Rich Mironov: AI Should Change Product Management... Just Not The Way You Think
One Knight in ProductShow Notes
On this episode, I speak to the returning Rich Mironov, longtime product management consultant, product leadership coach, and founder of Mironov Consulting. Rich has spent decades working with product leaders, particularly in complex B2B organisations, and is the author of The Art of Product Management and Money Stories. We explore what the current wave of AI-enabled software development really means for product management: why faster code does not automatically create more revenue, where product judgement becomes more important rather than less, and why the role of product may shift away from delivery management towards deeper discovery, commercial thinking, and go-to-market work. Episode Highlights
Faster software development does not mean faster revenue growth - AI may allow teams to generate code far more quickly, but customer budgets, market size, and demand do not expand at the same rate. More output only creates business value when it translates into adoption, differentiation, and revenue.
More products can mean more competition, not more opportunity - If it becomes dramatically easier for everyone to build software, markets may fill with more entrants and more features competing for the same customers. That can increase price pressure and make commercial differentiation harder rather than easier.
User attention becomes the scarce resource - Shipping dramatically more features pushes the burden of prioritisation onto customers, who have limited time and little interest in evaluating a constant stream of changes. Product teams still need to decide what matters enough to build, explain, and promote.
AI can automate obvious work, but judgement becomes more important as risk increases - Straightforward bugs and low-risk tasks may be good candidates for automation. As the size and impact of a change grows, teams still need to consider customer value, product coherence, economics, and the wider consequences of getting it wrong.
Not every customer request should become a feature - Faster implementation makes it tempting to connect feedback directly to delivery, but many requests are contradictory, poorly framed, commercially harmful, or only relevant to a small subset of users. Product management still requires deciding what NOT to build.
Today's product can quickly become tomorrow's feature - Lower development barriers make it easier for competitors and platform vendors to absorb standalone capabilities into broader products. Being able to build something is not enough; teams need a defensible reason why it should exist as a product in its own right.
Organisational incentives will shape how product managers use AI - If companies reward visible AI usage, coding, prototyping, or token consumption, product managers will naturally move towards those activities. That does not necessarily mean those activities represent the highest-value use of product management time.
Product management may become more 'barbell shaped' - As engineering needs less day-to-day coordination, product managers may spend less time in the middle of delivery and more time at the edges: understanding customers, markets, economics, and strategy before development, then supporting positioning, pricing, sales, and adoption afterwards.
AI transformation is an organisational problem, not just a tooling problem - Previous transformation waves showed that counting activity or mandating tool adoption does not necessarily improve outcomes. The larger opportunity comes from redesigning how work flows through the organisation and identifying where genuine bottlenecks and leverage points sit.
Commercial accountability needs to extend beyond Product and Engineering - If engineering can deliver more quickly, Sales and Marketing also need credible plans for turning that increased capacity into demand and revenue. Product leaders should connect roadmap expansion to explicit assumptions about markets, leads, quotas, adoption, and economic val
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