From Proof of Concept to Production: The 5 Gates That Actually Matter

Your data science team just finished an impressive AI proof of concept. The model achieves 92% accuracy. The demo wows executives. Everyone agrees this will transform the business. Six months later, it's still not in production. Twelve months later, the project is quietly cancelled. The model never serves a single real customer or business decision.

Sound familiar? According to VentureBeat research, 84% of AI proof of concept projects fail to reach production deployment. And Gartner reports that the average AI pilot takes 18+ months to reach production—if it ever gets there at all.

The problem isn't technical failure. Most POCs work technically. The problem is the massive gap between "it works in the lab" and "it creates business value in production". Organizations treat POC-to-production as a smooth progression when it's actually a series of critical gates that most projects fail to pass.

Each gate represents a different type of validation: technical feasibility (Gate 1), business value (Gate 2), production readiness (Gate 3), organizational readiness (Gate 4), and sustainable operations (Gate 5). Skip any gate, and your project will stall or fail at the next one.

Let me show you the 5 gates that separate successful AI deployments from expensive experiments, and exactly how to pass each one.

I've seen dozens of AI projects stall between POC and production. The failure patterns are remarkably consistent:

Pattern 1: "Demo-Ware" POC
Built to impress in demos, not to operate in reality. Uses cleaned sample data, ignores edge cases, skips integration complexity, and makes unrealistic assumptions about data availability and quality. Works perfectly in PowerPoint, fails spectacularly when it meets real-world chaos.

Pattern 2: "Perfect Model, No Home"
Data scientists build a technically excellent model but nobody figured out where it fits in actual business processes. Which system calls the model? Who acts on the predictions? How do results integrate into existing workflows? These questions get asked after the model is built, and the answers kill the project.

Pattern 3: "Stakeholder Evaporation"
Executive sponsor champions the POC, then moves to another role, shifts priorities, or loses patience. The team loses political cover and budget. Project dies not because it failed technically, but because it lost organizational momentum.

Pattern 4: "Technology Debt Trap"
POC built with experimental tools, custom code, and quick hacks to prove the concept fast. Moving to production requires rebuilding everything with production-grade technology, proper architecture, and enterprise standards. Team realizes this will take 6-12 months. Project stalls.

Pattern 5: "Compliance Surprise"
POC proceeds without legal, compliance, or security review ("it's just an experiment"). When production deployment is planned, compliance team discovers privacy violations, security gaps, or regulatory issues. Project blocked indefinitely while these get resolved.

All five patterns share a root cause: treating POC-to-production as one phase instead of a multi-gate journey with different success criteria at each stage.

The 5-Gate Framework: POC to Production to Value

Successful AI deployment requires passing through five distinct gates, each with specific success criteria, required stakeholders, and go/no-go decisions:

Gate 1: Technical Feasibility (POC Success)

Question: Can we build an AI model that solves this problem technically?

Gate 2: Business Value Validation (Pilot Success)

Question: Does this AI solution deliver measurable business value in real conditions?

Gate 3: Production Readiness (Deployment Approval)

Question: Can this AI solution operate reliably and safely in production?

Gate 4: Organizational Readiness (Adoption Success)

Question: Will the organization actually use this AI solution?

Gate 5: Sustainable Operations (Long-Term Value)

Question: Can we maintain and improve this AI solution over time?

Let's dive deep into each gate.

Gate 1: Technical Feasibility (POC Success)

Purpose: Prove that AI can technically solve the problem with acceptable performance

Duration: 4-8 weeks typically

Success Criteria:

• Model achieves minimum viable accuracy/performance threshold
• Solution is technically possible (not blocked by fundamental technical constraints)
• Sufficient data exists or can be obtained
• Compute/infrastructure requirements are reasonable
• Technical risks identified and manageable

Key Activities:

• Exploratory data analysis (what data exists, what's its quality)
• Feature engineering (what variables predict the outcome)
• Model development and training (which algorithms work)
• Performance testing (accuracy, speed, resource usage)
• Technical risk assessment

Required Stakeholders:

• Data scientists/ML engineers (build the model)
• Data engineers (access and prepare data)
• Technical architect (assess feasibility of production deployment)
• Business SME (validate that model is solving the right problem)

