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Today's Product Managers juggle customer feedback, feature prioritization, roadmap planning, stakeholder communication, and cross-functional collaboration, often spending more time managing work than driving product strategy. Generative AI and Agentic AI are changing this by automating repetitive tasks, accelerating decision-making, and delivering actionable insights.
The Generative AI & Agentic AI for Product Management course equips Product Managers with practical, hands-on skills to build AI-powered product workflows. Through live instructor-led sessions, you'll learn to automate backlog prioritization, generate user stories, analyze customer research, monitor competitors, build autonomous AI agents, and integrate intelligent workflows across Jira, Google Sheets, Docs, and Slack to improve product delivery and team productivity.
Know which features to build first, RICE-scored against your actual Jira backlog and business metrics, not gut feel
Turn a raw research transcript into an exec-ready insight summary in minutes, not a day of manual synthesis
Build agents that live inside your stack — Jira, Sheets, Docs, Slack — instead of one more disconnected AI tool
Get back 8 hours a quarter on roadmap planning, 4 hours a week on feature requests, and 2 hours per launch - time that goes back into strategy
Walk out with a deployed automation your team uses Monday morning, tested on your real data - not a certificate for a demo
Objective: Build 3 Gen AI scripts that directly solve your product challenges. Each script generates artifacts you use today: prioritized features, user stories, research insights.
PART 1A: FEATURE PRIORITIZATION
Content (Live Coding)
Your Feature Backlog → RICE Scores → Ranked Priorities
Live Demo: Your actual features
Customization for YOUR Product
Hands-on Lab
Deliverable: Prioritized backlog ready to share with team
USER STORIES & RESEARCH EXTRACTION (90 minutes)
Content (Live Coding)
Feature Brief → Complete User Stories
Research Transcript → Structured Insights
Live Demos: Your products
Customization
Hands-on Lab
Deliverables
Objective: Build 3 autonomous agents that continuously work for you. No manual prompting needed—agents run on schedule and auto-generate your weekly work.
AUTONOMOUS PRIORITIZATION AGENT
Learning: Design & Deploy
Agent Workflow Design
Outcome: Weekly priorities auto-refresh without your involvement
Build & Deploy Together
Safety & Guardrails
Hands-on Lab
Deliverable: Agent running on your Jira board
RESEARCH & COMPETITIVE INTELLIGENCE AGENTS
Learning: Design & Deploy
Research Synthesis Agent
Outcome: Research processed and available in 2 minutes
Direct impact: Customer insights move FAST
Competitive Monitoring Agent
Outcome: Weekly competitive brief auto-generated
Direct impact: You spot market shifts before competitors do
Build & Deploy Together
Hands-on Lab
Deliverables
Objective: Connect agents to your entire product stack. Learn advanced patterns for multi-agent workflows.
CONNECTING JIRA + SHEETS + DOCS + SLACK
Content
Your Current Workflow
Automation Opportunities
Integration Patterns
Live Setup: Your Stack
Hands-on Lab
ADVANCED MULTI-AGENT PATTERNS
Content
Agent Chaining
Conditional Branching
Parallel Execution
Error Handling & Monitoring
Scaling to Team
Troubleshooting Common Issues
Hands-on Lab
Objective: Each participant designs and deploys a COMPLETE automation project. Something you’ll actually use after training ends.
DESIGN YOUR PROJECT
Requirements
Example Projects
Quarterly Roadmap Automation
Feature Request Intelligence
Launch Readiness System
Your Project Design
BUILD, TEST & DEPLOY
Hands-on Work
Step 1: Code
Step 2: Test
Step 3: Deploy
Step 4: Present
Deliverables
There is no exam for this workshop.
As a Senior Product Manager, I wanted to understand how Generative AI and Agentic AI could be integrated into product strategy without having a technical background. This certification gave me exactly that. The trainers explained every concept with practical examples and real product use cases, making it easy to understand. I now feel more confident discussing AI features with engineering teams and identifying opportunities to build AI-powered products. The course is well structured, practical, and definitely worth the investment.
The Generative AI and Agentic AI certification helped me bridge the gap between AI concepts and product management. I particularly liked the hands-on demonstrations and industry examples, which made it easier to understand how AI agents can improve customer experiences and business workflows. The trainers were knowledgeable and always ready to answer questions. I highly recommend this program to Product Owners who want to stay ahead in the AI era.
