How to Find the Right AI Use Cases in Your Business

What makes a good AI use case?#

A good AI use case is narrow, frequent and checkable. It names a real task, the person who owns it and a result you can judge. It uses information you are allowed to use, and a mistake would be cheap to spot and fix. Broad goals like "use AI in sales" are not use cases yet.

Compare a vague idea with a testable one:

Singapore's Model AI Governance Framework says organisations should first decide on their commercial objectives for using AI, such as consistency, efficiency or new product features, and then weigh them against the risks. A testable use case makes that weighing possible, because you can see what the AI would actually touch.

How do you find candidate use cases?#

Find candidates by looking at the work, not the technology. List the tasks that recur every week, involve a lot of reading, writing, summarising or sorting, or cause delays and rework. Ask the people who do those tasks where the friction is. Then rewrite each idea as a specific task with an owner, inputs and outputs.

A simple discovery routine:

1. Walk through a normal week with each team. Note tasks that repeat and how long they feel.
2. Collect pain points. Where do errors, backlogs or repeated questions appear?
3. Look for language-heavy work. Drafting, summarising, classifying and searching documents are common starting points for generative AI.
4. Write each idea as a task. "When X arrives, produce Y, which Z checks."
5. Name an owner. Someone must be accountable for the result, not just for the tool.

The NIST AI Risk Management Framework includes a "map" function that establishes the context of an AI system. Among its outcomes, the business value or context of business use is clearly defined, and intended purposes, users and expected impacts are understood and documented. That is a useful test of whether a candidate is ready to score.

How do you score and prioritise use cases?#

Score each candidate on a few plain criteria: business value, how often the task happens, whether usable data exists, the risk if the AI gets it wrong and how easy outputs are to check. Favour tasks that are frequent, low-risk and easy to verify. Leave high-stakes decisions about people, money or legal matters until you have experience.

A simple scoring sheet, rated low, medium or high:

Decide how much human oversight it needs#

The Model AI Governance Framework describes three broad approaches. With human-in-the-loop, the AI only recommends and a person must act before any decision. With human-over-the-loop, a person supervises and can take over when something unexpected happens. With human-out-of-the-loop, the AI acts without human oversight. The framework suggests a matrix of probability and severity of harm to choose between them, and notes other factors, such as whether harm can be reversed. For early use cases, human-in-the-loop review is the usual safe default.

What does this look like in a Singapore business?#

Consider an illustrative scenario: a small events company in Paya Lebar, with a sales team, an operations team and a part-time finance assistant. The director wants to "use AI" but has no clear starting point. A short discovery exercise with each team could turn that wish into a few testable candidates.

The candidates might look like this:

• Sales: draft first replies to venue enquiries from a set of approved answers. Frequent, easy to check, low severity if a draft is wrong because a person sends it. A strong first pilot.
• Operations: summarise supplier briefing notes into a standard checklist. Useful, but notes vary in quality, so the team would need to check every summary at first.
• Finance: suggest which supplier invoices look unusual. Higher severity if wrong, and it touches payment decisions, so the director parks it until the team has more experience.
• HR: screen job applicants. The director rules this out for now, because errors could seriously affect individuals and applicant details are personal data under the PDPA.

The director picks the sales pilot, names the sales lead as owner and agrees what a good result would be before anyone opens a tool.

How do you test a use case before committing?#

Test a use case with a small, time-boxed pilot. Record how the task is done today, agree in advance what would count as success, run the pilot with a few people and real but non-sensitive work, then compare. Decide to scale, change or stop based on that evidence, not on how impressive the demonstration looked.

A pilot plan can be one page:

1. Baseline: how long the task takes now and how often it needs rework, measured the same way you will measure the pilot.
2. Success measures: for example, time saved per task, fewer corrections or faster replies.
3. Guardrails: what data may be used, who reviews outputs and what the AI must not do.
4. Duration and owner: a fixed period and one accountable person.
5. Decision: scale, adjust or stop.

The NIST AI Risk Management Framework says that after mapping context and risks, organisations should have enough knowledge to inform an initial go or no-go decision. Our guide to measuring AI return on investment, linked below, covers the before-and-after comparison in more detail.

Common mistakes#

The most common mistakes are starting from a tool instead of a problem, choosing a high-stakes task first and skipping the baseline. Others include ignoring data permissions, piloting without an owner and treating a polished demonstration as evidence. Each one makes it hard to tell whether AI actually helped, or to stop a project that should stop.

• Tool first, problem second. A tool in search of a task rarely produces a clear result.
• Starting with high-stakes decisions. Hiring, credit or legal judgements carry more risk and need more oversight.
• No baseline. Without "before" figures, you cannot show improvement.
• Ignoring data rules. Check what data may be used, and where, before the pilot starts.
• No owner. The NIST framework stresses clear roles and accountability for AI risks.
• Forgetting the people. Staff need to know how to use and check the tool, which is a training question as much as a technology one.

What to learn next#

Once you have a shortlist, decide how to deliver each use case and how to measure it. The guides below follow that order, and the category page and learning pathway show where they fit. If staff need hands-on skills to run pilots, look at training options and funding support next.

For staff who will build and test simple AI tools, our corporate AI training page explains how companies book seats for staff in GenAI Builder Sprint public classes, and our funding overview explains support that may apply, subject to eligibility.

Sources#

1. Model Artificial Intelligence Governance Framework (Second Edition), Infocomm Media Development Authority (IMDA) and Personal Data Protection Commission (PDPC), accessed 8 November 2026
2. AI Risk Management Framework, National Institute of Standards and Technology (NIST), accessed 8 November 2026
3. AI RMF Core, National Institute of Standards and Technology (NIST), accessed 8 November 2026

Frequently asked questions#

What is an AI use case?+

An AI use case is a specific business task where an AI tool could help, described clearly enough to test: who does the work, what goes in, what should come out and how you will know it helped.

Where should a business start when looking for AI use cases?+

Start from recurring work and real problems. List tasks that take time every week, involve a lot of reading, writing or sorting, or cause delays and rework. Ask the people who do the work, because they know where the friction is.

How do you prioritise AI use cases?+

Score each candidate for business value, how often the task happens, whether usable data exists, the risk if the AI gets it wrong and how much human oversight it would need. Favour tasks that are frequent, low-risk and easy to check.

How much human oversight does an AI use case need?+

Singapore's Model AI Governance Framework describes three broad approaches: human-in-the-loop, human-over-the-loop and human-out-of-the-loop. It suggests weighing the probability and severity of harm to decide which fits, alongside factors such as whether the harm can be reversed.

Should we pilot an AI use case before rolling it out?+

Yes. Record how the task is done today, run a small pilot with clear success measures and compare the results. The NIST AI Risk Management Framework describes using context and risk knowledge to inform an initial go or no-go decision.

What are common mistakes when choosing AI use cases?+

Starting from a tool rather than a problem, picking a high-stakes task first, skipping the baseline, ignoring data and privacy questions, and not naming an owner who is accountable for the result.