3-12)
- 280 drawings processed
- Five engineers (expanded in week 6)
- NCR rate: 4.9/month (28% reduction)
- ADIS scan time: 3 sec/drawing + 12 min engineer review = 12.05 min total
- Time savings: 10 min/drawing × 280 drawings = 47 hours saved
ROI
- Avoided NCRs: 1.9/month × $15k/NCR × 12 = $342k/year
- Time savings: 47 hrs/12 weeks × 4.33 = 203 hrs/year × $80/hr = $16k/year
- Total value: $358k/year
- System cost: $18k Edge tier + $4k/year maintenance
- Payback: 18 days
Decision
Shop proceeded to Ops tier (26 adapters) in Q2 2025, targeting full production deployment by Q4.How to Structure Your Pilot
Step 1: Define Baseline (Week 1)
Measure current state:- NCR rate (last 3–6 months)
- Error types (drawing ambiguity, missing specs, etc.)
- Cycle times (how long to review drawings)
- Engineer satisfaction (optional but useful)
Step 2: Set Success Criteria (Week 1)
Quantified targets:- Primary: Reduce NCR rate by 20%+
- Secondary: Scan time under 5 seconds, engineer review time under 15 minutes
- Tertiary: 80%+ engineer satisfaction
Step 3: Execute Pilot (Weeks 2-10)
Follow the 12-week framework:- Upload drawings
- Collect feedback
- Tune adapters
- Track metrics weekly
Step 4: Analyze Results (Weeks 11-12)
Compare pilot to baseline:- NCR rate reduction
- Time savings
- Cost avoidance
- ROI calculation
Step 5: Decide (Week 12)
Three outcomes:- Proceed: ROI proven, scale up
- Extend: Promising, need more data
- Cancel: ROI not there, exit cleanly
Related Resources on Implementation
For more on successful AI deployment:- LoRA, QLoRA, and the Future of Secure AI in Aerospace - How adapter tuning works during pilots.
- From Tribal Knowledge to Institutional Memory - What pilots capture beyond immediate ROI.
- The Real Cost of Poor Quality in Aerospace MRO - Understanding baseline CoPQ to measure pilot impact.
Conclusion
Good pilots aren't about "trying something and seeing if it works." They're about structured experiments with quantified results that enable confident decisions. The 12-week framework delivers:- Clear baseline measurement
- Quantified improvements
- ROI calculations
- Go/no-go decision criteria
Run a Pilot That Actually Proves Value
Get a pilot proposal with clear baseline, success criteria, and decision framework. No vague promises, just numbers.
Request Structured Pilot Proposal