This module helps leaders design the roles, responsibilities, and decision rights that keep enterprise AI accountable. Learners define the new accountability roles AI adoption creates — AI product owners, model owners, data and AI stewards, responsible-AI leads, and AI governance leaders — and map who decides what when an AI system shapes a consequential decision. Using established decision-rights frameworks such as RACI, DACI, and RAPID, learners assign a single accountable owner, set how much authority an AI system holds, and build the human oversight, escalation, and audit trails that catch a wrong recommendation. Built for AI transformation, organizational-design, and AI governance leaders with no coding background, the module turns AI governance from a policy document into a working accountability structure — closing the gaps where ownership over AI decisions otherwise falls through.

Which data and infrastructure decisions does an AI leader own, and which belong to the technical teams? This module helps non-technical leaders build the data and infrastructure foundations that let enterprise AI move from pilot to scale. Learners read the AI foundation as a three-layer stack of infrastructure, platforms, and applications; assess data readiness, data quality, and data governance; and weigh data fabric versus data mesh as answers to a fragmented data estate. They compare cloud strategy options — hybrid cloud, multi-cloud, and per-workload placement — and make build, buy, or partner decisions with a clear method. Built for AI transformation, data strategy, and digital transformation leaders with no coding background, the module turns data architecture, AI governance, and infrastructure into deliberate leadership decisions rather than delegated plumbing.

This module shows non-technical leaders how to take an AI operating model that already works in one business unit and extend it across the whole enterprise, without creating a central bottleneck or a fragmented shadow build. Learners choose among centralized, federated, and hub-and-spoke operating models; read organizational maturity and readiness before scaling; and split primary ownership between a shared central hub and locally adapted business units. They learn to operationalize AI governance so risk scales with the model, compose cross-functional fusion teams, and roll out the model unit by unit. Built for AI transformation, digital transformation, and enterprise AI leaders with no coding background, the module turns scaling AI, organizational design, decision rights, and change management into a repeatable, tool-agnostic leadership skill.