[BUSINESS_AREA] = Department or function for AI application [COMPANY_SIZE] = Organization scale and resources [CURRENT_TECHNOLOGY] = Existing systems and infrastructure [AI_OBJECTIVES] = Goals for AI implementation [EXPERTISE_LEVEL] = Internal AI/ML capability Step 1 → Opportunity Assessment & Strategy Identify high-value AI applications: AI opportunity mapping across [BUSINESS_AREA] processes Use case prioritization framework with impact/feasibility matrix Data readiness assessment methodology Competitive landscape analysis for AI in [BUSINESS_AREA] Cost-benefit analysis framework for potential applications Risk assessment approach for AI implementation Stakeholder impact analysis across organization Strategic roadmap development with phased approach Step 2 → Data & Technology Infrastructure Planning Establish technical foundation: Data requirement specification for target use cases Data governance framework development Data collection and preparation methodology Technology stack recommendation appropriate for [COMPANY_SIZE] Build vs. buy decision framework for AI solutions Integration planning with [CURRENT_TECHNOLOGY] Scalability and future-proofing considerations Security and privacy compliance planning Step 3 → Implementation & Project Management Create execution approach: Team structure and resource allocation framework Skill gap analysis based on [EXPERTISE_LEVEL] Vendor/partner selection criteria if applicable Project management methodology selection Implementation timeline with milestone establishment Success metric definition aligned with [AI_OBJECTIVES] Testing and validation methodology Change management and training strategy Step 4 → User Experience & Process Integration Design human-AI collaboration: Process redesign methodology incorporating AI capabilities User interface and experience design principles User acceptance and adoption strategy Feedback loop design for continuous improvement Human oversight and intervention framework Performance monitoring dashboard design Handoff protocol between AI and human workers Exception handling process development Step 5 → Governance & Scaling Strategy Establish sustainable AI practices: AI ethics framework and responsible AI guidelines Ongoing maintenance and improvement process design Model performance monitoring methodology AI governance structure appropriate for [COMPANY_SIZE] Regulatory compliance framework for AI applications Scaling strategy for successful pilot implementations Knowledge management system for AI initiatives Future capability development roadmap Pro Tip: Create an "AI value acceleration map" by identifying specific business processes where AI can create the most immediate value through automation of repetitive tasks, then use the credibility and resources gained from these "quick wins" to fund more ambitious AI initiatives with longer-term payoffs. This pragmatic, staged approach builds organizational momentum and stakeholder buy-in while developing the data infrastructure, skills, and governance practices needed for more sophisticated AI applications, significantly increasing the success rate compared to starting with complex, high-risk projects.