Transaction risk intelligence
Automated supplier statement reconciliation
Supplier risk intelligence
Managed AP recovery audit
Supplier file performance review
Forensic risk analysis
Consulting and bespoke reporting
Building your own AI has never been easier to justify.
The models get more sophisticated by the day, the tools affordable, and someone in your team has probably already built an agent or two.
Our team have a lot of sympathy for teams going down that route. We’ve spent years building and honing AI, and sometimes it can be a tough nut to crack!
Adoption is on the up. McKinsey puts regular AI use at 88% of organisations in at least one business function¹. So now finance teams ask: would it be better to own the solution ourselves?
But ambition is running ahead of readiness. Deloitte found that 74% of companies plan to use agentic AI moderately within two years. Only 21% have a mature governance model for autonomous agents.² If you choose this route, it’s wise to ensure your plans are robust!
So, should you build or buy? The decision rests on six factors.
1. Time to value
A working prototype is quick. A tool your team trusts enough to make decisions for them or act on takes time and money. There’s data testing, tuning, and the slow business of getting false positives down. Realistically that takes months, often longer. Not to mention having your team bug test constantly.
Bought software deploys in weeks, and a mature product should show measurable return quickly. When building AI yourself, the cost of waiting is easy to overlook. In the meantime, the original problem you had is still an issue.
If your problem is genuinely unusual, the chances are that few suppliers have software that can manage it as effectively as a custom-build. But it’s still worth checking around. A solution delivered by experts may be nearly there and the fix may take less time for them to develop than you.
2. Costs
There’s appeal in building yourself – you control the spend. But running your own AI isn’t a one-time project cost. There ere other costs to factor in. Engineering time, infrastructure, model fees, retraining, and the internal hours of reviewing outputs. This should include ongoing costs for maintenance and specialists.
Buy and you don’t control the price. Instead you negotiate a number which you can budget, forecast. If the supplier can provide proof of value, you can defend that figure to a board.
Building your own could looks cheaper at the start, but this depends on how long development lasts, and the value it then provides. Buying can be costly, especially if you pay a lump sum yearly, but is it worth it for the expertise?
3. Data breadth
Your AI is only as good as the data that it’s trained on.
This is where build projects can struggle. Let’s look at this from a risk detection angle again: Risk detection is a pattern problem. Spotting a duplicate invoice, or a suspicious change to supplier bank details, means recognising something you have seen before in a different context. Sometimes your data simply doesn’t have that context. An AI model cannot flag a technique it has never encountered. So a wide breadth of training material is integral to the success of your project.
It’s can be difficult to know if a software provider has the breadth and depth of data to train their AI effectively. Choose suppliers who have been developing their AI for years, with data across several sectors and teams. This access to data (and access to new data to learn from) is the hardest thing for a single organisation to replicate. Yes, even a shared service centre!
4. Fit and customisation
A tool you build matches your processes exactly, because you wrote it around them. Your data never leaves your environment. Updates land in the order you want them, when you need them. If you have an unusual structure or strict data residency rules, that counts for a lot.
Buying means accepting there may be some limits on customisation. How much depends on the vendor. Some can suit your needs perfectly with options for customisation. Others may have less sophisticated technology. So when you’re looking at your options, ask: how configurable is it, can it handle your setup, whether it’s several ERPS or business units.
It is also worth checking what the AI actually does. Plenty of products have added a chat window to software that works the same way it did before.
5. Keeping pace
AI models move fast.
If you build, someone needs to own it permanently, and update it often and solve issues as they come. You may need a specialist to do this, and they may need to be a new hire.
Buy, and updates, new modules and model improvements arrive without you lifting a finger. You pay for the software, the specialist knowledge and the constant upkeep. Software companies provide support services and are constantly working to remove bugs or improve the product.
6. Security and assurance
When you build your own AI, the assurance burden is entirely yours. Access control, penetration testing, audit logging, single sign-on, and the certifications your auditors and your own procurement team will ask to see. Some of this might fall to your IT team, so always get their OK before you proceed.
An established vendor will already have credentials to share with you, the software often logs information for audit purposes, and they also often supply SSO.
There’s another option
Sometimes the question you should truly be asking is not a this or that, but what if I can have the benefits of both?
There is now the option to buy the specialist layer and connect your own AI tools to it. Some software (FISCAL’s included) has AI connectivity built in. Your team can pull data and reports into whatever assistant or workflow they have already built.
That gives you detection models trained on far more data than you could ever hold in house, a support structure, and none of the burden of upkeep on the core AI. The only upkeep you do is on your own bespoke workflows. This might be the option for you if you have already invested in an AI assistant like Claude, Gemini, or Copilot.
So, build or buy?
It depends on your budget, your people, and how unusual your problem is. With engineering capacity, a genuinely bespoke process and time to spare, building can work.
For most finance teams it will not. A specialist team working on purpose-built software, honed over years of data and hundreds of implementations, is the safer bet. And with an AI connection, it can work even harder for you.
The six factors at a glance
| Factor | Building your own | Buying specialist software |
|---|---|---|
| Time to value | Months to deploy as a trustworthy agent. | Weeks to deploy, fast ROI. |
| Cost | You control it, but need to budget for potential issues. | The vendor sets it, but you can budget for it. |
| Data breadth | Limited to your own ledger. | Patterns learned across hundreds of organisations. |
| Fit and data control | Exact fit, data stays in house. | Varies by vendor, some third-party processing. |
| Keeping pace | Your organisation deals with every update and fix. | Updates and new modules included, with technical support. |
| Security and assurance | You build and certify it yourself. | Security as standard, with certification and, usually, SSO. |
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