The Thought Leader's Voice Podcast

Rachael Kinsella: Hello everyone! Welcome to the Thought Leader’s Voice. I’m Rachael Kinsella, your host today, Editor at iResearch Services. We’re thrilled to be joined today by renowned innovator, author and speaker on business strategy. Paul Nunes, Global Managing Director of Thought Leadership for Accenture research.

For more than 35 years at Accenture, Paul has researched technology-led changes in business and marketing strategy. His findings and writing on marketing and business strategy are regularly featured in publications worldwide and he writes a regular column on disruptive innovation for Forbes, with author Larry Downes.

Paul is co-author of three award-winning international best-selling books – we’ll be talking a little bit more about those later – and frequently speaks to industry and management conferences around the globe and at top business schools, including Harvard, Columbia, Wharton and Dartmouth.

Paul was a front-runner in pivoting strategies. He’s been speaking about this for a long time, even before it became fashionable, having been awarded a US patent in 2010 for his systematic method of improving organizational innovation processes. He is a Master of Science and Management from the Kellogg School of Management at Northwestern University.

A very warm welcome to you today, Paul, thank you for being here to share your insights on business innovation and future strategies today. We’re really looking forward to a lively and inspiring conversation.

Paul Nunes: Thanks, Rachael. Very glad to be here.

Rachael Kinsella: Great to see you. Thanks for being here. Well, we’ve got a lot to cover today. So, if I may, I’ll dive straight into our first question.

Paul Nunes: Please.

Rachael Kinsella: Amalgamation of an increasingly complex business landscape, the proliferation of data and the pressing desire to be at the forefront of competition have led organizations to focus on using analytics to make strategic business decisions. How important do you feel it is for businesses to embed data analytics and AI into their core strategy, rather than focusing on the past for insights in order to stay at the forefront of digital disruption?

Paul Nunes: That’s a great question Rachael, because it ties in to some of the research we’ve been doing very recently. Which has helped us to see the imperative for business now, something we call learning from the future. And that’s kind of counterintuitive, but what we’re seeing is that organizations have gotten pretty good at using at least some approaches to AI to create lessons from the past. And by that, I mean seeing and looking at behaviours and then interpreting that, and creating sort of predictive analytics on what somebody might do based on what they’ve done in the past. But what we’re seeing now is this: with the proliferation of data available to algorithms, and the new algorithms being developed, that we can actually start to look further into the future and start to make more predictive and better predictive guesses about what might happen and how to respond to that.

So, a couple of examples, some of the ways it’s being used, you think of car traffic, software for getting from one place to another, your typical sort of navigation software, before it would use sort of past patterns. But today, it actually starts to look at possibilities and do actually a sort of scenario planning of what might happen going forward. Should an accident occur? Should somebody say something here and there? So, starting to get much more forward looking, as opposed to assuming that simply because in the past a pattern happened, it might still be true today and tomorrow. So, this idea of being predictive is really important, going forward.

Rachael Kinsella: Great. And how is that application being used across different types of business?

Paul Nunes: Well, it goes to create what we call customer experience and the importance of creating a great customer experience. A lot of times we use customers’ own behaviour or large data sets of the behaviour that seem to predict what customers would want, but what we’re seeing now is that it’s a really complex problem. The weather might affect whether or not you want a cold drink or a hot drink. So even the temperature we might get off of a watch a smartwatch, right, could tell it maybe how you’re feeling about what kind of drinks you want, more than the fact that you wanted a certain type of drink, when you were at that place the last time. So now things like the weather, temperature, can matter your own body temperature, whether you’re hot from running or even determine the kind of drink you want. You want water? Do you want something like Gatorade or a sports drink? Or do you want a refreshing soft drink? So, we’re starting to see that. And in fact, what we’re seeing is 77% of companies that we surveyed, are starting to embed more internal and external data into the way that they’re assessing their offerings and decision making. So it’s really becoming the next step.

Rachael Kinsella: It’s really interesting. And I think, having that knowledge of your customer through behavioural data just becomes so valuable in, as you say, predicting future strategies, as well as looking at what’s happening now.

Paul Nunes: Well, that becomes table stakes, you have to have a large set of data to begin to understand and create the early predictions, and then that has to be modified and accelerated to be able to look forward and to do it in real time. So, it’s a challenge.

