Fusion Strategy: Navigating the Industrial Evolution

Unlock the secrets of "Fusion Strategy," the groundbreaking approach that's rewriting the playbook for industrial giants. On our latest episode, we sit down with Venkat Venkatraman, author of the upcoming book "Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future" who, along with co-author Vijay Govindarajan, is steering companies through a landscape where AI and real-time data are as critical as nuts and bolts. Venkat maps out for our listeners the fascinating terrain where digital and physical realms converge, elucidating how heavyweights in sectors like automotive and agriculture can not only survive but thrive by adopting innovative digital tools to gain actionable insights and drive strategic decisions.

Navigating the future of industry requires a skillful blend of technology and vision, and in this conversation, we dissect the role of AI in transforming traditional industries from the inside out. We scrutinize the "computer on wheels" phenomenon and discuss how companies that fail to integrate AI, data, and cloud connectivity might find themselves in the technological rearview. Moreover, Venkat shares his unique perspective on 'backcasting' and the imperative for a harmonized approach among company leaders to overhaul business strategies, ensuring longevity in an ever-evolving digital age. Join us for an episode that promises not just to inform but to equip you with the strategic foresight needed for the dynamic road ahead.

"Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future" comes out March 12, 2024 and available for preorder now.

Chapters

• 0:39 Industrial Fusion Strategy
• 16:04 Digital Fusion Strategy for Future Success
• 26:52 Updating Business Strategies for Constant Change

Transcript

J.P. Matychak: 0:40

Greetings everyone and welcome to another episode of the Insights at Questrom podcast. I'm JP Matychak and alongside me is my co-host, Shannon Light. Shannon, how are you? Great? Thanks, JP. It helps if I bring you up when the volume there you doing well, I'm doing well, great, well, today is another installment in a segment we like to call the Questrom Book Club, where we feature recent and upcoming books authored by Questrom School of Business Faculty, and today we welcome Venkat Venkatraman, the David J McGrath, Jr. Professor of Management in Information Systems.

J.P. Matychak: 1:13

He's considered one of the foremost authorities on how companies develop strategies to win with digital technologies, widely published in leading academic and practitioner journals, he has been recognized as a top-sided researcher by Google, scholar and Stanford University. He's lectured and conducted digital strategy workshops all over the world with companies such as IBM, merck, ericsson, bp and many more. He's also the author of the "Digital Matrix New Rules for Business Transformation through technology. And today we're discussing his upcoming book Fusion Strategy how Real-Time Data in AI Will Power the Industrial Future, co-authored with world-renowned innovation guru Vijay Govindarajan, a playbook that will help industrial companies combine what they do best to create physical products with what digital do best. Use algorithms in AI to parse expansive interconnected data sets to make strategic connections that would otherwise be impossible. Venkat, welcome to the show.

Venkat Venkatraman: 2:12

Thank you very much.

J.P. Matychak: 2:13

So let's start by setting the stage a little bit. It's hard not to read anything today and not see news about data, big data, AI and for the most part, I think people see those two things as only affecting the digital economy. But judging by the title of the book, it sounds like we should be preparing for data and AI to impact the entire industrial economy.

Venkat Venkatraman: 2:39

You're absolutely right, J. P. If you think about the time that I wrote the digital matrix in 2017, the big question at that time was how will digital giants and the startups start to influence traditional industries as they become digital? And most of the examples that I studied at that time were digital economy companies music, media, advertising, telecom, financial services. These are asset light sectors where the analog products could become digital and we don't need to actually have an analog form of those products.

Venkat Venkatraman: 3:25

When I finished the book and I started discussing the ideas with my colleague VG at Dartmouth, we realized that the industrial sectors have largely been untouched by digital technologies for so long, because you still have to make cars, you still have to make aircraft engines, you still have to make aircrafts and buildings and tractors and so on.

