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Hello and welcome. You're listening to
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Marketing Matters on the Wharton Podcast
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Network, our weekly podcast where we
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analyze the latest in advertising,
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marketing, customer behavior, new
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product launches, retailing, and
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pharmaceuticals. Uh, I'm Barbara Khan,
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the Patty and JH Baker Professor of
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Marketing, and I'm joined by my co-host,
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Americus Reed, the Whitney M. Young Jr.
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Professor of Marketing and the brand
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identity theorist. Hello, Americus.
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>> Hi, Barbara. So, uh, this is the first
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week of classes. It's an incredible sort
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of energy on the campus. I'm loving it.
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Uh, one of the things we talked about
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today is level setting.
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>> I just mentioned it's your first week of
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classes. I've been teaching
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seven weeks.
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>> Seven weeks in. So, you're like, listen,
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I may be I'm into it now. America,
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you're brighteyed and bushy tail, but
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hey, it's going to catch up with you.
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>> We're all on midterms. I don't know
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where you are.
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>> I'm introducing the syllabus. And so,
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yeah. No, it's it's it's fascinating
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that you mentioned that because you know
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all of the things that are going on
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campus. One of the things I talked about
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today, Barbara, is we're talking about
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marketing and people think marketing is
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just advertising, but I was trying to
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explain to my class marketing is about
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strategy. And in fact, marketing
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actually starts with science and ends
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with art. And so I'm very fascinated by
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this interface of data science,
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artificial intelligence as a new
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technology that is making its way into
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marketing strategy, various ways that
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it's happening and all of these
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different interfaces. And so have you
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got anything for me today that can help
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illuminate this and further allow me to
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make the case to my students that
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there's a lot going on under the hood
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with respect to marketing strategy? Oh,
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well actually we have a very interesting
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guest today who can talk about a lot of
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those things because he's have a long
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and varied career. I'd like to introduce
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Shawn Buick who's currently the senior
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vice president of artificial
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intelligence and data at Amgen which is
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a pharmaceutical or biotech or anyway
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health kind of related company but he
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has a long history starting out at
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Stanford as a psychology and biological
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science major all the way through Google
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and Facebook and Nike. So he's kind of
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been at the right place at the right
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time. Anyway, welcome Sean to our show.
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>> Thank you, Americas. Thank you, Barbara.
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It's so great to be here.
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>> So, let's build on on Americas's um
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quest or introduction in some sense or
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question and just quickly go through
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your background because you do have a
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knack of being in the right place at the
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right time starting well Stanford wasn't
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the right place. You should have been a
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Wharton, but okay.
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>> Had to get that plug in. Very, very well
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done, Barbara. But then uh I think you
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went first to Google then to Facebook
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then to Nike and now to Amgen just as
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each one of those areas was doing some
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pretty interesting area connecting
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marketing with data science and now what
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I really like to talk about is what
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you're doing at Amgen. But let's start
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with your history.
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>> Sure. Yeah. I got my start uh up at
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Stanford and um I was working in a
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neuroiming lab. So this was right around
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the time you know this these new brain
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scanners were be being able to gaze into
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the working human mind functional
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magnetic resonance imaging. Um and and
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there were kind of two things that
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happened there. I think the first was
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you know this new technology allowed us
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to for the first time answer some
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questions that for 70 years in cognitive
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science weren't really answerable. And
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so I became really convinced up until
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that point I'd been you know studying
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the science but I became convinced that
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technology was going to be really
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important in understanding the world.
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And then the second thing was I had to
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learn how to code because this these
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data sets were so much bigger than the
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reaction time and wet lab and um you
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know survey data that sort of everybody
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was using at the time. And so that's
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what really got me started um in in my
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career and I I got a job at Google right
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at the beginnings of what you would now
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call consumer data science.
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>> Um had had just a a you know wonderful
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set of mentors there as we were um sort
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of exploring what was possible with this
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field. I went to Facebook and helped
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them launch the the ads business um
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where I led the the measurement science
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team, the data science team on the on
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the ads business. And then I spent the
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last 10 years at Nike where um I helped
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them with their direct to consumer
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digital transformation leading their
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data science and analytics and customer
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insights teams there. But as you said, I
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made a big uh switch about a year ago.
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So now I met I'm at Amgen which um if if
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folks don't know is one of the world's
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leading biotechnology companies. It
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makes medicines um for things like uh
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cancer and inflammatory disease and
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cardiovascular disease and rare disease.
