Multiple departments inside one enterprise quietly built fifteen separate AI chatbots. Each had its own APIs, its own data pipeline, its own vendor contract, and its own infrastructure. None of them talked to each other. Duplicate spend crossed $1.5 million before anyone had a complete inventory, and it took a public facing AI incident at a peer organization to force the executive team to act. This session is the operational story of what came next, told through the product roadmap and governance decisions that made the difference.

We walk through the governance model and shared capability approach that reduced AI infrastructure waste by roughly 60 percent, cut duplicate cloud spend, tightened compliance posture, and shortened time to deploy new AI use cases from months to weeks. The focus is on the product decisions, the roadmap trade offs, the intake and architecture review process, the funding model changes, and the measurement discipline that made the transformation stick.

Attendees will see the actual AI Governance Committee charter, the intake process that catches duplicate solutions before budget is committed, the shared capability inventory that lets teams reuse authentication, data access, model hosting, and audit logging instead of rebuilding them, and the metrics dashboard that proves the model is working.

Attendees leave with three tangible artifacts:
1. AI Governance Committee charter template adaptable to enterprise product organizations.
2. AI Sprawl Audit worksheet for cataloging duplicate capabilities across product portfolios.
3. Five level Governance Maturity Scorecard for benchmarking product AI programs against peers.