Agile Visa Proprietary IP

The AI-Enabled Product Owner

Moving from Backlog Administrators to Market Predictors using Agentic Workflows and AI Synthesis.

The "Why" Behind the Course

Traditional Product Owners spend 70% of their time writing user stories, grooming backlogs, and explaining acceptance criteria. In the AI Agile Operating Model, these mechanical tasks are fully automated. The AI-Enabled PO reclaims that time to focus entirely on synthetic user testing, market strategy, and outcome prediction.

  • Target Audience: Existing POs, Product Managers, and Business Analysts.
  • Duration: 2 Days (or 4 half-days online).
  • Outcome: A deployed "AI PO Portfolio" (Custom GPTs, automated Jira workflows, persona simulators).

Day 1: Synthetic Discovery & Market Prediction

Morning: 09:00 - 12:30

Module 1: The End of Guesswork (Synthetic Personas)

How to stop waiting for customer interviews and use LLMs to simulate thousands of user interactions.

  • The paradigm shift: From human focus groups to AI Synthetic Audiences.
  • Lab: Building a "Customer Simulator" GPT loaded with past user transcripts and market data.
  • Lab: Pitching a feature idea to the AI Persona to identify friction points before a single line of code is written.
Trainer Action Do not let students use generic prompts. Provide them with a massive, messy CSV of dummy customer feedback and teach them how to "train" the agent to think like their target demographic.
Afternoon: 13:30 - 17:00

Module 2: Data-Driven Prioritization

Removing emotion and politics from the backlog using AI scoring models.

  • Feeding business metrics, technical debt, and competitor data into an AI reasoning engine.
  • Lab: Designing an automated WSJF (Weighted Shortest Job First) calculator using AI.
  • Defending AI-backed prioritization to C-level stakeholders.

Day 2: Orchestration & Automated Execution

Morning: 09:00 - 12:30

Module 3: Dynamic Backlog Generation

Writing user stories is no longer a human job. Orchestrating them is.

  • The "Epic-to-Story" automated pipeline.
  • Lab: Using an agent to slice a massive Epic into 15 INVEST-compliant user stories, complete with BDD (Behavior-Driven Development) acceptance criteria (Given/When/Then).
  • Identifying dependencies and edge cases via AI logic mapping.
Trainer Action Demonstrate how AI hallucination can ruin a backlog. Teach "Verification Prompts" where the AI is forced to critique its own user stories against the INVEST criteria before outputting them.
Afternoon: 13:30 - 17:00

Module 4: The Continuous Feedback Loop

Creating systems that automatically measure outcomes against initial hypotheses.

  • Feeding sprint telemetry (velocity, bug rates) back to the AI for continuous forecasting.
  • Lab: Designing the "AI PO Dashboard" - integrating Jira/Azure data with LLM analysis.
  • Capstone: Presenting the finalized AI PO Portfolio.

Facilitation & Positioning Strategy

The "Agile Visa" Narrative

Throughout the class, trainers must constantly reinforce the Agile Visa philosophy:

  • Rule 1: AI does not replace the Product Owner. AI replaces the administration. The human becomes the editor and strategist.
  • Rule 2: Never teach "Prompt Engineering" in isolation. Always teach "Agentic Workflows" (systems where AI performs tasks sequentially with human oversight).
  • Rule 3: The goal is ROI. Every module must tie back to how this saves the company money, speeds up time-to-market, or reduces risk.
Handling Skeptics You will have traditional Agile purists in the room who say, "Agile is about individuals and interactions over tools."

Your response: "Exactly. By offloading ticket-writing and data-crunching to AI, the Product Owner finally has time to actually interact with individuals (customers and stakeholders) instead of staring at Jira all day."