NPRODUCT
Insights

AI-native product leadership, in practice.

How B2B SaaS leadership teams rebuild product and engineering for AI: what to build, how to ship it in weeks, and how to make the advantage hold. Written by Guillaume Vives from operating experience, with numbers from a real transformation.

AI-native product strategy
How to build an AI-native product strategy for B2B SaaS
An AI-native product strategy for a $20M–$500M ARR SaaS company: why the bottleneck moved from building to deciding, and the seven pillars that turn that shift into revenue.
Customer insight and signal-driven decisioning
Signal-driven product decisions: turning every customer conversation into a ranked roadmap
How B2B SaaS teams use AI to analyze every sales, success and support conversation, rank requests by revenue at risk, and replace the loudest voice in the room with evidence.
Rapid AI-assisted delivery
From idea to production in 2–4 weeks: AI-native delivery for B2B SaaS
How AI-native delivery cuts idea-to-production from months to weeks: AI drafts spec, design and code behind human review gates, without adding headcount or risk.
Defensible product strategy
What makes a SaaS product defensible when AI makes features cheap
When any competitor can build your features in weeks, defensibility moves to proprietary data, workflow depth, AI value customers use and pricing tied to outcomes.
Revenue-backed product metrics and ROI
Revenue-backed product metrics: tying the roadmap to net revenue retention
How to measure product ROI in the AI era: link every release to its customer evidence and measure bookings, churn and adoption, with NRR as the number the board watches.
AI-first operating model
The AI-first operating model for product and engineering teams
How to redesign product, design and engineering around AI: small pods that own whole bets, redefined roles, human gates and a plan to raise both the floor and the ceiling.

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