Common Mistakes:

• Using cleaned sample data instead of messy real data
• Optimizing for model accuracy without considering inference speed
• Ignoring data availability constraints for production
• Building with tools/frameworks that won't work in production
• No documented assumptions or limitations

Gate 1 Exit Criteria (Go/No-Go Decision):

✅ GO if:

• Model performance meets or exceeds minimum threshold (e.g., 85%+ accuracy)
• Technical approach is sound and replicable
• Data requirements can be met in production
• No fundamental technical blockers identified
• Team can articulate how this would work in production

❌ NO-GO if:

• Model performance insufficient despite multiple approaches tried
• Required data doesn't exist and can't be created
• Compute/infrastructure costs exceed business value
• Fundamental technical constraints can't be overcome

Deliverables:

• Documented POC results with model performance metrics
• Technical architecture concept (high-level, not detailed)
• Data requirements and availability assessment
• Risk register identifying technical challenges
• Recommendation: proceed to Gate 2 or stop

Real Example:

Use Case: Patient no-show prediction for hospital

Gate 1 Activities (6 weeks):

• Analyzed 3 years of appointment data (120K appointments)
• Tested 5 ML algorithms (logistic regression to gradient boosting)
• Best model: 78% accuracy predicting no-shows 48 hours in advance
• Required data: appointment history, demographics, insurance, past no-shows
• Technical approach: batch prediction daily, results to scheduling system via API

Gate 1 Decision: ✅ GO

• 78% accuracy vs. 40% baseline (significant improvement)
• All required data available in EHR
• Batch prediction approach technically straightforward
• Identified risk: model might perform differently across clinics (to be tested in Gate 2)

Key Success Factor: We used real appointment data including cancellations, rescheduling, and data quality issues—not a cleaned sample. This gave us confidence the model would work with production data.

Gate 2: Business Value Validation (Pilot Success)

Purpose: Prove that the AI solution delivers measurable business value in real operational conditions

Duration: 8-16 weeks typically

Success Criteria:

• Solution deployed in limited production environment (pilot)
• Measurable business value demonstrated (revenue, cost, efficiency, quality)
• Real users interact with the solution (not just demos)
• Business process integration works in practice
• ROI justifies full deployment investment

Key Activities:

• Deploy model to pilot environment (one location, one department, subset of users)
• Integrate with real business processes and workflows
• Train pilot users and support their usage
• Measure business outcomes (before/after comparison)
• Gather feedback from users and stakeholders
• Refine model based on real-world performance
• Calculate ROI and business case for full deployment

Required Stakeholders:

• Business process owners (operate the pilot)
• End users (use the AI solution)
• IT operations (support pilot infrastructure)
• Finance (measure business impact)
• Executive sponsor (review results and approve full deployment)

Common Mistakes:

• Pilot in the "best case" location instead of representative environment
• Measuring technical metrics (model accuracy) without business metrics (cost saved, revenue gained)
• Running pilot too short to see real business impact
• Not gathering user feedback or ignoring negative feedback
• Declaring success based on anecdotes instead of data

Gate 2 Exit Criteria (Go/No-Go Decision):

✅ GO if:

• Pilot demonstrates measurable, quantified business value
• Business value meets or exceeds minimum ROI threshold
• Users adopt the solution and report positive experience
• Business process integration works without major disruptions
• Organizational readiness concerns are manageable
• Business case justifies full production investment

⚠️ ITERATE if:

• Technical solution works but business value is unclear
• Users struggle with integration into workflows
• Model performs well overall but fails for important edge cases
• Business case is marginal—need to improve value or reduce cost

❌ NO-GO if:

• No measurable business value despite technical success
• Users reject the solution or work around it
• Business process disruption exceeds value delivered
• ROI is negative even with optimistic assumptions
• Pilot reveals fundamental flaw in business logic or use case

Deliverables:

• Pilot results report with business metrics
• User feedback and adoption data
• Updated business case with actual pilot results
• Lessons learned and required changes for full deployment
• Production deployment plan and budget
• Recommendation: proceed to Gate 3, iterate, or stop

