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How AI rocketships redefine finance with automation

How do finance teams at AI-led companies leverage automation today to operate at unprecedented speed?

During an OnRamp San Francisco , leaders from AI companies, Cursor and Notion, and industry leaders, Airbnb and Poshmark, joined Megan Lin Gibbons to share the strategies they’re deploying to optimize productivity and hiring.

AI is your newest analyst — manage it like one

With AI technology advancing at breakneck speed, panelists agreed that hiring people who can tackle steep learning curves is critical.

Cursor actively works toward this goal by focusing on candidates’ adaptability over point-in-time skills. Instead of focusing on what candidates can do right now, they hire for their ability to learn as AI evolves. Every candidate goes through a one- to two-day onsite where they work side-by-side with the team solving real problems.

“Part of what we look for is how they can use external resources that are outside their core job to be better at what they’re actually tasked with,” said Vibhav Joopelli, who leads finance and accounting at Cursor. “That’s going to be one of the most important skills that separates talent over the next five to 10 years.”

The importance of hiring for AI fluency becomes clear once people are on the job. Poshmark SVP of finance and legal, Kaustubh Khandelwal (KK), observed that newer employees at Poshmark are taking more risks and experimenting with AI, while experienced team members show more skepticism.

“When you see manual errors, you can actually see them. But AI model errors are invisible,” he explained. “That is the difference in adoption and interpretation of the technology at this point.”

To that end, KK offered a memorable analogy for hiring: “Any person you’re hiring, they need to know how to manage a junior analyst on the team. And that junior analyst is AI.” He elaborated that success with AI depends fundamentally on a person’s curiosity, since the quality of questions someone asks is reflected in the output AI provides.

by building flexibility into job descriptions. Rather than prescribing a rigid list of duties and qualifications, roles include what procurement director Katie Loudon calls “intentional white space” for AI fluency and adaptability.

“The job I was hired for three years ago is very different from the job I’m doing today,” Loudon noted. “I’m more keen to hire someone that’s been curious and maybe failed than I am someone who’s just extremely resistant."

This focus on curiosity continues beyond hiring. Notion launched “Curiosity Cards,” which are dedicated budgets for employees to explore AI tools and learning opportunities. The philosophy behind this program is to create a safe space for employees to experiment — and fail — with AI.

“Sometimes you need to nudge people to be curious. We provided the funds, and people took advantage of it,” Notion assistant controller Jennifer La said.

Loudon takes a similar approach at Airbnb, giving engineers a $500 experimentation wallet to try different tools and report back on what works. She added that this hands-on experimentation is especially important at large, mature companies, where proprietary systems and strict regulations often limit AI tool adoption and ROI.

Use AI to upskill every employee and unlock better insights

AI is helping finance teams at these companies be more productive without increasing headcount, improve visibility into spend, and focus on higher-order tasks. At Cursor, has been helping employees build skills in new domains. Joopelli noted, “The biggest thing that AI tools have done, beyond make you operationally more efficient, is allow you to learn skills that you would otherwise not have been able to learn or not been able to learn as fast.” That’s enabled a culture at Cursor that pushes everyone, from recruiters to finance professionals to software engineers, to use tools like ChatGPT and Cursor itself to level up their data analysis skills.

For strategic finance organizations especially, this also reduces reliance on data experts to pull analyses and clean data. The result? The organization stays leaner without hurting performance.

At Notion, AI improved spend visibility and led to better strategic insights for leadership. J La emphasized that by automating expense coding and categorization, the accounting team can now provide financial planning and analysis with more concrete data on where employees are spending, such as on offsites and customer visits.

“In the previous life, we weren’t able to provide any value without seeing the strategic ’why’ behind the spend,” she said.

For KK, the focus is using AI to free up time for higher-value work. When he arrived, the team managed expenses on Google Sheets, a manual process that required the team to chase down employees for receipts and spend details. Automating expense management allowed the team to focus instead on “going deeper with the business.”

4 skills to build to keep up with AI’s rate of change

The panelists identified four areas finance leaders should focus on in the next 12 months to avoid falling behind:

• Data literacy. Joopelli emphasized data analysis and the ability to write SQL. He noted that the ability to understand how data originates and flows upstream through the organization’s systems allows finance leaders to move faster and make better decisions without outside help.
• Curiosity. J La thinks leaders should encourage employees to be curious about AI tools and constantly experiment with them. Since some finance professionals are still intimidated by AI, fostering a culture of open experimentation that encourages employees to adopt AI tools the company purchases is critical.
• Risk-taking. KK talked about the importance of taking calculated risks, something he thinks finance is still a bit hesitant to do. The senior finance leader believes the function should shift to a “let’s see what’s possible” mindset when it comes to automation, advanced analytics, and creativity.
• Storytelling. Loudon sees the ability to tell a compelling story that conveys your impact and ROI as the differentiating skill. “That’s something that’s harder and harder for AI to automate,” she added.

Looking two years ahead, panelists predicted a shift in the conversation around AI in finance. Joopelli said that the interfaces we interact with at work will look completely different. J La sees the conversation evolving from “can AI do this?” to “should AI do this?,” forcing finance teams to make tough judgment calls. KK envisions a future where he can ask AI over breakfast, “Hey, how’s the business doing?” and get not only metrics but also insights on the problems that need his immediate attention.

They all agreed that, with AI’s rapid ongoing evolution, finance teams should focus today on building the right culture, skills, and infrastructure to adapt to this bold new future.

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