Intelligence Allocation & BASICS
Intelligence allocation starts with a business outcome, locates the decision or knowledge bottleneck that limits it, sizes the value of removing that constraint, and chooses the least complex intervention that can prove the change. BASICS makes that sequence explicit before teams commit to a model or vendor.
Key takeaways
- The best AI use-case framework starts with business friction, not model capability.
- BASICS prevents teams from calling every automation idea an AI use case.
- Sequencing is the difference between a portfolio and a pile of experiments.
Decision brief
Intelligence allocation starts with a business outcome, locates the decision or knowledge bottleneck that limits it, sizes the value of removing that constraint, and chooses the least complex intervention that can prove the change. BASICS makes that sequence explicit before teams commit to a model or vendor.
Decision criteria
- The value pool is attached to an outcome with a baseline and owner.
- The scarce intelligence point is visible inside a real workflow.
- The selected intervention is simpler than plausible alternatives.
Evidence to check
- Observed workflow friction and decision latency.
- Financial value range with assumptions and sensitivity.
- Data readiness, adoption path, risk class, and sequence dependencies.
What I would do Monday
Run the first three BASICS steps on one proposed use case: name the business outcome, document the current friction, and size the value pool before anyone discusses models.
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