TV ads make it sound simple: switch on AI, and your business runs itself.
In reality, AI is brilliant at speeding up tasks. But it’s not a finance function. And if you let it operate without a human in the loop, it doesn’t remove risk — it often hides it.
This matters most once you’ve got a team, more transactions, and more moving parts. Because the cost of being “slightly wrong” compounds.
What AI is genuinely good at in finance
AI and automation can reduce admin when the process is already defined. Used properly, it can help with:
- Capturing receipts and extracting key details
- Matching bank transactions to invoices and bills
- Suggesting categories based on past behaviour
- Flagging anomalies (duplicate payments, unusual spend patterns)
- Speeding up first drafts (notes, reports, queries)
This is all valuable. I use automation extensively.
But it only works well when the underlying rules are clear.
The failure mode: confident errors at speed
Here’s the problem with “AI does it all”.
If the inputs are messy, AI doesn’t fix the mess. It scales it.
And it tends to be confidently wrong in the exact ways that are hardest to spot quickly.
Common examples I see in growing businesses:
1) Inconsistent coding creates fake signals
If costs are coded differently month-to-month, your profit report becomes noise.
You’ll see margin “improve” when nothing improved.
Or you’ll think overheads are under control while they’re drifting into the wrong categories.
That leads to bad decisions:
- pricing stays too low
- hiring happens at the wrong time
- costs don’t get challenged early
2) VAT becomes a slow-building risk
VAT is a classic area where consistency matters more than cleverness.
Automation can suggest VAT treatment, but it can’t reliably understand context without rules:
- what you sell
- how you invoice
- what evidence you hold
- what’s standard vs unusual in your business
If VAT is treated slightly differently each month, the risk builds quietly — and you only notice when you file or get asked questions.
3) Month-end turns into clean-up
When automation is bolted onto a messy process, month-end becomes a scramble:
- missing paperwork
- “uncertain” transactions parked in holding accounts
- reconciliations delayed
- reports produced late
Late reporting doesn’t just mean inconvenience.
It means decisions happen late — and the same issues repeat for another month.
4) Exceptions get missed (and exceptions are where money leaks)
AI is strongest with patterns. Most profit leaks happen in exceptions:
- a supplier bill that should have been challenged
- a customer who is unprofitable because they consume time
- scope creep that isn’t being billed
- recurring spend that nobody owns
These are judgement calls. They need a human to notice, question, and decide.
The role of a human in the loop
A good human finance function does four things AI can’t do reliably on its own:
1) Defines the rules
What goes where. What counts as direct cost vs overhead. How you want to see margin. How you tag jobs, clients, departments.
Your reporting should reflect how you run the business — not how the software guesses.
2) Spots what changed (and why)
Not “here are the numbers”.
Here’s what moved, what caused it, and what it means.
3) Converts reporting into actions
Finance is only useful when it leads to decisions:
- tighten scope control
- renegotiate a supplier
- fix debtor chasing
- adjust pricing
- pause discretionary spend
- change hiring plans
4) Creates cadence and accountability
Weekly and monthly rhythms protect the business:
- weekly cash/debtors/approvals
- fixed month-end close date
- clear owners for recurring costs and anomalies
That’s how you stop relying on heroics.
The simple approach that works
Here’s the framework I recommend if you want to use AI properly in finance:
- Define categories and rules that match your business
- Build a weekly rhythm (cash, debtors, approvals)
- Close monthly on a fixed date with a short commentary
- Then automate the repetitive parts (capture, matching, reminders, drafting)
In that order.
Because automation on top of a weak process doesn’t create control. It creates faster confusion.
If you want this set up properly
If you’re using Xero and you want automation to save time without creating risk, you need someone to design the system and keep it honest.
Want me to look at your current setup and tell you where AI helps — and where you still need a human in the loop?