Rachael Kinsella: And I think that it has been particularly well used in the financial services sector, looking at behavioural analytics, looking at those more predictive sources of data to look at elements such as credit worthiness, or to predict particular risks. How do you find that this kind of data and these kinds of analytics can be better positioned towards quantifying, measuring and predicting future risk?

Paul Nunes: Oh, risk is always an interesting thing. Because we already have certain ways of predicting and knowing that, but oftentimes, there’s the specificity again, of particular contexts, that can change. So, we need to be able to look more forward here again. So, for example, if you think of, whether somebody is likely to pay their bills, or a loan, paying back on a loan, well, if an unexpected medical emergency comes up, their risk profile may change immediately. And so you could predict whether or not the likelihood that they would get sick, but once it actually happens, all the future payments now become more questionable and need another level of analysis, so, would they be better able to pay if you were able to restructure the debt? Would they…all sorts of things that you can do to adjust the nature of the contract or the risk going forward.

So that’s what we’re starting to see, that as opposed to simply saying, well, you have this income, or getting smarter about your dentist, in the old days we used to say, for some credit cards and some of the early credit cards into risk assessment, we would say, well, dentists tend to need a lot of money when they get out of dental school, but they’re great because they tend to pay it back. So, we started to get these insights, but we got them at a pretty, pretty broad level. Now, what we were able to do is we’re starting to get much more granular. And we’re starting to be able to use more real time data, which is, alright, if I gave you some forbearance on this risk, on this debt, do I see that you start to pay back better than that, and we can actually start to adjust these things in real time.

And that’s an important next step really, is this idea of experimentation and testing. And it’s the next big step in AI, which is the first step in learning through AI is really, what can I infer? So, inference engines, right, work off large data sets, and what can I infer from that? But the next step is really well, how can I, as a system, get the next level of data I need, so that I can make even better decisions? But because I’m actually going off and creating and gaining that data? So how do I actually experiment and then use that experimental result in my predictions and behaviour? And that’s a very exciting next step in next generation in AI, and consumer behaviour prediction AI, and that’s going to help us manage risk tremendously. Because the best thing we want to do with managing risk is to improve the experience, right, we don’t want to have to deal with losses, what we want to do is minimize losses, and make it the best possible situation for the customer as well.

Rachael Kinsella: Sure. And also, from a regulatory compliance perspective, in terms of looking at moving away from those traditional risk models, for example, or looking at different data, cutting it up in different ways, looking at it in real time, as you mentioned, surely that will make things easier in terms of regulatory compliance as regulation evolves?

Paul Nunes: Yeah, well, regulation is a really interesting question, because regulation always sort of comes down for a number of different reasons to adjust and correct for markets, supposedly. And really, the good news of regulation, when it works well, is it protects consumers, and it creates a safe and fair environment. And I think if we assume good faith, everybody wants that. But with AI, and some of the power of new technologies is allowing us to do is even to get beyond that. Right? So, regulation, I’d say, well, you can’t foreclose on somebody. But once you get to that point, everybody’s losing. The power of the new technologies, is that, alright, if we can find new ways to avoid having to throw it into regulatory decision making, that’s probably better for everybody involved. So, can we restructure that debt? Can we, rethink contracts and find new approaches, and new solutions, in real time on the fly? And doing it at the personal level, part of the challenges you can think about is, regulation has always been a rather broad brush, and a rather blunt tool. We don’t have regulations for individuals because of the complexity, right, but we really actually want solutions for individuals.

Rachael Kinsella: Absolutely, and putting it at the customer level, and at that personal level, as you say, just takes it beyond regulatory requirements. And actually, it brings it all back to the customer and making sure that we’re on top of their needs, now and in the future.

Paul Nunes: Exactly.

Rachael Kinsella: I think these different methods, it’s really exciting to hear you talking about what’s being achieved and the research that you’ve been doing. Obviously, it has implications for the workforce, as well. In terms of the way we’re already very aware of the need for adaptability, and speed, and agility. And that’s come even more to the fore, even through the past two years with the pandemic, but it’s not going to end there. We’re going to have to keep evolving and keep innovating, but keeping up that pace of change. How do you feel that that’s impacting decision making authority across different areas of the business?

