AI can also help drive value creation, whether that’s automating routine tasks to drive cost savings or enabling top-line growth. LLMs, for example, can help predict customer behavior, create more accurate forecasts, and improve scenario modeling by processing a large volume of data and considering a multitude of variables. Augmenting the capabilities of financial planning and analysis (FP&A) teams would allow them to prepare for a wider range of potential outcomes, making planning more resilient and adaptable to changing market conditions.
Thanks to productivity gains like these, Brynjolfsson predicts that generative AI will be bigger than any of the technologies we’ve used over the last 20 years. He provided an example from new research he led on how generative AI is boosting call center productivity.
“A couple of years ago, we teamed up with a company founded by a Stanford professor and graduate that helps call centers do a better job,” he said. “And what we found was that the operators who used the AI model were dramatically more productive and more successful than the ones who didn’t. In fact, the least experienced operators became 35% more productive.”
Brynjolfsson went on to explain that the model learned from the most successful operators, listening in on their conversations and identifying phrases or suggestions that improved customer sentiment. The model then passed on those skills to the newest operators. “That’s the kind of tacit knowledge that was previously really hard to automate.”
Generative AI and the CFO Role
Gartner analysts recently noted that CFOs are best positioned to help lead the implementation of generative AI in corporations because they have more insight into opportunities to leverage the technology to reduce costs, improve productivity, and increase revenue streams. “The CFO should be on the frontier of the AI revolution,” Brynjolfsson said. “CFOs understand how to work with unstructured and structured data and do sophisticated analyses on that data, which is why they can make such a big impact.”
Brynjolfsson also sees human resources teams benefiting. “I did an estimate a few years ago and found that the value of human capital in the U.S. economy is a little over $200 trillion—10 times the value of the gross domestic product (GDP). But the problem with human capital is it’s very poorly measured and understood,” he said. “There are a lot of intangible skills in there. AI’s large language models can do a lot to capture and understand the value of your human capital.”
Given AI’s power to disrupt the economy, I asked Brynjolfsson about his take on the mood in Washington around regulating AI.
“I came away from my trip really impressed with how up to speed the government officials I met with were on generative AI,” he noted. “They understand that there’s a tidal wave coming that will be bigger than the impact of the pandemic on remote work, and they are taking it very seriously.”
Workday and Responsible AI
At Workday, we’re committed to responsible AI. We’ve been a vocal and early proponent of AI regulation that builds trust and enables innovation, and we support an ethical approach to AI that is grounded in trust and transparency. To leverage AI effectively, it’s critical to ensure that the data behind an AI recommendation is clean, accurate, and trustworthy. Given that more and more finance functions own the responsibility for data strategy at their companies, I also believe it will fall to the CFO and their team to ensure, through good data governance and management, that the data companies use to coach AI models is trustworthy.
Brynjolfsson closed out our chat on a positive note, predicting that AI could potentially double the productivity rate currently estimated by the Congressional Budget Office over the next decade. He also sees AI giving us more resources to address challenges we face on the healthcare front, such as cancer, and on the educational front, such as personalized education. The reason is AI’s ability to unlock human potential versus just seeing it as cost-saving automation.
“Any one of you who has tried to call an automated voice response system knows it can be very frustrating, especially when there’s a long tail of questions that we ask that aren’t common,” he said. “We humans are much better at dealing with exceptions than machines, so a good partnership is where AI can answer the more common questions and humans can deal with exceptions. AI has a much higher upside in terms of creating additional value than simply trying to take costs out.”
Read more of Workday’s insights on how AI and ML are driving the future of work.