AI risk management
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
-
Designing AI Agents That Can Safely Act for Your Users
A practical framework for defining agent authority, building safe action flows, measuring execution, and expanding autonomy without losing user trust.
-
How Autonomous AI Changes the Work of Product Leaders
A practical operating model for delegating ambitious work to autonomous AI while preserving human judgment, decision rights, security, and accountability.
-
How Product Leaders Should Govern Frontier AI Releases
A practical release gate for assessing frontier-model capability, misuse controls, enterprise privacy, and dependency risk before you ship.
-
Claude Fable 5.1: A Practical Adoption Guide for AI Leaders
A practical framework for deciding where Claude Fable 5.1 belongs, how to test its judgment, model whole-run cost, and govern sensitive use.
-
How Financial Product Teams Can Earn Consumer Trust
A practical framework for designing, measuring, and governing trust across high-stakes financial journeys without reducing it to a brand score.
-
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.
-
A Practical AI Evaluation Workflow for Product Teams
Build a repeatable AI eval workflow that turns vague quality debates into traceable failure rates, safer experiments, and clearer release decisions.
-
AI Agent Governance Infrastructure: A Practical Control Plane
A practical framework for governing AI agents through identity, scoped authority, transaction controls, audit evidence, and incident response.
-
Designing AI Products for Chip and Platform Volatility
A practical framework for choosing AI infrastructure, testing provider claims, and building exit paths before platform shifts reach customers.
-
Enterprise AI Agent Audits: A Framework for Safe Execution
A practical framework for proving that enterprise AI agents complete real work, stay within authority, leave evidence, and recover safely.
-
How to Critically Evaluate AI Answers Before You Act
A practical framework for testing AI claims, exposing assumptions, matching scrutiny to risk, and keeping human judgment in consequential decisions.
-
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.
-
How to Build Governance Infrastructure for Autonomous AI Agents
A practical control-plane blueprint for identifying agents, limiting delegated authority, monitoring behavior, and preserving recourse.
-
The Organizational Infrastructure Responsible AI Actually Needs
A practical operating model for assigning AI ownership, limiting agent authority, funding human oversight, and turning failures into safer systems.
-
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.
-
How Product Leaders Should Evaluate Emerging AI Tools
A practical framework for choosing, testing, and governing emerging AI tools without turning promising demos into unmanaged workflow clutter.
-
How to Design AI Benchmarks That Drive Product Decisions
A practical framework for building AI benchmarks that predict product behavior, expose risky failures, and support confident release decisions.
-
How to Roadmap AI Agents, Robotics and Dedicated Hardware
A practical framework for sequencing agent autonomy, robotics, and dedicated hardware without letting compelling demos outrun product evidence.
-
Designing AI Products That Learn Without Losing User Trust
A practical framework for placing AI in the workflow, capturing meaningful feedback, governing adaptation, and turning usage into safer product learning.
-
Frontier AI Updates: A Product Leader’s Adoption Playbook
A practical framework for turning model, inference, open-weight, agent, and multimodal releases into measured product and architecture decisions.
Weekly digest
One email a week on AI products, enablement and hiring. No fluff.
Browse topics
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
Work with me
45-minute consultation on AI product strategy, GTM and PM hiring — no charge.