Playbooks

Process-first guides for project managers navigating AI governance. These articles translate NIST, EU AI Act, ISO 42001, and related frameworks into PM workflows — charters, risk registers, oversight plans, vendor management, and more.

Change Management for AI Projects: Preparing People for a New Way of Working

AI change management goes beyond training people on a new tool. It requires helping them develop new mental models for working with probabilistic systems — and addressing the trust, accountability, and expertise questions that conventional IT change management was never designed to handle.

By AIPMO

/ 29 Jan 2026 · 14 min read

Third-Party AI and Vendor Management: Risks You Don't Control

When you deploy third-party AI, you deploy the decisions its developer made about training data, fairness testing, and risk mitigation — and you remain accountable for the outcomes regardless of who built the system. Here's what PMs need to know before signing the contract.

By AIPMO

/ 29 Jan 2026 · 14 min read

Monitoring AI Systems in Production: The Work After Go-Live

AI systems degrade in production even when no one touches the code. Post-deployment monitoring is not optional: for high-risk AI it's a legal requirement, and for all AI it's the only way to know whether the system is still doing what it was designed to do.

By AIPMO

/ 29 Jan 2026 · 16 min read

Testing and Validation for AI Systems: More Than Accuracy

Every AI project needs testing — but the test plan looks nothing like traditional software QA. The NIST AI RMF TEVV framework requires testing across five dimensions: accuracy, fairness, robustness, safety, and explainability. A system can pass every conventional test and still discriminate.

By AIPMO

/ 29 Jan 2026 · 17 min read

Stakeholder Engagement for AI Projects: Beyond the Usual Suspects

AI systems affect people who never appear on a traditional stakeholder register — and under the EU AI Act, informing them and giving them a mechanism to contest decisions is a legal obligation. Here's how to build a stakeholder approach that goes beyond the usual suspects.

By AIPMO

/ 29 Jan 2026 · 14 min read

Human Oversight in AI Systems: Designing for Control

Human oversight of AI isn't a design preference — it's a legal requirement. EU AI Act Article 14 mandates five specific capabilities in every high-risk AI system. As PM, each one is a project deliverable, not a governance aspiration.

By AIPMO

/ 29 Jan 2026 · 35 min read

Model Cards and Datasheets: Documentation That Matters

AI projects need documentation that doesn't exist in conventional IT: model cards, datasheets, and system cards. Under the EU AI Act, technical documentation must exist before deployment — and be retained for 10 years. Here's what each document must contain and why.

By AIPMO

/ 29 Jan 2026 · 17 min read

AI Risk Registers: Beyond Traditional Risk Management

AI risk registers use the same mechanics as conventional ones — identify, assess, mitigate, monitor. What changes is the taxonomy of risks, how likelihood behaves at scale, and the fact that the register doesn't close at deployment. Here's what needs to extend.

By AIPMO

/ 29 Jan 2026 · 17 min read

The AI Project Charter: What's Different

The AI project charter covers the same fundamentals as any charter — plus questions that conventional projects never ask: Why AI rather than an alternative? Who might be harmed? What decisions will never be automated? What data is this project permitted to use?

By AIPMO

/ 29 Jan 2026 · 13 min read

AI Impact Assessments: Running Them Like a PM

An AI impact assessment asks who could be harmed by this system — not just what could go wrong with it. Those are different questions. Here's what the frameworks actually require, why the PM needs to own it, and how to run one that produces decisions rather than paperwork.

By AIPMO

/ 28 Jan 2026 · 14 min read