AI risk management
- AI Strategy (315)
- Generative AI (106)
- IT Leadership (33)
- Leadership (76)
- Product Management (344)
- Product Management Leadership (306)
- Uncategorized (32)
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How to Give Autonomous Agents Context and Permission to Act
A practical operating model for agents that detect work, use current context, take bounded action, and escalate before consequences outrun control.
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How to Benchmark AI Models for Cost, Quality, and Risk
A practical framework for choosing AI models by cost per accepted result, workflow reliability, failure severity, and production economics.
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How to Design AI Systems That Keep Humans in Control
A practical framework for deciding what AI may do, where approvals belong, how to stop and recover actions, and how to preserve human judgment.
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How to Roll Out AI Without Dodging the Job Security Question
A practical playbook for making credible job commitments, testing AI on business outcomes, and showing employees how their roles will change.
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Governing AI Security Beyond the Open-Weights Debate
A practical operating model for product leaders to govern jailbreak disclosure, open-weight releases, agent containment, and incident-response readiness.
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How to Give Your AI Strategy Financial Staying Power
A practical framework for sequencing AI investment, testing unit economics, preserving optionality, and surviving timelines you cannot control.
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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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Embodied AI: A Product Leader’s Guide to Generalist Robotics
Use a task-first framework to choose embodied AI workflows, measure useful autonomy, design safety boundaries, and make a sound robotics investment.
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The July 2026 AI Evaluation Lab Leak: A Leader’s Playbook
A practical governance and containment playbook for testing dangerous model capabilities without turning an internal evaluation into a real-world incident.
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How to Evaluate AI Risks That Emerge After Deployment
A practical framework for testing stateful AI across long user trajectories, monitoring drift after launch, and resisting misleading satisfaction metrics.
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How to Lead When AI Expands Roles Before Titles Change
A practical operating model for using AI across functional boundaries without blurring accountability, review, or specialist ownership.
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How to Benchmark AI Humanizers Without Gaming the Test
A practical framework for testing AI humanizers across detector disagreement, content fidelity, writing quality, latency, and workflow fit.
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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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China AI Safety Governance at WAIC 2026: A Product Playbook
A practical playbook for turning China’s WAIC 2026 signals on loss of control, agent boundaries, and cyber risk into product decisions.
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Reliable Agentic AI Architectures: A Production Blueprint
A production blueprint for agentic AI covering bounded graphs, independent verification, safe tool execution, durable state, and eval-driven rollout.
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How to Scale AI Pilots Into Everyday Operating Work
A practical operating model for choosing the right workflow, setting AI boundaries, measuring value, and scaling with auditable controls.
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Cost-Aware AI Model Selection: Pay for Accepted Work
A practical framework to compare AI models by accepted-result cost, test quality, choose hosting, and route production work with safe fallbacks.
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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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AI Product Leadership: Faster Learning, Safer Systems
A practical framework for improving product discovery and delivery while matching AI evaluation, privacy, and governance to real-world risk.
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Browse topics
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