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NEW BUSINESS MODELS AND DIGITAL TRANSFORMATION

Interview with Gregory Lablanc, Distinguished Teaching Fellow, Haas School of Business

ABOUT OUR GUEST

Gregory Le Blanc Silicon Valley Innovation Center Interview Speaker

Gregory LaBlanc

Gregory La Blanc has been teaching at the Haas School of Business and Berkeley Law since 2005. His research interests lie at the intersection of law, finance, and psychology, in the areas of business strategy and risk management. He has been the recipient of numerous teaching awards at Berkeley, including the Earl F. Cheit Award for Outstanding Teaching (2009) and the Haas EWMBA Core Graduate Instructor of the Year (2004-2005). Gregory has been instrumental in developing and teaching innovative courses, such as Blockchain and the Future of Technology, Business, and Law, to keep Berkeley students on the cutting edge of the increasing complex and ever-changing business environment. Prior to his tenure at UC Berkeley, he held teaching positions at Wharton, Duke, and the University of Virginia. He has also worked outside of academia in the areas of competitive intelligence, litigation consulting, and advising consulting teams in finance, marketing, and strategy. Gregory received B.A. and B.S. degrees from the University of Pennsylvania before pursuing further graduate studies there as a University Scholar and graduate fellow. He later earned a J.D. from George Mason University and an LLM from Berkeley Law.

INTERVIEW TRANSCRIPT

Rahim Rahemtulla:
Hello and welcome to Silicon Valley Innovation Center. Here, at SVIC, we promote discussion on technology and business innovation through our executive immersion programs and online events. I’m Rahim Rahemtulla, SVIC Brand Ambassador. Today I’m talking to Gregory LaBlanc about building a successful business in the era of digital transformation. Don’t forget, if you have a question for Gregory, you can send that to us in real time. Just use the comment section of whichever platform you’re watching us through. So our guest today is a distinguished teaching fellow at the Haas School of Business. He’s also a founder of the Berkeley Faculty Network, which advances the consulting practices of UC Berkeley business and law faculty. So Gregory joins us now. Gregory, thanks so much for joining us today.

Gregory LaBlanc:
Thank you, Rahim.


Rahim Rahemtulla:

I’d like to start, if I could, by asking you a little bit about a presentation you gave for us recently at SVIC that was part of our Executive Immersion Leading Digital Transformation program. In that program, in your presentation, I should say, you advanced a view which I think is perhaps a little counter-intuitive in that you said digital transformation is not actually a problem of technology, it’s actually a managerial problem. So just tell us a little bit, how do you come to that conclusion? And what does that mean?

Gregory LaBlanc:
Throughout history, there have been technological advances and the application of those technological advances has been uneven and the companies that have been able to take advantage of it are the ones that have the managerial capabilities and the organizational structure that allows the technology to do its work. And, in fact, if we’re trying to figure out what’s driving technological innovation, no one’s going to invest in the kind of technology that will not find its way into organizations. And so it’s really my belief that the hardest part about digital transformation is getting the management right, getting the organization right, getting the structure right, getting the incentives right, getting the decision-making right. Technology takes care of itself, almost. You’ve got a lot of smart people out there, they’re trying to solve scientific problems and, in many ways, they’re operating autonomously. But figuring out ways to monetize it, that’s the hard part.

Rahim Rahemtulla:
And you say that in the world today, as a result of this digital transformation, you have dead companies and you have tech companies and that’s a very stark contrast. I think it sounds great as a soundbite, for sure, but I do wonder, is it really that black and white? Could there be companies – I assume there are companies out there – who, they’re not quite dead, they’re not quite tech companies either and are existing somewhere between these two poles and they could still go either way? Is that right?

Gregory LaBlanc:
Yeah. Look, I live in Berkeley and there are people that are making goat cheese and stuff like that without a huge amount of technology and there are psychiatrists and others that don’t use a heck lot of technology. But at the end of the day, I think, if you’re going to be a successful company and you’re going to scale and you’re going to make a big impact, you’re going to be a technology company. And it always kind of amuses me that in Wall Street you have this thing called the tech sector as distinguished from logistics and media and manufacturing, etc. This is silly. I mean, every company is a tech company. General Motors has more software developers than Google. It’s a software company which just happens to make cars. United Airlines is a software company that owns planes. UPS is a software company that delivers packages. Amazon is a software company that sells merchandise and cloud services. So Google’s a medium, Google’s an advertising company. It’s a tech company that does advertising. So I think all the different traditional sectors – media, manufacturing, automotive, etc. – they exist but within each one of those verticals they are the ones that are taking advantage of what technology has to offer. And those that are struggling, it’s just a matter of time. The clock is ticking and they’ll disappear. When the average lifespan of a company on the S&P is – I don’t know what it is now – like fifteen years.

