Data & AI Product Management Certification

GET CERTIFIED FOR A HIGH DEMAND ROLE. OWN THE PRODUCT VISION & ROADMAP. CONNECT TECHNOLOGY WITH VALUE.

HROI Certifications

options to fit your needs. Choose:

self-paced

OR

instructor-led

$1600

INSTRUCTOR LED

The Next Cohort Starts April 29th & Runs For 8 Weeks.

Classes Meet Tuesdays @8am PT.

JUST RELEASED! Agentic AI Product, Monetization, & Go-To-Market Strategy Modules.

$595

SELF PACED

Start & Learn On Your Schedule.

All Self-Paced Courses Include Office Hours.

Optional 1 on 1 Support Is Available.

AI product Management certification

Certification Shows You're Ready To Advance.

Instructor-Led courses include a verified digital certificate for LinkedIn and other platforms. Validate credentials with employers, and highlight your expertise.

Course Description

Data and AI have the potential to power new product categories, but businesses lack the capabilities to turn potential into anything tangible. This course teaches you how to turn an AI opportunity into a product, from 0 to ROI.


You’ll master frameworks that connect technology with value creation. The frameworks start at opportunity discovery and use case selection. You’ll use them to help frontline teams and C-level leaders find treasure.


The maturity frameworks guide you through turning use cases into initiatives that deliver value incrementally, quarter to quarter. The course reinforces theory with real-world case studies and exercises.


You’ll define initiatives in a new way by connecting business metrics to model metrics and setting the data team up for successful delivery.


The AI Product Manager sets the vision and owns the roadmap. The frameworks make those functions actionable even in early-maturity businesses.


You’ll leave capable of taking data and AI products from early features to a complete product to a complex platform.

Course Outcomes

Demand for AI Product Managers is growing faster than demand for technical data talent. CEOs need people to connect strategy with execution, and the AI Product Manager role is considered a critical success factor.


What You'll Do.

  1. Discover & Justify High-Value Use Cases
  2. Monetize Data & AI
  3. Work Effectively With Stakeholders & C-level Leaders
  4. Price AI Products & Features
  5. Develop Initiatives That Deliver Value Incrementally
  6. Define the AI Product Vision & Roadmap
  7. Manage Research Lifecycles & Advanced Machine Learning Initiatives
  8. Develop AI Go To Market Strategies
  9. Estimate AI Opportunity Size & Initiative Costs
  10. Lead & Persuade Without Authority

Benefits

Take advantage of my experience in person.


  • 12 hours of in-person instruction over 8 weeks.
  • Classes are recorded. You’ll have access to the recordings and slides for 3 months after the last class.
  • A 1 on 1 AI Product Strategy session for personalized learning and guided walkthroughs of implementation.
  • Office hours twice weekly and immediately after each class for deeper dives into key topics.
  • Access to the self-paced course and office hours for a year. Support continues after the course ends.

INSTRUCTOR LED

Benefits

Learn on your terms and timeline.


  • Over 20 hours of instruction covering Data and AI Product Management frameworks, case studies, and exercises.
  • Content is continuously updated with new case studies and recent developments.
  • Office hours twice a week for deeper dives into key topics.
  • Access to the self-paced course and 2 office hours per week for a year.

Get Started

Who Should Take The Course

People who get the most out of this course have at least 3-5 years working on a data team or data and AI initiatives. It’s important to have firsthand experience with the challenges this course is designed to address. That context is critical to support learning outcomes.


No previous strategy or product management coursework or experience is required. The curriculum is designed for those with a nontechnical business background as well as technical individual contributors without a business background.


No technical background is required either, and the course content is easily understood by non-data scientists. Understanding data, analytics, and/or machine learning is helpful but not required.


Built For Technical & Nontechnical backgrounds

The curriculum is designed for technical professionals with no business or product management background. Get certified in AI Product Management from any starting point.



The certification course also supports current technical product managers looking to transition into better-paying, more secure AI PM roles. Learn the product strategy, monetization, and GTM frameworks without all the technical jargon.

What People Are Saying About The Course

Why Get Certified?

