Finding your first high-value AI use case

When a business first decides to "do something with AI," the hardest question isn't technical. It's where to start. Pick the wrong first project and you spend a lot of money proving AI is disappointing. Pick the right one and you build the confidence and momentum for everything after.

Here's a simple, practical way to find the use case that pays off first.

Look for high-volume, high-friction, low-variety work

The best first AI use cases usually share three traits:

• High volume — it happens often, so even a modest improvement per instance adds up.
• High friction — it's slow, repetitive, or frustrating for the people doing it today.
• Low variety — the task follows recognizable patterns rather than requiring fresh judgment every time.

Support triage, document processing, data entry, and routine information lookups tend to score well on all three. Highly creative or high-stakes judgment work tends to score poorly — good later, risky first.

Follow the context-assembly cost

A useful signal: look for work where people spend most of their time gathering information rather than making decisions. If your team's day is full of hunting across systems to assemble a picture before they can act, that assembly is exactly what AI does well — and removing it frees people for the decisions only they can make.

Check that the data actually exists

AI can only work with what it can reach. Before committing, ask: is the information this task needs actually available, in systems we can connect to? A brilliant use case with no accessible data is a research project, not a first win. Sometimes the highest-value first step is simply making existing data reachable.

Size it so a win is visible

Your first project should be small enough to ship in weeks, not quarters, and measurable enough that success is obvious. "Reduce first-response time on this ticket category" is a good first goal. "Transform our operations" is not — it's unmeasurable and unfinishable.

Avoid the expensive-exciting trap

The projects that look most exciting in a strategy deck are often the worst first choices: broad, ambitious, judgment-heavy, and hard to measure. The unglamorous, well-scoped automation is what builds real capability and real trust. Save the ambitious vision for after you've earned confidence with a concrete win.

The best first AI use case is high-volume, high-friction, low-variety work where the data already exists and success is easy to measure.

Get the first one right and the second, third, and fourth get easier — because now the organisation believes, the data pipelines exist, and you've learned how to ship. Momentum in AI adoption is mostly earned one honest win at a time.