Rahim Rahemtulla:
Yeah.

Gregory LaBlanc:
I’m not putting any bets on companies that don’t have the organizational structure that enables them to be fundamentally be tech company.

Rahim Rahemtulla:
That’s a scary thought for some executives out there that this lifespan now, of a company, can be so short. What would you suggest, though, for a company that is perhaps somewhat hovering between these “dead and tech” worlds? What can they do if they want to turn themselves around and want to survive?

Gregory LaBlanc:
Well, actually, the principles of good organizational design haven’t changed in history – they’re the same. So a lot of what we’re seeing happening is you don’t need a whole new set of conceptual skills and tools to understand it. The principles of good organizational design were laid out decades ago and it’s just a question of adapting them for new cost structures.

So there’s a whole literature of good organizational design that talks about the learning organization, an organization that is able to adapt and learn and change the way it does things in response to new information. And, fundamentally, to use the modern language, it’s just about having sensors, actuators and processing. We didn’t use this like twenty years ago. We would talk about organizations that were structured so that they could sense the external environment, react to the external environment and act accordingly with the external environment. Now, we can use the language of sensors, actuators and decision-making. And sensors are just all about data, data gathering and data collection. The decision-making part is about evaluating that data, understanding what you can do with that data. And then, the actuator, that’s the part where implementing new business decisions based on the information that you’re getting. You can use the analogy of a nervous system and that’s one that business academics have been using for decades.

Rahim Rahemtulla:
And I wanted to ask you about that, Gregory, how companies should be reading all this data that they’re now going to be collecting if they’re not doing that. But before we go there, I wanted to ask you, because we had a question come in from someone in the audience and they’re saying, “Wouldn’t non-technology fields also become tech companies?” I think it’s what we’re talking about. So mining, agriculture, heavy industry, these fields. We don’t necessarily associate them–

Gregory LaBlanc:
Yeah.

Rahim Rahemtulla:
–with data, that it’s easy to collect information about what customers do with the products, but these too will become tech companies. Is that right?

Gregory LaBlanc:
More so, I think that that’s a false dichotomy. I was just reading about TGI Friday’s and how they dramatically improved their performance just because they started thinking like a technology company. They started tracking what their individual consumers were purchasing. They started tracking more carefully what their individual stores were selling. They started tracking more carefully what the contributions were, of the employees. And so, fundamentally, if you’re wired right, if you’ve got a good nervous system and a good respiratory system and a good circulatory system, then you can run the race. And that’s fundamentally about technology.

I mean, farming? Farming is probably one of the most technologically sophisticated industries in America. Hugely capital-intensive, hugely data-intensive. A company like Monsanto now has a platform called FarmView and they’ve managed to record, I think, something like 70% of the arable land in America. So they actually have mapped it out because of all the sensors that are attached to all the tractors and the harvesters. And, ultimately, all these tractors and harvesters will be or are already autonomous and they’re being driven by software in the cloud. And you’re fertilizing very specific, specific areas. You’re irrigating very specific areas. You’re harvesting according to a schedule that’s dictated by predictive analytics. Farming is hugely, it’s a very, very technologically sophisticated industry, at least if you’re successful in farming.

Rahim Rahemtulla:
Indeed. And it seems like, for sure, the boundary between tech, non-tech, that’s just blurring more and more, and no industry is going to be immune from that. But what does a company do, though, with all this data? I mean, is there a right way to read data? Are there wrong ways to read the data? Because I would imagine that once you have all of this information, there are many courses of action you can then take with using all of the data. So how does a company know that it’s taking the right steps? That it’s using the data as best as it can?

