A method for decisions that must work in the real business

A plan can be sensible and still fail at specification, handoff, approval, the first transaction, or an exception. Practical Intelligence makes those points visible before the outcome is declared.

How it works

Define the business result and its baseline. See how the system actually produces that result. Find the few conditions with the most influence, including what could go wrong. Test the proposed action against evidence, risk, and decision authority. Implement, watch for signals that require intervention, and check actual effects with the people responsible for the operating or financial measure. Feed what you learn back into the system. These steps form a diagnostic logic; urgent work can start where the failure occurs.

Decisions need an owner

Before a material change, when a signal crosses a threshold, and before claiming the outcome, we ask who is authorized, what evidence is enough, and what happens if conditions change. A meeting or a slide deck is not the decision.

What does a poor decision cost you?

One misaligned decision can erode value through supply chain gaps, integration delays, or a capability the organization cannot yet support. It can also stall when it overlooks how people earn trust, exercise authority, and adopt change. The cost shows up in results, time, and operating momentum.

A contract is not a result

A negotiated reduction can look successful in a contract even when prices are loaded incorrectly, people keep buying elsewhere, or actual volume changes. We test those possibilities against transactions and the agreed accounting basis before calling a difference “leakage.” This is an illustration, not a client result.

Where AI belongs

AI can help organize permissible inputs, compare alternatives, and draft working material. The human owner checks the sources, the business logic, and the consequences. When verification or authority is weak, strengthen controls or defer the action.

Learning in practice

A leader or team can apply the same questions to one live case through private or sponsored case learning. The output is a better decision record, an owner, and a follow-up action—not a certificate.