AI automation cost
How much does AI automation cost for a mid-sized business in Australia, and why do consultancy quotes vary so much?
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The cost of AI automation depends on scope: how many systems it connects and whether they have usable APIs, how varied the documents are, the volume, the accuracy you need proven, approval and audit rules, where it runs, and how much data clean-up and training it needs. Quotes vary because providers price different scopes, so compare them on the same process and accuracy bar.
Who this is for
Operations managers, application and IT leads, business analysts, continuous improvement leads and procurement staff at mid-sized to enterprise organisations, often multi-site, who are budgeting for AI automation and need to explain why one quote is several times another. It suits teams running SAP, Business Central, Dynamics 365, NetSuite, Pronto Xi, simPRO, AroFlo, Uptick, Xero or MYOB alongside Microsoft 365.
Solve8 is an AI consultancy and delivery firm working in Sydney, Brisbane, Melbourne, Perth and across Australia and New Zealand. We scope work in fixed-fee phases: discover one process at one site with a baseline, prove it in shadow mode on live work, build it into your systems on your infrastructure in Australia, then run it or hand over the code. There are no prices here because these drivers change the scope too much for one figure to mean anything.
The main drivers of AI automation cost
| Cost driver | Keeps the cost down | Pushes the cost up |
|---|---|---|
| Systems to connect | One or two systems with documented APIs and a test environment | Several systems, older on-premises ERPs, no test company, or data reachable only through file exports |
| Document variety and quality | A few standard forms or digital files from known senders | Hundreds of layouts, scanned or handwritten pages, and emails that mix several requests |
| Volume | A steady flow, large enough that automating each item is worth it | Spiky volume that needs queueing and monitoring, or volume too low to repay the build |
| Accuracy target and proof | A clear bar per field, with a person checking low-confidence results | Near-perfect accuracy on every field, or a long shadow period across many sites |
| Approval and audit requirements | One approver and a simple log | Several approval levels, delegations of authority, segregation of duties and evidence kept for auditors |
| Where it runs | Your existing cloud or Microsoft 365 environment in Australia, with security patterns already approved | A new environment, private model hosting, or a security review that starts from nothing |
| Data clean-up first | Item, customer and price data that is already consistent | Duplicate customers, inconsistent item codes or pricing rules held in people's heads |
| Change management and training | One team at one site, with a process owner who wants the change | Many sites, unions to consult, shift workers and new starters to train |
| Ongoing support and model running | Steady volume and an internal owner | Usage-based model charges on heavy volume, frequent rule changes and after-hours support |
One-off build costs and ongoing run costs
| Cost | One-off or ongoing | What to ask for in the quote |
|---|---|---|
| Discovery and proof | One-off | Which process and site, how the baseline is measured, the accuracy bar and who sets it |
| Integration and build | One-off | Every system connected, the records written back, how approvals and logs work |
| Training and handover | One-off | Who is trained, what documentation is delivered, whether the code is included |
| Hosting and model usage | Ongoing | How usage is charged, who holds the account, how costs grow with volume |
| Support and monitoring | Ongoing | Response times, what counts as a change request, how accuracy is reported |
| Upgrades and retesting | Ongoing | Who retests when the ERP, field system or AI model is upgraded |
How to compare AI automation quotes
Give every provider the same scope
One page naming the process, sites, systems, monthly volume and example documents. Quotes built on different assumptions cannot be compared.
Set the same accuracy bar
State the accuracy you need per field and what happens below it. A cheaper quote often assumes a lower bar or leaves exceptions to your staff.
Ask what happens after the pilot
Is the pilot production ready, a throwaway, or tied to the provider's platform? Ask for the next phase to be scoped up front.
Separate build from run
Ask for one-off and ongoing costs listed separately, including model usage at your volume.
Check what is excluded
Data clean-up, work with your ERP partner, testing time from your staff and security reviews. Exclusions cause most budget surprises.
Why phases limit the risk
Each phase has its own fixed fee agreed before it starts, and you can stop after any phase and keep the work, so the most you can lose on an idea that fails is the phase you are in. Solve8's defect quoting AI for a national fire protection provider runs this way: built in the client's own cloud in Sydney, it is entering shadow mode beside the quoting team before anyone relies on it. For a first pilot on its own, see What drives the cost of an AI pilot.
