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Well, great to have back with us Wharton marketing professor Stephano Pantoni as
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we talk about AI. Stephano is also co-director of Wharton's human AI research. Uh our conversation this month
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is about a new report that they have put out looking at how companies are implementing generative AI and how they
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are looking to gain an advantage on their competition. Stephano, great to talk to you again. How are you sir?
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>> Thanks Dan. Great to great to be here. >> And so this is a continuing study. This
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has been going on for a couple years as generative AI has kind of continued to develop.
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>> Yeah, we uh we're in the third year now. We basically discussed with our partner
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GBK Collective, a market research company. Basically just a matter of weeks after uh CHP was released in
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November 2022 and by summer 2023 we were out in the field collecting data. We did
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that in 2024 and we did it again this year. So now we've got three years of data which is nice. Where are we in
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terms of the development of generative AI and kind of the understanding that that companies and maybe more
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importantly seuite executives have on how much it is going to be used. >> Yeah. So the survey it's a large sample
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of senior executives and management directors and about half of the sample are vice president or seuite level. So
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it's a it's a a way of getting the pulse of senior leadership in large US
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companies. That's basically our sampling frame and we measure lots of questions.
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We ask them a lot of question about their own use of generative TVI and their organization and their functions.
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We have people across basically many industries and all functions and so we are getting bit of a picture or where
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companies stand in their geni adoption process. >> Obviously a lot of questions around gen
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AI and one which seemingly draws a lot of attention is how it will have an impact on labor. uh what do the
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executives feel like is the the path that we're going to see Gen AI and the impact it'll have because it's
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interesting you note in the report about entry level jobs and internships and how
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those may not be as impacted as much. >> Yeah. So the conversation on jobs about
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geni has been uh quite uh um you know scary or gloomy for already a while and I think when you get in conversation
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with people about AI and jobs you end up talking about Terminator and the Matrix
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in five minutes. So you know there's that kind of vibe and especially it's
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been gloomy around entry- level positions. people have talked about uh the end of entry- levelvel positions
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anywhere that basically now you can have those AI interns who do a lot of the work that maybe you get a summer intern
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on MBA graduate or something to do uh now using tools like deep research or other geni tools and so uh obviously um
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geni is going to have a big impact on entry-level positions because the people in the entry- level positions tend to be
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the people who do the work right the people above them tell people what to do but they at the entry will actually do
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it. So obviously they're going to be the ones who have to navigate and negotiate
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the impact of Genai tools on uh workflows and functions and so on. Now the interesting thing in our data is
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that when you hear um the answers of the senior leaders, you get a picture of a technology that is powerful, everybody
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agrees is going to have a major impact on every function. So it's not hyped,
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it's it's real. And at the same time though they tend to tell us that while
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human replacement concerns are high in fact people as a net effect think that the technology is going to have mostly
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the effect of augmenting or enhancing employee skills. So in a way they think that the technology is powerful but it's
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not quite doing what a human expert is doing and so we are hoping to see complimentarities between human
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expertise and AI. Now at the entry level that's where the conversation has been
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very negative. In our data we do see that people tell us that uh the negative impact on jobs is going to fall
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disproportionately on the junior positions relative to the senior position. They do say that. But
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what is missing from the conversation generally is another finding that we also get in the data which is while they
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say that the negative impact will fall disproportionately on junior people, they also say that the positive impact
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will also fall disproportionately on the young people. And the net effect is not
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so obvious. Maybe in fact if you look at the the numbers in our data indirectly it might be actually a good thing for
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ent certainly those jobs are changing the most. Why is it that that bo both you will see the negative and the
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positive uh in the entry level category? >> Because it's a disruptive technology.
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You know, these jobs are going to change a lot and so and new jobs are going to come and new ways of doing things are
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going to come up and uh people are going to be able to perform things and now and
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require much more labor and maybe you know specialized expertise. So lots of jobs are really going to be different.
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Uh but that doesn't necessarily mean you know need to mean that the jobs are
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going to disappear. So from the business perspective, we are seeing obviously bigger and bigger
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investments made by companies in and around uh AI. How much of this is basically a race against their
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competition right now? Yeah, there's a lot of FOMO I think where a lot of firms
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feel they have to spend money on Genai uh because everybody's doing it or because if they don't do they look like
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they're antiquated or whatever and uh there is a danger in that in so far as
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you know maybe some peer kind of uh uh envy of that kind for people to change. At the same time,
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what you don't want to do is to do something only because everybody else is doing and then you end up with this kind
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of a herd mentality and that's not helpful. I think the important thing is for companies to realize this technology
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has very impressive capabilities and then think very very hard about how those capabilities can be used to do a
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better business and that I think is what we don't know we are learning. >> What's the expectation then that of the
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type of impact that geni will have? You talk about the fact in the report this latest one that it won't necessarily be
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a narrow impact but it could very well be a pretty broad-based one. >> Yeah, we do we have lots of interesting
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question to speak to that and one of the section of the surveys about use cases and we see companies endorsing very
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large number of use cases already making progress on most of them. um general office work um is going to be impacted a
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lot but also a lot of uh functional applications like say marketing could be customer service versus marketing
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content creation things like that now what's interesting to me that this year
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we added for the first time some measures on return on investments which is a big question for companies are we
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getting the returns we were hoping to get out of these genai investments and the mood there has also been quite
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negative over the last months or two lot conversations seem to suggest that companies are struggling to see positive
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returns some anecdotal evidence of that and in our report actually we don't see
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that what we find is that 3/4 of the people who say they're measuring ROI which is the majority of the firms are
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reporting positive ROI modily or significantly positive >> so is that because of how the companies
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are using AI how it's being implemented and and in different areas of their business they they've kind of taken the
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the belief that we can use this in so many different areas that we can get a better ROA right now.
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>> Yeah. And you know, these are self-report survey data. So, you should always take uh the numbers, exact
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numbers with a pinch of salt because this is what they tell us. But I don't have a view of the hard data inside the
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companies. You know, they might be over reporting or maybe you're too optimistic
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about the results or they might not even know and taking a guess. So, there might
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be a lot of bias in these numbers. It is possible. These are self-reports. is a caveat that we always need to
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acknowledge with this kind of data. But I do think that it gives us a very good sense for the vibe, you know, how people
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feel about the technology and the vibe is good. The vibe is that this technology is delivering positive
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results. >> Finally, what are then the future expectations, you know, 10, 15, 20 years
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down the road in terms of the impacts of AI? >> Oh, we can't we can't speak to 10, 20
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years, 30 years is impossible. I I I always say I never make predictions beyond 24 months but uh uh I think what
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we're seeing is companies are continuing to invest. Um but there are some tensions and the tensions tend to be not
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so much on the capital spending or the um or the technology itself seem to be more on the human capital side. So
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companies report on difficulties in finding the right talent in upskilling and hiring and also some beliefs that
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the training is not really working as well as it should. There's a lot of interesting insights about that in the
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report too. But basically, I think that uh geni adoption is a big challenge for organizations. We don't have a template.
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We have to figure it out. And that challenge is actually more of a human capital challenge than a tech challenge.
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>> Stephano, great to talk to you once again. We will catch up with you again
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in a month and talk more about the world of AI. >> Great. Thanks, D. >> Thank you. Stephano Pantoni who is a
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marketing professor here at the Wharton School and co-director of Wharton Human AI research.