Why you need this
Enterprise AI adoption fails at rates up to 80%. The technology isn't the problem—it's the strategy, governance, and change management. Organizations that succeed approach AI as a business transformation, not an IT project.
This playbook provides the frameworks, templates, and checklists that distinguish successful enterprise AI programs from expensive failures.
Perfect for:
- CIOs and CTOs developing AI strategy
- VPs of Innovation leading AI initiatives
- Enterprise architects designing AI infrastructure
- Change management leaders driving adoption
- Consultants advising on AI transformation
What's inside
Part 1: AI Readiness Assessment
Organizational Readiness Dimensions
| Dimension | What to Assess | Scoring Criteria |
|---|---|---|
| Data maturity | Quality, accessibility, governance | 1-5 scale |
| Technical capability | Infrastructure, skills, tools | 1-5 scale |
| Cultural readiness | Leadership buy-in, risk tolerance | 1-5 scale |
| Use case clarity | Defined opportunities, success metrics | 1-5 scale |
| Resource availability | Budget, talent, time | 1-5 scale |
Readiness Score Interpretation:
- 20-25: Ready to scale
- 15-19: Ready to pilot
- 10-14: Foundation building needed
- Below 10: Significant gaps to address
Part 2: Building the Business Case
ROI Model Template
Quantifiable benefits:
- Labor productivity gains: [hours saved × cost]
- Error reduction: [error cost × reduction %]
- Speed improvements: [cycle time value]
- Revenue impact: [new capability value]
Costs to include:
- Technology (licensing, infrastructure)
- Implementation (internal + external)
- Change management (training, support)
- Ongoing operations (maintenance, updates)
- Risk mitigation (security, compliance)
Business Case Structure:
- Executive summary (1 page)
- Problem/opportunity statement
- Proposed solution
- ROI analysis with scenarios
- Risk assessment
- Resource requirements
- Timeline and milestones
- Success metrics
- Governance approach
- Recommendation
Part 3: Governance Framework
AI Policy Components
Acceptable Use Policy:
- Approved AI tools and platforms
- Permitted use cases
- Data handling requirements
- Output review requirements
- Prohibited uses
Data Governance:
- Data classification for AI
- What can/cannot be shared with AI
- Data retention and deletion
- Third-party data handling
- Cross-border considerations
Quality Assurance:
- Human review requirements
- Accuracy standards
- Audit trails
- Error handling procedures
- Feedback mechanisms
Risk Management:
- Risk categorization framework
- Approval workflows by risk level
- Incident response procedures
- Liability considerations
- Insurance requirements
Part 4: Vendor Evaluation
Selection Criteria Matrix
| Criterion | Weight | Vendor A | Vendor B | Vendor C |
|---|---|---|---|---|
| Capability fit | 25% | |||
| Security/compliance | 20% | |||
| Integration ease | 15% | |||
| Total cost | 15% | |||
| Support/SLA | 10% | |||
| Roadmap alignment | 10% | |||
| Reference quality | 5% |
Due Diligence Checklist:
- Security certifications (SOC 2, ISO 27001)
- Data handling practices
- Customer references (similar industry/size)
- Financial stability
- SLA terms and penalties
- Exit/portability provisions
- Regulatory compliance
- Integration capabilities
Part 5: Change Management
Stakeholder Analysis Template
| Stakeholder Group | Current State | Desired State | Key Concerns | Engagement Strategy |
|---|---|---|---|---|
| Executive sponsors | ||||
| Middle management | ||||
| End users | ||||
| IT team | ||||
| Legal/compliance |
Communication Plan:
- Why AI (business rationale)
- What it means for each group
- How it will be implemented
- When key milestones occur
- Where to get support
- What success looks like
Training Program Structure:
- Awareness (all employees)
- Fluency (regular users)
- Expertise (power users)
- Technical (builders and maintainers)
Part 6: Implementation Roadmap
Phase 1: Foundation (Months 1-3)
- Readiness assessment
- Governance framework
- Pilot selection
- Tool evaluation
Phase 2: Pilot (Months 4-6)
- Controlled pilot deployment
- Success metrics tracking
- Feedback collection
- Governance refinement
Phase 3: Scale (Months 7-12)
- Successful pilot expansion
- Broader training rollout
- Additional use cases
- Center of excellence development
Phase 4: Optimize (Year 2+)
- Advanced use cases
- Process integration
- Continuous improvement
- Innovation programs
How to use it
Start with readiness: Don't skip assessment. Accurate baseline prevents false starts.
Build the case first: Executive support requires clear ROI. Use the templates.
Governance before tools: Policies prevent problems. Establish before scaling.
Plan for change: Technology is 20% of success. Change management is 80%.
Want to go deeper?
- AI Strategy Basics — Strategic planning fundamentals
- AI Use Case Evaluator — Score your AI opportunities
- When Not to Use AI — Avoid AI for wrong use cases
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You may share and adapt for any purpose with attribution.