AI can do a lot. But that doesn’t mean it should do everything. The businesses that succeed are the ones that choose the right AI use cases from the start.
The most successful AI projects don’t start with “what can this tool do?” They start with “what do we need to solve?”
Choosing the right AI use cases is what separates the businesses that see real results from those that just burn time and budget.
Here’s how to find the opportunities that will give you the biggest return.
1. Start with a real problem to find the right AI use cases
If you begin with the tech, you risk bending your processes to fit the tool. That’s when AI becomes a distraction instead of a solution.
Instead, start with a clear business challenge or goal. Maybe it’s improving customer response times, reducing human error in reports or making better decisions with data. When you start here, AI use cases become solutions with purpose, not experiments looking for a reason to exist.
2. Look for repetitive, high‑volume tasks
One of the easiest wins for AI is to take on work that happens a lot and doesn’t require deep human judgement.
Think about customer query triage, routine reporting, basic data entry or processing large amounts of unstructured information. These are often low‑value for people but essential for your business. As a result, when AI handles the repetitive work, your team has more time for high‑value, strategic and creative contributions.
3. Find areas where speed or accuracy really matter
There are some areas where being faster or more precise has a direct impact on success. This is where the right AI use cases can make a huge difference. For example, if customers expect instant responses, AI‑driven chatbots or routing systems can keep you competitive.
If errors carry big costs in areas like compliance, safety or reputation. AI can help spot risks before they become problems.
Examples include fraud detection, predictive maintenance, quality checks in manufacturing or flagging anomalies in financial data.
According to McKinsey research, businesses that focus AI on high‑impact use cases see significantly higher returns.
4. Listen to your people
Your team often knows exactly where the friction points are. Ask them:
- Which tasks feel like time‑wasters?
- Where do mistakes keep happening?
- What processes slow them down?
They’ll give you ideas for AI use cases you might not have considered. Plus, involving them early makes it easier to get buy‑in when you roll out new tools.
5. Test small, then scale up
Even the best‑chosen AI use cases can go wrong if you try to implement them across the whole business on day one. However, starting with a pilot project allows you to measure results and make adjustments before rolling out on a larger scale.
Once you see a clear benefit, then scale it up.
This approach keeps costs down and builds confidence as you go.
Key takeaway:
The right AI use cases start with the right problems. Get clear on your challenges first, then choose AI to solve them.
When you pick well, AI moves from being a novelty to being a genuine business driver.
💬 Your turn:
What’s one area in your business you think AI could handle better than a human?
📌 PS:
My upcoming AI in Business course launching at the end of this month dives deeper into spotting and prioritising AI opportunities so you can focus your time, money and effort where it counts.