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Um it's been around for for over 40
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years. It was sort of one of the
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pioneering companies in the biotech
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revolution of the 1980s. Um, and my job
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here is to try to figure out how we can
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use AI and data uh to make better
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medicines for for patients.
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>> So, let me just build a little on this.
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Now, you've been there a year and um I
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mean I know you were what that health
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what did you say bio health or
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biological science undergrad major but
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that's not like major health
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credentials. So, but obviously they saw
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some advantage in what you did do for
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the last 10, 20 years. Uh, but how have
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you like learned about what Amgen does
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and h how did how did you get up to
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speed? How did you do that?
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>> Yeah. And and it's been a long time
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since I took my last biology class at at
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Stan Smith.
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>> Yeah. And I mean, the science here is is
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really complex. you know, it's genetics,
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it's it's biology, it's pharmarmacology.
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Um, and so it has been definitely a a a
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big ramp to to learn all about what we
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do at Amgen. And and you know, it is
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pretty different than online ads or
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shoes in a few in a few ways. I mean, I
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think the first is just the fact we're,
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you know, making medicines for for
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patients, which creates this incredibly
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high standard around reliability and
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safety and compliance and scientific
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accuracy. Um, but it is a scientific
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discovery, especially in in this space.
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Um, you know, where you're trying to
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take this molecule and turn it into a a
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medicine. It it uses really large data
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sets. It's actually surprisingly
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quantitative for me. I mean, there's
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definitely beers and labs and
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experiments, but it's also very
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quantitative. Um, and and it's
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increasingly relying on technology
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because the data sets are huge and
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these, you know, new artificial
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intelligence and machine learning
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algorithms we think um has has the
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potential to really change um sort of
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how we bring these these medicines to
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patients. So, so some of it is
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definitely new and and hard and and a
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ramp, but some of it feels, you know,
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much more comfortable, the the core
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first principles of data science or or
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the um, you know, underlying technology
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stack and the partners and things like
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that.
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>> So, if everybody is using AI, then is
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how do you differentiate? I mean, it
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seems like if you have AI, everybody's
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doing the same thing. Then how how does
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that work? No, you you bring up a great
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point, Barbara, and and I do think
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there's a lot of convergence now on how
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well these algorithms perform, you know,
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so in the old days, um you know, when I
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was getting my start as a as a data
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scientist, you know, your your magic
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sauce was very much like how good your
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your algorithm was, how good your model
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was. Um nowadays I think I think
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companies should be thinking about two
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things and and certainly we are here at
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at Amgen but I think if you look across
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uh you know my work work previously
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there was it was the same concepts you
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know the first is you the underlying
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data that you have as a company is is a
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pretty interesting differentiator and
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and I'm super excited about some of the
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data we have here at Amjen you know
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genetics data a long history of
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designing proteins um and molecules and
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and all those scientific experiments
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along the way that there's that you know
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that that set of data uh is is a really
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interesting differentiator
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>> and then I think increasingly what
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you're seeing is that there is an art
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and a science because you mentioned kind
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of the art and the science um of how do
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you implement these algorithms in a way
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that you know is impactful in a way that
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gets to scale quickly
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>> um and and realizes whatever impact
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you're trying to drive at at your
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company. And I think in both spots,
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there's going to be differences in how
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successful companies are. And so, um, I
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think I think that's kind of one of the
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most important and underappreciated
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parts of of how to win here.
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>> I love that point, Sean. One of the
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things that I talked to my class Sean is
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the idea of adoption of AI technologies
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and the statement that you made which is
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how do you make the company strategy
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infused deeply with the DNA of AI that
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seems not necessarily easy because you
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have different folks maybe even if it's
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a large company and you know aside from
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just having a bunch of young people who
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are sort of born into it in your company
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how do you go about creating that the
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right organizational culture and
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atmosphere
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where the adoption of these technologies
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and understanding of how to use and
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deploy them appropriately and well
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differentiated like Barbara said, how do
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you make that happen? What are the what
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are the things that you do to create the
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cohesiveness inside the building to be
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able to adopt that kind of culture?
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>> Yeah. I mean, this is a podcast, right?
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So, we have to have some controversy.
