Start with the problem, not the model
By Robert OrozcoSeptember 19, 20264 min read
Most stalled AI initiatives went wrong at the first step: they started from a tool instead of a decision worth improving.
A striking number of AI programs begin with an answer already chosen. A tool gets bought, a model gets picked, a pilot gets funded, and only then does anyone ask what problem it was supposed to solve. That order is backwards, and it is the single most common reason initiatives stall.
The alternative is not complicated, but it takes discipline: start from a decision or a process that is genuinely costly, slow, or risky today, and work forward from there. If you cannot name the business problem in a sentence a non-technical executive would recognize, you are not ready to choose a technology.
Three questions before any tooling
- What decision or workflow would be measurably better if this worked, and who owns it?
- What does "better" mean here: less cost, less delay, less risk, more capacity? Pick the one that matters most.
- If AI removed the constraint entirely, what would change downstream, and is anyone prepared for that change?
These questions do more than clarify scope. They surface the opportunities that are worth real investment and quietly retire the ones that only sounded exciting in a vendor demo. They also reveal when the honest answer is that AI is not the right tool yet, which is a valuable result, not a failure.
Value first, novelty never
Novelty is a poor filter. The most impressive-looking use case is often not the most valuable one, and the most valuable one is often unglamorous: a bottleneck that quietly taxes the organization every day. Ranking opportunities by value, feasibility, and risk, rather than by how modern they feel, is what turns a list of ideas into a plan leadership can commit to.
The output of this work is not a slide of possibilities. It is a prioritized portfolio, a build-versus-buy view, and a sequence: what to do first, what to do next, and what to deliberately leave alone. Get the problem right, and the technology choices get much easier. Get it wrong, and no model will save the project.
This piece relates to AI Strategy.