Finance is the function with the clearest rule about AI output and the least room to bend it: a number is right or it is not, and a wrong one travels into a board pack. This track treats generative tools as useful for structure and explanation, and never as a source of figures.
Language models produce text, and a calculation presented as text is produced the same way a sentence is. The output is often right, which is precisely what makes it dangerous: a team that has seen twenty correct totals stops checking the twenty first. The rule this track installs is that a model never originates a number that goes into a document. It can explain a number you computed, restructure a table you built, or tell you which number is missing. It does not supply the number itself.
Translation, mostly. Turning a variance analysis into three sentences an operations manager will act on is work finance teams do constantly and rarely enjoy, and it is work with no numeric risk because the figures are already fixed before the writing starts. The same goes for drafting the structure of a review, writing the question list for a budget conversation, or producing the first version of a commentary that you then correct. The gain is real and it sits entirely on the explanation side.
Instead of asking a model for the answer, give it your analysis and ask what would have to be true for it to be wrong. Ask which alternative explanation you have not considered, what a sceptical reader would attack first, and which assumption is doing the most work. This is the one use where the model's indifference to your conclusion is an advantage, and it is the exercise participants carry out of the track most often.
Diagnostic, acceptable use policy, interactive tracks by function, adoption follow-up. The four steps work together, and the free 30-minute call scopes all of them.
See the four stepsAI training for finance teams
Technically yes, and whether it may is a policy question about where that file is allowed to go. Even where the answer is yes on an approved tool, the rule about originating figures still holds: the commentary is a draft and every number in it is one you already had.
Useful and treated as draft code, which means tested rather than trusted. The exercise is to run any generated formula on a small case whose answer you already know before pointing it at the real file. Most errors show up immediately on a case of five rows.
We cover how to evaluate one before you sign: what it was trained on, what it does when it is uncertain, whether a person can see why it produced a given figure, and what happens to your data. We do not teach a specific forecasting product.
We go through how your teams use AI today, what is missing to frame it, and what the first quarter would look like. You leave with enough to decide, whether you work with us or not.