Which business process should AI improve first?

An example from a competitor or a conference can suggest a good idea. It is not enough to choose a project: you need to understand whether the same activity has value, usable data and suitable conditions in your own business.

I compare four things: expected value, feasibility with available tools, access to data and an internal owner. A weakness does not always rule out a case, but it needs an explicit response: what is missing, who can address it and how to check it before investing further.

The question changes from "which case excites us most" to "which case can we test with a useful outcome and clear checks". The choice remains a stated trade-off, not a combined score that makes the decision for the team.

This framework is Creaitivo's operating proposal, not a methodology validated by Gartner. Gartner's 2024 forecast highlighted data, risk, cost and value problems in GenAI projects. That provides context for useful questions, not evidence that these four criteria match those causes or guarantee success.

Each criterion answers a different question, and has to be assessed separately before being combined. Conflating them is the most common way a reasonable choice quietly turns into a wrong one.

Because the visibility criterion inside a company rewards outward-facing cases, sales and marketing, where the result is easy to put on a slide. MIT NANDA's 2025 report The GenAI Divide, a study of over three hundred enterprise AI initiatives, finds that more than half of generative AI budgets go to sales and marketing tools, while the highest and most measurable return shows up in back-office automation: finance, procurement, internal operations.

This is not an isolated finding. It is the predictable effect of a selection process driven by whoever has to show the result, not by whoever has to measure it. A back-office case rarely makes it into a board demo, but it is the one with cleaner data, an already documented process, and an owner who lives it daily: exactly the three criteria the flashy case usually lacks.

This is not a case for ruling out customer-facing cases on principle. It is a case for not letting a case's visibility substitute for the four-criteria assessment.

This comparison is illustrative, not an assessment of real projects. The judgments change with the data, systems and people available. Invoice reconciliation looks more ready in this scenario, but the business must test those assumptions before choosing it.

An observable output and a metric defined before choosing a tool. MIT NANDA's exploratory report highlights integration difficulties, but does not establish a universal cause for every project. The operating proposal is to move from "let's try AI on this" to "we want to improve this outcome, measured this way."

The difference is practical, not philosophical. If the metric does not exist before the project, it gets invented afterward to justify the time spent, and a metric invented after the fact almost always confirms the project worked, regardless of what actually happened.

The second thing that has to exist beforehand is a time limit for the go or stop decision. Without a date, a low-feasibility case keeps absorbing time on the promise it will work "once one last detail is sorted," and that detail never gets sorted.

One to three, not more. Not as an arbitrary rule of method, but because every extra case dilutes the internal owner's attention, and in most organizations that person does this work on top of their own job, not instead of it. Five parallel cases usually move slower overall than two run in sequence.

There is also a learning reason. The first case, whatever it is, teaches things about the company's adoption process (who approves what, where a data request stalls, who needs convincing) that the second case will use to move faster. Starting too many cases at once means paying that learning curve more than once instead of just once.

Pick the one with the shorter verification cycle. A case whose outcome shows up in two weeks, even if it is worth less than one whose outcome shows up in two months, produces the evidence needed to correct course, or to justify the next step to whoever is funding the project, sooner.

It is the same principle applied to the choosing itself rather than to execution: an output observable soon beats a bigger output observable late, because time passing without signals is time a project loses sponsors, not time it matures.

Beyond the speed of the verification cycle, three other signals help break a tie when the four main criteria stay close.