AI Governance and Organizational Architecture

Microsoft via Coursera

Go to class Write review

Details

Go to class

Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

9 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Unlock All Certificates

This course provides the framework for leading a high-impact AI rollout while maintaining a secure and resilient environment. You will learn to move beyond manual oversight by implementing automated technical guardrails—such as system-enforced data protection—to mitigate the risks of "permission sprawl" and data leakage. The course addresses the transition from slow, reactive "User Councils" to a proactive Strategy as Code model. You will learn to design an AI Center of Excellence (CoE) that functions as a Policy Engine, managing Architectural Debt and shifting labor from "content creation" to "human-in-the-loop" verification. By the end of this course, you will be able to architect a deployment strategy where AI accelerates business decisions through automated contracts and system-enforced accountability. This course is designed for professionals with significant experience in AI implementation, IT governance, or organizational leadership. Participants should have foundational familiarity with enterprise technology ecosystems, organizational management principles, and standard governance frameworks; hands-on technical AI experience is not required.

Syllabus

Architectural Guardrails: Evaluate Risk Posture Using Microsoft Purview AI Hub

Most AI governance conversations focus on what happens after deployment—monitoring outputs, managing compliance, responding to incidents. This module addresses the prior question: what does the organization's data environment look like before AI is given access to it, and what will AI surface that current governance structures have not accounted for? The Microsoft Purview AI Hub provides the primary risk signals required for an executive to authorize the consumption of corporate data by AI.

Architectural Guardrails: Mandate System-Enforced Protections

Identifying the risk posture is the diagnostic step. This module addresses the executive's response to that diagnosis—mandating the system-enforced protections that convert identified risks into governed structures. The distinction this module draws is between governance that instructs humans to behave correctly and governance that makes incorrect behavior architecturally difficult. Auto-labeling and Privileged Identity Management (PIM) are the two primary mechanisms through which executives mandate the latter, and understanding when and why to require them is the governance decision this module prepares executives to make.

Responsible AI: Communicate to the Board and Secure Sponsorship

Boards that hear responsible AI framed as an ethical obligation tend to treat it as a compliance cost. Boards that hear it framed as a structural risk management requirement tend to treat it as a governance investment. This module gives executives the communication framework to present responsible AI principles at the board level in the language boards actually respond to—risk, liability, competitive positioning, and fiduciary accountability—and to secure the sponsorship that makes responsible AI governance operational rather than aspirational.

Responsible AI: Institute the Compliance Review Process and Align with Microsoft Policies

Board sponsorship creates the authority for responsible AI governance. This module addresses what governance looks like in operational practice—a compliance review process that evaluates AI solutions against the eight responsible AI standards before deployment is authorized, and an alignment framework that maps the enterprise AI strategy to Microsoft's published responsible AI policies. The goal is a governance structure that is specific enough to govern real deployment decisions and auditable enough to be presented to regulators, institutional investors, and board audit committees.

Strategy as Code: Architect the AI CoE as a Policy Engine

An AI Center of Excellence that governs through meetings, guidelines, and manual review processes will always lag behind the pace of AI deployment. This module gives executives the architectural framework to redesign the CoE as a Policy Engine—a governance model where strategic intent is expressed as system configuration rather than advisory guidance, where Purview-driven insights trigger automated governance responses rather than manual reviews, and where the CoE's primary output is not a governance decision but a governance architecture.

Strategy as Code: Design AI Decision Systems with Automated Accountability

An AI decision system without automated accountability is a workflow automation with a governance liability attached. This module gives executives the framework to design AI decision systems where accountability is built into the workflow architecture: automated validation rules that enforce quality standards before AI outputs are acted upon, audit trails that document the accountability chain for every AI-assisted decision, and escalation mechanisms that route exceptions to human judgment without disrupting the automated workflow. The goal is not to slow AI down; it is to make AI-assisted decisions defensible at the speed AI operates.

Measuring Structural Success: Track High-Stakes Signals

The measurement gap in most AI programs is not a data availability problem—it is a signal selection problem. Organizations have access to extensive activity data about how AI tools are being used. What they frequently lack is a measurement framework that connects that activity data to the structural outcomes the investment case projected. This module gives executives the signal selection and tracking framework to verify structural ROI—distinguishing between metrics that confirm deployment and metrics that confirm value.

Measuring Structural Success: Diagnose Adoption Barriers

Structural signal underperformance has two possible explanations: the AI is not delivering the capability the investment case projected, or the organization is not adopting the AI in the way the rollout plan assumed. Distinguishing between these two explanations requires a diagnostic framework that identifies the specific organizational and cultural barriers impeding adoption—so that the executive's intervention is targeted at the actual problem rather than the visible symptom. This module gives executives that diagnostic framework, using Viva Insights and Viva Glint as the primary signal sources.

Project Module: AI Governance Charter & Risk

Learners receive a provided Enterprise AI Governance Charter submitted by a fictional governance team and produce an Executive Review and Authorization Memo. The charter is realistic but contains specific gaps that reflect the most common governance design failures at the enterprise level—structural protections that are defined in principle rather than specified as system configuration, a responsible AI compliance review process that lacks trigger criteria and accountable roles, and a measurement framework that tracks activity metrics rather than structural signals. Learners identify what is well-constructed, what is incomplete or misaligned, and the specific changes that must be made before the charter is board-ready, and deliver a final authorization decision.