I joined this certification to learn how AI can influence product decisions, and it exceeded my expectations. The sessions were practical, engaging, and focused on real-world implementation rather than just theory. I now have a much clearer understanding of Generative AI, Agentic AI, and how to evaluate AI use cases during product planning. It has already helped me contribute more confidently in product roadmap discussions. Great learning experience and excellent value for money.
Using ChatGPT ad-hoc and directing AI systematically across every stage of product work are very different skills. This course covers the full product lifecycle — not just story writing — and introduces Agentic AI, where systems run autonomously in the background instead of waiting on your next prompt. That jump, from occasional prompting to structured AI workflows across discovery, backlog, roadmap, and release, is what turns AI into a genuine career advantage instead of a convenience tool.
A standard PO manages the backlog, writes stories, and represents the customer. An AI-fluent PO does all of that — and also designs and directs AI-powered workflows: feedback synthesis agents that process thousands of customer signals automatically, roadmap scenario models that simulate trade-offs before the team debates them, and release communication drafts that take minutes instead of hours. The AI-fluent PO makes higher-quality product decisions faster, and has the data to defend them. That distinction is increasingly visible in hiring decisions and compensation.
You don’t need to be an engineer — but you do need basic Python comfort. This course teaches you to direct AI systems, including some hands-on scripting to connect agents to Jira, Sheets, and Slack, so completing the Python for AI course (6 hours) before enrolling is required. If you can follow a loop and a function, you’re ready — no deeper technical background needed.
The human judgment in discovery — deciding what matters, what to ask, and what to build — remains yours. What AI changes is the scale and speed of the input layer. Instead of reading 50 support tickets manually, an agent synthesises patterns across 5,000. Instead of spending three hours crafting an interview guide, AI produces a structured first draft in minutes for you to refine. The course covers both: Gen AI for accelerating your own discovery work, and Agentic AI for building feedback pipelines that run continuously without manual initiation.
You’ll design and deploy one complete, production-ready automation — built using at least two AI agents connected to at least two of your real tools (Jira, Sheets, Docs, Slack, or Drive). Choose the track that fits your role: – Quarterly Roadmap Automation — AI prioritizes, an agent organizes, and a roadmap draft posts automatically, saving ~8 hours per quarter – Feature Request Intelligence — an agent extracts, categorizes, and prioritizes customer feedback into a weekly summary, saving ~4 hours per week – Launch Readiness System — an agent checks go-live requirements and generates a go/no-go recommendation, saving ~2 hours per launch Along the way, you’ll also build the smaller Gen AI deliverables from Modules 1–2 — RICE-scored priorities, generated user stories, and a research synthesis agent — but your capstone is the one production system you deploy and keep.
Different role, different deliverables, different focus. The Project Management course covers the project lifecycle — initiation, planning, EVM, vendor management, closure. This course covers the product lifecycle — discovery, opportunity sizing, backlog, roadmapping, hypothesis testing, go-to-market, and retrospectives. The PM course is for people delivering projects; this course is for people defining and evolving products. There is minimal overlap between the two.
With structured prompting and the right context, AI-generated roadmap analysis is genuinely useful — not generic. The course teaches you how to feed AI your specific constraints: team capacity, business goals, customer data, and competitive signals, and how to structure prompts that produce scenario comparisons you can actually use in planning sessions. The goal is not to let AI decide your roadmap — it is to use AI to prepare the analysis that makes your decision faster, better-supported, and easier to defend to stakeholders.
Yes — Business Analysts working in Agile environments will find significant overlap with their day-to-day work. The modules on user story writing, acceptance criteria, stakeholder communication, requirements documentation, and data analysis are directly applicable to the BA role. Many BAs working alongside POs take this course alongside the AI for their specific role track to build a broader AI skill set across both functions.
The specific tools will evolve — but the skills this course builds are durable. Structured prompting, knowing how to frame a product problem for AI, understanding what agentic workflows are appropriate for which tasks, and being able to evaluate AI outputs critically — these are judgment skills, not button-clicking skills. The tools change; the judgment transfers. That is why the course focuses on principles and frameworks with today’s tools as the vehicle, not the destination.