One of the first assumptions people make about AI-driven expense categorisation is that it works flawlessly from day one. The reality is more nuanced, and understanding why helps set reasonable expectations.
A single receipt, multiple categories
Consider a supermarket transaction for €87.40 at a store like Dunnes Stores. The AI might log the entire amount under Groceries. But that receipt could include cleaning products, a birthday card, and a bottle of wine - items that belong in Household, Entertainment, or Gifts depending on how a business or household tracks spending.
The AI has no way to inspect line items from a card transaction alone. It sees the merchant name, the amount, and the date. That is the input it works with.
Where the myth comes from
Demos and product screenshots tend to show clean, unambiguous transactions - a flight booking goes to Travel, a restaurant charge goes to Dining. Those cases are straightforward and the AI handles them well. Edge cases rarely appear in marketing material.
What actually improves accuracy
Most tools allow users to correct miscategorised entries. Over time, the model learns from those corrections. A transaction at a petrol station that also sells food will eventually be sorted correctly if the user consistently recategorises it. The learning is gradual, not instant.
For someone just starting out, the practical takeaway is this: expect a settling-in period of a few weeks before the categories feel reliable. Manual review is still part of the process.