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The New CFO: How AI Is Rewriting the Finance Function | Figma CFO Praveer Melwani

50 min episode · 2 min read
·
Praveer Malwani,Seema Amble,Ivan Makarov

Episode

50 min

Read time

2 min

Topics

Career Growth, Productivity, Investing

AI-Generated Summary

Key Takeaways

  • ✓Hiring filter for AI-era finance: Screen candidates using case studies that test whether they understand the mechanics of the model they built, not just the output. Melwani specifically probes sensitivity analysis and business intuition during VP Finance interviews, because anyone can generate a model with an LLM, but few can explain why it works.
  • ✓Corp dev diligence via vibe coding: Figma's corporate development team now prototypes acquisition targets' products using Codex to assess technical moat depth before engaging the product team. They also build AI personas of each leadership team member to simulate investment committee feedback on M&A memos, cutting review cycle time before live presentations.
  • ✓Data quality is 95% of the automation problem: Before any AI workflow delivers reliable output, underlying data must be clean and consistently defined across systems. Melwani's T&E reconciliation across Niven, SAP Concur, and Ramp required fuzzy matching across sources — directional accuracy, not perfection, was the threshold needed to surface actionable policy changes within 30 minutes.
  • ✓Automation-first mindset as a recruiting tool: Finance leaders should explicitly position access to AI tools and a culture of internal automation as a hiring differentiator, particularly when recruiting from non-tech industries. Melwani reports team members citing tool access and learning environment as direct retention factors, making technology investment a talent strategy, not just an efficiency play.
  • ✓Headcount math shifts when business complexity stays flat: Melwani states directly that if Figma's business complexity stopped growing, the finance team would not need the same headcount within 12 months. Growth in products, acquisitions, and new markets currently offsets efficiency gains, but the directional trend points toward fewer people handling equivalent operational volume through automation.

What It Covers

Figma CFO Praveer Melwani details how AI transforms finance from backward-looking reporting into forward-looking strategy, covering automated collections workflows, AI-assisted M&A diligence, simulated leadership investment committees, and what intellectual curiosity plus data fluency means for hiring the next generation of finance talent.

Key Questions Answered

  • •Hiring filter for AI-era finance: Screen candidates using case studies that test whether they understand the mechanics of the model they built, not just the output. Melwani specifically probes sensitivity analysis and business intuition during VP Finance interviews, because anyone can generate a model with an LLM, but few can explain why it works.
  • •Corp dev diligence via vibe coding: Figma's corporate development team now prototypes acquisition targets' products using Codex to assess technical moat depth before engaging the product team. They also build AI personas of each leadership team member to simulate investment committee feedback on M&A memos, cutting review cycle time before live presentations.
  • •Data quality is 95% of the automation problem: Before any AI workflow delivers reliable output, underlying data must be clean and consistently defined across systems. Melwani's T&E reconciliation across Niven, SAP Concur, and Ramp required fuzzy matching across sources — directional accuracy, not perfection, was the threshold needed to surface actionable policy changes within 30 minutes.
  • •Automation-first mindset as a recruiting tool: Finance leaders should explicitly position access to AI tools and a culture of internal automation as a hiring differentiator, particularly when recruiting from non-tech industries. Melwani reports team members citing tool access and learning environment as direct retention factors, making technology investment a talent strategy, not just an efficiency play.
  • •Headcount math shifts when business complexity stays flat: Melwani states directly that if Figma's business complexity stopped growing, the finance team would not need the same headcount within 12 months. Growth in products, acquisitions, and new markets currently offsets efficiency gains, but the directional trend points toward fewer people handling equivalent operational volume through automation.

Notable Moment

Melwani describes how Figma's corp dev team built distinct AI personalities modeled on specific executives — including the CFO, CEO, and CRO — to stress-test investment memos before presenting them, effectively running a simulated leadership review that surfaces gaps and reduces live meeting cycle time.

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Episode Transcript

I think we're gonna be able to attract and build the world's best finance organization because so much what people want is the ability to learn and feel like they're being invested in and surrounded by people that wanna go and become the best at their craft. We'd love to hear a little bit about what made someone a great hire on your team, a great person. I want folks that feel extremely intellectually curious. You're coming in and trying to solve and try to understand the business really well. You wanna use AI as a tool to help you and empower you, but it's really, really critical to still be that thinker. We're in the era right now where a lot of slop is being called out. I've heard one CFO talk about how using AI just led to unlimited amount of docs that are being created every day. Anything that you're putting out there that you're asking someone to consume, it's gonna take them time, effort, and energy. 95% of the challenge there is just, like, getting your data in the right spot. What do we think changes in the next twelve to eighteen months? If our business complexity was staying the same, I think we probably wouldn't need the same number of people a year from now. For decades, finance teams spent enormous amounts of time figuring out what already happened. AI is starting to give them more time to decide what should happen next. In this episode, a16z partners Seema Amble and Ivan Makarab sit down with Figma CFO Praveer Malani to talk about what an AI native finance organization actually looks like. Praveer joined Figma in 2017 as its first business operations hire, years before becoming CFO and helping take the company public. Back then, automation meant writing hundreds of lines of SQL, waiting for queries to run, and stitching financial and product data together by hand. Today, team is building very differently. Collections workflows are automated. Corporate finance can reconcile messy data across multiple systems in minutes, and the corp dev team can use AI to prototype an acquisition target's product and run investment memos against simulated versions of Figma's own leadership team before presenting them. But Previr argues the real shift isn't simply about doing the old work faster. It's about changing what finance people spend their time doing in the first place and turning the CFO organization from what he calls a traffic cop into a business enabler. They also discuss what should never be outsourced to AI, why Excel isn't going anywhere yet, and what founders should actually look for in the next generation of CFOs. Welcome to the a 16 c podcast. I'm Sima Amble. I'm joined here by my partner, Ivan Makarov. We recently wrote a piece called You Need a New CFO. Our argument was that AI takes finance to the next level, and the c f... CFO's job increasingly includes building an automation and really designing the …

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