Too many AI conversations start with platforms, data foundations and governance frameworks. I prefer to start with the work: what can we help people accomplish, where does AI add value, and what could go wrong?

These five principles bring together how I approach those questions.

1. AI for Data

Use AI to make existing data useful. It can help interpret unfamiliar structures, connect sources and expose gaps, so useful work need not wait for a comprehensive data transformation. Start with the question and improve the data where the answer requires it.

Read more: AI for Data (Not Data and AI).

2. Augmentation over Automation

Prioritize expanding what people can understand, create and accomplish. Distributing AI capability puts experimentation in the hands of people who know the work, allowing useful applications and shared learning to emerge across the organization. That offers greater potential than a centrally selected portfolio of automation projects: the organization grows its capacity to discover and solve problems.

Read more: AI doesn’t create advantage—distribution does.

3. Reserve AI for Ambiguity

Concentrate AI where interpretation, exploration and judgment are needed. Its value comes from helping resolve uncertainty; applying it to already settled rules adds cost and variability without a corresponding benefit. Make the structure explicit as understanding improves, and keep reasoning focused on what remains uncertain.

Read more: Pattern: Reserve AI for Ambiguity.

4. Don’t Use AI as the Runtime

Use AI to build dependable automation, then capture stable logic in software. Making a model repeatedly interpret the same rules creates variability that teams try to contain with ever more evaluations and guardrails. Explicit, testable code preserves what has already been learned and makes repeated execution easier to verify, operate and reuse.

Read more: Anti-Pattern: Using AI as a Runtime for Deterministic Workflows.

5. Govern AI by Blast Radius, Not Capability

Match oversight to what an application can affect and how far mistakes can spread. Model sophistication alone says little about the consequences of its use. Bounded, reversible experimentation should be easy; broader authority, sensitive data and consequential actions warrant stronger assurance. This allows capability to spread while concentrating scrutiny where it matters.

Read more: Govern AI by Blast Radius, Not Capability.