Every part of the business now wants AI, and most of those requests arrive before anyone has agreed who approves them. This course is for the leaders stuck in the middle, who have to decide which initiatives get funded, what controls belong in policy, and who is accountable once an agent starts updating real records. No administrative access or developer tools required. You finish with an AI Governance Strategy Roadmap covering initiatives in your own organization.
Governance usually starts late. By the time anyone sits down to write a policy, half a dozen AI requests are already in flight across the business, one of them an agent that would update supplier records on its own, and nobody has agreed who signs that off. This course is written for the leaders who must resolve that without slowing the business down. You will do it without admin access, a developer environment, or any product permissions. Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Purview, Azure AI Content Safety and Entra Agent ID are reference points for decisions, not consoles to configure.
Much of the early work is deciding what to fund. You will score candidate initiatives on projected annual impact against estimated engineering effort, sort them into quick wins, strategic investments, nice to have, and reconsider, then match each one to a platform pathway from Microsoft 365 Copilot through to Azure Machine Learning. Those choices are tested against four levels of organizational maturity, because an organization still establishing AI use within its productivity tools is not ready to authorize an agent to update its asset management system.
Once the portfolio is settled, the question becomes who governs it. You will build an AI Center of Excellence from executive sponsorship through to a defined operating model, then weigh centralized, federated, advisory, and hybrid arrangements against the teams they oversee. Centralized control suits early maturity and high-risk work, though it eventually becomes a bottleneck teams work around. The same work converts the six Responsible AI principles into policy a team can follow, naming approved data sources, restricted topics, escalation triggers, required disclosures, review frequency, and an owner accountable for correcting inaccurate answers, then tests the result against the EU AI Act and ISO/IEC 42001. A quarterly Risk Radar review keeps that policy set current.
Policy only holds if the controls behind it are specified, which is where the course ends. You will determine which Microsoft Purview control categories apply to an initiative, and where severity thresholds belong for blocking, warning, human review, or escalation. Autonomous agents are treated in detail: what an agent registry must record, who owns each agent's identity and recertification date, and why approving the tools an agent may reach is a separate decision from permitting one agent to delegate authority to another.
The three assignments accumulate into one AI Governance Strategy Roadmap: what you would fund, on which platform, under whose oversight, with which controls, and what you would stop.Every part of the business now wants AI, and most of those requests arrive before anyone has agreed who approves them. This course is for the leaders stuck in the middle, who have to decide which initiatives get funded, what controls belong in policy, and who is accountable once an agent starts updating real records. No administrative access or developer tools required. You finish with an AI Governance Strategy Roadmap covering initiatives in your own organization.