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Can I bring up the MIT AI paper because
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it Yes. Definitely a controversial topic
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and I think one that's worth digging
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into a little bit because I think it it
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strikes right at the heart of of kind of
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your question. Okay. So for if
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>> if people are not tracking, there was a
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a paper by MIT that came out uh probably
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four weeks ago that you know the
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headlines have all said 95% of AI pilots
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in companies don't generate value. Um
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now that's the headline. The real story
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I think is is is twofold. So the first
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story is that if you're in a big company
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and you do a pilot, it never generates
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scale business value. That's the whole
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point of a pilot, right? Is it's little.
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Um now there's a muscle around how do
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you choose the right pilots? because
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pilots are only valuable if they teach
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you something you didn't know or derisk
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an investment by by you know trying it
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out in a specific zone or or sometimes I
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think what you as you mentioned you know
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shines a light on a success story that
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can absolutely inspire and teach your
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your colleagues and your workforce a
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path forward but lots of pilots lots of
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sort of poorly thought through poorly
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targeted random pilots is not a measure
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of success. And there's a lot of
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companies, I think, doing way too many
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pilots that are kind of not ever going
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to generate real value because then you
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have to have the practice and the and
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the skill set and the technology stack
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and the monitoring capabilities to take
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the winners and get them scaled and
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adopted and and that I think is a is is
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a real lesson um from this paper. Now,
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now why did these 95% of these programs
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in this paper fail?
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This isn't always in the headline. It
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wasn't the AI technology. It wasn't like
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the algorithms were crappy. It wasn't
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that the that the AI failed. It was
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everything else about it. There wasn't
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alignment on the budget. Business
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processes weren't evolved. There wasn't
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receptivity on the part of the people
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using it. They didn't get adoption. And
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and so that speaks exactly to your
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point, which is even if you have an
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amazing capability, if if you don't get
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everybody else lined up around it in a
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big company, you're never going to get
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the the business value out of it. And I
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think that's another piece that
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companies are struggling with is is how
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do you line up everything such that you
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can really pull these through to value
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um and and make an impact on the on on
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whatever mission you're trying to
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accomplish.
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>> Well, so how do you do it?
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No, it I I think it's this there's
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there's three pieces. I think you have
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to have the discipline around where are
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you going to do the pilots and what are
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they designed to teach you? And you
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know, here at Amgen, we have a we have a
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very rigorous process. It it's not
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anybody can run any pilot anywhere
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around AI. We're we're directing those
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pilots at specific zones that are going
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to teach us something.
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You have to have this muscle around how
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do you take a great pilot and and get it
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to scale. So and and and you know that's
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a technical question around you know do
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you have the right data infrastructure
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do you have the right um technology
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portfolio do the right monitoring uh
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programs to get these algorithms
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implemented and then there's all of the
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change management stuff that you know is
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not all that different than every other
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technology transformation that you guys
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have studied you know that we've looked
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at digital marketing you know 15 years
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ago direct to consumer you know 10 years
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ago So um but if but if you don't have
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all three of those I think it's really
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hard to make a big impact and and you
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know one or two of them isn't enough.
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>> So let me let me just summarize that. So
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like the first one is kind of how to ask
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the right questions right where to focus
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is the first thing and then and you can
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ask a lot of wrong questions but
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understanding where you're focusing is
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basically what you're saying and I could
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totally see how that'll differ from firm
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to firm to firm. So it wouldn't result
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in the same thing. And then once you get
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viable ideas, implementing and and
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bringing to and operationalizing and
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then scaling is a whole other issue. And
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then the third one is getting the
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organization around this new is that it
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>> Yeah, I think that's a great a great
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summary. It's it's you know the
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discipline around pilots, where you
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play, how you test ideas, how you turn
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the Yeah. turn turn the big ones the the
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best into impactful scale programs and
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then how do you line up all of the
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pieces that are necessary to to make to
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make the impact through those through
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those uh scale pilots for sure. So it
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seems like how to do it right would
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suggest that there's like some
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measurement system or some objectives
