Most organizations approaching generative AI are asking the wrong question.
They start with "What can AI do?" when they should start with "Where are we losing time, accuracy, or customer satisfaction right now?"
There's a real difference. The first approach leads to proof-of-concepts that impress executives but never scale. The second leads to implementations that change how work actually gets done.
I've seen this pattern repeat: a team gets excited about AI, identifies a use case that technically works, builds a prototype—and then discovers nobody's incentivized to use it because it doesn't solve a problem they felt acutely.
The discipline is in the diagnosis. Which customer-facing processes have the highest error rates? Where do your teams spend time on repetitive work that could be automated? What decisions are being delayed because you lack synthesis speed? Those are your real starting points.
The appinventiv piece walks through legitimate enterprise use cases, but they only work if you've already done this groundwork. Are you mapping your actual pain points before evaluating AI, or building AI use cases and hoping they stick?
Source and context
This commentary was originally published on LinkedIn in response to Generative AI for Business: Use Cases, Benefits & Strategy - appinventiv.com.