AI risk management
- AI Strategy (315)
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Reliable AI Coding Requires Four Kinds of Control
A practical framework for controlling requirements, context, verification, permissions, and recovery when teams build software with AI agents.
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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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A Layered Playbook for Package Supply Chain Security
A practical framework for reducing package risk through delayed adoption, script controls, provenance checks, dependency pinning, and AI guardrails.
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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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A Product Leader’s Playbook for Humane, Sustainable Growth
A practical operating model for balancing product growth with user agency, trust, community impact, and durable business value.
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How to Operate Always-On AI Agents Without Losing Control
A practical operating model for unattended AI agents, covering job design, permissions, task state, failure handling, cost controls, and scaling.
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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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How to Prove the ROI of an AI Product Before You Scale It
A practical system for tying AI product behavior to incremental revenue, real cost savings, and risk-adjusted launch, scale, or rollback decisions.
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How to Deploy an Operator AI Agent in Customer Operations
A practical playbook for selecting operator-agent workflows, designing safe actions, and measuring improvements in customer operations.
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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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AI Product Validation: From Promising Demo to Proven Value
A practical evidence ladder for validating AI demand, model quality, user value, safety, and unit economics before expanding production exposure.
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Auditable AI Code Review: A Practical Operating Model
A practical operating model for AI code review that makes approvals explainable, limits autonomy by risk, and improves through production evidence.
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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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Stop Forcing AI to Prove ROI: A Product Leader’s Playbook to Measure Real Business Value
AI ROI isn’t elusive—it’s mismeasured. I outline a practical playbook that ties leading indicators to revenue, cost, and risk through a clear driver tree and disciplined…
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Product Management Isn’t Dead: Why ‘Product Builders’ Will Win in the AI Era—and How to Upskill Now
“Is product management dead?” Not even close. The role is evolving as AI raises the baseline, compresses cycle time, and nudges the classic product trio toward…
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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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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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Agentic AI for Clinical Trial Operations: A Practical Playbook
A practical framework for choosing, designing, validating, and governing clinical trial agents while keeping accountability and audit evidence intact.
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Browse topics
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