Venkat Venkatraman: 3:49

But we asked the question there's a $75 trillion of GDP that's the industrial sector that's largely been untouched. What would happen if we digitize products? What would happen if we actually have real-time insights into how your car is driven on the road? What will happen if you have real-time insights to your tractors as they go about plowing in different farms in the world? And so we started connecting the digital economy to the physical economy and came up with the idea of fusion, which is the fusion not in the sense of energy sector, but fusion in the sense of infusing physical and digital. And so we believe that the next decade is really about digital transformation of asset heavy sectors where physical products are going to become digital, physical products, and we have already seen it. Look at today's speakers, yesterday's output devices, that today's input output devices that are essentially digital physical products, and we can go on and talk about many other things as we go along in this podcast.

J.P. Matychak: 5:07

So what inspired this exploration, then, of thinking about strategy and real-time decision-making, and that the impact that AI and data could have on this aspect of business?

Venkat Venkatraman: 5:21

Yeah, again, we went back to the industrial companies and asked ourselves a question what made them successful? They have historically spent a lot of time designing their product and making sure that it's economically viable at a price point that the customers will pay, and then they had a very efficient supply chain. Things that they did inside the company extended through the global supply chains and once they delivered the product, they had very little information on how the product actually performed on the field because we didn't add telematic capability. We didn't have real-time tracking capabilities and if you take an automobile as a classic example, where the automobile is a consumer product but the data about the product ended at the level of the dealer, so a GM or a Ford or a Mercedes would typically know where the car was sold, not to whom the car was sold often, and they never really had data on who actually drove the car and how the car performed on the road.

Venkat Venkatraman: 6:33

Now, with telematics, we have real-time capabilities in terms of what's happening on the field. So the shift in thinking for industrial companies and strategy is to go away from thinking about your competencies as products are designed or products are designed and manufactured, but products as driven or products as used by the customers, and once I know why your car fails, I can then understand whether yours is an outlier or is likely to be a pattern that we can begin to generalize so that we can take proactive corrective action. We couldn't do that before. Sensors were expensive, software was something that was hard-coded into the product, not real-time Cloud connectivity the 5G connectivity was not there. So when I did the research in 2016 for the previous book, these were ideas that we could see happening in the future, and by 2023-2024, they become real, and by the time we come to the end of the decade, this will become commonplace.

Shannon Light: 7:41

And to that point, how did these emerging technologies impact fusion strategy, as we continue to see how much growth, even just in months of AI, yeah, that's actually a great question If you think about.

Venkat Venkatraman: 7:59

Let me take an example again from Digital Matrix and connect it. One of the examples I used in the digital matrix was IBM Watson and the ability of IBM to create cognitive computing, which is really a precursor to all the generative AI that we see today. But at that time, what IBM had to do was to go inside hospitals, collect all the detailed data to digitize their understanding of cancer cure or heart disease cure or whatever the case might have been. At that time we didn't have the large foundation models and then suddenly chat GPT came with the foundation models and then we can now create real-time data and AI that sit on top of these foundation models. So we couldn't have really been thinking about fusion strategies in 2020 unless individual sectors started to digitize all their content not just structured data, but unstructured data and very few industries did that on a systematic basis. So today, in 2024, we can reasonably speculate on why the different industrial sectors are going to develop their large industrial foundation models, and that is going to be extremely valuable for them to then develop their fusion strategies.

Venkat Venkatraman: 9:26

So that's just simply one technology. The second one is really cloud and the 5G or 6G connectivity. So it's now possible to think about putting a 5G or a 6G modem on remote devices and collect real-time data on how they're performing on the field. We couldn't do that five years back. We didn't have this widespread cloud capability or the cellular capability, and then we got, obviously, the software capabilities and the applications, the development capabilities and all that is now possible. So multiple technologies are converging to make fusion real. Multiple technologies are converging to make fusion economically feasible for many companies to explore. The real battleground is going to be how companies are going to invest to accelerate towards this fusion future that we believe will be the defining activities for many, many sectors.

Shannon Light: 10:29

Would you say that that adoption of fusion strategy is likely the biggest challenge that a lot of these industries face?

Venkat Venkatraman: 10:41

Yeah, I think again. When we finished the book about six to eight months back, we were in presenting the ideas to very many companies and they're beginning to see the rationale of shifting from industrial age analog thinking towards this digital, real-time fusion thinking. And as companies begin to realize that if they don't do that, somebody else is going to come between them and the customers, they will put those sensors, they will reverse engineer the process, they will deliver new value-added services that otherwise legitimately should have gone to the companies that make the product. And so once they realize that you need to do this, both for offensive, proactive reasons, as well as for defensive, reactive reasons, then the companies realize that they need to actually focus on this much more seriously than they might have done in the past.

