AI Product Management(AIPM) –Live Training

  (Master AI Strategy, Product Discovery, Prototyping, Testing & Delivery)

This practical, industry-oriented course is designed for working professionals who want to build expertise in AI Product Management, including AI product discovery, strategy, requirements, prototyping, testing, delivery, and responsible AI decision-making.

The course combines Product Management practices with Artificial Intelligence, AI opportunity assessment, AI product requirements, AI interaction and workflow design, prototyping, quality evaluation, risk controls, stakeholder management, and product strategy.

Participants will learn how to identify AI opportunities, evaluate AI and non-AI approaches, define AI product requirements, collaborate with technical and business teams, test AI products, manage risks, plan launches, measure product outcomes, and defend AI product recommendations through a practical capstone project.

About the Instructor:

Live Sessions Price:

For LIVE sessions – Offer price after discount is 149 USD 129 USD 109 USD Or 12000 INR10900 INR 8900 Rupees

Free Demo On:

Indian Timings: 22nd October  @ 9 PM – 10 PM (IST)/

U.S Timings: 22nd October @ 11:30 AM – 12:30 PM (EST)/

U.K Timings: 22nd October @ 4:30 PM – 5:30 PM (BST)

Class Schedule:

For Participants in India: Monday to Friday @ 9 PM – 10 PM (IST)

For Participants in the US: Monday to Friday @ 11:30 AM – 12:30 PM (EST)

For Participants in the UK: Monday to Friday @ 4:30 PM – 5:30 PM (BST)

Course syllabus:

MODULE 01: Discover and Frame the Right AI Problem

✦ Understanding the AI Product Manager role
✦ Product ownership vs AI engineering responsibilities
✦ Identifying AI-suitable business opportunities
✦ Customer and workflow discovery
✦ Conducting customer interviews and identifying pain points
✦ Mapping existing workflows before proposing AI solutions
✦ Understanding market and business context
✦ Connecting AI opportunities with business strategy
✦ AI vs Non-AI solution evaluation
✦ Evaluating value, feasibility, cost, uncertainty, and trust
✦ Identifying assumptions and validating them with evidence
✦ Presenting an AI opportunity to stakeholders
✦ Making Continue, Pivot, or Stop decisions
✦ Creating a Discovery Brief and Opportunity Decision Memo
✦ Building a Stakeholder Map

MODULE 02: Align Stakeholders and Define the AI Product

✦ Identifying key AI product stakeholders
✦ Working with Users, Buyers, Engineering, Data, Design, QA, Operations, Legal, Security, Risk, and GTM teams
✦ Developing AI product strategy and product narrative
✦ Defining product promise and desired business outcomes
✦ Understanding strategic fit and business priorities
✦ Defining AI product requirements
✦ Understanding inputs, outputs, context, and constraints
✦ Designing fallback and human-oversight mechanisms
✦ Defining AI product acceptance criteria
✦ Prioritizing AI use cases
✦ Defining and scoping the AI MVP
✦ Evaluating value, evidence, effort, uncertainty, and risk
✦ Conducting stakeholder alignment workshops
✦ Resolving competing stakeholder requirements
✦ Defining decision rights and documenting product decisions
✦ Creating an AI Product Brief / PRD
✦ Creating a Prioritized MVP and Decision Log

MODULE 03: Prototype, Test and Evaluate AI Products

✦ Understanding AI prototype strategies
✦ Storyboard-based prototyping
✦ Wizard-of-Oz prototyping
✦ No-code AI prototyping
✦ API-assisted AI prototypes
✦ Selecting the right prototype based on product uncertainty
✦ Designing AI interactions and workflows
✦ Context management and structured outputs
✦ User control and human handoffs
✦ Designing fallback behaviour
✦ Understanding AI technical trade-offs
✦ Working with Engineering and Data teams
✦ Model and data considerations
✦ Retrieval, latency, cost, and vendor dependency
✦ Build vs Buy decisions
✦ Defining AI quality criteria and release thresholds
✦ Defining task success and acceptable error levels
✦ Designing AI test sets
✦ Normal, edge, adversarial, and role-specific test cases
✦ Analysing AI failures and identifying root patterns
✦ Testing privacy, bias, security, misuse, and transparency
✦ Designing human-oversight controls
✦ Making Improve, Narrow, Pilot, Monitor, or Stop decisions
✦ Creating an AI Test Set and Quality Scorecard

MODULE 04: Lead AI Product Delivery, Adoption and Measurement

✦ Building an AI product roadmap under uncertainty
✦ Sequencing experiments and learning milestones
✦ Managing dependencies and product risks
✦ Cross-functional AI product leadership
✦ Defining decision rights and working agreements
✦ Managing conflicts and escalations
✦ Communicating with executive stakeholders
✦ AI product launch planning
✦ Change management and user adoption
✦ User onboarding and enablement
✦ Managing affected business workflows
✦ Creating feedback loops after launch
✦ AI release-readiness assessment
✦ Working with QA, Engineering, Legal, Operations, and Business teams
✦ Defining fallback and escalation processes
✦ AI product metrics and review cadence
✦ Measuring adoption and task success
✦ Monitoring errors, incidents, cost, and business outcomes
✦ Defining pause and rollback triggers
✦ Creating an AI Product Roadmap
✦ Creating RACI and Decision Logs
✦ Creating a Launch Checklist and Monitoring Plan

MODULE 05: Integrate and Defend the AI Product Recommendation

✦ Connecting customer evidence with product strategy
✦ Connecting product scope with AI test results
✦ Reviewing risk controls and launch readiness
✦ Evaluating the complete AI product evidence chain
✦ Stakeholder simulation and decision-making
✦ Handling Engineering, Risk, Sales, Finance, and Executive challenges
✦ Presenting AI product recommendations to leadership
✦ Communicating decisions, evidence, trade-offs, and residual risks
✦ Identifying evidence gaps and next learning milestones
✦ Preparing the final AI PM portfolio
✦ Conducting an AI Product Decision Review
✦ Defending the AI product recommendation
✦ Presenting a final AI PM capstone project
✦ Preparing a 10-minute AI Product Decision Defence

Frequently Asked Questions (FAQ’S):

1. What is AI Product Management?
AI Product Management focuses on building and managing AI-driven products.

2. Who can join this course?
Product Owners, Project Managers, Scrum Masters, QE Managers, Developers, Engineers, and aspiring PMs.

3. Do I need an AI background?
A deep AI engineering background is not required.

4. What will I learn in this course?
AI product discovery, requirements, prototyping, testing, delivery, and measurement.

5. Will I learn AI product testing?
Yes, the course covers AI testing, quality criteria, test sets, and failure analysis.

6. Will I learn AI product strategy?
Yes, you will learn product strategy, prioritization, MVP definition, and stakeholder alignment.

7. Is there a practical project?
Yes, the programme includes an AI PM evidence pack and final decision-defence exercise.

8. What roles is this course suitable for?
It is suitable for professionals moving toward AI Product Management roles.

9. Will I learn to work with technical teams?
Yes, you will work with Engineering, Data, Design, QA, Security, and Business teams.

10. What is the main outcome of the course?
You will learn to evaluate, define, test, and defend an AI product recommendation.

Enroll For Free Demo

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For any other details, Call me or Whatsapp me on +91-9133190573

Live Sessions Price:

For LIVE sessions – Offer price after discount is 149 USD 129 USD 109 USD Or 12000 INR10900 INR 8900 Rupees

Sample Course Completion Certificate:

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Note:

To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.

To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.

We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.