Tag
AI Product Management
17 articles tagged with AI Product Management.
I Don't Deal in Hype: Why AI Product Leaders Must Also Build
Product leaders who have not felt latency or wrestled with hallucinations first-hand build AI strategies on fantasy. The case for builder-leader identity.
Tests Pass. Does It Think?
When AI writes the code, green CI isn't enough. The new discipline is understanding and defending the choices the model made — not just the ones you made.
The E-Shaped Product Leader: Stacking Skills Beats Silos
AI collapses the cost of cross-domain competence. The career advantage belongs to people who stack skills, not the ones who go deeper in a single silo.
The AI Pricing Stack: Usage, Outcome, and Hybrid Models
Per-seat pricing is dying but the replacement is not simple. A practical framework for AI pricing that covers usage-based, outcome-based, and hybrid models.
AI Governance Without Bureaucracy: A Framework That Ships
Most AI governance is either theatre or a bottleneck. A risk-tiered framework built from shipping AI features to AFSL-regulated Tier 1 banks in production.
How to Measure an AI Product (When Traditional Metrics Lie)
DAU, time-in-app, and NPS were built for a world where humans do the work. AI products need different metrics. A framework for what to measure and why.
Enterprise AI Adoption Playbook: Why Seats Go Unused
AI features that work in demos fail in deployment because adoption is a product problem, not a training problem. A playbook from rolling out AI to Tier 1 banks.
Build for the Model That Doesn't Exist Yet
Your AI product market fit depends on a model that has not shipped yet. Build your product architecture for the capability curve, not today's snapshot.
Your RAG Pipeline Is a Product Decision, Not an Engineering One
Chunking, retrieval, and grounding are not engineering details. They are product decisions that determine whether your AI feature helps or hallucinates.
Latent Demand Is Your AI Roadmap
The best AI products aren't imagined. They're discovered by watching how people misuse your existing ones. A framework for finding what to build next.
Product Discovery Is Dead: Why Prototyping Replaced It
The 6-week discovery sprint is a relic. When you can build a working prototype in a weekend, the fastest path to insight is shipping, not researching.
Taste Is the Last Skill AI Can't Commoditise
AI commoditises execution. The scarce resource is knowing what to build, for whom, and when to stop. That's taste, and it's the career bet worth making.
Your Agent Evals Are Vibes. Here's How to Make Them Infrastructure.
Most teams evaluate agents with manual chats and gut feel. A practical framework for eval suites that let you ship, starting with 20 examples, not 20,000.
The Translation Layer Is Dead. Here's What Replaces It.
Agentic coding compressed the PM translation layer to zero. The three skills that matter now: problem shaping, context curation, and taste as judgment.
AI Strategy Needs Hands-On Experience, Not Slide Decks
AI strategies fail because leaders set direction for a capability they have never used. You cannot strategise well for a material you haven't touched.
Stop Building AI Agents. Start Building SOPs Wrapped in Code.
A 5-step agent at 95% accuracy per step is only 77% reliable. The path forward isn't better agents, it's narrower ones. Three rules for workflows that ship.
The Product Manager Is Dead. Long Live the Product Builder.
Meta PMs vibe code prototypes for Zuckerberg. LinkedIn scrapped their APM program. The PM role is being redefined, and the new skillset is prompt, build, eval.