How to prioritize AI use cases in an enterprise
A simple value-vs-feasibility framework for picking the first project.
The hardest question in enterprise AI is not technical. It is “where do we start?” Most organizations have a long, unranked wish list: a chatbot here, a forecasting model there, an automation someone read about. The list grows, nothing ships, and the initiative stalls before it produces a single decision. Prioritization is the skill that breaks the logjam.
Two axes decide everything: value and feasibility
Every candidate use case can be placed on two axes. Business value asks how much a better decision here is worth, in revenue retained, losses avoided, or cost removed. Feasibility asks how ready you are to build it: data availability, decision frequency, and whether the organization can actually act on the output.
The best first project is rarely the most sophisticated one. It is the one with real value that you can actually ship.
- High value, high feasibility: your quick wins. Start here.
- High value, low feasibility: strategic bets. Fund the data work, but don't start here.
- Low value, high feasibility: distractions dressed as progress. Skip them.
- Low value, low feasibility: ignore.
How to estimate value without a six-month study
You don't need perfect numbers. You need defensible ranges. For each use case, estimate the size of the decision (how many times a year it is made), the current cost of getting it wrong, and the realistic improvement AI could deliver. A retention decision made 50,000 times a year with a 5% improvement in save rate has a value you can size on a whiteboard. That estimate is enough to rank.
Feasibility is mostly about data and action
Two feasibility questions matter more than model choice. First: does the data exist, in usable form, with outcomes attached? A model needs to learn from what happened, not just what was recorded. Second: can the organization act on the recommendation? A brilliant next-best-action engine is worthless if no process or team is positioned to execute it. Many failed AI projects were technically fine and organizationally impossible.
Frequency is the multiplier
A decision made once a quarter, however important, is a poor first AI project. You will wait years to learn whether it worked. A decision made thousands of times a day compounds: small per-decision gains add up fast, and the feedback loop tightens quickly. Favor high-frequency decisions for your first build.
From matrix to roadmap
The output of prioritization is not a single project. It is a sequenced portfolio: one quick win to build momentum and prove value, one or two strategic bets with the data work scheduled, and a clear “not now” list that stops the wish list from reasserting itself. This is exactly what an AI Opportunity Roadmap produces: a ranked, ROI-estimated portfolio with a recommended first pilot, in weeks rather than quarters.
OKEMA is a decision-intelligence company. Our team brings a PhD in Systems & Information Engineering (University of Virginia) and 8+ years in applied ML, causal inference, and reinforcement learning, with production work across Microsoft, Netflix, and DoorDash.