How to Use This Checklist
💡 Implementation Approach
This checklist covers 3 implementation phases over a typical 12-18 month AI project:
- Phase 1: Pre-Implementation (Months 1-3) - Planning, assessment, foundation building
- Phase 2: Implementation (Months 4-10) - Pilot, development, testing, deployment
- Phase 3: Post-Implementation (Months 11-18) - Optimization, scaling, measurement
Click checkboxes to track your progress. Items are organized by stakeholder responsibility (Executive, Technical, Operations, etc.).
Phase 1: Pre-Implementation (Months 1-3)
Goal: Build foundation, secure buy-in, and plan execution before writing code.
Strategy & Governance Executive Team
Appoint Chief AI Officer (CAO) or AI initiative owner
Single point of accountability. Reports to CEO/COO. Owns AI strategy and budget.
Form AI steering committee with cross-functional executives
Representatives from IT, Operations, Finance, Legal, HR. Meets monthly for oversight.
Define AI vision and strategic objectives (3-year horizon)
Specific, measurable goals aligned with business strategy. Not "use AI everywhere."
Allocate budget (Year 1: $500K-$5M depending on company size)
Include technology, talent, training, change management. Reserve 20% contingency.
Establish AI governance framework and policies
Ethical guidelines, bias testing requirements, approval workflows, compliance standards.
Use Case Selection Business & Technical Teams
Conduct AI opportunity assessment across all business functions
Workshop with department heads. Identify 15-20 potential use cases. Use RICE framework.
Prioritize 3-5 pilot projects (focus on quick wins: 3-6 month ROI)
Criteria: High business impact, proven technology, data availability, stakeholder support.
Conduct technical feasibility assessment for each pilot
Data availability/quality, integration complexity, infrastructure requirements, skill gaps.
Define success metrics and targets for each pilot
Example: "Reduce customer service costs by 25% while maintaining 4.0+ CSAT."
Create detailed business cases with ROI projections
3-year financial model, sensitivity analysis, risk assessment. Use templates from Lesson 18.
Infrastructure & Data IT & Data Teams
Audit existing data infrastructure and identify gaps
Data warehouses, lakes, quality issues, governance gaps, security vulnerabilities.
Select AI/ML platform (cloud vs. on-prem, build vs. buy)
Options: AWS SageMaker, Azure ML, Google Vertex AI, or vendor solutions (Salesforce Einstein, etc.)
Establish data pipeline for pilot projects
ETL processes, data cleaning, feature engineering, version control. Test with sample data.
Implement security and compliance controls
Data encryption, access controls, audit trails, GDPR/CCPA compliance, bias testing tools.
Set up monitoring and alerting infrastructure
Model performance dashboards, drift detection, error logging, uptime monitoring.
Team & Skills HR & Talent Teams
Assess internal AI skills and identify gaps
Survey employees. Map skills to project needs. Identify critical roles to hire or upskill.
Recruit core AI team (2-5 people for pilots)
Roles: AI Product Manager, ML Engineers/Data Scientists, MLOps Engineer. Use contractors if needed.
Design AI training program for employees
Role-specific modules: Executives (strategy), managers (use cases), employees (tools). 2-8 hours each.
Identify and recruit AI champions (1 per 20-30 employees)
Early adopters who will evangelize, support peers, provide feedback. Incentivize participation.
Communicate "no layoffs" policy if displacing tasks
Address job security fears early. Commit to reskilling, not layoffs. Builds trust and reduces resistance.
Phase 2: Implementation (Months 4-10)
Goal: Build, test, and deploy pilots while building organizational capabilities.
Pilot Development Technical & Product Teams
Recruit 5-15 volunteer users for pilot testing
Early adopters, not forced participants. Diverse representation across departments/roles.
Build Minimum Viable AI (MVA) in 4-6 weeks
Focus on core functionality. 70% accuracy acceptable for pilot. Iterate based on user feedback.
