The conversation around AI in product management has become tactically obsessed. Every article offers prompt libraries or promises faster documentation. This misses the point entirely.
The real challenge isn’t learning to prompt AI tools. It’s rethinking how product organisations work when artificial intelligence can generate, analyse, and iterate on requirements faster than human teams.
The Efficiency Trap
Current thinking suggests AI will make product managers more efficient. Write user stories faster. Generate better competitive analysis. Create documentation with less effort.
This efficiency focus is wrong because it assumes current working methods are correct, just slow. AI’s real value is in challenging basic product management assumptions.
Three Core Shifts
From Documentation to Conversation
Traditional product management relies on comprehensive requirements documents. AI can generate these instantly, but this reveals that extensive upfront documentation was always a sign of poor communication.
AI lets product teams maintain context through conversation rather than document handoffs. Instead of detailed user stories, product managers can have ongoing discussions with engineers about changing requirements, with AI helping track these conversations.
From Competitive Analysis to Rapid Testing
AI can analyse competitor features in minutes, not weeks. This isn’t just faster research. When everyone has instant competitive intelligence, advantage shifts from information gathering to execution speed and deep user understanding.
The new competitive advantage is how quickly you test hypotheses with real users, not how thoroughly you analyse competitor features.
From Feature Specification to Outcome Focus
When AI can generate feature specs from high-level user needs, the product manager’s role changes from specification writer to outcome coordinator. The critical skill becomes understanding which problems are worth solving and how solutions fit broader user journeys.
Real-World Example
OpenAI’s product management for their AI platform shows what AI-era product leadership looks like. Their product managers don’t focus on feature backlogs. They coordinate complex interactions between user behaviour, model capabilities, and safety considerations.
OpenAI describes their product management role as making their product “more capable, intuitive, and helpful in people’s daily lives.” This outcome-focused language assumes AI handles traditional feature specification work.
Implementation Steps
Weeks 1-2: Process Audit Identify which product management activities involve information synthesis, pattern recognition, or structured document creation. These are candidates for AI enhancement or replacement.
Try this: Map out your current weekly routine. How much time do you spend writing user stories, competitive research, stakeholder updates, feature specifications, or market analysis? Track which activities involve collecting information from multiple sources and synthesising it into structured outputs.
Weeks 3-4: Workflow Redesign Don’t use AI to complete existing tasks faster. Redesign workflows around AI’s capabilities. Use AI to generate multiple solution approaches for single user problems, then apply human judgement to select and refine the best options.
Try this: Instead of writing one feature specification, ask AI to generate five different approaches to solving the same user problem. Rather than researching competitors manually, have AI analyse multiple competitor products simultaneously and identify patterns you hadn’t considered. Replace weekly status emails with AI-generated summaries of key decisions and blockers from team conversations.
Month 2: Measure Outcomes Track whether AI-enhanced processes create better user outcomes, not just faster completion. Speed without improved user value is pointless acceleration.
Try this: Measure whether your feature releases have higher user adoption rates, not whether you wrote requirements 50% faster. Track if your team is solving more impactful user problems, not just shipping more features. Monitor whether your competitive positioning improves, not just whether you complete competitive analysis more quickly.
The Real Advantage
Product organisations that properly integrate AI won’t just work faster. They’ll work at a fundamentally different level of strategic capability. While competitors focus on prompt engineering, winning organisations will redesign their entire product development approach around AI-human collaboration. As I’ve always said AI really is impactful when framed as ‘Augmented Intelligence’ For now, in any case!
The question isn’t how to use AI for better documentation. The question is whether traditional documentation makes sense when AI enables entirely new ways of defining, testing, and implementing product solutions