AI Governance for Managers

Govern AI use cases, risks, policies, vendors, oversight, and responsible decisions as a manager.

New

Created byEngr. Ziyad Alghamdi

Last updated 7/2026

English

What you'll learn

Identify what AI governance means in practical management terms and why it matters for business teams.
Distinguish between low-risk, medium-risk, high-risk, and prohibited AI use cases.
Ask the right governance questions before approving, buying, piloting, or scaling an AI-enabled tool.
Recognize major AI risk categories, including data risk, privacy risk, security risk, bias, model behavior, vendor risk, operational risk, and reputational risk
Apply responsible AI principles such as fairness, transparency, explainability, human oversight, reliability, security, and accountability.
Design a practical AI governance operating model using inventories, intake forms, risk tiers, approval workflows, roles, policies, monitoring, and escalation pa
Evaluate vendor and SaaS AI features using practical due diligence questions about data use, model updates, security, documentation, and accountability.
Create a 30-60-90 day roadmap for improving AI governance within a business function or management team.

Course content

1 section • 10 lectures • 1h 9m total length

The Manager’s New Mandate5:56
The New Reality of AI in Business6:56
What AI Governance Means for Managers6:47
Responsible AI Principles Without the Poster Language7:03
The AI Risk Map Managers Must Own7:16
Data, Privacy, Security, Bias, and Model Behavior7:11
Transparency, Explainability, Human Oversight, and Decision Rights6:57
The AI Governance Operating Model7:44
Policies, Vendors, SaaS, and Regulatory Awareness6:56
The Manager’s Roadmap and Closing Call to Lead6:50

Requirements

No coding, data science, or machine learning background is required.
Basic business, management, operations, risk, compliance, HR, finance, product, legal, IT, or digital transformation experience will be helpful.
Students should have an interest in how AI is used in real organizations and how managers can govern AI responsibly.
A willingness to think critically about risk, accountability, policies, vendors, data, and decision-making is recommended.

Description

Artificial intelligence is no longer a future topic reserved for technical teams. It is already entering daily business work through productivity tools, analytics platforms, HR systems, finance workflows, customer service tools, vendor software, chatbots, forecasting models, and generative AI assistants.

For managers, this creates a new responsibility.

The question is no longer only: can we use AI?

The better question is: how do we lead, govern, approve, monitor, and scale AI responsibly?

AI Governance for Managers is a practical, business-focused course for managers, senior managers, business leaders, product leaders, HR leaders, finance leaders, operations leaders, risk and compliance professionals, and non-technical decision-makers who need to understand AI governance without becoming engineers or data scientists.

You will learn how to think about AI governance as a management discipline: a system of decisions, roles, policies, controls, and monitoring that guides how an organization selects, builds, buys, deploys, and oversees AI.

The course explains responsible AI principles in plain business language, including fairness, transparency, privacy, security, accountability, reliability, explainability, and human oversight. It also maps the major AI risks managers need to recognize, including data risk, privacy risk, security risk, bias, model behavior risk, vendor risk, operational risk, legal and regulatory risk, workforce risk, and reputational risk.

You will also learn how to structure an AI governance operating model using use case inventories, intake forms, risk tiers, approval workflows, decision rights, policies, standards, documentation, monitoring, escalation, and executive reporting.

The course includes practical guidance for vendor and SaaS AI features, where many organizations face hidden risk because AI is embedded inside tools they already use or purchase.

By the end of the course, you should be able to ask better questions in AI-related meetings, identify weak governance before it becomes a business problem, classify AI use cases by risk, understand what good oversight looks like, and build a practical 30-60-90 day roadmap for responsible AI governance in your team or organization.

This course does not teach coding or machine learning model development. It is not legal advice and does not certify compliance with any law or regulation. Instead, it gives managers a practical governance mindset for leading AI adoption with clarity, control, accountability, and confidence.

AI is not only a technical capability. In business, it is a management responsibility.

Who this course is for:

Managers and senior managers who need to understand how AI affects business accountability.
Business leaders responsible for approving, using, buying, or overseeing AI-enabled tools.
Product, operations, HR, finance, procurement, legal, risk, compliance, audit, and customer service leaders involved in AI adoption.
Non-technical decision-makers who want a practical governance view of AI without becoming data scientists or engineers.
Organizations and teams starting to create AI policies, use case inventories, governance committees, vendor review processes, or responsible AI controls.