AI Implementation Roadmap for Business

Design an AI implementation roadmap for [Company] in [Industry] with [Employee Count] employees. Include: 1) AI readiness assessment — evaluate data infrastructure maturity, technical talent, organizational culture, use case clarity, executive sponsorship. Score each dimension. 2) Use case identification — brainstorm 15-20 potential AI applications across departments, prioritize using value vs feasibility matrix, select top 5 for first year. 3) For each priority use case: problem definition, data requirements, expected business impact, build vs buy analysis, timeline estimate. 4) Data strategy — assess data availability and quality for priority use cases, data collection plan, data infrastructure needs (cloud, warehouse, labeling). 5) Technology stack — ML platform evaluation (AWS SageMaker, GCP Vertex AI, Azure ML, open source), LLM strategy (OpenAI, Anthropic, open source, fine-tuning), vector databases, MLOps tools. 6) Team structure — hire vs upskill plan, key roles needed (ML engineer, data scientist, AI product manager, MLOps), embedded vs centralized team model. 7) Governance framework — responsible AI principles, model monitoring, bias detection, human-in-the-loop requirements, AI policy for employees. 8) Change management — employee communication (AI as augmentation not replacement), training program, AI champions network. 9) ROI measurement — baseline metrics, measurement methodology, expected ROI timeline. 10) 12-month implementation calendar with milestones.