AI Readiness
Is your business ready for AI?
Most Australian businesses are being sold AI before anyone has asked what it would actually run on. The honest answer is that readiness is not a yes or a no. It is a question you can only answer one workflow at a time, and the answer usually turns out to be yes for something smaller and more boring than the thing you were pitched.
This section is about what AI can genuinely do inside a business that runs on Odoo, what has to be true before it works, and where it pays for itself first. No demos of things that only work on tidy sample data.
What AI can actually do
Strip away the marketing and every useful AI action inside a business is one of five things. If a task is not one of these five, AI is not the tool for it, and anyone telling you otherwise is selling.
Notice what is not on that list. Nothing here decides anything on its own. A person still approves, and on anything that touches the ledger a person always will. That is a design rule, not a limitation we are apologising for.
The three things that have to be true
Readiness gets talked about as a single gate you either pass or fail. It is not. There are three separate conditions, and only the first is genuinely all-or-nothing.
1. There has to be a system
One place where the transactions of the business actually live. If your stock sits in one system, your margins in a spreadsheet and your job costing in somebody's head, there is nothing for an AI to read and it will confidently invent the rest. This one really is a gate.
2. There has to be a task worth automating
Volume, times minutes, times what an hour costs you. A job done four times a month is not the place to start, however irritating it is. The first build should be high-volume and dull enough that nobody will miss doing it.
3. The data has to be good enough for that task
Not perfect, and not across the whole business — good enough for the specific workflow. Drafting quotes needs a current price list; it does not need ten years of tidy history. We scope the data each workflow depends on before anything is built, and where it is not there yet, that is a finding to work with rather than a reason to stop.
Still choosing a system? Start there instead — that is a different conversation, and a longer one. The rest of this page assumes you already have Odoo running.
Where it pays first
Three areas of a business hold nearly all the high-volume, repetitive, dollar-quantifiable work. We build in one of them at a time, starting with whichever one is costing you most.
AI for Accounting
The most defensible of the three, because almost all of the saving is time and rework you can put a number on before we start.
- Vendor bill capture, coding and approval
- Bank reconciliation suggestions
- Debtor chasing and collections
AI for Sales & CRM
The largest upside, and the one where you have to be most careful about what you count. Time saved is certain; deals won are an argument.
- Quotes drafted from enquiry emails
- Lead enrichment and scoring
- Quote follow-up and pipeline nudges
AI for Customer Service
Solid time savings plus the retention argument. The same twenty questions, answered over and over, is the classic case.
- Ticket triage and routing
- Response drafting from your knowledge base
- Escalation summaries and handover
We start with one. Doing all three at once sounds efficient and is the most reliable way to finish none of them. One area, one workflow first, measured before and after, then the next.
How a build actually runs
Three stages of thinking, then four stages of work. The thinking stages are where the money is decided; the work stages are where it is earned.
Assess
What is the most repetitive manual job in this part of the business, and who owns the outcome of fixing it? If there is no named person who will feel the improvement, there is no project. We stop here rather than build something nobody has asked for.
Investigate
Map the workflow as it is really done, not as the process document says. Count the volume, time the steps, apply your loaded hourly cost, and put a defensible annual number on it. You should be able to argue with that number before we build anything.
Operationalise
Inputs, actions, outcomes, approvals. What the AI can see, which of the five verbs it performs, what comes out, and exactly where a human confirms. The approval step is designed first, not bolted on afterwards.
Then the build itself:
- Scope and data. Confirm what the workflow needs, check the data is there, and take the "before" measurement. Without a before number there is no way to prove the after.
- Build. Check what Odoo already does natively before building anything custom — native is cheaper, safer and survives upgrades. Then the logic, the automation that hooks it into Odoo, and the approval step.
- Test and train. Run it on your real historical data and tune it until it is genuinely good, not demo-good. Document it, train the people who will use it, hand over the run guide.
- Go live. Watch the trust tail: accuracy climbing, checking falling. Capture the "after" number. Done is not "it runs" — done is that your team has stopped double-checking it.
What protects you
The uncomfortable questions about AI in a business are not technical ones. They are about control. Here is where we stand on each, plainly, so you can hold us to it.
Does our data train the AI?
No. Your data is used to do your work and nothing else. That is a commercial term in the agreement, not a promise in a sales meeting, and every build documents exactly what data goes where.
Who holds the data?
You do. It stays in your Odoo. Nothing about an AI workflow moves your records into somebody else's product, and nothing creates a system you have to keep paying to read your own information out of.
What if the AI vendor disappears?
This is the one almost nobody asks and everybody should. AI vendors get acquired, change terms and shut down. So every build's logic, prompts and configuration are documented and stored with you. If a vendor vanishes tomorrow, you still have the recipe, you still have your data, and the workflow can be rebuilt on something else. Nothing we build lives inside a black box we cannot open.
What access do you need to our system?
The least that will do the job. Some work needs no system access at all — design, prompt libraries and training. Most needs ordinary user-level access, the same as any staff member. Full backend credentials are for genuinely complex builds only, and we say so up front rather than asking for everything by default.
What if it gets something wrong?
It will, occasionally. That is why every workflow has an approval step and why we design it before we design anything else. AI drafts; a person confirms. On anything touching your ledger, that never changes, no matter how well it is performing.
What it costs
Each focus area is a fixed-price program, which is a deliberate departure from how we price everything else. We bill implementation work by time because the scope of an ERP project genuinely cannot be known up front. An AI focus area is different: the workflows are defined, counted and agreed before a line is built, so the scope is bounded and we can put a number on it and stand behind it.
The price is set against the value the workflows create, not against our delivery cost, and we work that value out with you in the open. Volume, times minutes, times your loaded hourly rate. You will see the arithmetic and you are welcome to argue with it.
The test we hold ourselves to
The program should pay for itself on hard savings alone — direct time and rework removed — well inside a year. Anything softer (faster decisions, higher conversion, customers kept) is upside we will show you but will not ask you to pay for. If a program only works once you count the soft benefits, it is not ready to sell.
The saving recurs every year. The fee happens once. That ratio is the whole argument, and it is why we would rather quote a real number than a small one.
Exact pricing depends on which workflows are in scope, so it comes out of the first conversation rather than off a page. Book a call and we will size it with you.
Common questions
Do we need to be on Odoo?
For this work, yes. Everything here is built into Odoo and hooks into what is already running. If you are not on an ERP yet, start with the system decision — that is the prerequisite, and it is worth doing properly.
How long does one workflow take to build?
Weeks, not months, once the scope is agreed. The long pole is almost never the build. It is deciding how the business should work, and then the trust tail afterwards, where people gradually stop checking every result.
Will this replace people?
The work we target is the work nobody wants: keying invoices, retyping enquiries into quotes, reading a whole ticket thread to catch up. Removing it gives you back capacity you are already paying for. We have yet to meet a finance or service team with nothing better to do.
Can we start small?
You should. One workflow, measured before and after. If the number is not there, you have learned something cheaply. If it is, the second one is an easy decision.
What if our data is a mess?
Then we find that out during scoping rather than three weeks into a build. Sometimes the answer is to clean it first; sometimes it is to pick a different workflow that does not depend on the messy part. Either way it is a finding, not a verdict.
Is Odoo's own AI enough?
Sometimes, and we check that first every time. Native features are cheaper, safer and survive upgrades. We would rather tell you the platform already does it than build you something you did not need.
Start with one workflow
A short call to work out which part of your business is losing the most hours to repetitive work, and whether AI is genuinely the right tool for it. If it is not, we will say so.