“In today’s market, generative AI and related companies are attracting the most attention and capital, but we believe the AI opportunity set is much larger and across all industries,” says Navneet Govil, executive managing partner and CFO of SoftBank Investment Advisers. 

SoftBank Vision Fund, the venture capital arm of Japanese conglomerate SoftBank Group, was founded in 2017 and is the world’s largest tech-focused investment fund. The fund’s AI investments are sorted into two main buckets: pure-play AI companies and startups that significantly leverage AI as part of their core operations. 

The Vision Fund 1 and 2’s investments in the former group include LegalOn, which provides AI contract review software to lawyers, and legal AI assistant ContractPodAi. SoftBank Vision Fund’s investments in firms that embed AI into their business includes TikTok maker ByteDance, warehouse automation company Symbotic, and digital temp agency JobandTalent.

“While we are very bullish on AI, at the same time, we’re very prudent about underwriting our investments,” says Govil.

Govil says the company first started to see the AI revolution as early as 2016, which is why SoftBank Vision Fund was created as a vehicle to make investments as the tech advanced. “Our founder, Masayoshi Son, has this incredible ability to see around corners,” adds Govil.

What CFOs are being tasked with in 2024 and beyond is how to ensure the dollars they spend on AI are invested judiciously. Nearly $18 billion was raised by AI companies in the third quarter of 2023 alone, PitchBook data compiled for Bloomberg showed, and not all of those investments will pan out. And even as the hype for AI continues to swirl, there are signs CFOs are cautious. When Deloitte surveyed North American CFOs and asked “How important is GenAI to achieving your business strategy?,” only 24% said it was important or very important. Conversely, 42% said it was not important or not very important. 

Financial chiefs must sort out whether they want to be an early investor in AI technology, be a fast follower, or wait to see if investments in AI are a bubble that could pop as it did recently for the metaverse. 

At SoftBank Vision Fund, the approach to investment is to look at four key criteria. They include a focus on AI leadership, determining if the product offered is a strong fit for the market, evaluating a future financial performance that’s sustainable with positive unit economics, and strong execution. 

To support SoftBank’s portfolio companies, in October, it brought together founders and executives from 12 startups with 12 leading AI experts for a two-day meeting in Silicon Valley. The focus of those meetings was to share best practices for AI use cases, discuss proprietary versus open-source models, and how to measure the return on investment. 

SoftBank itself is also sorting out how it, too, will invest and deploy AI. The company has an enterprise contract with ChatGPT that’s available for all employees. AI is being used by SoftBank’s investment team to help simplify complex technical concepts and review and summarize transaction documents. Legal and finance are also using AI to automate and simplify tasks.

“If we’re going to be AI investors, we have to be embracing this within our firm as investors,” says Govil.

The Vision Fund has booked two straight quarters of gains, though it has had to write down billions of dollars that it had invested in WeWork over the past few years.

“The journey starts with data,” says Bea Ordonez, CFO of financial services firm Payoneer.

For over a decade, financial institutions have recognized the power of data and are making investments to unlock it. But in the past year, with the advancements of generative AI, there’s now a democratization of data that requires some strategic investments. 

“We’re early in our journey and being experimental,” says Ordonez. Today, Payoneer is using a generative AI-based predictive model tool to drive more effective customer acquisition and onboarding decisions. The company also uses machine learning and data tools within the underwriting business. 

CFOs, Ordonez says, are using modeling to get better at ingesting and consuming data to guide financial planning and an organization’s overall strategy. “Using predictive modeling to really help us look forward to more quarters and have a better line of sight to how the portfolio is performing, those are areas that we’re definitely investing in,” says Ordonez. 

And because the financial industry is heavily regulated, Payoneer says the use of AI raises interesting questions about data privacy and protection. Through it all, CFOs should be closely involved. 

“I think it’s super important for the CFOs to be hand-in-glove with the folks in their organization who are driving that data transformation journey,” Ordonez says. “And we’re really focused on making those investments.”

“We are formally declaring that we are going to make AI at the core and the forefront of everything Fountain does from this point onward,” says Nico Roberts, chief business officer at Fountain, the creator of an applicant tracking platform that’s used to hire workers at clients like Bojangles and Sweetgreen. 

Fountain is in the business of processing massive amounts of data quickly. Nearly 100 million applicants have worked their way through the startup’s talent platform, with some employers seeing thousands of applications filled for openings in just a week. 

But a survey that Fountain conducted in 2022 showed that 60% of job applicants ended up abandoning the online form when it had more than 15 questions. And 81% of all candidates said they never received any communication from the employer to share updates on where they were on the hiring journey. 

Investments in AI can help reduce these pain points, Fountain says. AI can be used to evaluate an application form to determine the right number of questions that should be asked—perhaps no more than 10. Or analyze the language used in the job posting to ensure it isn’t filled with too much irrelevant corporate jargon.

To leap into AI more assertively, Fountain set aside some budget and created a small team of five. “We said, ‘Hey, we’re thinking about really exploring and going deeper on AI, what does that look like?’” says Roberts. The project, which was initially siloed, led to beta tests with customers, and eventually the AI offerings were shared more widely.

“I think that a lot of companies right now are still going through the decision-making process of, how do we want to tackle AI? What is our risk tolerance and threshold? And do we want to be part of the early adopters group?” asks Roberts rhetorically. “We are very gung-ho about AI.”