J.P. Matychak: 11:42

So what do you think are some of the roadblocks that stand in the way of organizations transitioning into this fusion strategy, and how might they be able to overcome them?

Venkat Venkatraman: 11:51

Yeah, I think there are many, many barriers or factors that inhibit companies make the transition. I think the most important issue is companies believing that the future success is going to be very different than their past success. If companies have been extremely successful in their development, then they look at the digital as a 10-year journey and they don't quite see that exponential shift happening in their visible horizon. Then they can say I'll wait. I'll wait for a few more years, because in technologies the one area that I've studied for the better part of 35 years the one common equation is the waiting equation.

Venkat Venkatraman: 12:45

Do I invest today or do I wait and invest tomorrow? Because I know that if I wait and invest tomorrow, much of the uncertainties can be resolved. Costs of technology will come down, there'll be more options for me to play with, but at the same time I lose the opportunity to have a first mover advantage. So the companies are now beginning to ask the question what's the value of waiting and what's the risk of waiting? And so that's the first and foremost barrier. The second one is really one of investment shifts. Companies know how to invest in what they're good at.

J.P. Matychak: 13:22

Right.

Venkat Venkatraman: 13:23

And successful companies fail because they overinvest in what they're good at today and underinvest in what they need to be good at tomorrow. So who brings this new thinking into the company? Is it the CIO? The answer typically is not, because the CIO quite doesn't understand what a product does. The CIO may understand what a process does and what set of organization changes need to happen. So it's got to be the manufacturing, it's got to be the marketing person, it's got to be the operations person, who may have competencies in their branches of industrial engineering and manufacturing engineering. But here we are really asking them to take the role of being the hybrid managers that understand manufacturing engineering and data sciences, industrial engineering and information engineering. So once these hybrid managers see the potential, then I think making the investment cases has been easier.

J.P. Matychak: 14:25

And do you see this move to a fusion strategy and even data and AI and this may not get touched onto the book, but it just kind of sparks the question Do you see this as having an impact in creating a larger gap in competition between companies or do you think that this has the opportunity to close the gap of competition? And, to say it another way, I've even noticed, with the adoption of AI technologies, the chat GPDs, the Google Gemini's of the world and whatnot smaller companies are able to process things that the likes that major organizations like McKinsey would be able to do with hours and hours of people power and human capital. Do you see this fusion strategy as a way to create a more level playing field for competition?

Venkat Venkatraman: 15:26

I think it's difficult to generalize at this point in time. But if you now take your question and start asking can a new company design a tractor that has got data and AI capability at the core, where it's able to show real-time data of what the tractor is doing on the field and Then design it, manufacture it and deploy it at scale over the next ten years? The answer is yes. Why? Because we can look back and Tesla mm-hmm as a relatively new company that had a different vision of what a car is going to be. Mm-hmm right computer on wheels connected to the cloud, with real-time data and AI, and All the incumbents laughed at the company because they saw it as essentially a faster golf cart.

Venkat Venkatraman: 16:19

You're right right, because nobody actually understood the entertainment center.

Venkat Venkatraman: 16:26

Nobody actually understood that it's possible to have an battery electric vehicle that has the same power as an industrial combustion engine. But then, once you have that, you can add a lot more things to that piece of equipment. Computer on wheels connected to the cloud. A tractor, essentially, can become computers on wheels Connected to the cloud, but except it's in a more regulated setting, if you will, not on, you know, wide roads, but in defined farms. And If John Deere doesn't do it, if caterpillar doesn't do it, if Mahindra tractors don't do it, somebody else will begin to do that right? So in that sense, it's possible for a new entrepreneur to think about creating this.

Venkat Venkatraman: 17:11

But the reason why I said I don't want to generalize is because it will require a significant amount of investment right, to actually create that product. And this is where we wanted to contrast between the digital transformation of the asset light sector and Digital transformation of the asset heavy sector. In the asset light sector, once you create an app, the marginal cost of producing the next app is near zero, and we had this Relatively scalable Infrastructure for distribution of the apps right Apple App Store and Android Play Store right. Here it's going to be much more difficult, right? You got to design the product, you got to develop the product. You got distribute the product. You got to maintain the product.

