Develop and manage hypotheses
How develop and manage hypotheses are reshaped as AGI capability advances.
The bottom line
Roughly 90% of the work in Develop and manage hypotheses is information-shaped — already within reach of AI delivery. The question here is not whether it shifts, but which tasks go first and who staffs the residual.
Why: Without child occupations seeded, the evaluation relies entirely on the APQC PCF top-level category lens ('Develop, Manage, and Deliver Analytics') and the process description ('Creating theories that explain empirical data' and 'feature selection'). These signals describe pure knowledge and information-transformation work rooted in data science, justifying a high digital scalar.
grounded in the economy graph · digital scalar 0.90 · digital
Browse within Develop and manage hypotheses
Related articles
No articles yet for this entity.
Recent capability events
No capability events for this entity yet.
How the work flows
Trigger: A data scientist or business analyst identifies a specific research objective or observes an unexplained pattern in preliminary empirical data.
- Identify the core business problem or research question
- Review preliminary empirical data and domain context
- Formulate initial explanatory theories
- Translate theories into specific testable hypotheses
- Determine required features and data points for testing
- Document and manage the lifecycle of the hypotheses
Outcome: A set of formally documented, testable hypotheses is established to guide feature selection and subsequent data collection.