Scoring use cases for finance — Impact × Feasibility with a finance rubric

Scoring use cases for finance applies a 2×2 model (Impact × Feasibility) to a longlist of AI ideas, with a finance rubric accounting for audit impact and cash effect. The four quadrants produce quick wins, gold nuggets, moonshots, and maybe-laters — for finance teams the simplest way to reduce a 30-item list to three explicitly debated choices.

A longlist of 30 AI use cases for finance is not a plan. It's a collection of hypotheses, and without a filter it runs into reality: not enough time, not enough budget, too many competing priorities in the close cycle. The Impact × Feasibility model is the simplest way to make the list manageable. Two axes, four quadrants, and an explicit discussion per use case about what you actually think.

For finance specifically, the model works well if you fill in the axes on finance criteria — not "impact" in general, but impact in hours of close time or euros of cash effect; not "feasibility" in general, but feasibility on data access, audit impact, and team capacity.

The four quadrants

• Quick wins (high impact, high feasibility): small effort, big difference. Do them now.
• Gold nuggets (high impact, low feasibility): big difference but not trivial. Require a serious project — but worth it. This is where strategic value sits.
• Moonshots (low impact, low feasibility): lots of work for little gain. Defer, unless there's another reason (learning, positioning).
• Maybe later (low impact, high feasibility): easy but not worth it. Pick up only if it can happen on the side.

Taking the quadrants seriously forces you to reject use cases. That's the point.

The Impact axis for finance — four drivers

1. Time saved in finance hours

The simplest to quantify. Hours-per-week × number of finance staff × hourly rate. For a four-person finance team where a use case saves 3 hours per week per person, you're at over 600 hours per year — at an effective €75 rate, that's €45,000 of direct value, on top of what the team can do instead.

2. Cash effect

Direct or indirect effect on working capital: lower DSO from better AR, lower DPO from better AP, fewer outflows from timelier VAT corrections, fewer wrong payments through better control. Cash is a harder driver than time savings in a board context — one day of DSO reduction on €5M of revenue = ~€14K of cash you don't have to finance. Two weeks of DSO reduction at that level = ~€190K.

3. Error reduction and audit impact

Fewer errors in postings, fewer VAT corrections, less rework in the annual report. Harder to quantify than time or cash, but important because errors get exponentially more expensive deeper in the cycle — an error slipping through the close and landing in the annual report costs many times what it cost at source. Audit impact is a sub-driver: a use case that strengthens auditability has strategic value, even if the time gain is small.

4. Strategic — steering on numbers

The least quantifiable but often decisive: a use case that lets the CFO steer the business faster and with more confidence. A rolling 13-week cash forecast that holds up every morning is strategic — not because it saves hours, but because it makes better steering possible.

A workable scoring approach: score each driver 1-3 (low/medium/high), sum, and normalize to 1-10. A use case scoring 3-3-2-3 lands at 11/12, which is 9/10. Little precision required, but disciplined.

The Feasibility axis for finance — four drivers

1. Data availability and quality

For finance, this is almost always the bottleneck. An agent proposing draft journals needs a clean chart of accounts with consistent cost centers. An AI AR flow needs a current open-items list with customer segmentation. A variance analysis needs a budget in the same structure as the actuals.

"We have it somewhere" isn't yes. Start by getting the data in proper order — that's often a project in itself, and the ROI of doing so unlocks multiple use cases.

2. AI technology and integration

Is the AI capability mature? For standard tasks (summarizing, classifying, extracting), yes since early 2024. For agentic workflows on your accounting system, there has to be an MCP integration or API connection — if there isn't, your project starts at the integration. From our experience: Exact integrations are standard to realize today, Twinfield and AFAS follow.

3. People — capacity and finance know-how

Building is often the easy part. Maintaining, handling edge cases, training users, keeping the audit trail — two thirds of the work sits there. A finance team without someone taking AI ownership has to adjust feasibility scores downward. For SME finance, this is often the bottleneck — no time to stay on top of it.

4. Audit impact and governance complexity

Specific to finance: a use case that needs access to the production ledger or creates a formal posting demands governance work that a marketing use case does not. Approval flow, audit log, four-eyes, roles — not impossible, but a project of its own. A use case in a sandbox tenant or on read-only data is an order of magnitude easier.

Score 1-3 per driver, sum, normalize to 1-10. A use case scoring 1-3-2-2 lands at 8/12, which is 7/10 — feasible, but with a data problem to address first.

Common mistakes in finance scoring

Optimism on feasibility. Anyone excited about a use case gives it scores too high. Antidote: have someone score it who didn't come up with the use case, and ask for the top two assumptions per score.

Confusing audit impact with strategic impact. "The auditor will like this" is a driver, not a license to inflate the time-saving score.

Lack of quantification. "This saves a lot of time" is not a score. A range (1-3 days DSO reduction expected; €30-50K annual cash impact) is usable; a general claim is not.

Scoring without an owner. A score nobody defends is a guess. Let the process owner (AP clerk, controller, finance manager) score, and review as a group.

The reasoning check

Once you have scores, let a reasoning model challenge them. Give the model the use case, your impact and feasibility scores, and ask explicitly:

• what are the top two assumptions under my impact score;
• what are the top two assumptions under my feasibility score;
• one reason this use case would not work in finance practice.

The answers are often sobering — and that's precisely why they're valuable. A score that survives a serious challenge is a much firmer basis for an investment decision than a score nobody pushed back on.

Where this lands in practice

• A 80-FTE scale-up scores 18 finance use cases. Two quick wins (AI-assisted variance commentary, board-pack sections via file creation) get picked up immediately. Three gold nuggets (Close agent, VAT agent, Cash agent) go on a roadmap with data integration as step one.
• A 120-FTE manufacturer scores 24 finance use cases. Surprise: their favorite idea (fully automated credit scoring per debtor) lands in moonshot because historical payment data sits in three systems. Six quick wins around bank reconciliation and invoice processing get priority; credit scoring is parked until the data integration is done.
• A 45-FTE professional-services firm finds their longlist skewed toward reporting. Scoring forces the question: why aren't we looking at VAT, at AP flow, at hours-registration analysis? Four new use cases get added.