• Conduct stakeholder interviews across product, engineering, data, and customer support to surface pain points, unmet needs, and technical constraints.
  
• Audit existing data infrastructure, model catalogs, and analytics pipelines to identify gaps in scalability, observability, and performance.
  
• Deliver a “Product Health Report” outlining quick wins, technical debts, and a high‑level roadmap for AI integration.

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• Facilitate immersive Design Thinking sessions where cross‑functional teams (UX, data science, engineering, business) align on user personas, journey maps, and AI use cases.
  
• Draft a detailed product specification—complete with feature prioritization, success metrics, and failure‑mode analyses—using Amazon’s PR/FAQ template.

• Rapidly prototype core AI functionality: fine‑tune a small LLM on domain‑specific data, deploy a microservice for inference, and validate against real user queries.
  
• Measure initial KPI shifts (e.g., response time reduction, accuracy improvement, customer satisfaction) to validate or pivot before full‑scale investment.

• Architect scalable model‑training pipelines (batch or streaming) integrated with your existing data lake or warehouse.
  
• Build automated CI/CD processes for model releases, ensuring bias scans, performance tests, and security checks occur on every version update.
  
• Implement live dashboards tracking end‑user engagement, model inference latency, cost per API call, and drift detection—enabling continuous optimization.