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When will AI show returns?
As researchers predict a productivity boom fueled by generative AI, companies are starting to work out what that might look like for their productivity and their profits.
Six months ago, companies were asking broad questions: “What is generative AI and how does it apply to our business?” said Lareina Yee, a senior partner at McKinsey. Without knowing all the answers, executives across industries rushed to assure analysts and investors on earnings calls following the release of ChatGPT, that they, too, had a plan in place to capitalize on the new technology.
Now the discussion is shifting from awareness and high-level strategy to more practical considerations: proving the technology works, and figuring out how to deploy it at scale. “All of a sudden, you move from, ‘What is this?’ to ‘How are we applying it in this business, and what are the results?’” Yee said.
The answers are of keen interest to board members, analysts and investors, given the technology’s potential impact. A recent McKinsey report estimates AI may add up to 0.6% in annual labor productivity growth over the next two decades, generating as much as $4.4 trillion in additional value each year across industries.
Some companies, such as online learning platform Coursera, have started tackling the question of what that might look like on their own balance sheets.
Coursera’s Chief Executive Officer Jeff Maggioncalda said his finance team is combing through the available research to identify which roles will benefit from an AI productivity boost. “We're now incorporating our expectations about generative AI back into the financial planning process. We're going to actually assume that people are more productive next year than they are this year,” he said. “And as we think about planning, we think about hiring, so now it's getting to brass tacks.”
According to the McKinsey report, about 75% of the potential value from applied generative AI will come in four business functions: customer operations, software engineering, marketing and sales, and research and development.
“It's not surprising that companies are able to start to size the potential value as they are prioritizing where to focus,” Yee said.
For Brex, a fintech startup that offers corporate credit cards and an expense management platform, the efficiency gains are most readily apparent for software development with new tools like GitHub CoPilot, a program that helps users write code, and for customer support with AI-powered chatbots, according to Michael Tannenbaum, the firm’s chief operating officer. With this new tech, he anticipates Brex will be able to add headcount more slowly in both areas even as the business continues to grow. He expects productivity boosts may take longer to show up in other areas, such as product management, marketing, and accounting.
As companies start strategizing for 2024, more executives are likely to start focusing on such issues in earnest. “You are going to see people absolutely ask questions like, ‘Are we using Copilot?’ ‘Can we do more with less?’ Especially on the software engineering side of things, because the expenses are so high,” he said.
While companies like Meta Platform have been rewarded for reducing headcount, Tannenbaum says that AI offers executives a more positive way to talk about cost savings — not through layoffs, but by boosting productivity and moderating hiring.
To be sure, many companies are nowhere near the point where it makes sense to translate the promise of generative AI into dollars and cents. Several of the companies Felicia Lyon, principal at consulting firm KPMG, has been working with see it as still too early to quantify — many haven’t yet made the leap from pilots to implementing the new technology more broadly.
Even when AI tools are successfully incorporated into the workflow, quantifying the impact on a business isn’t always straightforward. And then there are a series of decisions to be made about what to do with the productivity gains that do materialize — not least of which is whether to downsize or augment the existing workforce. “There's a lot of work to be done before you can definitively just say, ‘Oh, we're going to have 20% more revenue because we're using generative AI,’” Lyon said.
In the meantime, companies can expect analysts and investors to start asking more targeted questions about when they anticipate the widely-heralded benefits to materialize. “The market is going to keep asking us for the timeline horizon,” Lyon said. “The companies that start going faster and getting results and reporting numbers will set the stage for the rest of us to follow very fast.”