Executive AI Product Management Mini MBA Course

Overview

The Executive AI Product Management Mini MBA Course is a five-day course for product leaders who leave with an AI Product Portfolio. It connects customer problem discovery, AI opportunity assessment, product vision, data and model feasibility, responsible requirements, experimentation, roadmap decisions, cross-functional delivery, and adoption metrics. Participants apply product tools to convert evidence into governed product choices. Agile Leaders Training Center delivers this course on executive AI product management.

Who Should Attend

• Product leaders accountable for vision and portfolio outcomes
• Innovation leaders responsible for AI-enabled value propositions
• Digital leaders coordinating customer, data, and technology decisions
• Business owners sponsoring AI product investment
• Delivery leaders aligning multidisciplinary product teams

The course assumes participants make product or investment decisions and leaves out coding, model training, and platform configuration.

Departments and Industries

The course supports product decisions across customer-facing and operational settings.

• Product and innovation functions in financial services
• Digital experience teams in retail
• Service design and data teams in healthcare
• Connected product teams in manufacturing
• Digital service teams in public services

Learning Objectives

By the end of this course, participants will be able to:

• Diagnose customer problems suited to AI
• Analyze data and model feasibility
• Build an AI product vision and value case
• Prioritize responsible product opportunities
• Design experiments and adoption measures
• Create an AI Product Portfolio

Course Agenda

Day 1: Product Opportunity Discovery

• Jobs-to-be-Done Customer Problem Interview
• Opportunity Solution Tree for AI Use Cases
• Customer Journey Evidence Map
• AI Suitability and Value Hypothesis Canvas
• Product Vision and Outcome Statement

Day 2: Feasibility and Responsible Requirements

• Data Readiness and Provenance Checklist
• Model Capability and Limitation Assessment
• Human-in-the-Loop Decision Design
• Responsible AI Product Requirement Template
• Cross-Functional Risk and Assumption Log

Day 3: Experimentation and Product Decisions

• AI Product Hypothesis and Test Card
• Prototype Fidelity and Learning Plan
• Model Quality and Product Outcome Scorecard
• Experiment Evidence Review Method
• Value, Feasibility, and Risk Decision Matrix

Day 4: Roadmap, Delivery, and Adoption

• Outcome-Based AI Product Roadmap
• Product Requirement Document for AI Features
• Team Decision Rights and RACI Matrix
• Release Guardrail and Monitoring Plan
• Adoption Funnel and Product Health Metrics

Day 5: AI Product Portfolio Practice

• Suggested Exercise: Customer Problem Evidence Review
• Suggested Exercise: AI Feasibility Challenge
• Suggested Exercise: Responsible Requirement Critique
• Suggested Exercise: Roadmap and Metric Alignment
• Capstone Exercise: AI Product Portfolio

Practical Exercises

The course includes suggested activities for applying product decisions to realistic situations.

• Suggested activity: frame an AI opportunity from retail customer evidence.
• Suggested activity: assess healthcare data and model feasibility.
• Suggested activity: compare product bets using value, feasibility, and risk.
• Suggested activity: present an AI Product Portfolio and decision rationale.

FAQs

Who suits the Executive AI Product Management Mini MBA Course?

Product, innovation, digital, and business leaders responsible for product choices suit the course; it assumes experience influencing customer, investment, or delivery decisions.

How does AI product management differ from a general product management course?

AI product management adds data readiness, model limitations, responsible requirements, human oversight, evaluation, monitoring, and AI-specific adoption decisions to established product practices.

How are AI product opportunities prioritized?

AI product opportunities are prioritized through customer evidence, strategic value, data readiness, technical feasibility, responsible-use risk, delivery dependencies, and measurable outcomes.

What belongs in an AI product roadmap?

An AI product roadmap connects outcome hypotheses, experiments, data dependencies, model evaluation, guardrails, releases, adoption measures, and decision points.

How is AI product success measured?

AI product success combines customer and business outcomes with model quality, workflow performance, adoption, safety indicators, and operational monitoring.

Conclusion

Participants leave with an AI Product Portfolio linking customer evidence to opportunities, experiments, responsible requirements, roadmaps, and outcome measures. The work product improves comparison of product bets and clarifies decision ownership. It gives product teams a shared basis for investment, delivery, adoption, and monitoring.