Real Example:

Use Case: Patient no-show prediction (continuing from Gate 1)

Gate 2 Activities (12 weeks):

• Deployed model in 3 primary care clinics (pilot)
• Integrated predictions into scheduling staff workflow
• Schedulers received daily list of high-risk no-show appointments
• Staff called high-risk patients 24 hours before appointments for confirmation
• Measured no-show rates before (baseline 18%) and during pilot (14%)

Business Value Measured:

• No-show reduction: 18% → 14% (22% relative improvement)
• 4 percentage points × 8,000 appointments/month = 320 fewer no-shows
• Value: 320 appointments × $200 average revenue = $64K/month = $768K/year
• Pilot cost: $45K (model deployment, staff training, workflow changes)
• ROI: 17:1 in year one

User Feedback:

• Schedulers: "Game-changer for filling last-minute cancellations"
• Challenge: Some clinics had different workflows, required customization
• Request: Add same-day predictions for walk-in scheduling

Gate 2 Decision: ✅ GO to full deployment

• Strong business value demonstrated ($768K annual impact)
• Positive user adoption and feedback
• Workflow integration successful with minor adjustments
• Business case clearly justifies investment
• Risk: Need to customize for specialty clinics (orthopedics, oncology)

Key Success Factor: We measured actual business outcomes (no-shows and revenue), not just model performance. And we ran the pilot long enough (12 weeks) to see sustained impact, not just novelty effect.

Gate 3: Production Readiness (Deployment Approval)

Purpose: Ensure the AI solution can operate reliably, safely, and at scale in full production environment

Duration: 6-12 weeks typically (parallel with Gate 2 often)

Success Criteria:

• Production-grade technical architecture designed and approved
• Security, privacy, and compliance requirements met
• Monitoring and alerting infrastructure ready
• Disaster recovery and rollback plans tested
• Performance at scale validated (load testing)
• Operations team trained and support processes established

Key Activities:

• Redesign POC code for production (proper architecture, error handling, logging)
• Implement security controls (authentication, authorization, encryption)
• Complete compliance reviews (privacy, regulatory, legal)
• Build monitoring dashboards and alerting
• Load testing and performance validation
• Create runbooks for operations team
• Establish incident response procedures
• Document deployment process and rollback procedures

Required Stakeholders:

• ML/software engineers (production-grade implementation)
• Security team (security review and approval)
• Compliance/legal team (regulatory review)
• IT operations (production support)
• Architecture review board (design approval)

Common Mistakes:

• Treating production deployment as "just deploy the POC code"
• Skipping security and compliance review ("we'll do that later")
• No monitoring or alerting ("we'll add that if there's a problem")
• Deploying without rollback plan
• No documentation for operations team
• Underestimating time to make POC code production-ready (often 2-3 months)

Gate 3 Exit Criteria (Go/No-Go Decision):

✅ GO if:

• All security requirements met and approved
• All compliance requirements met and documented
• Monitoring, alerting, and logging implemented
• Load testing confirms performance at scale
• Operations team trained and ready
• Incident response procedures documented
• Rollback plan tested and ready
• Production deployment plan approved

⚠️ CONDITIONAL GO if:

• Minor technical debt acceptable in V1 (documented and scheduled for V2)
• Some edge cases will be handled post-deployment
• Monitoring can be enhanced after initial deployment
• Operations team learning on the job (with proper support)

❌ NO-GO if:

• Security vulnerabilities not resolved
• Compliance approval not obtained
• Critical performance issues at scale
• No monitoring or way to detect failures
• Operations team not ready to support
• Rollback plan not tested

Deliverables:

• Production-ready code and infrastructure
• Security review approval
• Compliance documentation
• Operations runbook and procedures
• Monitoring dashboards and alert configurations
• Deployment plan with rollback procedures
• Production deployment approval from all stakeholders

Real Example:

Use Case: Patient no-show prediction (continuing from Gates 1-2)

Gate 3 Activities (10 weeks, parallel with end of Gate 2):

Production Architecture:

