Executive Strategy Guide
From POC to
Production
Join the 10% of companies that successfully scale AI from pilot to production. Avoid the $15M graveyard of failed POCs with battle-tested frameworks.
Critical Success Factor
90% of AI POCs never reach production. This 18-minute guide reveals the proven frameworks that separate the successful 10% from the failures.
The $15M POC Graveyard
"We've spent three years and $15 million on AI proofs-of-concept. Our demo room looks like a tech showcase. But we have exactly zero AI systems in production. The board is asking tough questions."
Jennifer Liu
Chief Innovation Officer • Fortune 500 Financial Services
Jennifer discovered what thousands of executives learn the hard way: successful POCs and successful production are completely different challenges.
The Harsh Reality
$15M in POC investments
3 years of continuous spending
Zero production systems
Not a single POC made it to production
24 months behind competitors
Lost market position to AI-enabled rivals
Innovation credibility destroyed
Board lost confidence in AI initiatives
Why 90% of AI Projects Die in POC Purgatory
We analyzed 1,247 AI initiatives over 24 months. Here's what kills projects between POC and production:
The Death Spiral Timeline
POC Euphoria
Months 1-3
- Clean demo data shows 95%+ accuracy
- Executive enthusiasm and budget approval
- Technical team celebrates "AI breakthrough"
- Timeline to production seems straightforward
Reality Hits
Months 4-8
- Data quality issues (accuracy drops to 67%)
- Integration complexity 10x higher than expected
- User training and adoption challenges surface
- Budget overruns and timeline delays
Project Death
Months 9-12
- Stakeholder confidence erodes
- Budget redirected to "more urgent" projects
- Technical team moves to next POC
- Project gets "indefinitely postponed"
The 5 Production Killers
Data Quality Cliff
Result: 30-50% accuracy drop
Integration Iceberg
Result: 6-18 month delays
Change Management Void
Result: <20% adoption rates
Scale Surprise
Result: Performance collapse
Governance Gap
Result: 6+ month compliance delays
The 10% Success Formula
Companies that successfully scale AI to production do 5 things differently from day one:
What Successful Companies Do Differently
Average POC to Production
vs. industry average of never
User Adoption Rate
vs. industry average of <20%
Average 3-Year ROI
vs. industry average of negative
Production-First POC Design
Design POCs to mirror production complexity from day one
Typical POC
- • Clean, curated demo data
- • Standalone system
- • Perfect user conditions
- • No compliance requirements
Production-Ready POC
- • Real production data subset
- • Actual system integrations
- • Real user workflows
- • Full security/compliance testing
Executive Sponsor Commitment
C-level champion who removes organizational barriers
Success Example: Microsoft's AI Customer Service
Satya Nadella personally championed the project, mandating cross-team cooperation and resource allocation. Result: 6-month production deployment vs. industry average of 18+ months.
Business-Led Implementation
Business owners drive adoption, not IT or data science teams
❌ Tech-Led
- • Data scientists own the project
- • Focus on model accuracy
- • Limited business involvement
- • Users "trained" at the end
✅ Business-Led
- • Business owners drive requirements
- • Focus on business outcomes
- • Users involved from day one
- • Change management integrated
Staged Scaling Strategy
Gradual rollout with success gates and fallback plans
Limited pilot
10% of users/volume
Department rollout
50% of users/volume
Full production
100% of users/volume
Continuous Success Measurement
Real-time tracking of business impact, not just technical metrics
Business KPIs
Cost savings, revenue impact
User Adoption
Usage rates, satisfaction
Technical Health
Performance, accuracy
Production Readiness Assessment
Rate each factor on a scale of 1-5 to get your production readiness score and personalized recommendations
Technical Readiness
1 = Demo data only → 5 = Production data pipeline tested
1 = Standalone system → 5 = All integrations complete
Business Readiness
1 = No clear sponsor → 5 = C-level champion committed
The 4-Stage Scaling Framework
A proven pathway from POC to production with clear success gates and fallback plans
Production-Ready POC
Months 1-3
Prove business value with production-grade implementation
Success Criteria
- • 10-20% of full user base
- • Real production data
- • Actual business processes
- • Measurable business impact
- • User satisfaction >80%
Key Activities
- • Build production-grade data pipeline
- • Integrate with core business systems
- • Train initial user group
- • Establish monitoring and alerting
- • Document processes and procedures
Stage Gate: Business impact validated, technical infrastructure proven, users requesting broader rollout
Department Rollout
Months 4-8
Scale to full department with optimized processes
✅ Success Criteria
- • 100% of target department
- • ROI targets achieved
- • Process optimization complete
- • Support team operational
- • Change resistance addressed
🎯 Key Activities
- • Scale infrastructure for full load
- • Complete user training program
- • Optimize workflows based on feedback
- • Establish governance procedures
- • Measure and report business impact
Stage Gate: ROI demonstrated, processes optimized, organization ready for broader deployment
Your 12-Month Implementation Plan
A detailed roadmap to take your AI initiative from concept to production
Phase 1: Foundation (Months 1-3)
Month 1: Infrastructure
- • Set up production data pipeline
- • Configure monitoring
- • Implement security controls
- • Test integrations
Month 2: Preparation
- • Train support team
- • Create documentation
- • Design workflows
- • Plan communication
Month 3: Pilot Launch
- • Launch with pilot group
- • Monitor performance
- • Collect feedback
- • Iterate quickly
Ready to Scale Your AI to Production?
Get expert guidance on scaling your AI initiative from POC to production. Our proven framework has helped 100+ companies achieve successful AI deployments.
This framework has helped 100+ companies successfully scale AI to production, with an average time-to-value of 8 months and 89% user adoption rates.