AI Engineering Executive by Brown
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Lead AI at the Enterprise Level — With Rigor, Not Hype

A boardroom-ready executive program that takes senior technology leaders from AI awareness to confident command — covering ML engineering, generative AI evaluation, governance, and phased strategic implementation in one integrated curriculum.

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AI Engineering Executive by Brown Tech Academy Industry

My standard for this program is simple: when the hardest AI decision lands on your desk, you should already know how to think through it.Pauline Smith EdD

What you'll learn

What you'll be able to do

  • Design and lead a phased enterprise ML engineering initiative complete with KPIs, governance checkpoints, and a realistic budget framework
  • Evaluate generative AI foundation models for business value, IP risk, and responsible deployment readiness across your organization
  • Build governance structures that address ethics, privacy, cybersecurity, and regulatory compliance for AI systems at scale
  • Define and track operational performance metrics — including model quality, resilience, and continuous improvement cycles — for production AI
  • Engage cross-functional stakeholders and communicate AI strategy, risk, and progress to executive and board-level audiences
  • Assess AI accessibility, human oversight requirements, and sustainability considerations across multiple enterprise sectors

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 · 28 lessons

1

Machine Learning Engineering for Enterprise

Establishes the engineering foundations — software, data, model development, and lifecycle management — that underpin reliable enterprise ML systems.

  • 1.1The ML Engineering DisciplineIncluded
  • 1.2Data Engineering and Pipeline ArchitectureIncluded
  • 1.3Model Development, Testing, and ValidationIncluded
  • 1.4Deployment, Monitoring, and Lifecycle ManagementIncluded
  • 1.5Operational Performance Metrics and Continuous ImprovementIncluded
2

Generative AI — Capabilities, Architecture, and Business Value

Gives executive leaders a rigorous understanding of how generative AI foundation models work and where they create measurable enterprise value.

  • 2.1How Foundation Models WorkIncluded
  • 2.2Generative AI Output Types and Use CasesIncluded
  • 2.3Evaluating Business Value and ROIIncluded
  • 2.4Intellectual Property, Data Provenance, and Legal ExposureIncluded
  • 2.5Human Oversight and Responsible Use PrinciplesIncluded
3

AI Governance, Ethics, and Regulatory Compliance

Builds the governance structures, ethical frameworks, and compliance capabilities executives need to deploy AI safely at scale.

  • 3.1AI Governance Frameworks and Organizational StructuresIncluded
  • 3.2Ethics, Fairness, and Bias MitigationIncluded
  • 3.3Privacy by Design and Data Protection ComplianceIncluded
  • 3.4Cybersecurity Risks and AI-Specific Attack SurfacesIncluded
  • 3.5Accessibility and Inclusive AI DesignIncluded
4

Strategic Planning and Phased Implementation

Equips leaders to translate AI ambition into a disciplined, phased enterprise initiative with clear milestones, budgets, and governance checkpoints.

  • 4.1Assessing Organizational AI ReadinessIncluded
  • 4.2Designing a Phased ML Engineering InitiativeIncluded
  • 4.3KPI Design and Governance CheckpointsIncluded
  • 4.4Budget Frameworks and Financial AssumptionsIncluded
  • 4.5Evaluation Methods and Sustainability PlanningIncluded
5

Stakeholder Engagement and Executive Communication

Develops the communication strategies and stakeholder management skills needed to lead AI initiatives across functions and up to board level.

  • 5.1Mapping and Managing AI StakeholdersIncluded
  • 5.2Communicating AI Strategy to Executive and Board AudiencesIncluded
  • 5.3Change Management and Cross-Functional AlignmentIncluded
  • 5.4Communicating Risk, Incident Response, and TransparencyIncluded
6

Applied Executive Capstone — Enterprise AI Initiative

Integrates all modules into a high-stakes applied exercise where participants design and defend a complete enterprise AI initiative.

  • 6.1Sector Analysis and Strategic Use-Case SelectionIncluded
  • 6.2Initiative Design — Phases, KPIs, and Governance PlanIncluded
  • 6.3Budget, Risk, and Stakeholder Communication PlanIncluded
  • 6.4Executive Presentation and Peer ReviewIncluded

Who it's for

Is this you?

Chief Technology Officers

You're setting the enterprise AI direction and need a rigorous foundation in ML engineering, governance, and phased implementation to lead credibly and manage board-level accountability.

VPs of Engineering

You're translating AI strategy into execution and need the fluency to evaluate model readiness, operational metrics, and deployment risk before they become production failures.

Enterprise AI Strategists

You're building the business case and roadmap for AI adoption and need structured frameworks for ROI evaluation, governance design, and stakeholder alignment.

Chief Data Officers

You own the data assets and pipelines that AI systems depend on — this program equips you to govern their use responsibly, including privacy by design, provenance, and compliance.

Risk & Compliance Executives

You need to evaluate AI-specific regulatory exposure, cybersecurity attack surfaces, and ethics frameworks with enough technical depth to ask the right questions and hold the right people accountable.

Digital Transformation Leaders

You're driving enterprise-wide change and need to assess AI readiness, manage cross-functional resistance, and communicate AI strategy with precision to skeptical executive stakeholders.

Questions

Frequently asked

Your teacher

A note from your teacher

Pauline Smith EdD

Pauline Smith EdD

If you're reading this, you've probably already sat in the room where someone presented an AI roadmap that sounded compelling and felt hollow. You've reviewed vendor proposals built on benchmarks that don't map to your environment. You've been asked to sign off on an AI initiative without a clear governance structure, a credible budget, or any honest accounting of the risk. That is a genuinely uncomfortable position for a senior leader — and it's not a knowledge problem in the ordinary sense. You understand technology. You understand strategy. What's missing is the specific, integrated fluency that lets you evaluate an ML system's deployment readiness, stress-test a generative AI business case, design governance that actually holds, and communicate all of it to a board with confidence. That's the gap this program is built to close. Every section of this curriculum was designed around the decisions you will actually be held accountable for. Machine learning engineering is covered as a leadership discipline — so you understand what makes a data pipeline fragile, what model validation should look like before production, and what operational metrics you should be demanding from your teams. Generative AI is evaluated as a portfolio of capabilities with specific risk profiles: IP exposure, data provenance, human oversight requirements, responsible deployment readiness. These are not abstract principles. They are the questions your legal team, your board, and your regulators are already asking. Governance is treated here with the same rigor as engineering. You will design frameworks that address ethics, fairness, privacy by design, AI-specific cybersecurity vulnerabilities, and accessibility — because enterprise AI adoption at scale creates obligations across all of those domains simultaneously. And the program culminates in a capstone where you build and present a complete enterprise AI initiative: phased, governed, budgeted, and board-ready, reviewed by peers who are working through the same caliber of challenge. This is not a survey course, and it is not designed to make AI feel approachable. It is designed to make you the most prepared person in the room when the decisions are hardest. I built it for leaders who are ready to work at that level — and I'd be glad to have you in it.

Pauline Smith EdD

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  • 6 modules, 28 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