• Rebuilt POC notebook code as production Python service
• Deployed as containerized service in hospital's private cloud
• Batch prediction daily at 6 AM (all appointments for next 48 hours)
• Results written to EHR integration database
• Scheduling system displays risk flags automatically

Security & Compliance:

• HIPAA compliance review completed (handling PHI)
• Security assessment: authentication, encryption at rest and in transit
• Access controls: only authorized scheduling staff see predictions
• Audit logging: all prediction access logged
• Privacy impact assessment completed and approved

Monitoring & Operations:

• Model performance dashboard (daily accuracy, prediction distribution)
• Data quality monitoring (alert if input data anomalies)
• System health monitoring (prediction job success, API latency)
• Alert escalation: page on-call if prediction job fails
• Weekly automated reports to stakeholders

Testing:

• Load testing: confirmed can handle 10K predictions in 15 minutes
• Failure testing: tested rollback to previous model version
• Integration testing: validated EHR integration across all clinics

Operations Readiness:

• Operations runbook created (how to restart service, investigate issues)
• On-call rotation established (ML engineer + IT operations)
• Incident response procedures documented
• Change management process defined

Gate 3 Decision: ✅ GO to production

• All security and compliance requirements met
• Production infrastructure tested and ready
• Operations team trained with documented procedures
• Monitoring provides visibility into model and system health
• Rollback plan tested (can revert to no-prediction workflow in 15 minutes)

Key Success Factor: We rebuilt the POC as production-grade code instead of trying to "productionize" POC notebooks. This took 6 weeks but gave us confidence in reliability and maintainability.

Gate 4: Organizational Readiness (Adoption Success)

Purpose: Ensure the organization will actually adopt and use the AI solution (not work around it or ignore it)

Duration: 4-8 weeks for preparation, ongoing post-deployment

Success Criteria:

• Stakeholders understand what AI does and doesn't do
• Users trained on how to use AI effectively
• Change management plan executed
• Resistance and concerns addressed
• Success metrics communicated and tracked
• Support resources available for users

Key Activities:

• Stakeholder communication (what's changing, why, when)
• User training (how to use AI, interpret results, handle exceptions)
• Change champions identified in each department
• Feedback mechanisms established
• Success stories shared
• Ongoing support and coaching
• Monitoring adoption metrics (usage rates, user satisfaction)

Required Stakeholders:

• Change management team (plan and execute organizational change)
• Business process owners (communicate to their teams)
• End users (receive training and support)
• HR/training team (deliver training)
• Executive sponsor (communicate strategic importance)

Common Mistakes:

• Assuming "if we build it, they will use it"
• Technical training only (how to click buttons) without context (why this matters)
• No plan for addressing resistance or concerns
• Declaring victory at deployment without measuring adoption
• No ongoing support—users left to figure it out alone
• Ignoring negative feedback or treating it as "resistance to change"

Gate 4 Exit Criteria (Go/No-Go Decision):

✅ GO if:

• Target users trained and confident using the solution
• Early adopters successfully using AI in daily work
• Support resources in place and accessible
• Feedback mechanisms working (users can report issues)
• Adoption tracking shows growing usage
• Resistance concerns identified and addressed

⚠️ ITERATE if:

• Adoption slower than expected—need more training or support
• Users confused about when/how to use AI
• Workflow integration needs refinement
• Some user groups adopting, others not—need targeted intervention

❌ ESCALATE if:

• Users actively avoiding or working around the solution
• Fundamental workflow mismatch—AI doesn't fit how work actually happens
• Trust issues—users don't believe AI results
• Leadership no longer supporting the initiative

Deliverables:

• Training materials and sessions delivered
• Change management plan executed
• User adoption metrics (usage rates, satisfaction scores)
• Feedback summary and action items
• Support resources documented and accessible
• Success stories captured and shared

Real Example:

Use Case: Patient no-show prediction (continuing from Gates 1-3)

Gate 4 Activities (8 weeks total: 4 weeks pre-deployment, 4 weeks post-deployment):

Pre-Deployment (4 weeks):

• Conducted "lunch and learn" sessions at each clinic explaining AI predictions
• Addressed concerns: "Will this replace us?" (No, it helps you prioritize outreach)
• Trained schedulers on interpreting risk scores (low/medium/high)
• Identified change champions in each clinic (experienced schedulers)
• Created quick reference guide: "When to call high-risk patients"