Paul Nunes: Yeah, well, it’s very clear that a number of factors have happened in the past, even couple of years, with COVID, that decision making needs to be brought closer to the need and accelerated so that it happens faster, more in real time. So, a couple of points on that, one is, we can see what the pandemic and its diverse impacts across businesses or global businesses, but even regionally, and some of the stuff that we talked about, and COVID, all the different regional regulations, create this need for localized decision making. Are you going to open the restaurants? Well, that’s not a decision for an executive at the top of a 3000 national chain restaurant can’t make right? How are you going to treat employees? So, we can and need as executives today, to understand what has to be held fast from a corporate level, whether that’s for branding purposes or risk purposes, or regulatory purposes, but also it must be made loose, to bring it to the local level so that we actually serve our customers locally and our employees, our talent, and our business in general and our brand at the local level.

So, we’re seeing a real fascination and now 82%, I believe it is of our folks that we surveyed in a recent study said that they’re actually operating more like a federation, a broad federation, than they are, like a centralized company in the past 24 months, largely due to COVID, of course. But actually, from our additional research, we’re starting to see that that’s likely to remain, and much the same way that flattened organizations, which are sort of a trend, from anywhere from 15 to 20 years ago, as you’ve researched the history of business right, sometime about 20 years to 10 years ago, we started to really embrace this idea of flattened organizations. And really it was communications technology that allowed us to do that.

Today, technology is going to allow us to from the core, from the centre, evaluate and understand the decisions and see the decisions that are being made locally and understand when to intervene. And that’s going to allow us the power to distribute and to make it more normal to distribute lots of decision making to what we call the edge of the business. Because in the past, it might be months, years, or you may never, you might never have known what decision was made on a car loan, or a car sale price, you know, at the edge.

Today, the nice thing is you can decentralize that decision making but you can create centralized visibility, such as start to recognize patterns and start to make better, centralized decisions about broad patterns of decision making around the organization. And that’s an exciting new capability. For me, it’s really exciting as a business researcher for years, to think of that as a new level of how we organize and manage businesses today.

Rachael Kinsella: Yeah, absolutely. And how does that feed into getting the right skill sets across different teams? And making sure that there’s the right data and predictive capabilities in terms of what skills are going to be needed by the different levels of the organization?

Paul Nunes: Yeah, tremendous question, because there it happens on multiple fronts. So, at the simplest level, we simply need a lot more data scientists. In fact, Accenture itself has been off working with universities, and a number of different programs trying to accelerate the, you know, the student training in analytic capabilities, and particularly even the scientific analytic capabilities, you know, how do you code and manage this kind of software, have statistical knowledge? So, you know, statistics, which is always a bit of a challenge, it was a little bit of a challenge for me in school, we need lots of, we’re going to need hundreds of thousands more of those folks.

But then it also is a function of bringing decision making and teaching to decision making skills and empowering employees at every level of the organization. And that’s not really something that we want to do – in the old days, hierarchy, you know, the textbook definition of hierarchy in an organization was created so that decision making could be made by better trained, more experienced folks at a higher level. So, it was always the idea that well, we try and put less experienced, more affordable people at the frontline, and then we’ll use fewer, you know, more experienced people in the hierarchy to make the big decisions. Well, all of that’s getting turned on its head now.

Rachael Kinsella: Yes.

Paul Nunes: Now we’re recognizing that we want employees at every level to be thinking, engaged, capable, data gathering, setting up the questions and the problems and recognizing: well, I can answer this, I can make this decision or maybe I need a little help. And maybe the help I need isn’t from the hierarchy. Maybe it’s from something lateral in the organization, which knows better. So maybe it’s from another country in the same region rather than going to the multinational core in a different continent. So that’s what we’re seeing. It’s really interesting. And so, we need talent of all and because of that ability to make the very nature of the jobs, you know, how we do the processes, the processes themselves, need to be redesigned. For a process that involves local decision making. So, if sales are done at the salesperson, not the manager or the senior manager level, that’s a different process. And the computer you know, the technology enablement of that process changes as well.

Rachael Kinsella: Absolutely. And do you think that’s a problem solved by better visibility of the right data across the organization and different individuals having access to the right kinds of data?

