An AI Adoption Framework for FP&A Teams
If you are in FP&A and looking to implement AI more on your team, I have a guide that might help.
To be clear, this was created with AI but heavily influenced by my professional experience. You could probably create something that looks like this on the surface with a single prompt, but that is not what I did.
Someone in my network asked me how I thought about AI, and I used it as an opportunity to collect my thoughts. I connected Notion to Claude, and we iterated structure and ideas over the course of a week. I also incorporated thoughts from other people's posts that I found helpful.
The guide is more of a reference than a read-through document, and it discusses:
- Common FP&A pain points and where AI works today
- How AI generally gets adopted: prompting, workflows, automation, and agents
- Use cases I have implemented and other areas I am looking to try
One thing worth pulling out
The number that surprised me most while writing it is how low real adoption still is. The strictest finance-specific surveys put active AI use inside finance functions at 11 to 18 percent. The much higher figures you see in general surveys come from banks, fintechs, and large technology companies with engineering teams behind them. They do not describe a VP of Finance running a lean team at a growth-stage company.
That changes what being behind actually means. If you are prompting well and have a workflow or two running, you are further along than the headlines would suggest.
This was a helpful way for me to think through the topic, so I thought I would share it. Comments and feedback are welcome.