Advanced AI Governance Executive by
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Lead AI with Authority — Not Just Ambition

This rigorous, portfolio-driven credential program gives C-suite executives and senior leaders the governance frameworks, risk tools, and board-ready deliverables to own responsible AI leadership in their organizations — from day one.

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Advanced AI Governance Executive by Brown Tech Academy Industry

"The standard I hold this program to is simple: when you sit across from your board or your regulator, you should be the most prepared person in the room."Pauline Brown Smith, EdD

What you'll learn

What you'll be able to do

  • Build and present a board-ready enterprise AI governance roadmap aligned to your organization's risk appetite and strategic objectives
  • Design a Responsible AI framework that operationalizes fairness, transparency, accountability, privacy, and human oversight across the model lifecycle
  • Construct an AI assurance scorecard and executive KPI dashboard to monitor governance effectiveness, model quality, and regulatory readiness
  • Conduct structured bias assessments, robustness tests, and model documentation reviews as part of a repeatable model assurance process
  • Develop and maintain an enterprise AI risk register that integrates cybersecurity, operational resilience, and third-party AI risks
  • Distinguish established AI governance best practices from emerging capabilities to make defensible, audit-ready decisions under regulatory scrutiny

How it works

A school that adapts to you

This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.

We learn your level

A quick placement check tailors your starting point so you're never bored or lost.

Lessons adapt as you go

Each lesson is written for your pace and your goal, adjusting as your skills grow.

Your AI coach keeps you moving

Checkpoints, feedback, and gentle nudges turn progress into a real result.

The curriculum

What's inside your school

6 modules · 29 lessons

1

AI Governance Foundations and Executive Accountability

Establishes the strategic and structural foundations of enterprise AI governance, including executive roles, accountability frameworks, and model lifecycle oversight.

  • 1.1The AI Governance Imperative for Senior LeadersIncluded
  • 1.2Executive Roles, Ownership, and the AI Governance StructureIncluded
  • 1.3AI Model Lifecycle OversightIncluded
  • 1.4Data Stewardship and Cybersecurity in AI GovernanceIncluded
  • 1.5Building Your AI Governance RoadmapIncluded
2

Responsible AI — Ethics, Fairness, and Regulatory Compliance

Operationalizes Responsible AI principles — fairness, transparency, privacy, accountability, and human oversight — and maps them to the regulatory landscape.

  • 2.1Core Principles of Responsible AIIncluded
  • 2.2Designing a Responsible AI FrameworkIncluded
  • 2.3Navigating the AI Regulatory LandscapeIncluded
  • 2.4Contractual Obligations and Third-Party AI ComplianceIncluded
  • 2.5Human Oversight Mechanisms and Appropriate EvaluationIncluded
3

Trustworthy AI and Model Assurance

Builds the technical and process competencies executives need to validate, document, test, and continuously improve AI models throughout their lifecycle.

  • 3.1Principles of Trustworthy AI and Model ValidationIncluded
  • 3.2Explainability and Model Documentation StandardsIncluded
  • 3.3Bias Assessment and Fairness TestingIncluded
  • 3.4Robustness Testing and Incident ResponseIncluded
  • 3.5Continuous Quality Improvement in the AI LifecycleIncluded
4

AI Assurance — Scorecards, KPIs, and Governance Audits

Equips executives to evaluate governance effectiveness through structured assurance reviews, scorecards, and board-level performance metrics.

  • 4.1AI Assurance Framework and Structured Executive ReviewsIncluded
  • 4.2Building an AI Assurance ScorecardIncluded
  • 4.3Designing an Executive KPI Dashboard for AI GovernanceIncluded
  • 4.4Assessing Regulatory Readiness and Audit PreparednessIncluded
  • 4.5Distinguishing Established Practice from Emerging AI CapabilitiesIncluded
5

Enterprise AI Risk Management

Develops a comprehensive enterprise AI risk register integrating cybersecurity, operational resilience, third-party risk, and continuous risk monitoring.

