AI Readiness
AI for Accounting in Odoo
Finance is where AI pays back first and argues least. Almost everything it removes here is time and rework you can count before anyone builds anything, which means the business case is arithmetic rather than optimism.
Three workflows carry most of the value: getting supplier bills into the ledger, matching the bank, and chasing what you are owed. All three are high-volume, repetitive, and done today by people you would rather have doing something else.
The three workflows we build
Each one is scoped, quantified and approved before it is built. You will know what it is worth to you before you commit to it.
1. Vendor bill capture, coding and approval
Someone opens a supplier invoice, reads it, keys the lines, decides the account, the tax code and the analytic, and routes it for approval. It is the single most repeated task in most finance teams and it is almost entirely mechanical.
- Verbs: Extract, then Classify
- Where a person confirms: The bill is drafted and coded; your team member confirms it before it posts. Nothing reaches the ledger unreviewed.
- What it needs from your data: A chart of accounts that means what it says, and supplier records that are not duplicated three ways.
2. Bank reconciliation suggestions
Matching statement lines against invoices and bills, line by line, month after month. The obvious matches are dull; the ambiguous ones are the only part that needs judgement, and they are a small minority.
- Verbs: Suggest, with Classify behind it
- Where a person confirms: Matches are proposed, not applied. Ambiguous items stay manual by design rather than being guessed at.
- What it needs from your data: Reconciled opening balances and consistent supplier and customer naming.
3. Debtor chasing and collections
Writing follow-ups, deciding who to escalate, remembering who was promised what. This one usually slips first when the team is busy, which is exactly why the money sits out longer than it should.
- Verbs: Draft, then Suggest
- Where a person confirms: Chases are drafted and prioritised; a person sends anything going to a significant account.
- What it needs from your data: Accurate ageing and contacts that are actually current.
How we work out what it is worth
Volume, times minutes, times what an hour actually costs you. No index, no benchmark, no percentage lifted from a vendor's whitepaper. We do it with your numbers, in front of you, and you are welcome to push back on every input.
Worked example — illustrative figures, not a client result
Take bill entry alone, for a business processing 200 supplier bills a month, at 8 minutes each, with a loaded finance cost of $45 an hour. That is 320 hours a year on one task. If a drafted-and-confirmed workflow recovers 75% of that time — never 100%, because a person still checks every bill — you recover 240 hours, or $10,800 a year.
That is one line, on one workflow. Coding errors avoided, reconciliation time and debtor chasing are three more. The point of the exercise is not the total; it is that every figure in it is one you supplied and can argue with.
Hard, soft and made
We separate the value into three kinds and we are careful about which we lean on:
- Hard — direct cost removed. Hours no longer worked, rework no longer needed. This is the defensible part, and accounting is nearly all of it.
- Soft — efficiency and quality. A faster month-end close means earlier decisions, which is real but harder to price.
- Made — cash freed or recovered. Better collections shorten the time your money sits with someone else.
In the room we lead with hard savings only. If the program does not pay for itself on those alone, it is not ready to sell to you, and we would rather find that out at the start.
Why we start in finance
We are an accounting-led practice, so this is home turf, but that is not the main reason. Finance is where the case is easiest to prove and hardest to argue with.
The volumes are known. The time per task is measurable. The cost of an hour is on the payroll. And the error cost is real: a bill coded to the wrong account or the wrong tax code does not stay a small problem — it turns up in the BAS, in the management reports, and eventually in a conversation with your accountant.
It is also the area where a bad result shows up fastest, which keeps everyone honest. If a workflow is producing rubbish, the ledger says so within a fortnight. That is a feature.
What has to be true first
Less than people expect, and it is specific rather than general. Bill drafting needs a chart of accounts that means what it says and suppliers that appear once, not a decade of tidy history. Reconciliation needs opening balances that reconcile. Debtor chasing needs contacts that are current.
Where something is not there yet we find it during scoping and tell you, and you decide whether to fix it or start with a different workflow. It is a finding, not a verdict. If the underlying problem is bigger — the ledger itself is not trustworthy — that is a stabilisation job, and it should happen before anything is automated on top of it.
Common questions
Will AI post to our ledger without us seeing it?
No. Every workflow that touches the ledger has an approval step designed in before anything else, and it does not get removed however well the thing performs. AI drafts; a person confirms.
Does it work with Australian tax codes?
Yes, because it works with your Odoo configuration rather than around it. GST treatment, BAS and the rest come from how your chart of accounts and tax codes are already set up. If that setup is wrong, the workflow will be faithfully wrong — another reason we look at the accounting first.
What will our accountant think?
In our experience they like it. Cleaner coding and fewer corrections at year end means less time on the same recurring fixes. It is worth telling them what you are doing before you start.
How much of our team's time does the build take?
The scoping conversation and the testing are where you are needed. Testing on your real historical data is not optional and it is not quick — that is where a workflow goes from working to trusted.
Can we see it on our own data before committing to the rest?
That is exactly what the test stage is for. Nothing goes live on the strength of a demo running on somebody else's tidy sample file.
Start with the bills
It is the highest-volume, most mechanical task in most finance teams, which makes it the easiest place to prove the number. A short call is enough to size it.