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you and some like metrics that you have
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along the way in order to see if you're
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making prog is that kind of the way it
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goes or how do you know you're on the
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right track? Yeah, I look I think there
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are um you know a whole host of
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technical things we do to make sure
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these models are performing well and you
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know having thoughtful evaluation
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frameworks and um you know looking at
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their performance and um all of that
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stuff is is sort of the job of the data
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science team or the AI team. But I think
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you know the the bigger question goes
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back to that conversation we were just
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having around is it actually making an
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impact on your strategy. You know is it
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is it driving whatever outcomes you as a
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business are trying to are you trying to
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trying to enact. And that I think is the
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place where sometimes you know the plot
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gets a little bit lost. you focus too
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much on the technical implementations or
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the technology roadmap or the or you
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know the path that the that all the um
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the data and the algorithms and the
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technology stack need to be um all right
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and and sometimes you lose track of the
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the organizational and business
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partnership. I mean we understand what
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you're saying Sean which is like the the
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specific role of the technology needs to
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be managed in a way that is built around
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the synchronization of its adoption
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within the organization and how to in
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your case how to figure out the bi the
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specific biology that produces new
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opportunities for wellness products or
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health related uh protocols and things
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of that nature. So we definitely
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understand that it's just challenging
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because and whenever I hear a talk Sean
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on AI and you're a guru of this I sit
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back and I have to like suspend a lot of
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my judgment because I just don't know
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what you're talking about in terms of
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the technical pieces of it. You know all
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of these s sort of things. I understand
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the logic of it but I want to know much
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more to Barbara's point about you know
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how does this work? What is a what's an
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example of a specific pilot? if you
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could share that, you know, things that
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we've that you guys have done. Maybe
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that's the secret sauce and you can't
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talk about it, but like taking us
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through like here's something we did
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that's, you know, more public
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information now and here's an example of
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how we really brought in AI to really
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help us drive a specific understanding
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of a biological element that then turned
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into something that we could quickly
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scale and create something that adds
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value to the marketplace. Is there an
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example you could share with us?
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>> Sure. Yeah. I mean I think what is
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exciting and you know part of why I
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wanted to come to Amgen is I really do
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think biotechnology is is a spot where
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um some of the biggest advances in AI
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and data science are going to happen
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over the course of the next few years. I
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mean I I am so excited about what's
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possible and there's been some big
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breakthroughs and you know just last
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October
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um the winner of the Nobel Prize in
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chemistry was shared between the Google
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deep mic guys Dennis and a professor at
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the University of Washington David
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Baker. And you know what that that
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breakthrough um was was an algorithm
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called alphafold which was solving this
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like incredibly complicated
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uh problem in science which is how do
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you predict the way po proteins fold if
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you know the
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>> the the two-dimensional sort of
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structure of the protein can you predict
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the three-dimension structure and that
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>> interesting
00:18:24
>> you know fortunately that actually
00:18:26
happened after I joined Amgen because it
00:18:28
attracted a whole bunch of news to the
00:18:30
fact that AI and data science had had a
00:18:33
potential potentially, you know, a big
00:18:35
impact on on this field. But since I had
00:18:37
joined a few months earlier, it looked
00:18:39
like I was super smart and my timing was
00:18:41
perfect as opposed to uh joining joining
00:18:43
after the the news coverage. But that's
00:18:45
an example, I think, of a big
00:18:47
breakthrough. And, you know, there are
00:18:48
relatively few of those along the way. M
00:18:52
>> um you know I think the the more common
00:18:56
instantiation of like an incredible
00:18:58
impact of a of a data science uh project
00:19:02
is actually just much more simple. So
00:19:04
let me give you one.
00:19:05
>> Okay.
00:19:06
>> I did not know any of this a year and a
00:19:08
half ago but but it turns out to be a
00:19:11
good medicine
00:19:13
the the molecules have to be a certain
00:19:16
very narrow range of viscosity. M
00:19:19
>> so like imagine
00:19:21
>> you have a molecule it works pretty well
00:19:23
but it's like peanut butter like too
00:19:25
thick
00:19:26
>> you can't inject that into the human
00:19:27
body it's not going to work and so one
00:19:29
of the most important criteria our
00:19:31
scientists have to consider when they're
00:19:33
designing these molecules is like how
00:19:35
viscous is the solution of these of
00:19:38
these proteins after after we uh
00:19:40
engineer them and you know even that
00:19:43
developing a a machine learning
00:19:44
algorithm
00:19:46
>> takes into account all the historical
00:19:48
experiments
00:19:49
>> got it
00:19:50
>> and makes a prediction as to whether
00:19:51
these molecules are going to be in the
00:19:54
right zone to be a medicine
00:19:57
>> can dramatically improve the efficiency
00:20:00
of our scientists. So you know they
00:20:02
don't have to make a hundred of these
00:20:04
molecules and then see how viscous they
00:20:07
are. they can use this algorithm and it
00:20:09
dramatically cuts may not it may not be
00:20:11
right all the time but it at least
00:20:13
improves the efficiency of those
00:20:15
scientists lets them direct their energy
00:20:17
their creativity their time against a
00:20:20
higher um fidelity of candidate right a
00:20:22
small smaller pool more likely to be
00:20:24
successful so you know those are the
00:20:26
type of like blocking and tackling yeah
00:20:30
incremental work
00:20:31
>> that you know before we had access to
00:20:33
all of that data being digitized and
00:20:35
before our algorithms were powerful
00:20:37
enough to make strong predictions, you
00:20:39
couldn't couldn't reduce that workload.
