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How to Structure Prompts for a Reliable AI Resume Coach
A practical prompt architecture for building an AI resume coach that stays grounded in evidence, rewrites consistently, and can be tested before release.
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Trustworthy AI Product Engineering: From Demo to Daily Use
A practical operating system for making AI outputs traceable, uncertainty actionable, failures bounded, and quality measurable in customer workflows.
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AI Product Management Skills: A Practical 12-Month Roadmap
A 12-month roadmap for building AI literacy, evaluating prototypes, shipping reliable workflows, and scaling product governance.
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Operationalizing AI: A Practical System for Scalable Growth
A practical operating model for choosing AI use cases, designing controlled workflows, measuring value, governing risk, and scaling what works.
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A Practical Governance Model for Enterprise AI Support Agents
A practical operating model to control AI support-agent autonomy, meet service obligations, preserve audit evidence, and manage change safely.
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Beyond Accuracy: The Trust-First Evaluation Metrics I Use to Scale High-Impact AI Products
Trust—not accuracy—determines whether AI features earn adoption, retention, and long-term impact. In this piece, I share the layered metrics I use to evaluate model quality and…
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How to Run AI-Augmented Workflow Experiments That Matter
A practical playbook for testing AI-assisted workflows with clear hypotheses, full-job metrics, guardrails, and evidence-based autonomy decisions.
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AI-Ready Data Governance: A Practical Trust Framework
A practical operating model to trace AI inputs, enforce access, automate quality controls, and prove trust without slowing delivery.
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Own Your AI: 4 Essential Roles to Supercharge Support and Prevent Performance Drift by 2026
AI doesn’t fail because models are weak; it fails when no one owns performance. In this third installment of my 2026 customer service planning series, I…
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AI Product Owner in 2026: The High-Impact Role Every Team Needs to Win With AI
The AI Product Owner is the keystone role that converts AI strategy into measurable outcomes. I explain how it differs from traditional Product Management, the core…
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Mastering Data Governance in the AI Era: Move Fast, Reduce Risk, and Unlock Trusted Insights
AI is changing the rules of data governance, and the winners will combine speed with trust. In this piece, I share a practical blueprint for building…
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How We Built an AI Sleep Coach: CBTI, Voice AI, and a Product Playbook for Better Rest
I break down how Rest turned CBTI into a voice-first AI sleep coach that feels personal, safe, and effective. The team discovered a high-intent niche in…
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High-Quality Data, High-Velocity AI: My Product Playbook for Governance, Trust, and Scale
High-quality data is the fastest path to real AI impact. I outline how data contracts, privacy-by-design, and a unified analytics platform create the foundation for trustworthy,…
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AI Won’t Replace Engineers—Engineers Using AI Will: A Practical Playbook for Your Next Move
AI is automating tasks, not eliminating software engineers. The real opportunity is to shift from rote implementation to systems thinking, product discovery, and responsible AI Strategy.…
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How to Evaluate AI Voice Support in Real-World Conditions
A practical framework for testing AI voice support across live conversation, backend actions, failure recovery, escalation, and rollout readiness.
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Agentic AI for Incident Response: A Practical Operating Model
A practical operating model for scoping, governing, evaluating, and rolling out incident-response agents without surrendering human control.
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AI at Home, Impact at Work: Experiments That Supercharged My Product Leadership
I reflect on a standout All Things Product conversation that reinforced how low-stakes AI experiments at home can dramatically improve how we build AI-powered products at…
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How to Build an Evaluation-Driven AI Innovation Strategy
Build an AI innovation system that links portfolio bets to customer outcomes, release gates, and clear evidence for killing, refining, or scaling them.
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AI-Enabled Product Management: A Practical Operating Model
A practical operating model for using AI in discovery, planning, and execution while keeping evidence, judgment, and accountability in human hands.
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What I Learned from Trainline’s Agentic AI: Building a Trusted Travel Assistant at Scale
Agentic AI only works at scale when orchestration, tools, and guardrails are designed together. Trainline’s approach demonstrates how to pair scalable reasoning with deep domain context,…
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