Paul Nunes: Rachael, that’s a great question, because that highlights the whole thing of technology augmentation. We talk a lot about automation, but Accenture in a recent book with our Chief Technology Officer, and a good friend of mine, Chief Researcher and colleague, Jim Wilson, we talked about human plus machine. And what we recognize is that it’s not about automation, itself, automation is going to happen, as always happens, continues. But where the exciting thing for businesses today is at the intersection of humans and machines. So how do you augment human behaviour and human capability? And you know, in some ways, the easiest way to think of that is exoskeletons, right, the things that the fancy term for like putting a big, you know, machine on your back, and you see those sci fi movies banging around, you know, and, you know, humans that can lift up a car. And there is a lot of importance to that, you know, in terms of some futuristic designs, but it also goes to the simplest things of, you know, the handhelds that you see increasingly with store employees, right? Like, how do I bring the technology right to the transaction, but not remove the human experience, the human touch, the human understanding, empathy, decision making capability, from the equation. So, we see lots of really interesting things that will be called the missing middle, where we’re bringing technology together with humans, to create augmented processes that really create the best customer experiences and grow the business.

Rachael Kinsella: Absolutely, yeah, I think it’s really key that the processes reflect the nature of the workforce and how that’s evolving. And as you say, it’s a big task. So, there’s multiple ways it needs to be addressed. So, it’s really interesting to see how organizations are moving forward in those structural changes in the strategic change with the tech enablement. And it’s quite exciting, actually, to see that much movement, even in recent years.

Paul Nunes: Yeah, some of the research that we’ve done, particularly because of the financial services industry, for example, right, there’s the whole advisory side of the business.

Rachael Kinsella: Of course, yes.

Paul Nunes: And there’s been a lot of writing for years about is that all going to go away, you know, arguments were that it was all going to become electronic trading, and it was going to become electronic advisory, or we were going to just create one big algorithm where you put in your finances and advisory was going to be pressing a button with the computer output. Hasn’t happened. Why? People don’t want a pure computer answer entirely, because this is important stuff. This is their money. This is trust. This is, yeah, I’m particularly understanding. I mean, one of the challenges that we talk a lot about is the challenge of whether or not you can trust the algorithm, the computer, understanding the, and having transparency and visibility into how the computer made the decision.

Rachael Kinsella: Yes, sure.

Paul Nunes: So, allowing a computer to decide all your investments, well, people are gonna want some visibility into, why is that? [Laughs].

Rachael Kinsella Yeah [laughs].

Paul Nunes Well, the computer says, profile. And so humans that can work with computers there, I think are pretty good in financial services. they use an agent advisor as well. You want to use the computer because there’s just too many possible offerings, right, there’s 1000s of stocks, it could be in there’s industries, there’s sector analysis, right. So, the data is profound. And yet, the decision making at the end comes down to a real personal interaction. That has to be almost certainly, has to be intermediated by a real person who gets the technology, but also gets the people, the customers that they’re serving.

Rachael Kinsella: Yes, yeah. So, there’s that dual need skill set and capabilities in that you’ve got to understand the technology and what it’s doing and why it’s doing it. But also, you need to understand the customer needs, you need to understand the products and what you’re offering. So that’s, that’s quite a change in skill set, particularly for sectors like the financial services, where it’s been very traditional models, up until very recently. You know, I think there’s been some real embracing of technology across financial services, and in competing with FinTechs and other tech organizations, but then there’s also been some reticence to adopt new models and to rely on technology, as you said, that perhaps underlying this trust, which needs to be ironed out with transparency, as you say, so you can you can see why it’s working, how it’s working, and that is effective, so that you can trust it to make decisions.

Paul Nunes: You can see Rachael, why it’s threatening, right?

Rachael Kinsella: Yes.

Paul Nunes: It threatens your job, the robot can simply replace the kind of advice that you are creating. And it’s also threatening in that sense that they have to be able to reskill and upskill themselves into a new world. So, there’s a very real threat there.

Rachael Kinsella: How do you think we can combat that outlook, that approach yes, it is a threat, but obviously looking at it from the positive side, in terms of how the technology is helping the very manual processes or decisions that are more far more easily done through technology, through the computer? How do you think we can? Is it a case of education? Is it having the right people in place to you to be able to talk to the technology?

