Three tasks that already delegate well today
First: status reports. AI tools can produce a first draft from schedule, budget, and metrics data, with statements on project progress, timeline status, and financial standing. Second: meeting summaries. AI assistants can join meetings, log decisions, and automatically assign tasks with owners – a task that reportedly used to cost two to three hours per week. Third: risk register analysis. Existing risk entries can be enriched with suggested mitigations and a probability estimate based on the data on file.
The consistent principle: co-pilot, not autopilot
The same principle runs through nearly all current sources for all three use cases: AI delivers a draft, not a decision. Timelines, budget, scope, client communication, resource allocation, and the final assessment of a project risk stay human responsibility – regardless of how convincing the AI draft looks.
Why this line isn't a formality
This line isn't a pure precaution – it connects directly to the core skill described in a previous module. An AI-generated status report can look plausible and complete while containing an outdated budget figure or a misread schedule shift. Blurring the line between draft and decision transfers exactly the error-proneness the judgment module warned about directly onto your own project decisions.
Practice section: a simple two-question rule
Two questions help before every delegation decision: is this about turning existing data into a draft (delegable), or a decision with real consequences for budget, timeline, or stakeholder relationships (not delegable)? And: would an error in this draft reliably stand out before it's used? Only when both questions point toward delegable should an AI draft be used further without an additional review step.