Processes

What will AGI do for Develop and manage hypotheses?

AI-deliverabilitydigital

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.

A data scientist or business analyst identifies a specific research objective or observes an unexplained pattern in preliminary empirical data.

Trigger
A data scientist or business analyst identifies a specific research objective or observes an unexplained pattern in preliminary empirical data.
Outcome
A set of formally documented, testable hypotheses is established to guide feature selection and subsequent data collection.

The work itself

Grounded Work Profile

Measured by

  • Hypothesis Validation RateprocessProfile
  • Hypothesis Formulation Cycle TimeprocessProfile
  • Feature Relevance ScoreprocessProfile

Key steps

  • Identify the core business problem or research questionprocessProfile
  • Review preliminary empirical data and domain contextprocessProfile
  • Formulate initial explanatory theoriesprocessProfile
  • Translate theories into specific testable hypothesesprocessProfile
  • Determine required features and data points for testingprocessProfile
  • Document and manage the lifecycle of the hypothesesprocessProfile

How AGI delivers it

Four ways AGI delivers for Develop and manage hypotheses

  • Services-as-Software

    Get the professional outcome delivered as software, priced on results, not headcount.

    Services.do
  • Autonomous Agents as digital employees

    Hire a digital employee that does the job under earned, supervised autonomy.

    Agents.do
  • Business-as-Code

    Encode how your work runs, once, as software that executes itself.

    Platform.do

Value flow

How Develop and manage hypotheses connects

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