data governance
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How to Operationalize Amplitude AI Visibility Upgrades
A practical operating model for using Amplitude AI Visibility to speed segmentation and reporting without weakening governance or trust.
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Build vs. Buy in an AI-First World: My Framework to De-Risk Decisions and Own Your Data
Build vs. buy shows up in every product organization, and AI is changing the calculus without eliminating the tradeoffs. In this first-person framework, I walk through…
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Building Physician‑Grade AI When Trust Is Everything: Inside Healio’s Proven Playbook
Physicians won’t trust AI until it proves itself. Here’s how Healio built a physician-grade assistant with RAG and hybrid search across trusted sources like PubMed, designed…
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AI Product Governance: A Practical Operating Model for PMs
Turn AI ethics into an operating system with risk tiers, release gates, clear ownership, vendor checks, monitoring, and rollback plans.
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A Practical Framework for AI-Era Build-versus-Buy Decisions
A practical framework for separating strategic AI IP from commodity tooling, comparing true ownership cost, and preserving a safe exit as scale changes.
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AI Transformation Is an Operating Model, Not a Feature Roadmap
A practical operating model for turning scattered AI pilots into measurable outcomes, safer releases, and a faster organizational learning loop.
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Governed Agent Analytics: From Support Signals to Adoption
A practical model for linking support performance to product adoption while enforcing clear data, access, experiment, and scale controls.
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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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Amplitude Browser SDK: Turn Web Vitals Into Product Decisions
A practical model for sending Web Vitals through Amplitude, protecting page performance, and tying LCP, INP, and CLS to product growth.
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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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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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Unlock Real-Time Product Insights: Amplitude + OpenAI MCP in ChatGPT, Without BI Bottlenecks
Connecting Amplitude analytics to ChatGPT via OpenAI’s MCP brings product insights directly into the flow of work. I can ask natural-language questions about activation, retention, and…
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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 Turn Unified Product Analytics Into a Growth System
Build a trusted path from product behavior to business outcomes, then use shared metrics, experiments, and governance to make better growth decisions.
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Evidence-Driven Product Analytics: From Signal to Decision
A practical operating system for turning behavioral signals into sound hypotheses, reliable experiments, and product decisions your team can defend.
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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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How to Build Self-Service Analytics Teams Actually Trust
A practical operating model for giving product teams faster answers without sacrificing metric consistency, data quality, privacy, or analyst leverage.
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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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