00:20:40
You couldn't shape shape where they're
00:20:42
spending their time and attention.
00:20:43
>> Interesting.
00:20:44
>> That's the type of stuff just across the
00:20:46
board we're looking at of how do you you
00:20:48
know add a little bit of data science
00:20:50
into the day of a protein engineer or a
00:20:52
biologist or a geneticist um or a
00:20:55
process engineer um you know a supply
00:20:58
chain expert, right? And and and just
00:21:00
incrementally move the needle on how
00:21:02
efficient and effective they can be. So
00:21:05
Sean, one of the things I'm interested
00:21:06
in as you're telling us the critical
00:21:08
role that AI does play and how it can
00:21:11
really make things better. One of the
00:21:12
things people are really afraid of is AI
00:21:15
replacing other, you know, people
00:21:17
working at a company. But the way you're
00:21:20
describing it, it doesn't sound like
00:21:22
that to me, and I wonder if you agree
00:21:23
with it. It actually sounds like there's
00:21:26
more room for people who know how to use
00:21:30
AI and are trained to use AI and it
00:21:32
might even provide more jobs than less.
00:21:35
I almost got that impression from what
00:21:37
you're saying, but I don't know if you
00:21:38
want to go so far as to say something
00:21:40
like that.
00:21:41
>> Yeah, I'm no economist, so I won't weigh
00:21:43
in on on maybe the um the economics of
00:21:46
it, but I'm I'm thinking about it in in
00:21:48
two ways. I think as we're implementing
00:21:51
AI systems today, that human AI
00:21:55
interface is actually critically
00:21:57
important. And so, you know, we call it
00:21:59
human in the loop. that's kind of the
00:22:01
the term folks folks are using, but
00:22:03
particularly when it pertains to like
00:22:06
scientific inquiry and ensuring quality
00:22:10
and compliance and adhering to like, you
00:22:12
know, the incredibly high standards of
00:22:14
of scientific accuracy that we have to
00:22:17
adhere to. Having a human inside that
00:22:20
system engaging with the technology and
00:22:23
the AI algorithms themselves and the
00:22:26
output is is critically important. And I
00:22:28
I don't see that changing anytime soon.
00:22:31
>> The other thing is as we as we build
00:22:33
these systems,
00:22:35
>> there's a whole set of new roles that
00:22:37
are being created
00:22:39
>> that exist on on this edge. I mean,
00:22:41
look, just just to zoom in on my career,
00:22:43
I have never had a job
00:22:46
>> in which there had been a prior
00:22:48
incumbent in that job over the last 25
00:22:52
years.
00:22:53
about you, but
00:22:54
>> yeah,
00:22:55
>> I don't want to overextend from an N of
00:22:57
one, Barbara, but I I think everyone
00:22:59
would agree that's dangerous in in
00:23:01
science and marketing.
00:23:04
>> But I do think it speaks to this is an
00:23:07
exciting time. I mean when I look at my
00:23:11
whole career and and know I've been
00:23:13
around data science since the really
00:23:15
early days this is the single most
00:23:18
interesting exciting moment that I've
00:23:22
seen in that time. I think the
00:23:24
capabilities you know the combination of
00:23:27
computing power and data that's that's
00:23:30
been digitized and available um and the
00:23:33
performance of these of these new models
00:23:35
is is really transformational. I think,
00:23:38
you know, tools like chat GPT are are
00:23:41
just incredible. I'm sure many of your
00:23:42
listeners are are big users of it.
00:23:45
>> Yeah, I'm sure they use it all the time.
