Wharton AI expert Ethan Mollick says despite hype, little is known about AI’s real-world impact. He shared emerging evidence on generative AI in workplaces, highlighting early insights for HR leaders on adoption, productivity, and potential challenges.

New York

A Wharton School expert has shared findings from recent studies on artificial intelligence in the workplace, revealing notable improvements in work quality and productivity where AI has been implemented. In controlled trials at major companies, AI use led to significant gains, but the expert notes that broad organizational adoption is still lacking.

The expert stresses that the impact of AI depends on how organizations choose to integrate it, warning against using AI solely for automation and job replacement. Instead, they advocate for a balanced approach involving leadership vision, widespread employee access to AI tools, and dedicated teams to develop and scale effective use cases. The expert also highlights that traditional training methods for AI usage are rapidly evolving, requiring organizations to adapt their strategies.

Wharton expert: Limited AI evidence emerging in workplaces

I mean, we have some evidence about AI's impact on both work and AI's impact on how things go. Actually, some of the early papers, I was lucky enough to work with my friends at Harvard, MIT, and the University of Warwick. We went to Boston Consulting Group, did a randomized controlled trial with 8% of the workforce. We found 40% improvements in the quality of work, 26% increases in productivity. We did another one at Procter & Gamble with 776 employees. We found individuals using AI performance as well as teams. So we know the impact is there, but it isn't happening because the organization isn't quite adopted the right way. The job replacement thing is a choice, right? And I worry that without imagination, organizations will think automation is the way to go. How do I replace people? How do I use this productivity gain? And by the way, that's part of why your employees aren't showing you productivity gain, is they fully know that if they show a 20% productivity gain, that they're not going to get that back as a benefit to them. So I think that part of this is about trying to rethink what we want to accomplish with these tools. How do we do that? So that's part of what this whole day is for these people and leaders, to think about how do we reimagine how, where, why, what we're doing, and how do we use AI to help us do that, as opposed to tell us how to do that, right? Yeah, so there's a few things. My model for AI use in companies, speaking to lots of them, is leadership, lab, and crowd. You need three things. What, leadership? Lab and crowd. Lab and crowd, OK. So leadership is you guys, and you have a lot of decisions to make. Because what I hate are memos from CEOs saying you need to use AI, or we're all in trouble. That's not an inspiring memo for the future. Similarly, you have to use AI. It's not a mandate that gets translated to something. So what processes do you want to change? How do you incentivize people to use this? What's your vision of the future? That's the leadership level. OK. The crowd level is everyone needs access to these tools, or at least large numbers do. That means that you have to actually give them access to Chachi Petit, or Gemini, or Claude. And there's actually, the training is kind of where, as somebody who studies the training of AI and has taught a lot of people, almost everything we knew about training people four months ago doesn't apply anymore. The models don't need any of that. If you're learning chain of thought prompting, none of that works anymore. But you need your crowd to be using things. They're going to find use cases. Because it turns out, AI is much more like a human than a machine. I mean, it's a machine. But if you're good at working with people, you'll be good at working with AI. So your crowd is everyone using these things. And the lab is, you need your own little R&D arm, which is not, by the way, an IT department or a technical department. It is your best AI users. And they're going to be the ones who take ideas that come out of the crowd, reward people for them, and turn those into products that everyone uses right away. So you need all three of those elements together.

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