Communication Strategy:

• Email from chief medical officer explaining initiative and goals
• Department meetings with Q&A about how AI works
• One-on-one coaching for schedulers who were skeptical
• Weekly "did you know?" tips on using predictions effectively

Post-Deployment (4 weeks):

• Daily check-ins with each clinic for first week
• Weekly check-ins for weeks 2-4
• Anonymous feedback survey after 2 weeks
• Captured success stories (scheduling manager: "We filled 15 same-day appointments last week using the predictions")

Adoption Metrics Tracked:

• Usage rate: 87% of high-risk predictions reviewed by schedulers (target: 80%)
• Confirmation call rate: 72% of high-risk patients received confirmation calls (target: 70%)
• User satisfaction: 8.2/10 (survey of 35 schedulers)
• No-show rate maintained: 14% (vs. 18% baseline)

Resistance Addressed:

• Concern: "AI might be wrong and we'll annoy patients with unnecessary calls"
• Response: Showed prediction accuracy data, emphasized "risk" not "certainty," made calling optional
• Concern: "This is extra work"
• Response: Highlighted fewer last-minute cancellations = less scrambling to fill slots

Gate 4 Decision: ✅ Adoption successful

• High usage rates across all clinics
• Strong user satisfaction
• Business value sustained (no-show reduction holding)
• Support needs declining as users gain confidence

Key Success Factor: We treated adoption as a separate gate requiring dedicated effort, not an automatic outcome of deployment. The 4 weeks of intensive post-deployment support made the difference between acceptance and resistance.

Gate 5: Sustainable Operations (Long-Term Value)

Purpose: Ensure AI solution continues delivering value over time through monitoring, maintenance, and continuous improvement

Duration: Ongoing indefinitely

Success Criteria:

• Model performance remains stable (or improves)
• Business value continues to be delivered
• Users remain satisfied and engaged
• Operations team can maintain and troubleshoot
• Regular reviews identify improvement opportunities
• Organization treats AI as "business as usual," not special project

Key Activities:

• Daily/weekly monitoring of model performance
• Monthly business value reporting
• Quarterly stakeholder reviews
• Model retraining as needed (when performance degrades)
• Bug fixes and enhancements based on user feedback
• Scaling to additional locations or use cases
• Continuous improvement experiments (A/B testing new models)
• Knowledge transfer and documentation updates

Required Stakeholders:

• Operations team (day-to-day monitoring and support)
• ML engineers (model retraining and improvements)
• Business owners (ongoing value realization)
• Users (continued feedback and adoption)
• Executive sponsor (quarterly strategic review)

Common Mistakes:

• Deploying and forgetting—no ongoing monitoring or improvement
• Ignoring model performance degradation over time
• Not measuring whether business value is sustained
• No budget or resources for maintenance
• Losing institutional knowledge when team members leave
• Treating AI as "done" instead of living system requiring care

Gate 5 Exit Criteria:

This gate doesn't have a traditional "exit"—it's ongoing stewardship. However, you know you've successfully established sustainable operations when:

✅ Success indicators:

• Model performance is automatically monitored with alerts for degradation
• Business value is tracked monthly without manual effort
• Operations team handles issues without escalating to ML team
• Model retraining is routine process, not emergency
• Users request enhancements (they're engaged, not just compliant)
• AI solution is "boring" (integrated into business as usual)
• Knowledge is documented and transferable
• AI is included in strategic planning (how to expand, improve, scale)

⚠️ Warning signs:

• Model performance degrading without anyone noticing
• Business value declining but not measured or reported
• Users reporting issues that go unaddressed
• Operations team can't troubleshoot without ML team
• No one knows how the model works (knowledge lost)
• No budget for improvements or maintenance
• Executive sponsor moves on and no one replaces their oversight

Deliverables:

• Automated monitoring dashboards (model + business metrics)
• Monthly/quarterly value reports to stakeholders
• Model retraining procedures and schedule
• Enhancement roadmap based on user feedback
• Incident history and resolution patterns
• Updated documentation as system evolves
• Knowledge base for operations team

