data governance
- AI Strategy (315)
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Why AI Adoption Stalls After the Pilot—and How to Fix It
A practical guide to finding the data, integration, authority, context, and ownership failures that keep promising AI pilots from delivering.
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How to Evaluate Corporate Data for AI Training Deals
A practical framework for testing enterprise data rights, quality, privacy, model fit, and economic value before committing to an AI training deal.
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A Practical Privacy Control Model for AI Agent Trace Analytics
A practical control model for collecting useful AI agent traces while limiting exposure across capture, redaction, access, exports, and retention.
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AI Agent Data Privacy: A Product Leader’s Launch Playbook
A launch-ready framework for mapping AI agent data flows, minimizing collection, redacting traces, setting retention, and testing every control.
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How to Find Commercial Opportunities in Emerging AI Markets
A practical framework for turning AI market shifts into paid wedges, validating demand, and evolving repeatable services into defensible products.
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Persistent Memory for AI Assistants: A Product Playbook
A practical guide to deciding what an AI assistant should remember, operating its memory lifecycle, and measuring whether users can trust it.
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How to Design Persistent Memory for Reliable AI Agents
A practical framework for deciding what an AI agent should remember, retrieving it safely, correcting stale facts, and proving that memory helps.
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How Product Leaders Should Govern Open and Closed Frontier AI
A practical framework for choosing open or closed frontier models, limiting agent blast radius, and building security brakes your team can test.
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Persistent AI Agent Memory: A Knowledge Graph Blueprint
A practical blueprint for modeling, governing, retrieving, and evaluating knowledge graph memory that an AI agent can safely use over time.
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How to Build Persistent Business Memory with AI Knowledge Graphs
Learn how to design a time-aware, evidence-linked knowledge graph that lets AI recover current decisions, their history, and provenance.
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Enterprise AI Ownership: What Your Company Must Control
A practical framework for deciding which AI assets, learning loops, contracts, and platform layers your enterprise must control as models change.
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How to Design Production AI Guardrails for Sensitive Workflows
A practical control-plane design for routing sensitive data, constraining agent actions, enforcing approvals, testing failures, and preserving auditability.
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How to Build Trustworthy AI Diagnostics for Women’s Health
A practical framework for defining clinical scope, communicating uncertainty, preventing automation bias and governing sensitive health data.
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Connecting Product Analytics, Attribution, and Growth Decisions
A practical framework for linking journey context, governed product data, AI-assisted analysis, and measurable action without overstating attribution.
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Migrate Analytics Platforms Without Chaos: 7 Proven Lessons to Plan, Move, and Land Cleanly
Migrating analytics platforms doesn’t have to derail roadmaps or erode trust. I share seven battle-tested lessons—shaped by work with Human37 and Amplitude—that help teams align on…
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Secure System Access for AI Agents: A Phased Control Model
A practical framework for giving AI agents narrowly scoped system access through phased permissions, independent controls, and measurable safeguards.
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Supercharge Insights with Amplitude Agent Connectors: Connect Notion, Slack, Linear & More
When insights and actions live in different tools, teams lose speed and clarity. Amplitude’s Agent Connectors help by uniting Notion, Atlassian, Slack, Linear, and more with…
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Package Hack Wake-Up Call: My Playbook for Securing Cowork, Coding Agents, and Secrets
A recent wave of malicious package hacks pushed me to level up my security posture without slowing down my AI workflows. In this piece, I break…
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How to Build a Resilient Experimentation Program at Scale
A practical operating model for producing trustworthy decisions through layered evaluation, governed measurement, and reversible delivery at scale.
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Behavioral Customer Data for Proactive SaaS Retention
Learn how to turn behavioral customer data into clear risk reasons, timely retention plays, and a weekly product and customer success operating loop.
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Browse topics
- AI Strategy (315)
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