Venkat Venkatraman: 17:58

So we will see it happening in sectors where the industrial companies are asleep at the switch, where the new companies come in. But the generalization that we want the listeners to take away from that is a digital industrial company that understands the power of Infusing mechanical engineering with data and AI Will do much better than a mechanical engineering company that doesn't understand the power of the data and AI. And the same argument holds good for any human that is able to take advantage of Generative AI will do better than someone who still thinks that gen AI is simply for the, for the, you know, for play and not for work. Right, and so that's where the shift is going to happen.

Shannon Light: 18:44

And I mean all of that seems to play a big role in just the long-term planning for a company. So, to that point, with Companies were not going to, you know, be willing to adopt this, do you see the potential of them? Just you know that company could just being completely unsuccessful, or what's your take on?

Venkat Venkatraman: 19:11

that. Yeah, I you know. Obviously we are all biased in how we think about the prescription coming out of our own research, I think, our General prince prescription coming out of our researchers. If companies believe that the next five years are going to be exactly like the last five years, they will find themselves at a comparative disadvantage Compared to companies that are prepared to look at the future as being very different. And we also write in our book this idea of the distinction between forecasting and back casting. Forecasting is projecting from today into the future, where we typically assume that things that we want to happen In the future will continue to happen because that's a future we like, because we'll be successful in that future.

Venkat Venkatraman: 20:03

Backcasting really challenges the company to take a position in 2030 and say what will the future be in 2030? Not seen from our lens, but from the lens of the customers that are going to use your product. What will have to be true for that future to become real? And In that future, will my company be relevant? Will my competencies be relevant? Will I still have a role to play in get a fair share of that value in that future? So the moment we take an outside in perspective, future backward perspective rather than inside out and Project from today.

Venkat Venkatraman: 20:45

Companies start to realize that much of the competencies that they've been good at today May not be relevant in the future. Right, because core competencies are not about what you're good at today. Core competencies are always about will customers give you a premium For you being good at that? So I may be good at many things and and today I may be able to extract value from the, from the customers, but that doesn't necessarily guarantee that the customers tomorrow will give me that same value, but even a fraction of that value, in the future.

J.P. Matychak: 21:20

So this obviously has implications up and down an organizational structure, you know, especially for those that are, you know, traditional products and whatnot. So what can leaders do? That sort of see the vision of this fusion strategy but then need to build that, that groundswell of support within an organization that may have the the inertia of change, resistance against them, to really kind of shift the focus.

Venkat Venkatraman: 21:50

Yeah, I think the CXO team has got to take a coordination role rather than a delegation role. Okay, because what has happened? When we had the first few years, few decades, of digital transformation, the CEOs realized that we need to transform our processes. With big ERP systems and CRM systems, you know the big SAP implementation. That's three years and five years of implementation. They made the investment and then they delegated it down to the CIO or the chief human resource officers and chief operating officers.

Venkat Venkatraman: 22:26

Now the fusion strategy is really challenging the status quo and, if you believe that the future is not an extrapolation of the past, we now have to rethink the data flows inside the company, the data flows that extend to my suppliers and to my partners and to the distributors, and have to reimagine what the factory of the future will look like. We got to reimagine what the product of the future will look like. We have to reimagine the control center, the operation center, that these data starts to flow back in. Many of those things are relatively new ideas.

Venkat Venkatraman: 23:07

Right, we saw with the digital technology people started tracking real-time data of conversations about our products in social media. That was relatively new. Suddenly, I need to know what people are saying about my product on, you know, google or Facebook or Twitter or you know wherever else the case may be. So they created listening centers, social media listening centers. The equivalent idea for product companies is to understand real-time data of how my products are performing on the field. That requires data, not just something that the marketing department does, but the marketing department shares with the operations department, with the procurement department and with the suppliers. So what we write in the book is this idea called digital twin.

J.P. Matychak: 23:56

Okay.

Venkat Venkatraman: 23:56

Where the digital twin has been well understood in terms of designing a product.

