AI in Finance, the Whole Map in One Page
Everything on this site is AI in finance, which makes this page the map rather than another article. Six areas, and the pages in each one that get read most.
I run an FP&A function for a living, so every one of these came out of work that had to survive a real close. Nothing here is a demo I built to have something to write about.
If you have never used AI at work, start at the bottom of this page. If you know what you are trying to fix, pick the area and go.
The finance work itself
The biggest area on the site, and the one people arrive on most. Variance, break-even, pro forma, depreciation, reconciliation, the close. The analysis you were doing before AI showed up, done faster and with fewer manual steps.
Start at finance automation, which has the AUTO framework for deciding what to automate first and what to leave alone.
- Rate and volume variance analysis, the one that explains why the number moved rather than that it moved.
- A break-even analysis template you can fill in rather than build.
- Building a pro forma analysis, from the assumptions up.
- Closing the books and account reconciliation, the two that eat the most days.
- Automating accounting processes, and where the limits really are.
- How to use AI in accounting, seven jobs it does well and three it does not.
AI in Excel
Most finance work still happens in a spreadsheet, and there are now five separate routes AI takes into one. They are gated behind different subscriptions, which nobody writes down until you are about to demo something.
AI in Excel goes tool by tool and names the gate each time, including the =COPILOT function and the date it stops working.
- Dynamic ranges in Excel, the most read page on this site.
- Excel techniques worth knowing and the add-ins worth installing.
- AI for Excel formulas, mostly for reading the ones you inherited.
- ChatGPT against Claude against Copilot, on the same spreadsheet.
- Copilot in Excel, if your employer already pays for it.
- Where Excel stops and Power BI starts, if you are deciding between them.
Power BI and reporting
Once the same report goes to the same people every month, it stops being a spreadsheet problem. This is the area with the widest gap between what people build and what gets opened.
Power BI for finance is the hub, financial dashboards is where most people start, and reporting in finance covers what happens to the output after it leaves your hands.
- Building a budget dashboard, the most read page in this area.
- Variance analysis in Power BI, with the sign convention decided once.
- Dashboard design, which is mostly about what to leave out.
- Building a Power BI dashboard with Claude and DAX for finance.
The whole shelf, in one email
The prompts, templates and skill files behind these pages
Every prompt, template and AI skill file I use across the site, plus one short email a week. Free, and it lands in your inbox in about a minute.
Which AI tool to use
The honest answer is that the differences matter less than people think, and the one you already have access to is usually the right one. The differences that do matter are about what each one can see, not how clever it is.
The AI tools for finance I still use is the roundup, including the three I dropped and why.
- Claude for finance, ChatGPT for finance and Copilot for finance, each on its own.
- Perplexity for finance research, which quietly does one job better than the rest.
- Office Scripts, the button in Excel almost nobody clicks.
- The stack I run every day, end to end.
Agents and automation
An agent is a tool you brief once and let run, rather than one you prompt every time. This is the newest area on the site and the one where the gap between the demo and the Tuesday is widest.
AI agents for finance is the hub, and agentic AI for finance covers what separates a real agent from a macro with better marketing.
- n8n for finance and Power Automate for finance, the two most read builds here.
- Building your first agent, step by step.
- Claude Skills and custom GPTs, for work you repeat.
- Copilot Studio agents, if your employer is a Microsoft shop.
Careers, governance and the awkward questions
The part nobody puts in a demo. What you are allowed to put into these tools, who owns the output, and what happens to the job.
Will AI replace finance jobs is the honest read, and it is not the one either side of that argument wants.
- An AI policy for finance teams, with a fill-in template.
- Getting buy-in for finance automation, which is usually the real blocker.
- Rolling AI out to a team without breaking the close.
- AI fundamentals and the AI glossary for finance, if the vocabulary is the barrier.
- Data quality and data governance, which decide whether any of this works.
If you have never used AI at work
Do not start with a tool. Start with a job you already do that you resent.
Take one thing you rebuild every month, open whichever AI your employer already pays for, and ask it to explain the file back to you. Not to build anything. Just to read it. Twenty minutes, no budget, no procurement, no policy conversation.
That one exercise tells you more about whether this is worth your time than any pilot will, and it is the same first step I give every team I work with. When you want the vocabulary, the glossary is written for exactly that. When you want the wider picture on where this is going, what changed this month is updated as things move. And when you want the specific jobs rather than the overview, AI use cases in finance sorts them by where they land in your month.
And if you would rather be handed the map than read it, everything on the site is in one list.