AI use cases for business: how to prioritise, SHIFTLABS

Guide

A good AI use case is a repetitive, frequent task, fed by accessible data, where a mistake costs little. Rank candidates by scoring impact, feasibility and cost, then set aside any whose risk is too high.

6 min read
Updated 15 October 2026

Contents

Five tests for spotting a good AI use case

An AI use case is a specific task: a trigger, input data and an expected result. Five tests rule out most ideas before any costing.

A repetitive task: it follows the same pattern each time, and its rules can be written down, exceptions included.
Volume: it comes up often enough, across enough people, for the time saved to add up to hours each month.
Available data: the documents and information it needs exist in digital form, stay accessible and are up to date.
A bounded risk: a mistake is spotted quickly and costs little, or a person reviews the result before it goes out.
A business owner: someone knows the task, can describe it and will use the result.

Where to look: measured work, not wish lists

A brainstorm produces a wish list. To rank candidates you need measured hours, so start from each team’s real work.

Have each person describe a typical week: tasks, duration, frequency, tools.
Sort each task into a family: data entry and checking, research and consolidation, communication and meetings, reporting and documents, customer and supplier relations, expertise and decisions.
Note the volume, the time per occurrence, the data used and the consequence of a mistake.
Write each candidate as one sentence: “when [trigger], [who] does [what] from [data] to produce [result]”.

Examples of task families: finance, procurement, operations

Here are candidate task families in three functions, with what works for and against each.

Examples of task families: finance, procurement, operations
Function	Candidate tasks	What works in its favour	Watch-out
Finance	Matching invoices to statements; checking supporting documents; chasing customer payments; writing variance comments in the monthly report	Explicit rules, steady volume, data already digital	Ledger entries stay validated by a person; closing dates are fixed
Procurement	Chasing suppliers; matching order, receipt and invoice; comparing quotes; updating supplier master data	Repetitive documents with a stable structure, known counterparts	Master data is often incomplete; choosing a supplier remains human
Operations	Entering orders received by email; tracking delivery exceptions; sorting incoming requests; consolidating activity reports	High volumes, standardised steps, verifiable result	Many exceptions; tools with no export or interface

Scroll the table sideways.

The scoring grid: impact, feasibility, cost, risk

Score each shortlisted case from 1 to 5 on impact and feasibility, measure its cost as a payback period, then run it through the risk filter. These criteria overlap with those in published guides (value, feasibility, governance); the grid adds payback and a decision rule. Thresholds are reference points to adjust.

Feasibility also depends on how mature the organisation is. The express diagnostic (12 questions, 3 minutes) gives a first, indicative reading.

The scoring grid: impact, feasibility, cost, risk
Criterion	What is measured	Rather unfavourable	Rather favourable
Impact (score 1 to 5)	Hours per year: frequency × duration × people, then the value of the time freed	A few hours a month (1)	Among the largest volumes on your list (5)
Feasibility (score 1 to 5)	Accessible data, written rules, open tools, an available team	Scattered data, vague rules (1)	Clean data, written rules, few exceptions (5)
Cost (payback period)	Full first-year cost ÷ monthly value: set-up, running, review, team support	More than 18 months (1)	Under 6 months (5)
Risk (filter, not scored)	Consequence of an undetected mistake	A regulated decision, or one that affects a customer or an employee, without review	A mistake is detectable; human review before release

Scroll the table sideways.

Common mistakes when prioritising

Six mistakes skew the ranking before the first trial. Several are documented in public guides (sources at the end of the page).

Starting from the tool, not the task: a fashionable tool does not tell you which work deserves help.
Deciding in committee: teams know which tasks waste their time, and use cases imposed from above tend to go unused.
Estimating impact by gut feel: a painful task is not always a costly one. Without measurement, you rank irritants, not hours.
Counting development and forgetting the rest: running costs, human review and team support all weigh on cost.
Looking at the data after choosing: information scattered across inboxes or scans changes feasibility, and so the ranking.
Not measuring the starting point: without hours, lead times and error rates from before the project, no gain can be demonstrated.

From ranking to a first use case in production

A ranking is only worth something if it leads to a first measured use case. Four steps are enough.

Name a business owner for each selected case.
Measure the starting point: hours, lead times, error rate.
Launch a narrow scope, with human validation before anything goes out.
Compare with the starting point after a few weeks, then extend or stop.

Frequently asked questions

Which AI use case should we start with?

One scoring at least 3 on both impact and feasibility, where a mistake is easy to spot: a repetitive data entry, checking or chasing task, reviewed by a person before it goes out. Avoid the showcase project: aim for a first measured gain within weeks.

How many use cases should we run at once?

You can cost three to five, but launch only one to three at a time. Each needs a business owner, accessible data and a starting measurement. Beyond that, attention scatters and reviews pile up.

Do you need a lot of data for an AI use case?

Not necessarily large volumes. The useful information (documents, messages, exports) must be accessible when the task is done, up to date and representative of real cases. If it is scattered or missing, prepare the data first, then score the case again.

How do you estimate a use case’s gain before launching it?

Multiply frequency, time per occurrence and number of people to get hours per year, put a value on them, then subtract the full first-year cost. Those hours only become a financial gain if they are redeployed. The gain simulator gives an order of magnitude from your own assumptions, calculated in your browser; it is not a commitment.

Which use cases should we avoid at the start?

Rare tasks, processes that change every month, cases with no usable data, and decisions affecting a customer or an employee without human review. A high-impact case that is hard to deliver waits until its data and rules are ready.

Can we prioritise use cases without a consultancy?

Yes, with the grid above, on two conditions: measure the real work rather than relying on what people say, and have the ranking reviewed by someone who knows the trades. The interview example shows how a task is described and quantified with the teams.