Venkat Venkatraman: 24:02

We think the digital twin has to extend beyond product design to manufacturing, but also performance.

Venkat Venkatraman: 24:12

Once I designed the digital twin of actual performance of the product in the field, then I'll know why the product fails in a specific situation and by understanding across these different settings I get a much better insight into why my product fails.

Venkat Venkatraman: 24:31

And that data, if it is functionally kept siloed, then the marketing department will then not share it with the manufacturing department, or manufacturing department may not share it with the product design department and the product design department may not share it with the suppliers. The moment we take the CXO and give them the charter of end-to-end visibility with this real-time data and AI, they realize that this is a weak signal that I'm observing in the field. That's going to be extremely valuable for me to make proactive corrections rather than think of this as what we call in the book systems of records rather than systems of data graphs. And the systems of data graphs are really understanding how my products perform in the field. So we are introducing in the book ideas and mechanisms by which the senior leaders can put in place organizational mechanisms by which they can create, this infusion of physical and digital domains.

J.P. Matychak: 25:34

And it sounds like it really requires the true breaking down of departmental and functional silos in order to be successful.

Venkat Venkatraman: 25:43

Yes, because successful companies have relied on functional excellence. They're good at R&D, they're good at manufacturing, they're good at marketing, and cross-functional excellence was not something that was expected except at the senior level, but even then they relied on functional expertise to say if something is an R&D problem, I rely on you, if something is a marketing problem, I rely on somebody else. So now, by using the word fusion, we are really beginning to say that it's the infusion of disciplinary thinking, infusion of mechanical engineering with data, as I said earlier, infusion of industrial engineering with information sciences. These are new ideas that will make successful companies differentiate from others that still are thinking about functional excellence rather than cross-functional or fusion thinking.

J.P. Matychak: 26:52

So let's talk about the book itself. You both decided to write this as a workbook almost right and a guidebook for leaders to implement this new way of thinking. Talk about your reasoning behind that, and you know, in particular, what strikes me and it's something that you said earlier on in the conversation was that, even since you finished the book, to now look how the world has changed. So you wrote this as a guidebook. How are you planning on keeping this updated for executives and leaders to keep up with the times? Because, I mean, we thought technology was rapidly changing before. It seems exponentially faster.

Venkat Venkatraman: 27:46

Yeah, I think it's a limitation of the book publishing cycle, if you will right.

J.P. Matychak: 27:52

Right.

Venkat Venkatraman: 27:54

We finished. The major drafting of the book was done by August of last year. It goes through reviews. It comes back with some comments. We might have revised it and basically sent it in just around Thanksgiving, Right, and the book is coming out in March. So that's at least six months of thinking.

Venkat Venkatraman: 28:14

And nothing has happened and you know that's okay in many, many disciplines, but it's not okay in a discipline that we are really talking about. That is changing pretty much every day, right? So we did two things. One is the book is coming out in March. We, hopefully will have the playbook with the templates that will allow companies to think about much of the ideas that we've been talking about during this podcast. Sometime in the August September timeframe, the Harvard Business Review Press will decide how they want to distribute the templates that make the playbook actionable and, even though we write about many of the actions in the book, we'll provide some templates to make that happen.

Venkat Venkatraman: 28:59

We also wanted to do something, which is to practice fusion in terms of making physical digital book work. Now, if you think about most of the books, there is a physical book and there's a digital book, which is basically a static version of the book, and then there's an audio book, which is basically an audio version of the static book. The form doesn't change the substance of what's in the book and what we are now trying to do is to update the material since the book is finished, expand the material because, by design, the book is 200 plus pages and we may refer to a case in two paragraphs when the richness of the research that we might have done is maybe six to eight pages, but the actual material may be a lot more than six, seven, eight pages. So what we are doing is we're actually creating a fusion strategy coach.

