What Does AI Mean for FP&A in SaaS?

Introduction

AI is supposedly transforming how companies operate—or at least that’s what all the LinkedIn “hype” posts say. But if that’s true, why does finance still feel the same?

I’ll admit, I use AI to clean up my emails (spelling and grammar were never my strong suit—hence, a career in numbers).

But on a serious note, can AI actually help finance teams—especially in SaaS, where small teams are under pressure to deliver revenue-driven insights?

Can it analyze historical data and improve forecasting assumptions, or are we better off sticking to Excel and hoping we can eventually push things into Power BI?

1. The Potential of AI

What AI Promises:

• Greater accuracy, deeper insights, and unmatched speed in analyzing datasets and uncovering patterns.

• Automates manual tasks in the monthly close and forecast process.

• Frees up time by handling repetitive forecasting tasks, allowing for more strategic decision-making.

The Reality Today:

• Many SaaS companies are still in the early stages of AI adoption, largely because their data is scattered across multiple systems and requires heavy manual work. 

• AI can help, but until the data process is fixed at the source, it won’t add much value for analysis or forecasting.

2. The Challenges AI Faces

Complexity of SaaS Revenue:

• There is a lot of nuance in how different companies write, book and record their customers sales contract

• This makes it hard to have a blanked forecasting solution or one size fits all, since there are always “one-off” or special instances that usually apply to some of the larger customers

Data Issues in SaaS:

• Because companies grow so fast what usually happens is they scale at a rate faster than their back office can keep up

• This leads to messy, overly complex and siloed data as back office functions do their best to play catch up with all of rapid growth from the front office 

3. How SaaS Companies Can Leverage AI for Forecasting

Start Small:

• Pilot AI tools for specific analysis or questions by feeding it a cleaned dataset.

• Use the insights it provides as a starting point for understanding AI’s capabilities and limitations for analyzing your business

Invest in Data Infrastructure:

• Clean, well-organized data is the foundation for effective AI models.

• The "garbage in, garbage out" rule applies—if your data is unreliable and inconsistent, AI will only amplify those issues in its analysis.

Conclusion

AI has the potential to transform SaaS FP&A, but it requires a thoughtful implementation. SaaS leaders should see AI as a tool to enhance—not replace—their strategic insights.

If you're interested in how AI can enhance your finance function, feel free to DM me or set up a call.