Paul Nunes: Well, it’s very definitely, it’s a combination of leadership, and like you say, training and upskilling, I think it’s a function also leading with the opportunity, more than the risk, right? I mean, when they see sales increase, when they see the number of customers that can be managed by a single agent, which therefore means a lot more potential income for the individual, I think they start to see the upside now.

It has to be done through a directed, sort of reskilling, individuals can’t be expected to kind of take the risk in the chance entirely by themselves, some will. But broadly, it’s a function of risk, but also a redesigning of the processes in ways that are evolutionary, more than revolutionary, in some ways. What we see happening with this is, it’s not taking it a bridge too far, expecting, folks to completely change the way they interact with their client base now, but a lot of times, when they see the opportunity, and they see the goodness. And you’ve got to make it usable. So, a lot of the work that we do is just about how do you put the right user interface? How do you put the right usability approaches in place? Because it can’t be just going off and using. Yeah, you’ve got to have some guardrails, and you’ve got to have some trial and error kind of experimentation and learning with that, we can’t just sort of throw them in the deep end of the pool and say, swim.

Rachael Kinsella: Of course, an integration as well, in terms of, I really like the way you put it, that evolution rather than revolution. I think it’s, you know, having the opportunity to integrate it within existing processes, existing ways of working, and also that educational piece, at every level, from leadership and from within the team. So, people know what’s happening.

Paul Nunes: What I learned early on from technology assessment, and I used to spend some time early on in my career in a technology assessment group, is that technologies can’t be embraced faster than people can tolerate. So, people are the mitigating factor of technology evolution. We can invent stuff, we can bring it to market, but you look at even electric cars and a number of other things. And you say, well, why don’t we have a lot of these technologies in place, everywhere? And it’s simply the mitigating factor of people’s ability to change can never be underestimated or overestimated really.

Rachael Kinsella: That’s a really important point. And, also, not just using technology and adopting it for the sake of it, actually having a business case behind it and being clear on what value it’s adding and how it’s going to help you either better communicate or better serve your customers or work more effectively, more productively. And there’s also the other end of the spectrum where people will just want to try every new thing. And as you say, there has to be a certain level of trial and error and testing before you integrate anything into the organization, for the longer term, but there can be a bit of a trend for just using every new bit of tech, whether that’s for marketing purposes, or project management. So, making sure that it’s been just a fad. I think.

Paul Nunes: Rachael, you really hit on a great point there, which is the new opportunity and ability through technology that we have for rapid experimentation. And what people know, is MVP, minimum viable product,  love that name, MVP, what does that got to do with anything? [laughs] Minimum viable product. But this idea of testing and learning, so you’re exactly right, that even the very way that we change processes now are and offerings is no longer sort of Big Bang, but is really a function of using the technology capabilities itself to introduce in parts of the market, that is segments, and then testing and learning and testing again, in order to bring the right products and beneficial products to market.

Rachael Kinsella: Yeah, it’s definitely a more scientific approach. And I think it’s interesting to see that evolution as organizations are using scientific disruption within their business, that they’re becoming ever more scientific, as a company, and applying science and the right technology to tackle particular challenges, whether those are business challenges, or wider global challenges, which leads very nicely into sustainability, which, of course, is the other enormous challenge that we all face. And we’re looking at technology’s ability to support in being a more sustainable business, to be able to offer sustainable products to clients. But also, again, there’s that whole process of embedding that within the organization and making it part of the fabric of the company. And indeed, cost is a big barrier. As you mentioned, that the human response and approach can often be a barrier. So, do you feel that there are particular solutions that you’re seeing that can help companies become more socially and sustainably responsible?

