Goldman Sachs CIO Margo Argenti told VentureBeat that nearly a year after ChatGPT was released, the company has yet to put generative AI use cases into production.
With all the generative AI hype swirling among evangelists, one might think that the Fortune 500 is galloping wildly towards putting large language models (LLMs) into production and turning corporate America into one big chatbot. To that, I say: “Whoa, Nelly!” — meaning, think again.
That’s because for all the C-suite executives out there feeling generative AI FOMO and getting pressure from CEOs to move quickly to develop AI-centric strategies, things are actually moving far slower than you might imagine (or AI vendors, who warn companies about falling behind, might want). As I reported back in April, there’s certainly no doubt that executives want to access the power of generative AI, as tools such as ChatGPT continue to spark the public imagination. But a KPMG study of U.S. executives that month found that a solid majority (60%) of respondents said that while they expect generative AI to have enormous long-term impact, they are still a year or two away from implementing their first solution.Goldman Sachs CIO says company is ‘deeply into experimentation’
Consider Marco Argenti, CIO at Goldman Sachs — who told me in a recent interview that the leading global investment banking, securities and investment management firm has, nearly a year after ChatGPT was released, put exactly zero generative AI use cases into production. Instead, the company is “deeply into experimentation” and has a “high bar” of expectation before deployment. Certainly this is a highly-regulated company, so careful deployment must always be the norm. But Goldman Sachs is also far from new to implementing AI-driven tools — but is still treading slowly and carefully.
While Argenti told me that he thinks “We’re all anxious to see results right away” in areas like developer and operational productivity, as well as revolutionizing the way knowledge workers work and producing content, when I asked him what it would take to put its experimental use cases of generative AI into production, he said it required “feeling comfortable about the accuracy.” He added that this needs to hit a certain threshold “in which we feel comfortable that the information is correct and the risks are actually well managed.
