Enterprise GenAI deployment requires strategic thinking beyond technology selection. Use case prioritization matrix: x-axis = business impact (revenue, cost, risk reduction), y-axis = implementation complexity. Priority order: 1) Quick wins (high impact, low complexity): employee productivity tools, code assistance, internal knowledge search. 2) Strategic bets (high impact, high complexity): product personalization, customer service automation. 3) Experiments (low impact, low complexity): creative tools, analytics assistance. 4) Avoid (low impact, high complexity). Build vs buy framework: Buy (SaaS) for commoditized use cases (code review, meeting notes, HR FAQ). Build for competitive differentiation, proprietary data advantages, unique workflows. Platform decisions: Azure OpenAI for Microsoft-first organizations (security, compliance, existing contracts). AWS Bedrock for multi-model, AWS infrastructure alignment. Google Vertex AI for data analytics pipelines. Governance framework: AI committee with legal, IT security, compliance, business stakeholders. Review process for new AI tools. Acceptable use policy for employees. Data classification system defining which data can be sent to external AI APIs. ROI measurement: establish before/after metrics, attribute productivity gains to AI (control groups), track total cost including compute, licensing, engineering, training. Change management: identify AI champions in each department, required training program, success story communication, address job security concerns proactively.