Gregory LaBlanc:
Yeah. I mean, someone once used the analogy, “you’re trying to find the fish of information in a sea of data.” Right? Companies can be easily overwhelmed with all the data and some companies think that it’s a data strategy to just collect massive amounts of data and then you’re done. Well, you’re not done. The hard part is in collecting the right data and understanding what to do with it. So I would start not by collecting massive amounts of data but start by asking, what kinds of decisions could we make if we had better data? What kinds of things could we achieve if we had better data? Because, typically, what’s happening is, in a typical business decision-making context, you run up against limitations, limitations of ignorance. So if you’re in media, you say, “Well, I’d like to market to males 18 to 36 instead of females 50 to 70.” And it’s like, “Why did you even pick up those categories?” They say, “Well, because we know something about them.” “Well, what would you do if you knew something in a much more fine-grained way?” “Oh, well, then we could do all sorts of other stuff.” “Okay, bingo! Now you know what it is that you need to go and get data on.” So there’s something called data-driven discovery which is you have all this bunch of data and you’re just playing around with it, you look for insights. And that’s like fishing in the sea. I think it’s much better to say, what kinds of things can we do or could we do if we had better data? And then that sort of directs your data-acquisition strategy. And, now, I’m not saying you shouldn’t do the others as well, because that’s going to happen once you have the data, you’re going to start mining around it, but, really, having an understanding of what data can do for you.

And then, making sure that you have a good complementary set of talents inside the organization. You need domain expertise and you need data science expertise. If you just have domain expertise, then you’re just relying on gut. If you just have data scientists, then these guys are really good with correlations, but they don’t really understand causation, their inferences are often incomplete, they’re looking at the wrong spot. They do stuff that they think is fun and cool and interesting, but not necessarily stuff that’s super useful, so that interface. And one of the things that I try to teach here at the Haas Business School – because I teach primarily business people, I don’t teach the engineers as much but when I teach both groups – I emphasize the importance of being like the human API. You know, ping the person you can sit at that human interface and translate the domain knowledge to the technical folk and the technical stuff back to the domain folks. If you can create those teams that are truly functional in solving business problems, then that’s what lies at the heart of data-driven companies decision-making.

Rahim Rahemtulla:
And I think we’ve seen that the most successful companies, among the biggest disruptors that we’ve seen, that is essentially what they do. And this leads to a new business model which I know you also got into in your presentation. This is the “as a Service” model, isn’t it? Because then we are essentially just using that data, matching supply and demand in real time, getting people not necessarily the products, but actually the service that they want at the end of the day. And so, we’ve seen that with Airbnb and Uber, always the big names that come to mind in this discussion, but I get the sense from what you’re saying that a lot more of that is still going to come because there are still a lot more industries which are yet to adopt this “as a Service” business model.

Gregory LaBlanc:
Yeah, absolutely. There’s two points there. One point is that analytics is now a commodity. You can get analytics from everywhere, you can get analytics in a box, you can get data scientists off the shelf. There’s really no competitive advantage from analytics. If you don’t have it, you’re dead. Okay, that’s a given. But if you do have it, then, okay, now you’re just on par with everybody else. And so your competitive advantage has to come from the priotary data. It has to come from something that cannot be easily replicated. And one of the things that I always say is whoever has more data wins. And so if you get a leg up on your competitor with respect to not just data about specific customers but the aggregate marketplace, then it’s going to be very difficult for a competitor to catch up. Facebook has data that no one else will ever have and if they carefully guard it and make sure that others can’t just jump in and cut and paste it for their own applications, then they’ll always be in a good place.

But the second point you’re referring to is, what do you do with this data? Well, when you sell a product, typically, the relationship is very transactional, it’s like you sell something and then you’re done. And so you’re losing an opportunity to continue to make use of that data by not just selling the product but also offering guidance on how to use the product. And so that’s why we’re seeing more and more product companies becoming service companies. I think that the product business model is on its way out.

And people talk about the culture of the millennials, how they don’t want to buy stuff, they just want to use stuff and have experiences. Okay, that’s not what’s driving it; that’s the consequence, that’s because of the availability of this. The fact that you can get a car by the minute, by the hour, by the day. In fact, you don’t need even to get a car; you get a ride. It fundamentally changes the way you think about this and so your asset acquisition strategy disappears. In my view, capital expenditures will be replaced by operating expenditures for most consumers. Now, that just displaces it one level and I understand that, but there’s not going to be that many things that we all really need to purchase as individuals. Obviously, some coats and so forth. And I’m saying this with all these books behind me, which were mostly given to me. But I think that at the business level too.

Rahim Rahemtulla:
Yeah.