Benefits

AI Product Manager Roles Are Seeing Rising Demand & High Salaries

Get Access To A High-End Career Path With More Options For Advancement

The Frameworks & Case Studies Prepare You To Interview Successfully

Greater Security From Automation, Layoff Cycles, & Team Reorgs

Become More Strategic While Staying Close To Product Development

Advantages

An Instructor With Real-World Experience On Multiple AI Products

The Innovative Course Design Prepares You To Do The Job VS. Memorize Facts

Students Report Long-Term Results & Career Impacts

Longevity: One Of The 1st Certifications Of Its Kind With A 5 Year Track Record

Exclusivity: Be One Of The Few Certified AI Product Managers

Your Learning Outcomes Are Our Highest Priority.

That's why support doesn't end when the class is over. You get access to office hours and email support.

Content Developed From Over A Decade In AI Working With A Range Of Clients.

Current & Former Clients

future proof your career today.

Data & AI Product Management: From 0 To ROI

8 Weeks Of Classes & Content

90 minutes of exercises, case studies, and instruction. Classes are recorded to review or catch up if you miss a session.

Drop-In Office Hours For Discussion

Get all your questions answered Mondays at 6 PM PT and Wednesdays at 8 AM PT. The hour after each class is another Q&A session.

A Self-Paced Companion Course

The self-paced course reinforces content and covers foundational concepts. You'll have access for 1 year.

1 on 1 Support Included

You get a 1 on 1 AI product strategy or career coaching session to personalize the content and improve your outcomes.

Instructor Led Course Overview

Week 1

  1. Turning An AI Story Into Results: The Role Of A Data & AI Product Manager
  2. Introducing A Modern AI Platform Architecture (SAP Joule Case Study)
  3. Anatomy Of An Insight: C-level Buy-In (Walmart Case Study)
  4. Facilitating Opportunity Discovery
  5. Turning Data Into A Competitive Advantage
  6. How To Build An AI Product Vision: Pragmatic Futurism


Week 2

  1. Using The Maturity Models To Get From Opportunities To Products
  2. What Happens Without Frameworks? (HRTech Case Study & Guided Exercise)
  3. Introduction To Agentic AI Product & Monetization Strategy
  4. How Do We Get There? The Journey From Data To AI
  5. Top-Down & Bottom-Up Opportunity Discovery (Autonomous Vehicles Case Study)
  6. Opportunities To Monetize Generative AI ('Amazon Interests' Exercise)


Week 3

  1. How Do We Become Strategic Partners? Coalition Building Strategies & C-level Communications
  2. Pitfalls & How To Recover From Platform Mistakes
  3. Starting An Initiative At Level 0 Maturity: Workflow Mapping (Email Marketing Case Study)
  4. How Product Platforms Are Built (Insurance Tech Case Study & Exercise)
  5. Building The Vision & Product Roadmap
  6. Managing Innovation & Research For Value


Week 4

  1. Breaking Opportunities Into Initiatives
  2. Delivering Returns Incrementally & Building AI Platforms Iteratively
  3. Feature, Product, Platform: Following The Maturity Models To Execute
  4. Information Architecture: Aligning Data Gathering With Workflow Mapping
  5. Exercise: Applying Workflow Mapping To Your Specific Use Cases
  6. Architecting Data Structures For Model Consumers (Siemens Supplier Discovery Case Study & Exercise)


Week 5

  1. Implementing The Maturity Models
  2. AI Product Design Fundamentals
  3. Opportunity Estimation (Microsoft Copilot Case Study & Exercise)
  4. Data Product Pricing Strategy (X & Reddit Case Studies & Exercises)
  5. AI Product Pricing Strategy (Google Bard Case Study & Exercise)
  6. SAP Joule Aligning Platform Pricing Strategy With The Business Model Case Study & Exercise


Week 6

  1. Seeing Around Corners: Pragmatic Innovation Strategy
  2. Case Studies In GenAI Product Disruptions
  3. The Agentic AI Product Cycle
  4. LLM Product Cost Estimation
  5. Using Data As A Competitive Advantage With LLMs