Venkat Venkatraman: 30:03

Oh, interesting which will be a Gen AI coach, which you think of it as a virtual VG and virtual Venkat oh, where a reader can potentially ask questions and, to the extent that we can train the material to help you answer that question. We have gone beyond just simply giving you a static book or a digital book, and so at this point in time, we have tested it out with about 40 executives who've had a chance to read the early draft of the book and we have asked them to just test out the coach to see whether the coach allows them to go beyond what we have written in the book. And we're getting good. We have gotten very good feedback on the coach. We're gonna test it out with a group of about 100 students at Tuck who is gonna take the strategy class in March and April. We're gonna do the same thing. They're gonna read the book, they're gonna use the coach and then see if the by using their prompts right we're not gonna give them the prompts using their prompts whether they're able to get deeper insight and understand the connections across the concept beyond what they would get from the book. And that will also help us develop the playbook with the templates and the prompts by the time that comes out sometime later this particular year.

Venkat Venkatraman: 31:31

We believe that the idea of a reader posing a question that the book is able to answer in the language of the reader is really where the book should go, and we're basically thinking of this as the first stage of this transformation, and what we haven't quite decided is how frequently we'll update it and how will we host it, where will it be hosted, how long this will be maintained, how to charge for it. All these are open questions, but we believe that we should at least let the initial readers, the initial buyers of the book, have an opportunity to ask questions that otherwise we would have answered using an online forum or using an email forum. But here we hope that we can get the Gen AI to provide first level answers, and that's gonna be our experiment with the launch of the book.

Shannon Light: 32:37

Currently, are the those who have tested out this coach? Is that living on an online platform where it's similar to say, something like chat, gpt, where you type in it's at this point?

Venkat Venkatraman: 32:51

in time, a proprietary platform in which we give the link and we give them a password so that only they can access it, because we don't wanna make it open at this point in time. It also raises interesting questions about intellectual property Because which is my next question Absolutely the book is copyrighted by the Harvard Business Tribunal. So we are still working through all those issues. But the executives then had a chance to then test out these ideas. Right, they could ask about their company. Their company might not have been done a discussion in our book. And then we are seeing how well the coach answers that question and the way we have trained it is actually looking back. It is very simple, but we struggled through it for a very long time because we wanted to make it a simple coach, without too many options given to the reader, and finally we came down with a simple idea of what I do in the classroom, right when somebody asked me a question.

Venkat Venkatraman: 34:02

What I do in the classroom is connected back to what I know and connected back to what the students know. So what we have trained the coaches to do the following you ask a question. It refers back to the core ideas from the book. It doesn't say chapter five or doesn't say chapter seven, because it synthesized across chapters. It writes an answer in about 250 words and, in making it conversational what it does, it ends the answer with two questions for you. One that goes deeper into the question you asked me, which is what I would do in the classroom. Right, you're asking me, I'll expand more on it. The second question is to laterally connect across concepts or across industries.

Venkat Venkatraman: 34:51

So, if the question is about farming, one follow-on question may be how does this idea apply in transportation or in mining or in construction? And if you asked me about farming, the second question may go deeper into the farming how do the digital twins apply in farming? How do we begin to think about systems of data graphs in farming? Right, because that's the logical connection by creating this Socratic thinking, which is what we do in the classroom, into the Gen AI, we are able to generalize and have one general coach that will continue to have conversation with you till you find that you've gotten the level of answer you can get from this. So so far, it's text-based. It's not simulating my voice. I'm not trying to create a Gen AI version of my answer using my voice. It's text-based, but these are easy extensions we could do. We could do an AI avatar with my voice, an AI avatar with my video, but first we want to make sure that the concepts work, and that's what we're doing so far.

J.P. Matychak: 36:05

Wow, truly an opportunity to both transform the business world and how we do business, but also the potential to transform how we learn as well. Incredible. Venkat Venkatraman is the David J McGrath, Jr. Professor in Information Systems at the Boston University Questrom School of Business. His upcoming book Fusion Strategy how Real Data in AI Will Power the Industrial Future, is coming out on March 12th 2024. In is available for pre-order now on Amazon. Venkat, thank you for joining us. This has been incredibly interesting.

Venkat Venkatraman: 36:41

Thank you very much for having me.

J.P. Matychak: 36:45

Well, that will wrap things up for this episode of the Insights at Questrom Podcast. I'd like to thank our guest again, Venkat Venkatraman. Remember for more information on this and previous episodes, along with other insights from Questrom experts, visit us at insights. bu. edu. For Shannon Light. I'm JP Matychak, and so long.