AIPGF Practitioner Tailoring by Size, Complexity, and Risk

Tailoring in Practitioner scenarios is about proportionate design, not permissive shortcuts. The framework should adapt to context, but essential governance must still survive the adaptation.

What to understand

The strongest tailoring decision looks at several variables together:

• project or programme size
• operational and stakeholder complexity
• AI-related risk and possible harm
• data sensitivity
• external visibility
• current maturity of governance capability

A low-risk internal drafting use might justify lighter review and documentation than an AI-supported prioritization decision that affects escalation, suppliers, or stakeholders. But both still require governance that is visible and owned.

Example

A PMO wants a single AI governance checklist for every initiative. That may be convenient, but it is not always proportionate. Practitioner judgment is about deciding what must stay common and what should change with risk, complexity, and context.

Common pitfalls

• Using size as the only tailoring variable.
• Assuming a high-pressure delivery context justifies skipping controls.
• Treating “internal use” as automatically low risk.