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