Building an AI Product Practice Operating Model

This case study covers the design and deployment of an AI Product Practice Operating Model. Developed for product organizations running parallel AI pilots, this model establishes governance, evaluation, and model-tiering standards to scale AI initiatives efficiently.

Product squads launched AI pilots ad-hoc. There was no shared standard for model selection, eval metrics, prompt grounding, or cost attribution. This led to high API token costs, security vulnerabilities, and duplicate learning loops across squads.

First Principles Analysis: AI scale is not just an engineering problem; it is an operating model problem. Unlike traditional software, AI is non-deterministic. The product organization needed a structured process for model evaluation (evals), task-routing, and security tiering, ensuring that teams do not start coding until they document their model selections and safety boundaries.

Executive leadership lacked visibility into AI return on investment. Product teams wasted cycles repeating the same LLM mistakes, and security and compliance teams flagged safety issues late in production cycles, causing release blocks.

We designed and rolled out the AI Product Practice Operating Model:

• Model Selection Framework: Created standardized templates for documenting model choices, cost trade-offs, and prompts.
• Eval Suite Standards: Standardized evaluation rubrics (measuring grounding, faithfulness, and latency) before production deployment.
• Release Governance: Set up a cross-functional review board to audit safety, security, and cost per run parameters.

Established shared libraries for RAG evaluations, model-routing rules, and prompt safety guardrails.

Samadhan advised on the operating model, template designs, and leadership rituals, leveraging his experience leading HealthTech AI portfolios to build scalable governance structures.

Delivered an operating model that aligned product, engineering, and compliance. Enabled faster launch decisions, reduced token cost wastage, and established consistent safety guardrails across all AI features.

Key Product Leadership Takeaways:

1. Templates beat heroics: Scaling AI requires structured checklists and templates. Documenting model choices and evals before writing code ensures process consistency.
2. Evaluate early and often: Set up eval runs early in the design cycle. Waiting until production to measure latency and grounding leads to major rework.