The AI Product Management Lab
Analysis, frameworks and interactive tools for product managers in the AI era: from continuous discovery through spec gates and stakeholder alignment to OKRs, delivery stability and product-market fit. Every piece follows the same structure – TL;DR, evidence, interactive tool, matching use cases – and is anchored in the Teklens cycle.
Three pillars. One system for AI product work.
Every post feeds one of these hubs – building topical authority instead of scattered posts.
Product Strategy
Setting course while everything moves – roadmap, discovery, compliance and GTM on one line.
Product Execution
From PRD to release: spec, tickets, QA and rollout on one line – even when models drift.
Product Operations
From data to growth – analytics, experiments and retention for AI products that survive token costs.
The anatomy of a Jira ticket for AI product management in agentic engineering
A good Jira ticket is a prompt in 2026 – context-complete enough for human and AI agent alike. The anatomy, with BMAD, context engineering and honest evidence.
Marc Gasser · Serial Founder · GTM & MarketingModel dependency: why model-agnostic workflows are becoming a boardroom issue
On 12 June 2026 a US agency pulled a three-day-old frontier model offline worldwide – by directive, overnight, without warning. The case shows what product teams long suppressed: dependence on a single AI provider is a strategic risk. How model-agnostic workflows defuse it.
Marc Gasser · Serial Founder · GTM & MarketingDiscovery stays human, execution becomes the spec: how product managers work with AI in brownfield
Discovery and strategy stay human; execution belongs to AI agents working against an agreed spec. Why gates don't cost speed but save it – with evidence from METR, GitClear, DORA and Anthropic.
Marc Gasser · Serial Founder · GTM & MarketingThe AI roadmap: how to unify feature requests and GenAI initiatives in one plan
Classic feature planning and AI initiatives follow different logics – one delivers predictably, the other experiments. A framework that unifies both in one roadmap, with RICE scoring and code reality as the corrective.
Marc Gasser · Serial Founder · GTM & MarketingAI product discovery: finding problems AI should actually solve
Most failed AI features were solutions in search of a problem. A discovery framework against “AI for AI's sake”: four questions every AI initiative must answer before the first prompt.
Marc Gasser · Serial Founder · GTM & MarketingThe EU AI Act for product managers: compliance as a design principle, not a brake
Risk classes, fines up to 35 million euros, deadlines shifted by the Digital Omnibus – what the EU AI Act concretely means for product teams in DACH and which five building blocks belong in the product now.
Marc Gasser · Serial Founder · GTM & MarketingGTM strategy for AI products: convincing sceptical B2B buyers
Only 46 per cent of people worldwide trust AI systems – fewer still in wealthy countries. Why the AI hype works against you in DACH B2B sales, and how to rebuild positioning, pricing and launch around trust.
Marc Gasser · Serial Founder · GTM & MarketingAgile for AI: sprints that survive model training
Scrum assumes effort and outcome are coupled. Model work breaks that assumption. How to adapt sprints, definition of done and reviews without losing delivery discipline – with CRISP-ML(Q) and CD4ML as the map.
Marc Gasser · Serial Founder · GTM & MarketingThe MVP for AI: test the wizard before you build the machine
Custom models are the most expensive way to refute a wrong hypothesis. How Wizard-of-Oz tests and foundation models validate AI ideas in days – and when custom ML is worth it at all.
Marc Gasser · Serial Founder · GTM & MarketingModel drift: when your product quietly degrades
91 per cent of ML models degrade over time – without a single line of code changing. What data drift, concept drift and AI ageing mean for product teams, and what monitoring looks like that PMs can understand and steer.
Marc Gasser · Serial Founder · GTM & MarketingPM, data scientist, ML engineer: who does what in the AI product team?
AI features rarely fail at the model and often at the hand-off: responsibility seeps away between product, data science and engineering. The role boundaries that work – and the two questions every team must answer.
Marc Gasser · Serial Founder · GTM & MarketingWhy Spotify's product management won't work for your business – even less so with AI agents
Published on pedalix.com in February 2023, more relevant than ever in 2026: why copied squad autonomy fails – and why the very same lessons now decide whether AI agents bring your team speed or chaos. Substantially revised.
Marc Gasser · Serial Founder · GTM & MarketingKPIs for AI products: prompt success beats MAU
MAU and churn only see a dying AI feature once it is dead. The four measurement layers AI products need – from acceptance rate to unit economics – and how to build feedback loops that make quality measurable.
Marc Gasser · Serial Founder · GTM & MarketingCustomer journey mapping for AI: onboarding that builds trust
54 per cent of people are wary of AI – your onboarding decides whether your feature is one of them. The four moments of the AI journey, Microsoft's human-AI guidelines in practice, and an audit for your flows.
Marc Gasser · Serial Founder · GTM & MarketingProduct-led growth for AI SaaS: adoption that carries itself
PLG means the product is the sales channel. AI features can power that engine – or eat the margin. How time to value, freemium dosing and upgrade triggers work for AI products.
Marc Gasser · Serial Founder · GTM & MarketingProduct management glossary: Jira & Linear terms explained
The most common product-management terms in software development, explained briefly – with a direct mapping of Jira and Linear terminology.
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