Like any change you make in your business, you will want AI to add value. But like any other tool, the business case for AI must be sound. Adopting AI tools first and hoping value will appear later is a common mistake. It’s also costly, and one you should avoid.
It’s far more effective to identify specific, high-impact problems that tech can fix. With AI, there are some obvious, quick wins.
Start with repetitive, time-heavy tasks
AI delivers immediate value when applied to work that is repetitive or rule based. These tasks tend to be time-consuming and high-volume. They’re also labour intensive for staff. At the same time, they don’t need deep human judgement. Things like:
- handling invoice questions and basic finance requests
- summarising documents or emails
- analysing tone in customer communications
- extracting information from forms or spreadsheets
In many organisations, these small tasks add up to a significant workload. When AI supports or automates parts of these processes it frees up time. That allows staff to focus on things like improving operations or service.
Strong processes come first
It’s important to remember that AI doesn’t fix broken processes. If a workflow isn’t clear or is inconsistent, AI can in fact make the problem worse. It’s the same with data. If you put garbage in, you get garbage out.
Before applying AI to any task, ask if:
- the process already works well
- the steps are clear and consistent
- you understand what good outcomes look like
AI performs best when it enhances something that already functions well.
How to identify good AI candidates in your business
A simple way to find use cases is to speak with teams across the organisation and ask:
- Which tasks take up the most time each week?
- Where do bottlenecks occur most often?
- What work feels repetitive or manual?
- Which queries get answered repeatedly?
Look for areas where volume is high and variation is low. These are your ideal starting points.
It’s also helpful to pilot small projects rather than attempting large transformations. Testing AI in one department or on one workflow allows you to measure value and adjust controls. Doing so will build confidence and trust in the process, and foster buy-in from teams.
Realistic examples in action
Across many organisations, AI is already in use. For example, some of our clients are:
- handling support tickets quicker by auto categorising them
- providing instant responses to common HR or IT questions
- summarising customer interactions for quicker follow-ups
- scanning invoices for key information
- drafting first versions of routine communications
These aren’t futuristic transformations. They’re focused improvements that solve real business problems. And they augment rather than replace your teams – which is what you want your tools to do.
Take a structured, sensible approach
Keep in mind that the most successful AI adoption doesn’t start with buying tools.
It starts with:
- Understanding the business problem
- Strengthening the underlying process
- Selecting the right AI support
- Piloting and measuring results
- Scaling carefully
This approach keeps risk low and value high.
Want help identifying the right AI use cases for your business? Our free webinar – Demystifying AI for business – explores realistic AI adoption for SMEs.