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Everything around AI right now is expecting great change and also great success. But there maybe should be also
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some concerns thrown out as well. Is it an absolute lock that all of this will succeed in the way we expect it to?
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There may be a need for a touch of skepticism as we move forward here at least right now. pleasure to be joined
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to discuss this by with Kate Lambertton who's vice dean and professor of marketing here at the Wharton School.
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She wrote about this in a LinkedIn post recently and she joins me right now. Kate, always great to talk with you and
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chat. Thanks very much for your time. >> Thanks so much for having me. I >> I I guess it probably is a trap that we
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could potentially fall into that when you see something new and it's expected
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to be so good that we expect it to run perfectly right from the get-go. It's it's quite interesting. You know,
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people have studied this in the context of even individual relationships. You know, people have great first dates.
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They leave a first date thinking they found this unicorn who's exactly like them. It's only as you get to know
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humans that you see the dissimilarities between you. It takes time and it takes experience. So, it is a it is a very
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common human pattern to see something we think is wonderful and to want it to be
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as good as it can be for us. So there's nothing nothing unreasonable or unexpected in the fact that we do this.
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At the same time though, one does begin to wonder, especially with regard to business, whether we could think about a
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process for thinking about innovation that doesn't perhaps lead to as many disappointments and and wasted
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opportunities as we sometimes see. So when you have situations like this, is it the positive mindset of the people
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that are using the product or is it the marketing tool or maybe it's a combination of both that's kind of
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pushing this forward and kind of driving this narrative? >> Yeah, it is absolutely both. So on one
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hand, new technology is incredibly exciting. There's a lot to talk about. So you get a lot of buzz. You get you
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get earned and unearned media. Everyone's talking about it. You also attract a set of early adopters who love
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the novelty and who are willing to look past possible bumps in the road because they find the technology so exciting.
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And those early doctors of course are critical because they often are operating as beta testers for
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technology. Um but their reviews may be slightly more positive than would be given by somebody who comes to the
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technology later who didn't have as a cue a need for it or who was less comfortable with the technology to begin
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with and for whom those snags and you know discontinuities are going to be more disturbing. So how then do we kind
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of navigate our path here so that we can look for the success maybe expect it to a degree but also
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have a little bit you know keeping the reinss pulled in maybe just a touch as we move forward here. Yeah, I think if
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we if we stop for a minute and we learn from history, it helps. You know, some of us are old enough to have lived
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through the dotcom boom. And this is both similar to that context and different, right? It's similar in that
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for a few years, everything that had do on the end got massive investment. And it was it was people said things like
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the old business models are out the window. They don't matter anymore. It's
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different universe. Everything is changing. When somebody tells you everything is changing, you should
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probably slow down. Um because humans remain humans, the market has some stable components and new technologies
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are always challenging and right now because they are developing so quickly even to say everything is changing
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because of this technology is something you should question because this technology is not the same thing next
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week. So so we can look back but what we can also recognize is that the present situation is also a little bit
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different. um which is to say that you have major players that have really diversified portfolios. I mean, if
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you're Google, first of all, you know, some of those types of firms out lived
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through the dotcom bubble. They lost a lot of value, but they came back because they were diversified. And in this case,
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you have these companies that have lots of different ways to weather the ups and
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downs that are going to come with new technology development. And that I think we have a a higher uh proportion of
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those working in AI right now. And so that may allow us to have a little bit more confidence um that as a whole the
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technology will continue to develop in positive ways. But those firms, it's just a business fundamental. Those firms
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that don't have any diversification that are putting all their eggs in the basket
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of a single type of AI or a single use of AI, the likelihood is a lot of those are going to get shaken out because
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those uses and those forms are going to keep changing faster than they can keep reinventing their business. It's it's
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the larger companies that are more diversified that can ride that wave. >> Yeah. Because if you're a startup a and
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your path to success is reliant on AI right now, you I I I think you can't really make that allin investment
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because you're putting your faith in a technology that we're still learning so
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much about right now. >> Yeah. And it's going to change in two weeks. I mean I have I have worked with
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some firms who >> a year and a half ago decided to spend millions of dollars developing an AI
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tool of their own and now you could develop that AI tool in a week and so it's a that is a very tricky kind of
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investment to make unless you're so confident that you are going to stay ahead of that technology and again that
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the larger more diversified firms can weather that kind of change more quickly and I think can find more sustained use
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cases for the technology. You know, I also think of of things like it's a more analog example, but I think
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of things like we work, right? Um, some of us lived through that too. That was a
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time when people said everything about real estate is going to change. Um, and were there new models emerging? Sure,
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there always, you know, real estate like any industry has innovation and it it looks for ways to increase efficiency
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and provide more value. It's absolutely true. Um, but if you at that point went
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all in on a single business model without thinking about the value that might actually still exist in the older
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ways of doing work, you set yourself up for for a big a big risk and possibly a big loss.
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>> So, is this a little bit like the old line, the more things change, the more
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they stay the same? >> Well, you know, again, I think there are a lot of new things being introduced
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right now. There are things that are going to change but there are still business fundamentals and I you know we
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are at a business school so I think I can say this what a business school does it observes
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changes in technology changes in all the solutions that humans create over time and business school scholarship is about
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being able to to generalize across innovation cases and find predictable uh indicators of value creation and
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delivery over time, right? So, for example, you know, a business school professor might write a research paper
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that says, you know, innovations in this context that have these general traits tend to do well in these certain types
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of markets. But that takes time. You have to observe a lot of cases to come to those kinds of conclusions. But what
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we hope is that our students learn those meth methods of of taking perspective that they learn the ways to analyze
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cases of innovation and different contexts so that they can better identify the technological innovations
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that are going to have sustained value over time. To do that they also need to understand the market. You know markets
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have fairly perennial needs as well. So they need to understand what other substitutes in the market might meet
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this and why this technology might be better. So those those fundamental perspectives and models I think can be
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applied to lots of cases of technological innovation. When we find cases where they can't be applied, well that's
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interesting too. That tells us more that we need to that we need to start observing to develop empirical
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generalizations about. >> Kate, great conversation. Thanks very much for your insight today. All the
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best. >> Thank you. Have a good one. >> You got it. Kate Lambertton, vice dean
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and professor of marketing here at the Wharton School.