AI Adoption · Written and maintained by Haink’s AI adoption team · Updated July 2026 · 7 min read

AI Use Case Prioritization: Value vs Feasibility

AI use case prioritization is the discipline of scoring candidate AI projects on business value against feasibility and data readiness — and starting where all three are high, not where the idea is most exciting. Once a company decides to adopt AI, its problem flips: not too few ideas, but too many that all look promising. Prioritization is what turns a wish list into a sequence.

The stakes are higher than they look. Your first AI project doesn’t just deliver its own result — it decides whether the organization believes in the next one. Choose a winnable first use case and you build trust, capability and momentum. Choose a dazzling but infeasible one and you spend your credibility proving it can’t be done.

Start from value, not from excitement

The single most useful rule in prioritization is counterintuitive: enthusiasm and ROI are poorly correlated in AI, but feasibility and ROI are not. The most exciting use case in the room is frequently the least feasible — it requires data you don’t have, a workflow no one has mapped, or a level of accuracy the business can’t yet trust. A disciplined process starts from business value and works backward to whether the thing can actually be built, now, with what you have.

Score every candidate on three axes

Reduce each candidate to three questions, scored on a simple 1–5 scale:

A simple weighted score keeps the ranking honest — for example, Score = (Value × 0.5) + (Feasibility × 0.3) + (Strategic fit × 0.2), with data readiness acting as a gate rather than a weight: below a threshold, the use case is parked regardless of its total. Weights are a tool for conversation, not a verdict; the discussion they force is worth more than the number they produce.

The value–feasibility matrix

Plot each scored use case on a 2×2 of business value against feasibility. The quadrants tell you what to do.

A healthy first portfolio leads with two or three quick wins to earn belief and build capability, adds a small number of carefully staged strategic bets, and consciously declines the money pits — naming what you are not doing is part of the discipline. Balancing the near-term and the ambitious is its own topic; see quick wins vs strategic bets.

The test for a first project

For the very first use case, apply a stricter filter on top of the score. If a use case cannot be described in one sentence, measured in one metric, and piloted in under 60 days, it is too big to be a first project. Scope it down until it passes, or pick another. A narrow win that ships beats an ambitious program that stalls — and the narrow win funds the credibility for the ambitious program later.

The mistakes that pick the wrong project

• Scoring on excitement. The loudest idea wins the room and loses the pilot. Anchor on value and feasibility, not novelty.
• Ignoring data readiness. High business impact with poor data availability is not a good first project — it is a future project, once the data infrastructure is in place. Treat data as a gate, not a footnote. See data readiness for AI.
• Scoring alone. Prioritization done by one function produces one function’s blind spots. It needs four perspectives in the room: the business owner, the technology leader, the data leader, and risk & compliance. Each catches a different way the use case fails.
• Confusing a list with a plan. A ranked list is not a roadmap. Sequencing over time, with dependencies and capacity, is the next step — see building an AI adoption roadmap.

How to run the prioritization

1. List the candidates. Every proposed use case in one place, each in a single sentence naming the business problem it solves. (Finding candidates in the first place is its own step — see where AI creates business value.)
2. Score each on the three axes, with business, technology, data and risk owners together.
3. Plot them on the matrix and separate quick wins from bets, fill-ins and money pits.
4. Sequence and assign. Start with quick wins, stage the bets, and give each greenlit use case a named owner and a success metric before anyone builds.

The honest verdict: when the top-ranked use case is “wait”

Sometimes the highest-scoring candidate on value fails the data gate — and the honest output of prioritization is not a project but a prerequisite: fix the data, then reassess. That is a result, not a failure of the exercise. A prioritization that only ever produces “build this now” isn’t ranking anything; it’s rationalizing a decision already made. The value of doing it properly is that it occasionally tells you to wait — and saves you a pilot that would have joined the 95% that fail.

Frequently asked questions

What is AI use case prioritization?
The discipline of scoring candidate AI projects on business value against feasibility and data readiness, then starting where all three are high. The output is a ranked, sequenced portfolio.

How do you prioritize AI use cases?
Start from business value and work back to feasibility. Score each candidate on value, feasibility and data readiness, plot them on a 2×2 matrix, and begin with high-value, high-feasibility quick wins.

How do you choose the first AI project?
Pick one you can describe in a sentence, measure with one metric, and pilot in under 60 days — sitting on data you already have. A high-impact idea with poor data is a future project, not a first one.

What is the value-feasibility matrix?
A 2×2 of value against feasibility: high/high are quick wins (start here), high/low are strategic bets (plan carefully), low/high are fill-ins, low/low are money pits to avoid.

Why do companies pick the wrong first use case?
They choose on excitement rather than feasibility, or ignore data readiness. Scoring with business, technology, data and risk owners together prevents it.