AI Use-Case Scorecard

How to prioritize AI use-cases: the impact/effort method

Prioritizing a client's AI opportunities comes down to one question: which idea returns the most value for the least work? The impact/effort method makes that question answerable instead of political. You score every candidate use-case on two weighted axes, divide impact by effort, and rank the results. The discipline that makes the ranking hold up in a discovery review is locking the criteria and their weights before you score a single use-case, so the order falls out of the client's stated priorities rather than your gut.

The impact criteria (and why each is weighted)

Impact answers "what does the business get if this ships?" The four default criteria carry the weight a typical diagnostic gives them, and each axis sums to 100 so the math stays normalized:

• Revenue upside (30) — new revenue or revenue protected if this ships. Weighted highest alongside cost because it moves the number a CFO watches.
• Cost reduction (30) — hard dollars or hours removed from the operation. The most defensible benefit in most AI cases, which is why it shares the top weight.
• Risk reduction (20) — compliance, error, or reputational risk this lowers. Real value, but harder to bank, so it sits a tier below.
• Time to value (20) — how fast the client feels the benefit after go-live. Speed-to-payback matters, but it modifies the case rather than being the case.

The effort criteria (higher means more work)

Effort answers "what will it actually take to deliver?" Score these so a higher number means a heavier lift. The same four-criteria, sum-to-100 structure applies:

• Data readiness (30) — is the data clean, available, and labeled? The single most common reason AI projects stall, so it carries top weight.
• Integration lift (30) — systems to connect, APIs, infrastructure. Weighted alongside data because plumbing, not models, usually drives the timeline.
• Regulatory burden (20) — approvals, audits, data residency. Real drag in regulated industries, lighter elsewhere, so it sits a tier below.
• Change management (20) — how hard it is to get people to actually adopt it. Quietly decides whether the benefit ever materializes.

The priority signal: impact divided by effort

Each axis is normalized to 0–100 (a use-case that scores a perfect 5 across every criterion maps to 100). The priority signal is simply:

Priority = Impact (0–100) ÷ Effort (0–100)

A higher ratio means more value per unit of work, so it goes sooner. Ranking by this ratio is what turns a pile of equally loud ideas into a sequenced roadmap you can defend line by line.

The four quadrants and what to do with each

Plotting impact (vertical) against effort (horizontal), split at the midpoint of 50, sorts every use-case into one of four quadrants. Each carries a different recommendation:

• Quick Win (high impact, low effort) — lead with these. They build momentum and trust before you ask for a bigger commitment.
• Big Bet (high impact, high effort) — worth it, but scope carefully and stage it. These are roadmap items, not week-one work.
• Fill-In (low impact, low effort) — do these when there is slack. Useful, never the headline.
• Money Pit (low impact, high effort) — say no, or reshape the scope until it leaves this quadrant. Naming these explicitly is often the most valuable output of a discovery.

That sequence — lock criteria, score, rank by the ratio, sort into quadrants — is the same prioritization discipline an Audity discovery runs across a client's whole opportunity set, then packages as a client-ready deliverable.

Related free tools for consultants

Once you have a ranked roadmap, these tools carry it through the rest of the engagement:

• Calculate ROI on your top-ranked use-cases — turn the quick wins into a defensible return projection.
• Price the engagement — size and price the discovery that produces this roadmap.
• Turn findings into pipeline — convert the diagnostic into the next round of client work.