April 30, 2026
This is the fifth blog in Mike’s 8-part series about how AI can become a force for clarity and acceleration across the broader software development lifecycle.
Problem discovery is hands-on and social, encompassing interviews, observation, and research into user and stakeholder needs.
Primary artefacts include interview notes, journey maps, and business observations. These are synthesised into actionable insights that guide decision-making and are typically expressed as an initial set of user stories.
Within the file-based framework, discovery artefacts live inside iteration directories created via /iter. This structure provides initial context for the LLM while remaining navigable and reviewable by humans.
The quality of problem discovery depends heavily on how insights are captured, synthesised, and shared.
Discovery interviews provide some of the most valuable context available to product teams. They surface business drivers, success metrics, and how users experience their work and tools.
Modern AI transcription tools for interviews and meetings have matured significantly. In this workflow, interviews are captured using Granola, chosen for its ability to:
These summaries provide concise, consistent inputs to Claude Code while preserving nuance and reducing processing time.
Synthesis through instructions and orchestration
Once interviews are complete, Claude Code ingests structured summaries and produces a synthesis in minutes. A predefined template specifies the output structure, while the /synth command applies the appropriate context and instructions to generate the file.
Each synthesis is timestamped and records its source materials, enabling iterative refinement and historical review. The LLM can also propose follow-up questions to address gaps in understanding. All artifacts remain human-editable markdown files, preserving transparency and accountability.
This synthesis marks the transition from understanding problems to deciding what to build.
Read the full Evolving product discovery for AI delivery eBook
Mike Mitchell is a Product and Strategy Principal at Equal Experts, helping organisations adopt modern product practices and integrate AI into their product lifecycle in practical, sustainable ways.
His current focus is on helping product teams do more meaningful strategic work with AI, both by accelerating the routine work that crowds it out, and by making AI a more proactive, multi-modal collaborator.
Mike brings 35 years of experience in enterprise software, with a foundation of engineering followed by two decades of product leadership. He is a founder of multiple startups, has advised dozens more, and has led multiple enterprise transformations. Just prior to EE, Mike served as a fractional Chief Product Officer, guiding early- and growth-stage firms to deliver value faster through stronger product strategy and operating models. He holds a degree in Computer Science & Engineering from MIT.
Mike’s next blog in this series covers turning insights into delivery during Solution Discovery.
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