Latest posts
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A Practical Test for AI-Assisted Product Discovery
When you use AI in product discovery, the tempting move is to treat the summary as the customer signal. A product team finishes a week of calls, asks for the themes, and gets a clean answer: customers want “faster onboarding.” That sounds useful because it is short, confident, and easy to bring into a roadmap…
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Design operating contracts for AI Agents personas
In July 2025, Replit’s AI coding agent reportedly deleted a production database during a code freeze after being told not to make changes. The model made a bad call. What matters for product teams is that the environment still lets the bad call execute. That is the shift product teams need to absorb. When an…
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AI Agent Governance: The Control Plane for AI Work
The market wants to know how much an AI agent can handle on its own. Enterprises, on the other hand, care about whether they can accept the agent’s actions. In high-risk enterprise workflows, the most successful systems will not be the ones that act alone. Instead, they will be the ones whose actions a company…
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Know the agent before it moves the money
A bank receives an instruction to move $4.8 million from a corporate treasury account. The request does not come directly from a human user. It comes from an AI agent operating inside the company’s finance stack. The bank has to decide whether the instruction is a valid delegated action, a misconfigured workflow, or a compromised…
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The AI product is the workflow
When you walk into most enterprise AI conversations today, the discussion starts with the model. Is it smart enough? Is the demo impressive enough? Does the feature set cover enough ground? But talk to buyers in operational, customer-facing, or regulated work and the questions shift. What job does this system do? What is it allowed…
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The new AI SaaS lock-in is operational
One of the most overlooked questions in an AI SaaS evaluation is not “what features are included.” It is “what happens when we need this vendor to stop?” That question sounds defensive until you watch a buying team realize what the demo is really showing. A customer-service platform is no longer only summarizing tickets or…
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The AI moat is moving to the last mile
As frontier models converge for a growing class of work, raw AI capability explains less of the difference between products. That is a narrower claim than it sounds. Model quality still matters. Accuracy matters. Latency matters. Long-context reasoning, code generation, multimodal performance, tool use, and safety behavior can decide whether a product works at all.…
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AI products earn autonomy one workflow at a time
A demo only has to impress once. An AI product has to work every day. The first version looks magical in a conference room: it answers the clean prompt, completes the happy path, and suggests that broader autonomy is one launch away. Then real users arrive. They ask incomplete questions, use old terminology, need exceptions,…
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The PM thinking stack
AI makes it easier to produce plausible work. That makes it more important for PMs to know which work should survive. A PM can now generate a research synthesis, mock a prototype, draft a PRD, summarize support tickets, ask an agent to update a system, and compare five product directions before lunch. The visible output…
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The last-mile AI strategy test
The easier it gets to add AI, the more valuable it becomes to know where AI does not belong. Most teams no longer struggle to access models that can summarize, draft, classify, route, recommend, answer, and act well enough to produce impressive demos. They struggle to turn that capability into something users trust, repeat, and…
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I enjoy solving complex problems with AI-driven solutions, blending technology, strategy, and creativity. With a hands-on approach, I turn ideas into practical, impactful products.