  • 5.1AI Risk Taxonomy and the Enterprise Risk RegisterIncluded
  • 5.2Cybersecurity and Operational Resilience in AI SystemsIncluded
  • 5.3Third-Party and Supply Chain AI RiskIncluded
  • 5.4Risk Mitigation Strategies and Escalation ProtocolsIncluded
6

Board-Level Leadership, Stakeholder Engagement, and Portfolio Capstone

Synthesizes all prior learning into a board-ready governance presentation and a complete AI governance portfolio demonstrating executive authority and readiness.

  • 6.1Communicating AI Governance to the Board and StakeholdersIncluded
  • 6.2Stakeholder Collaboration and Cross-Functional Governance IntegrationIncluded
  • 6.3Five-Year AI Governance Roadmap and Continuous Improvement StrategyIncluded
  • 6.4Reflective AI Leadership and Personal Governance PhilosophyIncluded
  • 6.5Capstone — Enterprise AI Governance Initiative and Portfolio PresentationIncluded

Who it's for

Is this you?

Chief Technology Officers

Needs a defensible governance architecture to oversee AI deployments across the enterprise and communicate accountability clearly to the board.

Chief Risk Officers

Requires a structured AI risk taxonomy, a living enterprise risk register, and escalation protocols that integrate with existing risk management frameworks.

Chief Compliance Officers

Must navigate the EU AI Act and evolving regulatory landscape, manage third-party AI compliance obligations, and maintain audit-ready documentation.

Aspiring Chief AI Officers

Building the governance credentials and portfolio needed to step credibly into an enterprise AI leadership role and own it from day one.

Digital Transformation VPs

Driving AI adoption across business units and needs governance guardrails, KPI dashboards, and cross-functional accountability structures to do it responsibly.

Senior Risk & Compliance Directors

Executing AI governance on the ground and needs repeatable assurance processes — bias assessments, model reviews, scorecard reporting — that satisfy senior leadership and regulators alike.

Questions

Frequently asked

Your teacher

A note from your teacher

Pauline Brown Smith, EdD

Pauline Brown Smith, EdD

If you're a senior leader reading this, I suspect you already know the stakes. AI decisions are landing on your desk — procurement choices, model deployments, vendor contracts, regulatory inquiries — and the frameworks most organizations have in place weren't built to handle them. You're being asked to sign off on systems you didn't design, defend choices to boards that are increasingly alert to AI liability, and manage risk in a regulatory environment that is moving faster than any compliance function anticipated. That is an uncomfortable position for any executive. It's the position this program was built to resolve.

I designed AI Governance Executive for leaders who carry genuine accountability — not for observers of the AI conversation, but for the people who will be named in the board paper, the audit report, or the regulatory inquiry. That means the curriculum doesn't traffic in abstraction. Every concept — from Responsible AI principles to model assurance to third-party risk — is operationalized into something you can deploy: a framework, a register, a scorecard, a governance roadmap. By the time you reach the capstone, you will have built a portfolio of governance artifacts that reflect your organization's actual risk profile and strategic context.

What I have tried to be rigorous about is the distinction between what is established governance practice and what is still emerging. In a field where confident-sounding guidance sometimes outpaces evidence, I think it is an executive's obligation to know the difference — and to make decisions that will hold up under audit scrutiny. That discipline runs through everything we cover: from bias assessment and robustness testing to regulatory readiness reviews and board communication. The standard we work to is not 'informed enough to have the conversation.' It is 'authoritative enough to lead it.'

The executives I most want in this program are the ones who understand that responsible AI leadership is not a reputation exercise — it is an operational discipline. If you are ready to treat governance with the same rigor you would apply to financial controls or enterprise risk management, this program will give you the architecture, the tools, and the portfolio to prove it. I look forward to working through this with you.

Pauline Brown Smith, EdD

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  • 6 modules, 29 lessons
  • AI-adaptive lessons tuned to your level
  • Quizzes & checkpoints to lock in progress
  • Your own AI learning coach
  • Learn on any device, at your pace
  • Full access for as long as you're subscribed