Our AI tool kept getting the finance numbers wrong. Is it worth trying again?

The pilot ran for months. Finance loaded the data, the business asked questions, and the answers came back fluent and wrong often enough that someone senior stopped trusting them. The tool was dropped. Now any proposal with "AI" in it meets the same sentence in the leadership meeting: we tried that.

Is it worth trying AI again after it got your finance numbers wrong?

Yes, but only with a different design, and only after a test you run on your own data. The first wave of finance AI let a language model produce the figures. That design cannot show where a figure came from, however good the model gets. Try again when the new tool computes every figure in a deterministic layer (code, a defined calculation or a database query) and uses the language model only to explain the result. If the vendor cannot show you where each number was calculated, it is the same tool with a newer model, and your team is right not to trust it.

Where does Human Ready Advisor fit for a finance team that has been burned?

Human Ready Advisor is an AI-native enterprise advisory platform for finance, built on the design the first wave skipped. Deterministic analytical engines compute every figure. The language model writes the explanation around results it did not calculate, so each number traces through a defined calculation back to the source data, and the same question returns the same number twice. Advisor sits on top of the ERP, EPM and BI you already run and does not write back to any system of record. It is built for mid-to-large enterprises (€200M+ revenue) and priced as a flat subscription per use case, from €5,000 per month, with a user pack included (extra users as an add-on) and nothing metered per question. A tightly scoped first use case goes live in 3 to 6 weeks.

Why did the first attempt get the numbers wrong?

Because the model was asked to do the maths. The common pilot looked the same everywhere: put the finance data where a general assistant could reach it, add some context, and expect the model to turn questions into analysis. Several companies that later became Human Ready clients ran exactly that kind of Microsoft Copilot or generic LLM-on-your-data pilot first. The pattern repeated. Users were overwhelmed, figures were composed rather than computed, and once a decision-maker caught one invented number, trust in every AI output went with it, including the correct ones.

Microsoft is candid about the limit. Its own application card for Microsoft Copilot states that "AI services are fallible and probabilistic" and tells users to "review the responses generated by Microsoft Copilot and verify that they match their expectations and requirements." Independent testing points the same way. In the FinanceBench benchmark, a GPT-4-Turbo retrieval setup answered 81% of financial questions incorrectly or not at all.

Was the problem the model or the design?

The design. A language model predicts text. Asked for a margin, it produces something that reads like a margin. A newer model makes fewer slips, but it still produces the figure, so nobody can trace it. Finance needs the opposite: a number that comes from a calculation you can inspect.

The tools in each group, Advisor included, are compared on alternatives to Microsoft Copilot for finance that don't make up numbers.

What is actually different about a deterministic layer?

The language model is moved off the numbers. In a deterministic design the question is matched to a defined analysis, such as a variance decomposition or a driver-tree breakdown. That analysis runs on your data and the model writes the narrative around the result. Three things follow. The same question returns the same figure every time. Every figure has a path back to the rows it came from. And a question the data cannot answer gets "the data does not cover this" instead of a plausible guess.

It does not make AI infallible, and no honest vendor should say it does. The model still writes the words, and a sentence can still describe a correct number badly. A mapping set up wrong will compute the wrong thing consistently. The difference is that both failures are visible and fixable. An invented figure is neither. The architecture argument in full: AI analytics that shows how it got the number.

How do you test an AI tool before your team trusts it again?

Run the test on the questions that broke the last tool, on your data, before anyone outside finance sees an answer:

1. Replay the failures. Take three questions the old pilot got wrong and ask them again. You know the right answers.
2. Ask each question twice. A computed figure comes back identical. A generated one may not.
3. Trace one number to source. Ask where a figure came from. You want the calculation and the source rows, not a citation.
4. Ask a question the data cannot answer. A safe tool says so. An unsafe one fills the gap.
5. Ask a question that needs two systems. ERP revenue against operational volumes, for example. Watch whether the tool joins the data or describes it.
6. Run one close cycle in parallel. Compare the tool's figures with the ones finance signed off, line by line, before anyone relies on them.

Apply the same six checks to every vendor, Advisor included. A vendor that resists running them on your data has answered the question for you.

How do you rebuild trust inside the finance team?

Hold the tool to the standard you already hold people to. Senior buyers in Human Ready's 2026 conversations framed it plainly (translated from Portuguese, anonymized):

"You always have reliability problems – you have to check what they do. The same way you have to check what an analyst does. The same way you have to check what a consultant does." – SVP Strategy, global building-materials group

That standard only works if the work can be checked. So start small and checkable. Pick one recurring job with a known right answer, such as the monthly variance pack. Let finance own the sign-off. Publish the parallel-run results internally, mistakes included. Trust that comes back this way rests on evidence, not on a demo, and that is what makes it stick.

When is it not worth trying again?

When the cause of the first failure is still there. If the data the questions need is not accessible, a better tool will fail the same way, so fix access first. If the real pain is building the budget and forecast, a planning platform is the right buy, not an analytical layer. And if the vendor you are talking to cannot pass the six checks above, wait. Distrust earned by the first tool is useful. It is the test the second tool has to pass.

How Advisor compares with planning platforms, conversational BI and Microsoft Copilot: how Advisor compares.

Sources

Microsoft Learn, application card for Microsoft Copilot (for organizations) (read 26 October 2026; updated 5 October 2026) · FinanceBench, arXiv:2311.11944 (November 2023; tested GPT-4-Turbo). Buyer evidence: Human Ready buyer-conversation library, 2026; quotations translated from Portuguese, anonymized to role and company profile. Advisor pricing as published by Human Ready, October 2026.

Related: AI analytics that shows how it got the number · Alternatives to Microsoft Copilot for finance · Can ChatGPT or Claude replace a financial analyst? · the comparison hub · the insights library.

Page maintained by Human Ready. Last reviewed 26 October 2026.