What Australian guidance says about buying AI
The National AI Centre's Guidance for AI adoption tells organisations buying an AI system to ask the developer or supplier for proof that it has been properly tested, and to make clear who is responsible for each part of the supply chain when vendors, developers and integrators are involved. Both belong in a quote comparison.
The NSW Government's AI procurement essentials recommend involving a finance officer early for budget preparation, cost-benefit analysis and ongoing cost monitoring, and a legal adviser on intellectual property. They also note that many third-party AI systems receive automatic updates after the initial risk assessment, which is a reason to ask who retests the system when the model changes.
Who should be involved in scoping
Process owner
Defines the process, the accuracy bar and what success looks like.
Application or IT manager
Confirms which systems have usable APIs, where it will run and the security requirements.
Finance
Checks the baseline, benefit assumptions and ongoing cost model.
Procurement
Makes sure every provider quotes the same scope and that code ownership is in the contract.
Related work
- AI business case toolkitA one-page business case, vendor checklist and IT security questions.
- What drives the cost of an AI pilotThe factors behind a first pilot and how phase gates contain it.
- AI ROI calculatorTurn hours and rates into a directional saving estimate.
- ERP partner or AI consultancyWho fits which job, and ten questions to ask any provider.
Sources
National AI Centre, Guidance for AI adoption: foundations: https://www.ai.gov.au/staying-safe-and-responsible/essential-ai-practices/guidance-ai-adoption-foundations
NSW Government, Artificial intelligence (AI) procurement essentials: https://www.info.buy.nsw.gov.au/resources/artificial-intelligence-ai-procurement-essentials
Questions
Why do AI consultancy quotes vary so much for the same process?
Providers usually price different scopes without saying so. One quote may cover a demo on sample documents, another a tested system connected to your ERP with approvals, logs and training. The accuracy bar, data clean-up, who handles exceptions and who owns the code explain most of the gap.
Does running AI automation on our own infrastructure in Australia cost more?
It can add setup work if no suitable environment exists, but it often shortens the security review and keeps data residency simple because the system sits inside controls you already run. You also hold the accounts and the code, so changing providers later does not mean a rebuild.
Is it worth paying for a discovery phase before getting a full AI automation quote?
Usually, for anything beyond one simple integration. A build quote written before anyone has measured the process, seen real documents or checked the system APIs has to carry large allowances for unknowns. A short fixed-fee discovery replaces those with a measured baseline and a defined scope you can take to any provider.
Should we or the provider hold the cloud and AI model accounts?
Usually you. With the accounts in your name you see usage charges directly, can set alerts and limits, and keep the system running if you change providers. Ask each provider to estimate model usage from the shadow mode results at your real volume instead of a guess.
Which processes give the best return on AI automation?
Processes with steady volume, many hours of retyping or checking, measurable errors and a clear rule for a correct result, such as turning emailed orders, defect reports or supplier invoices into ERP records. Low-volume or judgement-heavy work rarely repays a custom build, and a standard ERP feature may cover it.
Related
- AI business case toolkitA one-page business case, a vendor checklist and the security questions IT will ask, so you can take an AI pilot to your COO, CFO and CIO. Copy it, print it or forward it.
- Should our ERP partner do our AI quoting and order entry, or should we use an AI consultancy?ERP partner or AI consultancy for AI quoting and order entry? When each one fits, when to hire, how they work together and ten questions to ask any provider.
- Our AI pilot worked in testing but nobody uses it six months later. What went wrong, and should we fund the next phase?Why an AI pilot that worked in testing goes unused months later, how to find the cause, and how to decide whether to fund the next phase, fix it or stop.
- AI strategyWhat to fix first, and the business case.
- Process automationLess retyping, chasing and manual checks.
Have this problem across your branches?
A 30-minute review with a senior consultant. You leave with a written one-page summary you can share internally.
Fixed-fee phases, agreed before we start. Stop after any phase.