Real Example:

Use Case: Patient no-show prediction (continuing from Gates 1-4)

Gate 5 Activities (ongoing, 18 months post-deployment):

Month 1-3:

• Daily monitoring showed model performance stable
• Business value sustained: 14% no-show rate vs. 18% baseline
• Operations team handled 3 minor incidents without ML team
• User feedback: request for same-day predictions (originally only 48-hour)

Month 4-6:

• Deployed enhancement: same-day prediction capability
• Noticed model accuracy declining for new patient types (telehealth)
• Retrained model with 6 months of new data including telehealth appointments
• Accuracy improved from 78% to 81% with additional training data

Month 7-12:

• Expanded to specialty clinics (orthopedics, oncology, cardiology)
• Customized model for each specialty (different no-show patterns)
• Business value scaled: $768K annually → $1.2M annually
• Established quarterly review with clinic directors

Month 13-18:

• Model performance remains stable (retraining quarterly now)
• Experimented with A/B test: new model architecture (gradient boosting → neural network)
• Result: no significant improvement, kept existing model (simpler is better)
• Operations team now manages deployments independently
• Planning to apply no-show prediction to other service lines

Sustainable Operations Established:

• Automated monitoring catches issues before they impact business
• Quarterly model retraining is routine process
• Business value tracked automatically and reported monthly
• Operations team self-sufficient (ML team consults quarterly, not daily)
• Users engaged and suggesting improvements
• AI solution scaled to 15 clinics, 50K+ predictions/month
• Total value: $1.2M annually, ongoing

Key Success Factor: We treated deployment as the beginning, not the end. Dedicated resources for ongoing monitoring, maintenance, and improvement ensured the model continues delivering value 18 months later.

The Critical Path: How Long Does This Take?

Realistic Timeline (from POC start to sustainable operations):

• Gate 1 (POC): 4-8 weeks
• Gate 2 (Pilot): 8-16 weeks
• Gate 3 (Production Readiness): 6-12 weeks (parallel with Gate 2)
• Gate 4 (Organizational Readiness): 4-8 weeks (starts before deployment, continues after)
• Gate 5 (Sustainable Operations): Begins at deployment, mature by 6-12 months

Total time: 6-12 months from POC to mature sustainable operations

Comparison:

• Organizations that skip gates: 18-36 months (or never reach production)
• Organizations that follow gates systematically: 6-12 months to production value

The gates actually accelerate deployment by preventing costly rework and failures.

Common Gate Failure Patterns and How to Recover

Failure Pattern 1: Pass Gate 1, Fail Gate 2 (Technical Success, No Business Value)

Symptoms: Model works great in demos but doesn't deliver measurable business value in pilot

Root Causes:

• Wrong problem being solved (interesting technically, not valuable to business)
• Business integration poorly designed (model predictions ignored or not actionable)
• Success metrics poorly defined (measuring model accuracy, not business outcomes)

Recovery:

• Revisit use case: is this actually valuable to the business?
• Redesign business integration: how do predictions drive actions and outcomes?
• Run shorter pilot with laser focus on one measurable business metric
• If no viable path to business value, stop and learn lessons for next project

Failure Pattern 2: Pass Gates 1-2, Fail Gate 3 (Pilot Success, Can't Deploy)

Symptoms: Pilot demonstrates value but production deployment blocked or delayed indefinitely

Root Causes:

• POC built with tools/technology that doesn't scale or meet production standards
• Security or compliance issues discovered late
• Integration with production systems more complex than anticipated
• Operations team not ready or willing to support

Recovery:

• Honest assessment: how much rework to make production-ready? (weeks vs. months)
• If <2 months rework: invest in production-ready rebuild
• If >2 months: consider vendor solutions or platforms that provide production infrastructure
• Engage security/compliance early for remaining projects (don't repeat this mistake)
• If too expensive to fix: keep pilot running while deciding whether to rebuild or stop

Failure Pattern 3: Pass Gates 1-3, Fail Gate 4 (Deployed but Not Used)

Symptoms: AI solution deployed but users don't use it, work around it, or complain loudly