Week 7

  1. Generative AI Monetization Opportunities & Use Cases
  2. Alignment & Guardrails For Agentic AI Products
  3. Agentic System & Platform Design
  4. Making The Shift To Outcomes-Based Products & Platforms
  5. Innovating Workflows For GenAI
  6. Case Studies In Agentic Systems (Salesforce Agentforce & Google Agent Builder)


Week 8: The Final Exam

  1. Saving Apple Vision Pro: Data & AI Product Ecosystem Opportunity Discovery Exercise
  2. Introducing AI Product Go To Market Strategy (GTM)
  3. GTM From Feature To Platform (Copilot 365 Release Strategy Case Study & Exercise)
  4. Saving OpenAI: Pivoting To Products After Missteps Exercise
  5. Building An Ecosystem Business Model: Customers, Partners, Developers (Hugging Face Case Study & Exercise)
  6. GTM: Engineering A Marketing Plan & Sales Funnel

Meet Your Instructor

Your learning outcomes and objectives are my highest priority.


I have a 3-layered professional background: Technology, Leadership, and Strategy.


Over 25 years in technology, I started my career in software engineering and moved into data science in 2012.


I am one of the only published authors on Data and AI Strategy (From Data to Profit, Wiley 23) and one of the most experienced practitioners. I built Data and AI Strategies for 10 of the largest companies in the world, 21 SMEs, and 3 startups.


V-Squared is one of the oldest data science consulting firms. I founded it in 2012 as a technical consulting practice but quickly realized that I needed C-level buy-in if I wanted to get the budget for advanced machine learning projects. I began developing the frameworks I teach in 2015. I taught my first cohort in 2017 and offered the course publicly in 2020.


Since 2014, I have been called a Data Science and AI Strategy expert by IBM, Intel, SAP, LinkedIn, NVIDIA, and others. I am a globally recognized thought leader, followed by Gartner, Walmart, Uber, Microsoft, Salesforce, MongoDB, Deloitte, and more.


Thank you for giving me the opportunity to teach. It is an honor to teach and one I greatly enjoy.

Self-Paced Course Overview

1. Introduction

  1. Course Support, Content Overview, Topics, & A Lesson On Pricing
  2. My Background & The Course Design
  3. Breaking Bad Habits
  4. Why Is Now The Time For AI Strategy & Products
  5. What Is An AI Product Manager?
  6. Why Do Businesses Need AI Product Managers & New Approaches?

3. Opportunity Discovery

  1. The Big McDonalds Reveal
  2. Ford & Being Distracted By Screens
  3. Why Change & Adopt New Frameworks?
  4. Why Did I Choose McDonalds & Ford?
  5. Becoming Strategic Partners With CxOs
  6. Top-Down Opportunity Discovery
  7. Bottom-Up Opportunity Discovery
  8. Breaking Initiatives Down
  9. McDonald's Top-Down Opportunity Discovery Exercise
  10. McDonald's Top-Down Opportunity Discovery Exercise Answer
  11. Ford Top-Down Opportunity Discovery Exercise Answer


6. The Product Platform & Defining AI Products

  1. Introduction To The Product Platform Exercise Estimating ROI For The iPhone Camera
  2. iPhone Exercise Answer Walk Through
  3. Introduction To The Product Platform Mentorship Marketplace Exercise
  4. The Mentorship Marketplace Exercise Answer
  5. AI Product Examples TikTok
  6. AI Product Examples Recruiter ATS
  7. AI Product Examples Netflix Recommender
  8. AI Product Examples Amazon Prime Pricing
  9. AI Product Examples Citi Credit Card Fraud Detection
  10. Product Platform Monetization Review


8. How Platforms Are Built & Developing The Roadmap

  1. Use Case Assessment Points
  2. AI Product Platform HRTech Exercise Learn From My Mistakes
  3. Problem, Data, & Solution Space Definitions
  4. How Platforms Were Built
  5. How Platforms Were Built Exercise Answer
  6. Blurring The Lines Between Products And Operations
  7. Breaking Initiatives Down And Meeting The Business Where It Is
  8. Introduction To T-Shaped Platforms
  9. From Opportunities To Roadmaps


