data governance
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Unlocking AI Agents: The Real Barrier Is Readiness—Not Capability—Here’s How to Scale
The biggest barrier to scaling AI Agents isn’t model capability—it’s organizational readiness. I break readiness into five types (content, scope, procedural, data, execution) and show why…
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Governed AI Analytics in Financial Services: A Playbook
A practical operating model for deploying AI analytics with clear data boundaries, human review, audit evidence, and measurable business value.
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AI-Enabled Enzymatic Recycling: A Product Leader’s Playbook
A practical framework for turning AI-designed recycling enzymes into an industrial product, with stage gates for data, lab proof, scale, and cost.
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From Internal FinOps Agents to Customer-Embedded Optimization
Learn how to turn internal FinOps agents into governed, customer-embedded workflows that improve cloud cost-to-value and product learning.
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How to Scale Session Replay Without Sacrificing Privacy
A practical operating model for scaling session replay with strict capture controls, performance budgets, targeted sampling, and measurable governance.
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Supercharge Claude and Cursor with Amplitude Plug and Play: Your AI Analytics Expert in One Install
I’m introducing Amplitude Plug and Play, a new AI plugin for the Claude and Cursor marketplaces. With a single-install, it turns your favorite AI client into…
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Developer-First Amplitude Instrumentation You Can Trust
A practical workflow for making Amplitude events reviewable, testable, and trustworthy from tracking-plan design through production monitoring and repair.
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AI Product Data Security: A Practical Playbook for PMs
A practical playbook for mapping AI data flows, assigning risk, selecting vendors, and embedding security controls without blocking delivery.
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Build an AI Toolbox That Improves Product Management
A practical operating model for choosing AI tools, grounding them in product evidence, measuring workflow gains, and scaling with clear guardrails.
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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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How to Ship Responsible AI Products in Regulated Healthcare
A practical delivery model that turns healthcare AI data boundaries, evaluation, staged rollout, and production monitoring into release decisions.
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Bad Advice from Your AI Clone? Ethics, IP, and How Product Leaders Protect Quality
AI “clones” can sound like you while giving advice you’d never give. In this analysis, I unpack the ethics, IP risks, and product-quality pitfalls of building…
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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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Human-in-the-Loop Mastery: Proven Oversight Tactics That Elevate AI Quality and Trust
Human-in-the-loop oversight is the most reliable lever I use to improve AI quality and build trust. By combining retrieval-first pipelines, prompt engineering, and eval-driven development with…
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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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Multi‑Agent Systems Demystified: Why One AI Isn’t Enough—and How I Ship Faster With Many
Multi-agent systems turn a single clever model into a reliable product engine by coordinating specialized agents—planner, executor, and verifier—inside robust AI workflows. I use a retrieval-first…
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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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