Gregory LaBlanc:
So at the business level, surely we’ll buy physical things, but the actual cost of that functionality will be much more around the services that come embedded in the physical object–

Rahim Rahemtulla:
Sure.

Gregory LaBlanc:
–whether it’s from manufacturing or services.

Rahim Rahemtulla:
And then from the consumer’s side, it seems sort of intuitive that having things as a service makes sense for us. We can just use as much as we need when we need it. We don’t have to buy a boat, we can just go, use one when we want to use one, for example. From the business side, does it also make sense? Is this something that they should also want to do? Providing things as a service makes sense from a business side? Is it a business case? Is it profitable? Or would they rather sell me the boat?

Gregory LaBlanc:
Well, if you can sell it, sometimes that’s fine, but at the end of the day, you’re not going to be able to. And there’s two things driving this. I used to teach Operations and capacity utilization is the number one cost driver for most businesses conceptually. And the amount of idle stuff that we have, the amount of idle capacity – and not just physical capacity or physical goods, whether it’s planes or tracks or cars or real estate and so forth – but also idle human capacity. How much time do people sort of stand around waiting for work or waiting for something to happen? They are on call and so forth and so anything you can do to reduce this idle capacity.

And my argument is that most of our industrial revolutions came about because we were able to extract more productivity from the same amount of capital and the same amount of labor – that’s what productivity improvement is. And that fundamentally comes from having better information: knowing how to match supply and demand, knowing how to ramp up production and ramp down production, knowing what to do with this surplus capacity, because you can find alternative uses through flexibility in both labor and capital.

So what we’re going to see, I think, is a rapid increase in not just output as a function of the amount of stuff we have, but also just GNP numbers will not reflect the amount of extra satisfaction that we get. So when we start increasing our capacity utilization on cars from 3% to 75%, it’s not going to show up in GNP numbers, typically, the way we’re used to seeing these things show up because we’re going to make fewer cars.

Rahim Rahemtulla:
Yeah.

Gregory LaBlanc:
We’ll have fewer parking lots, there will be less stuff sitting around going to waste and we’ll get more juice out of every orange. So I don’t think of it, really, as consumers driving this. I think of it as the logic of costs and old-school, old-fashioned understanding of the tradeoffs embedded in operations. That’s what is driving this.

Rahim Rahemtulla:
Agreed. Sounds like there are so many more interesting areas that we can take this discussion. Like, say, just raising this idea of how all of this capacity utilization, where does this show up in the traditional measures of economic activity that we’re used to? It sounds like a whole other interesting problem. But I’m afraid we’re almost out of time today and so I’ll just have to press you to close our discussion. I think I know what the answer might be, but if you had to say, what is your one essential piece of advice for me, for companies out there that want to survive in the digital world that we find ourselves in now? If you had to boil it down for us just in one takeaway, what would that be?

Gregory LaBlanc:
Well, a lot of it is about how you can conceive of your business, how you think of your business. If you think of it as a product business, then you need to start thinking of it as, what would it be to be like a service business? Now, if you think of yourselves as a non-technology business, you have to ask yourselves, what would it mean for this to be a technology business? If you’re making hardware, you need to ask yourself, well, what would it mean if this really were all about software? And then, finally, what would it mean for your business to be fundamentally a data company where information and data lies at the heart of your competitive advantage? So sometimes it’s about just reorienting your thinking about what your business model actually is and sometimes just having those conceptual adjustments opens up a lot of doors. You start to see things that you didn’t see before. And that’s the first step, I think, on the way to reinventing your business model.

Rahim Rahemtulla:
Fantastic. Well, Gregory, thank you very much. Products into services, collect data. I think, excellent notes to end our discussion today. So thank you very much for joining us.

Gregory LaBlanc:
Well, thank you, Rahim.

Rahim Rahemtulla:
A pleasure. And thank you to all our listeners and viewers. If you’d like to have a chance to see Gregory present, why not sign up for our Leading Digital Transformation Immersion Program? We’re going to be having another one in October, October 8th to 12th. Have a look at our website – siliconvalley.center – for all the details. Next week I’ll be joined by Dr. Maya Ackerman, the CEO and Co-founder of Wave AI. She’s going to be joining me for a discussion on the future of the workplace. That’s Tuesday August 14th at 11 a.m. PDT. So I do hope you’ll be able to join us then. From me, from Gregory, our guest today, goodbye.

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