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.

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 not on the technology stack. The focus is on the governance decisions, 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. We walk honestly through what did not work, including a failed first attempt at centralized approval that had to be redesigned into a lighter federated model within six months.

Key takeaways:
1. Identify duplicate AI capabilities across your organization that create 60 to 80% waste in engineering effort and infrastructure spending.
2. Design reusable AI capabilities (APIs, libraries, containerized services) that multiple teams can leverage across different products.
3. Build governance intake processes that catch duplicate solutions before spend is committed.
4. Calculate ROI of platform thinking using metrics like reduced cloud spend, faster delivery cycles, and improved compliance.