Why shared context is the missing layer in AI Product Delivery

This is the second blog in Mike’s 8-part series about how AI can become a force for clarity and acceleration across the broader software development lifecycle.

The growing velocity gap between product and engineering highlights a persistent challenge: the communication gap between these disciplines. If product intent, technical constraints, and evolving context are not shared effectively, from the outset, this gap widens rapidly.

As Equal Experts has shown in building proven patterns within AI-enabled engineering teams, a shared context across discovery and specification is the mechanism through which product and engineering teams remain aligned as speed increases.

Beyond this foundation, we have devised an additional set of guiding principles to shape the solution:

• Portable design – works across LLM ecosystems
• Transparent artefacts – everything is human-readable and editable
• Scalable structure – multiple teams can operate from the same foundation
• Iterative process – supports incremental discovery and development
• Human-in-the-loop checkpoints – critical milestones require explicit review

“These principles point toward a concrete question: how should product discovery artifacts be structured so they can be shared, evolved, and reliably used by both humans and AI?”

About the author

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 the architecture behind AI-enabled Product Discovery.