AI investment
Leaders are under pressure to “do something with AI” long before they have a usable way to decide what actually deserves investment. That pressure is understandable. The problem is that the organization usually starts spending before it has separated a meaningful opportunity from a fashionable distraction.
A fundable AI opportunity should satisfy four tests.
The opportunity should change a decision, workflow, or operating outcome that matters to someone with budget and accountability.
Value does not need to be perfectly forecast, but it should be specific enough to compare against cost, feasibility, and risk.
Data, governance, support ownership, and team structure need to exist or be buildable within a realistic sequence.
If the concept works only in a controlled environment, it is not yet an investment case. It is a learning exercise.
Most weak portfolios fail because they start with the technology, not the decision. They treat AI like a list of capabilities to sprinkle across the business instead of a set of disciplined bets that need a reason to exist.
That is why the first useful question is not “What can AI do here?” It is “What high-stakes decision, customer outcome, cost structure, or delivery bottleneck are we trying to change — and what would make that change real?”
This is the exact problem the readiness sprint is designed to solve.