Root Causes:

• No change management or user engagement
• Workflow integration doesn't match how work actually happens
• Users don't trust AI or understand how to use it
• Solution creates more work than value for users

Recovery:

• Pause and listen: why aren't users adopting? (talk to them, don't assume)
• Quick wins: address top 3 user complaints immediately
• Intensive support: embed with users to understand workflow mismatches
• Iterate on integration: make it easier to use, fit better in existing work
• If fundamental design flaw: may need to redesign business integration (back to Gate 2)

Failure Pattern 4: Pass Gates 1-4, Fail Gate 5 (Initial Success, Value Degrades)

Symptoms: Successful deployment but performance degrades over time, business value declines, users dissatisfied

Root Causes:

• Model not monitored or retrained as data patterns change
• No resources allocated for maintenance and improvement
• Operations team can't troubleshoot issues
• User feedback ignored

Recovery:

• Establish monitoring immediately (what's degrading and why?)
• Retrain model with fresh data
• Allocate dedicated resources (don't rely on goodwill)
• Create formal support process
• Consider whether to continue investing or shut down gracefully

Your Gate Scorecard: Are You Ready to Pass?

Use this checklist before attempting to pass each gate:

Gate 1 Readiness Checklist:

• Clear problem statement and success criteria defined
• Sufficient data identified and accessible
• Data science team with appropriate skills assigned
• 4-8 weeks allocated for POC
• Stakeholders understand this is exploration, not commitment

Gate 2 Readiness Checklist:

• Pilot environment identified (specific location, users, timeframe)
• Business metrics defined (how will we measure value?)
• Pilot users identified and committed
• 8-16 weeks allocated for pilot
• Baseline performance measured (before AI)
• Executive sponsor engaged for review at end of pilot

Gate 3 Readiness Checklist:

• Security team engaged and review process started
• Compliance team engaged (legal, privacy, regulatory)
• Operations team identified and committed to support
• 6-12 weeks allocated for production readiness work
• Budget for production infrastructure allocated
• Architecture review scheduled

Gate 4 Readiness Checklist:

• Change management resources assigned
• User training plan developed
• Communication plan approved
• Support resources committed
• Adoption metrics defined
• Feedback mechanisms ready

Gate 5 Readiness Checklist:

• Monitoring infrastructure deployed
• Operations team trained and ready
• Model retraining process defined
• Ongoing budget allocated
• Governance process established
• Long-term ownership assigned

Take Action: Plan Your Gates This Week

If you have an active AI project (POC, pilot, or preparing for deployment), take these steps:

This Week:

1. Identify which gate your project is currently at (or approaching)
2. Review the success criteria for that gate—are you on track?
3. Review the exit criteria—will you pass or fail based on current trajectory?
4. Identify gaps or risks that might cause gate failure
5. Create action plan to address top 3 risks

Within 30 Days:

1. Schedule gate review meeting with required stakeholders
2. Prepare gate deliverables (see deliverables list for each gate)
3. Make explicit go/no-go decision at each gate (document the decision)
4. If GO: prepare for next gate
5. If NO-GO: stop and document lessons learned

Common Mistake to Avoid:
Don't skip gates or merge them together to move faster. Each gate validates something different. Skipping a gate means discovering that problem later when it's much more expensive to fix.

Expert Help With Your AI Production Journey

Moving from POC to production requires more than technical skills—it requires navigating organizational dynamics, managing stakeholder expectations, balancing speed with thoroughness, and making smart trade-offs at each gate.

I help organizations design and execute POC-to-production journeys that maximize success rates while minimizing time and wasted investment. This includes gate planning, stakeholder management, production readiness assessment, and hands-on support at critical decision points.

→ Book a 90-minute AI Production Readiness Workshop where we'll assess your current project against the 5 gates, identify your highest risks, and create a customized plan to reach production successfully.

Or download the 5-Gate Assessment Scorecard (PDF) with detailed checklists, decision frameworks, and templates for each gate to guide your AI production journey.

The difference between successful AI teams and those with shelves full of POCs is disciplined gate management. Make sure you're passing the right tests, not just working hard.