How should my finance team learn to use AI?
It depends on what your finance team spends its time on and what tools it is allowed to use. If most of the week goes into writing, explaining and checking, such as variance commentary, board and management reporting, policy questions and vendor or contract review, hands-on training built around those tasks pays off quickly. If the pain is in data plumbing, like messy exports, manual matching or systems that do not talk to each other, training helps less and you probably need some engineering. Either way, train on your own work, inside approved tools, with review rules finance already trusts.
Who this is for
CFOs, controllers, FP&A leads and finance operations managers who want their team using AI on real work, without weakening accuracy or controls.
The short version
- Train on the documents and tasks your team already handles every month, not on generic examples.
- Teach finance-specific habits: check every number against the source, keep a person accountable for every output and never paste confidential data into personal accounts.
- Start with writing and analysis support, then move to repeatable workflows once people trust the tools.
- Measure the change on named tasks at 30, 60 and 90 days.
Why finance teams need a different kind of AI training
Most general AI training assumes the main risk is a weak paragraph. In finance, the main risk is a wrong number that looks right. AI chat tools can misread a table, round inconsistently or state a figure with confidence that does not appear anywhere in the source. A finance team needs to learn where AI is strong (summarizing, drafting, explaining, restructuring information) and where it must be checked line by line (arithmetic, totals, anything that will reach a lender, investor, auditor or board).
Finance teams also handle some of the most sensitive data in a firm: payroll, bank details, forecasts and deal terms. Training has to cover what may and may not go into an AI tool, and that depends on your firm’s policy and the plan you are on. If you do not have a policy yet, start with how to write an AI acceptable use policy.
What to cover, in order
1. Safe setup and data rules. Which tools are approved, which account to use, what data is allowed and how to handle client or employee information. Keep this short and specific. Our page on keeping client data safe with AI covers the main controls.
2. Writing and explaining. This is usually the fastest win. Good exercises include:
- Turning a variance table and a few bullet notes into a first draft of month-end commentary.
- Rewriting a technical accounting explanation for a non-finance manager.
- Drafting answers to recurring questions about expense, travel or purchasing policy.
- Summarizing a long contract, loan agreement or vendor proposal into key terms for review.
3. Analysis support. Asking AI to suggest reasons for a variance, list questions to ask a budget owner, outline a forecast scenario or check a spreadsheet formula. The output is a starting point for a person’s judgment, not a conclusion.
4. Checking and review. Teach a simple review routine: compare every figure with the source, ask the tool to show where each number came from, and treat anything unsupported as wrong until proven otherwise. This habit matters more than any prompt.
5. Repeatable workflows. Once people are comfortable, turn the best prompts into shared templates for monthly tasks so quality does not depend on who is doing the work. See how to create prompting standards.
A simple training format that works
| Step | What happens | Why it matters |
|---|---|---|
| Pick 3 to 5 tasks | Choose recurring finance work that takes real time | Training stays tied to value |
| Hands-on session | People do those tasks live, using their own anonymized or approved data | Skills transfer to the real job |
| Shared templates | Save the prompts and review steps that worked | The team works the same way |
| Follow-up | Short check-ins over the next few weeks to fix what is not working | Habits stick instead of fading |
| Measure | Compare time and review rounds on the same tasks before and after | You can show whether it worked |
For timing, see how long it takes to train a team on AI. For the measurement method, see how to measure whether AI training worked.
Where training is not enough
Some finance pain is not a skills problem. If the work is mostly moving data between systems, matching transactions or rebuilding the same report from several exports, people can learn to use AI well and still lose hours every month. That is where a custom workflow or agent, connected to your systems with proper controls, does more than training. Our page on AI agents for month-end close explains what that looks like, and custom AI vs off-the-shelf tools helps you decide whether it is worth building.
Common mistakes
- Training on toy examples. If the exercises are not your team’s real tasks, little changes on Monday.
- Skipping the review routine. Speed without checking creates errors that cost more than the time saved.
- No approved tool. Training people before they have a sanctioned account pushes them toward personal accounts.
- Treating the controller as a blocker. Bring the person responsible for controls into the design early, so the new way of working is approved rather than tolerated.
- One session and done. Without follow-up, most teams drift back to old habits within weeks. See why AI adoption stalls.
When you do not need us
If your team already has approved tools, a clear data policy and someone internally who uses AI well on finance work, an internal champion program may be enough. See internal AI champions vs an outside trainer.
How Ruby helps
- AI training: hands-on sessions for finance teams, built around your monthly tasks, with templates and follow-up.
- AI strategy: choosing which finance workflows to start with and setting a baseline so you can show the result.
- AI engineering: building connected workflows or agents where training alone will not remove the manual work.
If you want a training plan built around your finance team’s actual work, book a call with Ruby.