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title: What building an AI-native finance function taught me
description: OpenAI CFO Sarah Friar shares five lessons from building an AI-native finance function at OpenAI. Key initiatives include pursuing a zero-day close (real-time...
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og:description: OpenAI CFO Sarah Friar shares five lessons from building an AI-native finance function at OpenAI. Key initiatives include pursuing a zero-day close (real-time...
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# What building an AI-native finance function taught me

**[OpenAI](https://daily.dev/sources/openai)** · 10 min read · 1 upvotes · 1 comments

## Summary

OpenAI CFO Sarah Friar shares five lessons from building an AI-native finance function at OpenAI. Key initiatives include pursuing a zero-day close (real-time reconciled financial position) and continuously updated automated forecasting. Lessons cover: enabling broad AI access paired with structured hackathons (producing tools like IR-GPT), redesigning workflows from source data to decision rather than just automating steps, empowering finance professionals to build their own tools using ChatGPT and Codex, maintaining clear human accountability and governance controls, and measuring AI ROI through operational metrics like cycle time and forecast accuracy rather than seat counts or token usage.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://openai.com/index/building-an-ai-native-finance-function>

## Questions this post answers

### What is a zero-day close in finance and how does AI enable it?

A zero-day close aims to give leaders a real-time, reconciled, and traceable view of a company's financial position at any moment, rather than reconstructing it after a period ends. It connects approved spending plans, general-ledger actuals, purchase orders, accruals, and transaction details in a continuously reconciled view. AI prepares initial variance explanations and flags exceptions, while finance teams validate numbers and own the final sign-off.

_Finance teams pursuing real-time close initiatives track approaches like this on daily.dev._

### How can finance teams measure ROI from AI tools beyond seat count or token usage?

Measuring AI ROI in finance means asking four questions per workflow: Did AI complete work that mattered? What did it cost including employee time, review, and rework? Was the result good enough to use? Did it help move faster or make a better decision? For the close, metrics include cycle time, share of transactions reconciled automatically, exceptions requiring review, and time to explain a variance. For forecasting: accuracy, refresh frequency, and scenario turnaround time.

_Teams deciding how to justify AI spend in finance find frameworks like this through daily.dev._

## Community discussion

Top comments from developers on daily.dev.

**@kartiknvj** · 0 upvotes

> Finance is a good stress test for AI reliability, because the tolerance for silent errors is basically zero. I'd be curious how much human-in-the-loop you kept for numbers that actually hit a ledger. Where did you decide automation wasn't worth the risk?

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---

Tags: [#ai](https://daily.dev/tags/ai), [#automation](https://daily.dev/tags/automation), [#openai](https://daily.dev/tags/openai), [#chatgpt](https://daily.dev/tags/chatgpt), [#finance](https://daily.dev/tags/finance)

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