10. Generative AI Product & Platform Strategy

  1. Introduction To The Fourth Platform
  2. Why Generative Interfaces And Products Have Rapid Adoption Cycles
  3. Disrupting Education Workflows And Adaptations
  4. Disrupting Streaming Exercise
  5. Disrupting Streaming Exercise Answer
  6. SAP Joule Generative Interface Case Study
  7. Saving ChatGPT Exercise
  8. Saving ChatGPT Exercise Answer


12. Managing A Technical AI Product & Research

  1. Introduction And Do You Need A Technical Background
  2. Revisiting The Problem, Data, And Solution Space Breakdowns
  3. Nike SNKRS App Introduction
  4. Causal KPIs And Setting Up For The Experiment
  5. Setting The Conditions For An Experiment And Research Phase
  6. Nike SNKRS App Experiment 1
  7. Managing The Research Lifecycle
  8. Nike SNKRS App Experiment 2

2. Data & AI Strategy Foundations

  1. Introduction To The Business, Operating, & Technology Models
  2. Exploring The Technology Model
  3. Continuous Transformation & Its Impact On Products
  4. Steps That Launch & Derail AI Strategy
  5. The Capabilities Maturity Framework & Progression
  6. The Transition From Opacity To Transparency
  7. The Product Maturity Model & Strategic Decisions
  8. The Data Maturity Model
  9. Opportunity Discovery Without The Frameworks

4. The Maturity Models

  1. Introduction To Product Maturity
  2. The Product Lifecycle Maturity
  3. Human Machine Maturity Model
  4. The Data Generation Maturity Model
  5. Opportunity Estimation
  6. Opportunity Estimation Exercise Copilot

5. Introduction To The 4 Main Platforms & AI Product Strategy

  1. Building A Single Vision For AI Products
  2. The 4 Main Platforms

7. AI Product Pricing Strategy

  1. Introduction To Pricing Strategy Basics
  2. Introduction To AI Pricing Strategy
  3. Do Subscriptions Still Make Sense?
  4. GPT-4 vs GPT-3.5 Pricing Exercise
  5. GPT-4 vs GPT-3.5 Pricing Answer And Copilot Pricing Exercise
  6. Copilot Pricing Exercise Answer
  7. Data Product Pricing Strategy
  8. Amazon Textract Pricing Exercise
  9. Amazon Textract Pricing Exercise Answer
  10. The 3 Body Problem
  11. The Dangers Of Discounting


9. The Operations & Decision Platforms

  1. Introduction
  2. How Are Operating Platforms They Monetized?
  3. Operating Platform As A Product
  4. What Happens When Workflows Are Disconnected?
  5. Creating Alignment With Customers
  6. Introduction To The Decision Platform
  7. Understanding Platform Layers
  8. Why Data Gathering Is Harder For Decision Platform Features
  9. Decision Chains And ROI


11. Managing An AI Product

  1. Introduction And Understanding Tractable
  2. Saving Tractable Exercise
  3. Saving Tractable Answer
  4. Introduction To Competitive Analysis
  5. Who Are Your Stakeholders
  6. High-Level Pushback Strategies
  7. Business Culture Causes Resistance
  8. The Business Culture Must Adapt For Us To Get Buy In
  9. A Game Of Alignment
  10. Managing Stakeholder Expectations
  11. The Board Would Like A Word: Communication Strategy For CxOs & The Board


13. AI Product Go To Market Strategy

  1. Introduction
  2. The Path To Breakeven
  3. The Path To Profitability
  4. Scaling The SAM
  5. Scaling The TAM
  6. Extending The Platform
  7. Adding New Products

CASE STUDIES

Case studies from a range of industries bring the concepts to life and show how frameworks are implemented to drive results.

✓ Amazon ✓ Christie's ✓ X/Twitter
✓ Microsoft ✓ OpenAI ✓ American Airlines
✓ Citi ✓ Nike ✓ Infiniti
✓ Progressive ✓ Intel ✓ Tesla
✓ Apple ✓ Meta ✓ Zoom
✓ Tractable ✓ Disney ✓ NVIDIA
✓ McDonalds ✓ TikTok ✓ Netflix
✓ Ford ✓ Reddit ✓ Bain & Co

create your own opportunities. define your impact.

Need More Information? Questions?

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