Conduct bias and fairness testing before deployment
Test across demographic groups. Measure disparity ratios. Aim for <1.2x. Document methodology.
Implement human-in-the-loop workflows for high-stakes decisions
AI recommends, humans approve (initially 100%, gradually reduce as trust builds).
Deploy pilot to limited user group (Month 5-6)
Start small (5-10 users), expand gradually (50-100 users by Month 8). Monitor closely.
Establish weekly feedback loops with pilot users
Surveys, interviews, usage analytics. Track satisfaction, pain points, feature requests.
Iterate and improve based on user feedback (2-week sprints)
Fix usability issues, improve accuracy, add requested features. Show users you're listening.
Change Management Change & Communications Teams
Launch internal AI awareness campaign (Month 4)
Town halls, newsletters, success stories. Explain vision, address concerns, celebrate early wins.
Conduct role-specific AI training for pilot users
Hands-on sessions (not lecture). 2-4 hours. Record for future users. Provide job aids.
Set up support helpdesk for AI questions
Dedicated Slack channel or email. <24 hour response time. Track common issues.
Measure adoption metrics weekly (usage rate, frequency, satisfaction)
Target: 70%+ active usage by Month 8. If below 50%, investigate barriers urgently.
Address resistance and concerns proactively
1-on-1s with resisters. Understand root causes. Provide extra support. Some may never adopt (accept this).
Go/No-Go Decision Executive Team (Month 10)
Measure pilot results against success criteria
Compare actual vs. target KPIs. Example: Target 30% cost reduction, achieved 27% = GO.
Conduct user satisfaction survey (target: 4.0+ out of 5.0)
If below 3.5, significant improvements needed before scaling. Identify top 3 pain points.
Calculate actual ROI vs. projected (pilot phase)
If actual ROI <50% of projected, investigate root causes. Revise business case for scale phase.
Make Go/Pivot/Kill decision for each pilot
GO: Scale to production. PIVOT: Fix issues and test 90 more days. KILL: Terminate and reallocate budget.
Secure budget for scale phase (if GO decision)
Typically 3-5x pilot budget. Present results to CFO/board with updated business case.
Phase 3: Post-Implementation (Months 11-18)
Goal: Scale successful pilots, optimize performance, measure business impact, and build sustainability.
Scaling & Production Technical & Operations Teams
Build production-grade infrastructure (eliminate pilot "duct tape")
Robust error handling, failover systems, 99.9% uptime SLA, automated retraining pipelines.
Conduct comprehensive security and penetration testing
Third-party audit recommended. Address all critical/high vulnerabilities before full rollout.
Deploy to full user base in phased rollout (25% → 50% → 100%)
2-3 weeks per phase. Monitor performance at each stage. Rollback capability if issues arise.
Train all end users (not just pilot participants)
Scale training program. Mix of live sessions, recorded videos, self-paced modules. Mandatory completion.
Transition from project team to operational support team
Dedicated support (2-3 FTEs), on-call rotation, escalation procedures, knowledge base.
Optimization & Measurement Analytics & Product Teams
Implement continuous monitoring of model performance
Daily checks: Accuracy, latency, error rates. Weekly: Drift detection. Monthly: Comprehensive audit.
Establish model retraining schedule (monthly or quarterly)
Automated pipeline. Retrain when drift >5% or accuracy drops >3%. Test before deployment.
Measure business impact at 3 levels (Technical → Operational → Business)
Not just "model accuracy improved." Show: Cost reduced $X, revenue increased $Y, NPS improved Z points.
Conduct quarterly business reviews with executives
Report KPIs vs. targets, ROI realized, lessons learned, roadmap for next quarter.
Gather ongoing user feedback and prioritize improvements
Quarterly surveys (30-40% response rate target), usage analytics, support ticket analysis.
Knowledge & Scale Center of Excellence
Document lessons learned and best practices
What worked, what didn't, recommendations for next projects. Share with organization.
Replicate successful patterns to other departments
If customer service AI succeeded, apply to sales, then operations. Reuse 60-70% of work.
