How to identify AI opportunities inside real workflows
The best AI opportunities are usually found inside a specific, recurring decision, not in a general brainstorm about what AI could do.
Most organisations start an AI initiative by asking a broad question: where could AI help us? That question rarely produces a workable answer, because AI value depends on the specific structure of a decision, not on the technology's general capability.
A more useful starting point is to look at recurring decisions and tasks that already exist in the operation. Where does a person regularly gather evidence from multiple sources, weigh a small number of options, and choose an action inside a known set of constraints? That pattern, repeated often enough, is where AI assistance tends to earn its place.
Once a candidate decision is identified, three questions determine whether it is a genuine opportunity: is the data available and trustworthy, is the decision structured enough to support a defensible recommendation, and is there a clear point at which a human can review and approve the outcome. If any of these is missing, the opportunity needs more groundwork before a build is justified.
Opportunity identification is therefore less about technology scanning and more about operating discovery: watching how work actually happens, not how a process diagram says it happens.