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- AI Strategy (429)
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- Product Management Leadership (373)
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How to Build a Trusted AI Product Platform That Scales
A practical operating model for building AI platforms that earn trust through governed data, verifiable outputs, controlled actions, and measurable outcomes.
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Product Experimentation for AI Systems: A Practical Playbook
A practical playbook for testing prompts, retrieval, and policy changes with decision-ready metrics, safe rollouts, and reliable instrumentation.
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How to Scale AI Customer Experience Without Losing Quality
Build an AI CX quality system that combines risk-based monitoring, explicit scorecards, human review, and a closed product improvement loop.
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Stop Flying Blind with AI Agents: Put Users at the Center with Pendo Agent Analytics
AI agents often launch fast but learn slowly because teams track deployments instead of outcomes. Enterprises are spending 93% of their AI budget building agents and…
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How to Build a Continuous Improvement Loop for AI Support
A practical operating model for measuring AI support outcomes, learning from failures, testing fixes, and shipping improvements without regressions.
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A Practical Strategy for Foundational AI Platforms and Analytics
A practical framework for choosing platform capabilities, closing governance gaps, linking AI evaluation to analytics, and scaling adoption.
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Behavioral Analytics That Crush Fraud: Spot Anomalies, Prioritize Risk, Act with Confidence
Fraud teams don’t need more alerts—they need clarity, speed, and confidence. Behavioral analytics delivers that by surfacing true anomalies, prioritizing risk by impact, and enabling fast,…
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From Chaos to Clarity: My Proven Playbook to Scale an Analytics Taxonomy That Sticks
Too many teams drown in scattered events and conflicting metrics. I share the playbook I use to scale an analytics taxonomy that actually lasts: start with…
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Reliable AI Agent Orchestration: A Production Playbook
Learn how to bound AI-agent failures, control tool side effects, trace every handoff, and release orchestration changes without betting user trust.
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How to Build AI-Ready Product Analytics and Experiments
A practical system for linking AI quality, user behavior, risk, cost, and controlled experiments to customer outcomes before you scale.
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How to Scale Trustworthy Enterprise Analytics With AI Agents
A practical operating model for grounding, evaluating, governing, and scaling AI analytics agents without losing metric consistency or stakeholder trust.
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Build CX Scores You Can Defend: My 5-step playbook for transparent, trustworthy AI metrics
Support leaders shouldn’t have to take a metric on faith. I explain how we built CX Score to be transparent, statistically validated, and aligned with expert…
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Building Reliable AI Agent Systems: A Product Leader’s Playbook
A practical operating model for scoping, evaluating, shipping, and scaling AI agents that stay safe, observable, and economical in production.
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Reliable AI Infrastructure: A Product Leader’s Playbook
A practical framework for exposing silent AI failures, hardening runtime paths, controlling releases, and turning SLOs into product decisions.
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The Safety of Speed: 180 Deploys a Day, 12‑Minute Releases, 99.8%+ Availability
Speed and safety are not opposites—they reinforce each other when you ship in small, frequent batches. By automating our pipeline end-to-end, we move from merge to…
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Agentic AI for Construction Tendering: A Product Playbook
A practical playbook for narrowing the tendering wedge, designing specialist agents, evaluating each decision, and earning safe autonomy.
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How to Govern AI Agents With Product Analytics That Drives Action
A practical operating model for instrumenting AI agents, enforcing safe authority, setting release gates, and proving business outcomes.
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Context-Driven AI Product Engineering That Survives Production
Learn how to design context contracts, retrieval pipelines, evaluations, and ownership so an AI feature stays grounded and useful in production.
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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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Enterprise AI Foundations: An Operating Model That Scales
A practical blueprint for data, governance, evaluation, and decision rights that turns scattered AI pilots into a scalable enterprise capability.
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- AI Strategy (429)
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