AI is bacon. The economics decide where it belongs.

The bacon analogy is intentionally simple. Bacon can be wrapped around something, sprinkled over it, or mixed into it. AI is similar in one important respect: it can touch almost any part of an industry.

That flexibility is exactly why a company needs a disciplined way to decide where AI belongs.

Possible is not the same as valuable

A use case should survive an economic test. What burden disappears? Which decision improves? Whose capacity is released? What becomes faster, safer, more accurate, or more commercially useful? What happens if the system is wrong?

If those questions do not produce a meaningful answer, adding AI may create more complexity than value.

AI can be applied almost anywhere. The job is to identify where it changes the economics enough to deserve implementation.

Context changes the implementation

Sprinkling AI into a marketing workflow is different from placing it inside a regulated financial process. The second case may require specialized controls, evidence, security, auditability, and implementers who understand the operating environment.

The model is only one part of the system. Data quality, permissions, human checkpoints, failure modes, and the consequence of a bad decision determine what responsible implementation looks like.

Start with one defensible application

A company does not need to solve every AI opportunity at once. It needs one workflow where the current burden, the expected value, the accountable owner, and the acceptance threshold can be made explicit.

That first implementation becomes a learning system. It shows the organization how to evaluate opportunities, how to preserve judgment, and how to decide what earns the right to scale.