Build AI Center of Excellence (if 3+ projects active)
Centralize expertise, standards, governance. Hub-and-spoke model (enable, don't control).
Launch next wave of AI projects (Horizon 2 and 3)
Use lessons from pilots. Tackle more complex use cases. Allocate 70-20-10 resources.
Celebrate success and recognize contributors publicly
Awards, bonuses, promotions for AI champions. Creates positive culture and momentum.
Implementation Timeline Templates
6-Month Fast Track (Small Pilot)
| Month |
Phase |
Key Activities |
Deliverables |
| 1-2 |
Planning |
Use case selection, team formation, data assessment, governance setup |
Business case, project charter, team roster |
| 3-4 |
Development |
Build MVA, bias testing, user training, pilot deployment |
Working AI prototype, trained pilot users |
| 5 |
Pilot Testing |
User testing, feedback collection, iteration, performance monitoring |
Pilot results report, user satisfaction data |
| 6 |
Decision & Scale |
Go/No-Go review, production deployment plan, budget approval |
Scale roadmap, updated business case |
12-Month Standard (Department-Wide)
| Month |
Phase |
Key Activities |
Deliverables |
| 1-3 |
Foundation |
Strategy development, CAO appointment, budget allocation, opportunity assessment |
AI strategy document, steering committee formed, 3-5 prioritized use cases |
| 4-6 |
Pilot Build |
Infrastructure setup, team recruitment, data preparation, MVA development |
Working pilots (2-3), trained pilot users (20-50 people) |
| 7-9 |
Pilot Testing |
User testing, iteration, change management, early results measurement |
Pilot results report showing business impact |
| 10 |
Go/No-Go |
Executive review, ROI analysis, scale decision, budget approval |
Approved scale plan and budget |
| 11-12 |
Initial Scaling |
Production deployment (50-80% of department), full training rollout, support setup |
Production AI serving majority of users |
18-Month Enterprise (Multi-Department)
| Months |
Phase |
Key Activities |
Deliverables |
| 1-3 |
Strategy |
Vision development, executive alignment, comprehensive opportunity assessment, CoE planning |
3-year AI roadmap, $3M-$10M budget approved |
| 4-8 |
Pilot Wave 1 |
3-5 pilots across departments, infrastructure buildout, training program development |
Successful pilots with proven ROI |
| 9-10 |
Scale Planning |
Lessons learned synthesis, scale roadmap, CoE formation, budget allocation |
Scale plan for 5-10 use cases |
| 11-15 |
Enterprise Rollout |
Production deployment across departments, organization-wide training, CoE operational |
AI integrated into core workflows, 60-80% adoption |
| 16-18 |
Optimization & Wave 2 |
Performance optimization, measurement, Horizon 2/3 projects launch, culture anchoring |
Sustained business impact, next-gen AI roadmap |
📥 Download Printable Version
Get this checklist in formats you can print, share, and use offline with your team.
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Pro tip: Print this checklist and post it in your project war room. Check off items weekly in team meetings.
📝 Knowledge Check
Test your understanding of AI implementation!
1. What is the first step in an AI implementation checklist?
A) Buy expensive AI tools
B) Define clear business objectives and success metrics
C) Hire an entire data science team
D) Deploy AI across the organization
2. Why is data preparation critical for AI implementation?
A) Data is not important
B) Any data works for AI
C) Quality data is the foundation for accurate AI models
D) Data preparation can be skipped
3. What should be part of an AI implementation roadmap?
A) Phases, milestones, resources, and timelines
B) Only technical specifications
C) Vague goals without details
D) Roadmaps are unnecessary
4. How should organizations handle AI implementation risks?
A) Ignore all risks
B) Wait until problems occur
C) Avoid AI entirely
D) Identify and proactively mitigate potential issues
5. What indicates successful AI implementation?
A) Spending the most money
B) Achieving defined business outcomes and ROI
C) Having the most AI models
D) Implementing without measuring