Who Owns It When the AI Is Wrong?
“When AI gives customers bad recommendations, who answers for it? Operational AI governance requires clear decision rights, outcome accountability, and context auditability.”
Context Engineering, One Year Later
A year after coining context engineering, the idea evolved from a simple observation into infrastructure, tooling, and a knowledge management discipline.
AI-Human Partnership
What 2026 Looks Like to Me
2026 is AI's reckoning year. LLM leaderboards miss the point — what matters is ROI, accountability, and designing human-AI workflows for real operational value.
Pickles and Strategy: Why Collaborative Thinking with AI is Hard Work
AI pushes toward resolution even when questions deserve to stay open. Effective AI collaboration means learning to hold tension and resist premature answers on complex problems.
Who Decides? Architecting Human-AI Partnership
Combining the DARE framework with the Partnership Matrix to define clear decision roles in human-AI collaboration. Someone must always be the accountable Decider.
Stop Tweaking Your Prompts: Start Managing Your AI Teammate
Forget prompt engineering frameworks. The management skills you already have — clear delegation, context-setting, verification habits — are what actually drive better AI results.
All of My AI Friends Are Psychopaths
AI exhibits the behavioral patterns of psychopathy without the psychology. Understanding AI as alien pattern-matching, not human thinking, changes how we work with it.
The Partnership Matrix: How Humans and AI Can Work Together
“A 2x2 framework mapping stakes and uncertainty to define how humans and AI should collaborate, from full automation to human-led command decisions.”
Finding My Writing Style with AI
AI writing follows distinct patterns that mirror programming structures — loops, conditionals, variable assignments. Recognizing them is the first step to finding your own voice with AI.
Personal Context Engineering: The Power of Starting Small with AI
Start using AI with low-stakes personal tasks to build intuition for what works. The skills you develop — context-setting, structured requests — transfer directly to professional use.
Context Engineering
When Every Department Defines ‘Customer’ Differently
When sales, support, and billing define ‘customer’ differently, forcing consensus breaks operational context. A federated approach preserves each team’s meaning.
Everyone is Talking About Context Engineering
Context engineering is bigger than prompt optimization. It’s a discipline of preserving meaning across boundaries — practiced by historians, lawyers, and translators long before AI.
Customer-based Context Engineering
Most companies organize data around internal systems, not customers. Customer-based context engineering creates a unified index across systems without replacing them.
Context Is What Holds the System Together
“Systems share data easily but lose meaning at every handoff. Designing for coherence — shared context across teams, tools, and time — is the real systems challenge.”
The Human Multiplier: Why Domain Expertise Makes AI Exponentially More Powerful
Domain expertise multiplies AI's effectiveness exponentially. Research shows experts ask better questions, spot errors, and build on AI outputs in ways novices cannot.
Context Engineering: Why Feeding AI the Right Context Matters
How you organize and present information to AI matters more than the model itself. A framework for context engineering across knowledge architecture, integration, and implementation.
Dirty Jobs
Accountability Without Authority: Why the Product Operating Model Breaks in Service Businesses
The product operating model assumes digital teams can own outcomes end-to-end. In service businesses, they can’t — here’s how to structure shared accountability.
Product Management Thinking Needs Translation for Service Businesses
Standard product metrics fail at service companies where software supports the business, not the other way around. Measure call deflection, service efficiency, and operational outcomes instead.
The Dirty Jobs of Digital
Operational industries force humans to act as middleware between broken systems. AI can finally eliminate swivel-chair coordination work — if leaders redesign workflows instead of just automating them.
AI in Product Leadership
The Interface Roadmap: Beyond the Chat Box
AI interfaces shouldn’t default to chat boxes. Teams that build platform logic and preserve context can embed intelligence where users already work.
Back to the Future: Why AI Needs the Business Logic You Already Have
“Your teams already have the business logic AI needs — it's just implicit. Making tacit decision-making explicit is the real challenge of AI integration.”
Why Every Product Roadmap Needs Meaning
AI-driven velocity without purpose leads teams to ship faster while mattering less. Product leaders need a context roadmap alongside their feature roadmap.
The New Product Leadership: Designing for People, Machines, and Uncertainty
AI won’t replace product leaders, but it transforms the role. Five practices for leading hybrid human-AI teams through uncertainty and probabilistic systems.
Leading Products You Don’t Fully Control
“AI products behave unpredictably, creating a three-way accountability relationship between teams, systems, and users. Product leaders must shift from controlling outputs to shaping system behavior.”
Why AI-First Products Require a Different Playbook
AI-native products behave differently than traditional software. Product leaders must move beyond features and backlogs into systems thinking and emergent behavior.
The AI-Augmented Leader: Using Machines to Lead More Humanly
New research shows AI-augmented individuals match two-person teams while feeling more energized. Product leaders should use AI to reclaim strategic thinking time, not just automate tasks.
Fewer Reports, More Impact — Why Most Product Leaders Aren’t Ready
Senior product leadership roles are vanishing as AI handles coordination work. The future of product leadership is designing decision systems, not managing headcount.
Forget Agile, This Is How AI Will Actually Change Product Teams
AI won't fix Agile by automating ceremonies. Product leaders must choose: use AI to build Feature Factory 2.0, or redesign how teams collaborate and make decisions.
The End of Traditional Product Management?
AI is permanently flattening product organizations, not just during downturns. The PM role is splitting into technical Super ICs and strategic advisors as coordination work gets automated.
I Lost My Job, Then I Saw the Future of Product Leadership
AI and economic pressure are flattening product organizations. The traditional PM career ladder may not survive as smaller, AI-augmented teams replace layers of management.