00:23:47
>> And then, you know, I really do think
00:23:48
one of the biggest challenges in data
00:23:50
science over the last 20 25 years has
00:23:53
been just it's been really restricted to
00:23:55
people who are really highskilled like
00:23:58
you know they know we know Python, know
00:24:00
how to use the technology, work at a
00:24:02
company that has big data sets. But the
00:24:04
other thing that's really fun here is is
00:24:06
this has really democratized the power
00:24:08
of data and algorithms in a way that
00:24:10
almost everybody out there listening can
00:24:12
probably find, you know, a handful of
00:24:14
spots even just in their own lives,
00:24:16
whether it's making shopping lists or
00:24:18
planning vacations or automating
00:24:19
workflows, you know, getting work done
00:24:21
faster. That's going to have a pretty
00:24:23
big impact. So, I'm I'm really excited
00:24:25
about, you know, the positive impacts
00:24:27
that that these new technologies are
00:24:28
going to have on on us at work and us
00:24:30
outside of work. Yeah, Sean, this is uh
00:24:33
super intriguing to me. I I want to, you
00:24:36
know, ask this question because I think
00:24:37
it's important when we have the gurus
00:24:38
available to us to put them into the
00:24:40
time machine and give us some insight
00:24:43
into their crystal ball about what is
00:24:45
the state of the world in your domain,
00:24:48
artificial intelligence, uh data
00:24:50
science, all of these things. Um what is
00:24:53
that going to look like in your view
00:24:55
five to 10 years from now?
00:24:57
Well, I am too good of a data scientist
00:25:00
to get busted by making predictions too
00:25:02
far into the future. So, I'm gonna I'm
00:25:04
gonna tell you what I think now and then
00:25:05
maybe gaze just a little bit more
00:25:07
approximately into the future. But um
00:25:09
you know, look, I think the biggest
00:25:12
mistake people make in a moment like
00:25:14
this around a big technological uh
00:25:17
revolution, evolution of of data science
00:25:19
and algorithms and data is that they
00:25:22
over focus on the here and now. the
00:25:25
failures of of technologies, the hype
00:25:28
cycles that turn out to be, you know,
00:25:30
overheated and they underestimate in the
00:25:34
medium term how pervasive and impactful
00:25:37
some of these technologies can be. And I
00:25:39
I think that's the big disconnect I see
00:25:42
right now between kind of the here and
00:25:44
now. you know, we've all had a moment
00:25:46
where the algorithm didn't do what we
00:25:47
wanted it to do or there's not enough
00:25:49
data to make the prediction or, you
00:25:51
know, chatbt gives you a lousy answer.
00:25:53
And and those things are true. Um, but I
00:25:56
I do think in the medium term, folks are
00:25:59
underestimating how big of an impact uh
00:26:03
this this new set of capabilities is
00:26:05
going to have. And that's a big part of
00:26:07
why I came to Amjen is I'm I'm really
00:26:09
bullish on what I think is is possible
00:26:11
here. Now, you got to do all the stuff
00:26:13
we talked about to to actually make the
00:26:15
impact at at scale. Um, but I I think uh
00:26:19
this is a really exciting space to watch
00:26:20
and folks should folks should lean in
00:26:22
and watch it.
00:26:24
>> Well, Sean, thank you so much for
00:26:26
joining us today. It's always a pleasure
00:26:28
to have you. And um where can our
00:26:30
listeners go to to keep up with what
00:26:32
you're doing? Do you have a website or
00:26:34
or public facing side of Amgen so people
00:26:37
can figure out, you know, how amaning
00:26:39
into AI? Yeah, definitely. If you're
00:26:42
interested in either the science or the
00:26:45
AI of what we've talked about today,
00:26:47
check out amjen.com. There's a bunch of
00:26:49
great stories and teams there keep
00:26:51
everybody up to date on what we're
00:26:52
working on.
00:26:54
>> Okay. Well, that's great. Thank you very
00:26:55
much. And that's all we have time for
00:26:57
today. We'd like to thank our producers,
00:26:59
Aaron TR and Marissa Rena. And thank you
00:27:03
all for listening. We'll be back next
00:27:04
week. Till then, this has been Marketing
00:27:06
Matters on the Wharton Podcast Network.
00:27:09
I'm Barbara Khan here with America's
00:27:12
Reed.
00:27:16
[Music]
