“You can’t deliver the right product if you’re only building it right.”

Dual-Track Agile separates discovery (understanding problems) from delivery (building validated solutions). With AI, both tracks become faster—but PMs must ensure that speed doesn’t lead to shallow understanding.

Why Dual-Track Works:

• Keeps teams in continuous discovery while not blocking engineering bandwidth.
• Encourages learning loops, reduces the risk of building the wrong thing.

How AI Elevates It:

• In Discovery:
  • LLMs summarize competitive intelligence from dozens of sources in seconds.
  • GPT-based tools draft user personas from CRM data.
  • Cohort behavior analysis using AI reveals latent needs.
• In Delivery:
  • AI turns epics into story breakdowns based on past sprint patterns.
  • Copilot-based engineering assistants accelerate implementation.
  • Observability bots monitor story movement and flag blockers proactively.

Expanded Use Cases:

• Figma plugins like Galileo AI that generate multiple design mockups in discovery.
• Jira Assistant bots that auto-comment updates and detect scope creep.
• Test case generation tools like Diffblue or CodiumAI auto-creating unit tests for each delivery story.
• AI-generated retrospectives summarizing what went wrong and what improved.
• Trello/ClickUp integrations that automate workflow transitions using GPT logic.

Tools:

• Figma + Galileo AI, Atlassian Intelligence for Jira/Confluence
• CodiumAI, Testim, Katalon for automated test generation
• Notion AI, Supernormal, Product Discovery tools like Maze + GPT

Case Example: Netflix’s Studio Engineering team uses dual-track AI workflows where discovery personas and hypotheses are validated using AI-processed feedback, while LLMs help automate Jira board hygiene and backlog grooming. Gojek leverages AI to simulate discovery interviews and generate potential user objections.

Risks & Watchouts: Too much AI in delivery can decontextualize dev work. Sprint goals may become diluted if AI is auto-prioritizing stories. And in discovery, AI hallucinations can lead to false positives. Always pair AI outputs with structured user validation.

Tags: AIProductManagement, FeaturePrioritization, PrioritizationFrameworks, RoadmapTools