There is a tendency to describe AI expense categorisation as a universal solution. In practice, how well it performs depends heavily on the type of spending a business generates.
Two different businesses, two different experiences
A freelance graphic designer based in Cork might have a small number of recurring expenses: Adobe Creative Cloud, a co-working space membership, occasional client lunches. These are consistent, clearly labelled transactions from recognisable merchants. AI categorisation handles this profile with relatively little friction.
A small construction firm is a different situation. Expenses arrive from multiple sources - hardware suppliers, subcontractors, equipment hire companies, fuel for several vehicles. Some suppliers issue invoices with vague descriptions. Some transactions happen in cash. The AI encounters more ambiguity and produces more miscategorisations as a result.
The volume and variety problem
AI models are trained on large datasets, but they perform better on common transaction types. Niche industries with specialist suppliers or irregular spending patterns require more user corrections before the system becomes reliable. That correction process takes time and attention.
What this means for someone starting out
Before adopting a tool, it is worth auditing a recent month of transactions. If most expenses come from well-known merchants with clear categories, the AI will likely be useful quickly. If the spending is varied and irregular, plan for a longer adjustment period and more manual input in the early weeks.