AI coding assistants have made it trivially easy to ship software faster — and that is precisely the problem. Human developers used to absorb the gaps in a vague spec by asking questions, reading between the lines and quietly steering toward something useful. AI tools do not work that way. They take incomplete requirements at face value and build them literally, which means small ambiguities at the front of the process compound into expensive mistakes at the back. The result is shops moving at AI speed in directions they never meant to go.
Jeff Keyes, Field CTO at Allstacks and a 30-year veteran of Microsoft, Atlassian and Planview, sat down with Alan Shimel to preview his Flowtopia 2026 talk on a new operating model for product management. His thesis is blunt: the smartest organizations he is working with have flipped the script and stopped treating AI transformation as an engineering problem. The real leverage sits upstream, in how product teams scope work and write requirements before a single line of code — human or machine — gets generated.
Keyes and Shimel work through why more than 80% of companies still cannot point to meaningful ROI from their AI tooling investments. The pattern Keyes keeps seeing is faster output stacked on top of unclear intent, which produces velocity metrics that look impressive while value realization stalls. He argues requirements quality has quietly become the highest-leverage variable in AI-assisted development, and that PM craft — not prompt craft — is what separates the teams pulling ahead from the ones spinning.
What that operating model actually looks like in practice is where the preview gets concrete. Keyes lays out how PM teams need to rebuild around clarity, traceability and feedback loops that hold up when an agent, not a junior dev, is doing the implementation. With AI accelerating both the right work and the wrong work in equal measure, the organizations that fix product management first will compound an advantage the rest of the field cannot catch.