Paul Nunes: Well, certainly there are Rachael, and it’s so important today as we see it becoming an issue on so many different levels in so many different places and parts of the organization. What we’ve been working hard to sort of get our arms around is and we have a good amount of research and data and insight already about how you do that. But part of the challenge is finding the right ROIs and the biggest levels of impact, right? So, there are simple changes in sort of the consultants always kind of like a two by two, but we really do have to break it out into what requires a major investment? And what’s going to have a large impact at the upper right-hand corner? And what can we do kind of today, that’s really simple? And if we just put in recycling bins? And the interesting thing, when we look at that two by two, is that there is no reason not to put in recycling bins in the offices, right? It’s low cost, it’s not the biggest impact, maybe, but it’s an impact. So, part of it is the argument, well, does it matter? If it’s not a big impact, should we even bother? And I think there’s what we’re seeing is the important thing of building the culture a sustainable mindset. So, we talked a lot about why it’s so important to have a sustainable mindset. Well, you’re not going to do the big stuff unless you embrace the mindset. And oftentimes, the mindset comes from just changing the behaviours of the little stuff. So, we see the need to create broad perspectives and programs around the whole thing. The nice thing too, is that the costs, we find the costs are obviously coming down, and you can use technology to help bring down some of those costs.

One of the great examples, I like is a national coffee chain, that was able to give away its food at the end of the day, because it was able to simply empower that process with technology to find out, well, what are the soup kitchens, where are the places that can use the food? And to integrate and simplify that supply chain to make sure that you could get it out the door.

Rachael Kinsella: That’s a fantastic example.

Paul Nunes: Exactly. They didn’t want to waste the food, but they were wasting it before because there was a real cost. And really an insurmountable cost in a way, of having local coffee shops trying to figure out where to give the food to well, it needs to be facilitated. And so, we’ve seen great new ways. And that’s just one example of a lot of ways that we’re seeing technology as a force for good for sustainability. So even if that just takes out the need of creating X amount of food product, if we all just didn’t waste food, right, we’d have enough and we’d be able to reduce our environmental impact. So there’s really, there are new ways to be to rethink the ROI. And like I say, the ROI of impact, you have to have the mindset first.

Rachael Kinsella: Yeah, I mean, it’s something that sounds relatively simple, and, and quite small steps, but actually it really builds up and something like simplifying a supply chain, and being able to just solve that problem that’s been going on for such a long time, through technology, I think is a fantastic example. And there must be many of those that we could probably draw upon across different sectors. But it’s common sense, isn’t it? Using the technology to solve a problem, but to fill that gap where there wasn’t the opportunity to previously, either through human means, or otherwise.

Paul Nunes: And the other great opportunity that we’re seeing across organizations and the complexity of it is there’s so many different ways to create sustainable value from lowering the carbon footprint of even your information technology, your IT technologies.

Rachael Kinsella: Absolutely, yes.

Paul Nunes: The greening of IT is something that we’ve researched and published on for a long time and are doing more now. So, it’s in product design, the circular economy. We talk a lot about and do a lot of research at Accenture on how do we create circular things? The other interesting challenge that we’re seeing is sustainable supply chains and value chains: if you want the biggest impact, sometimes you have to go further up or down the value stream. And oftentimes, the solutions are tied into bigger ecosystem or value chain solutions. So, you know, I might have a way of producing a component that is greener. But unless the buyer of that component, the OEM, is willing to, sacrifice that and communicate to the end customer, why they chose this component versus another. So, the nature of what we’re starting to see is that telecoms and technology is helping us to actually think about and see and imagine solutions that can only occur when the value chains and the supply chain, get together to make it happen, because you can imagine the transaction costs of saying, well, alright, I could do this in a greener way. But how do I know if my upstream or downstream partners are going to value that or even tolerate that?

Rachael Kinsella: Yes.

Paul Nunes: And that fear, and I think we’ve talked about in the past, that fear of like, well, “I’d like to, but if it doesn’t perform as well, my customer’s going to go elsewhere.”

Rachael Kinsella: Yes.

Paul Nunes: And I know even coffee cups, you look at how often have we reinvented coffee cups? I’m trying to think of simple examples, right? But it’s like, well, coffee cups have to perform by keeping coffee warm. And so, we’ve gone from Styrofoam to, well, the cup gets hot, well, we put little paper things around it, corrugated cardboard, but that’s not great. So, then we do a two-layer cup. But we’re forever trying to fit performance. And consumers, we find are, unfortunately, not as behaviourally responsive as they are emotionally responsive, shall we say, something we call in marketing revealed preference, which is, “Hey, I’m gonna tell you, I am never going to drink out of a Styrofoam cup.” But then they go to a store, and that’s all they have – the Styrofoam cups – and they take the Styrofoam cup, or they say, “Ouch, I burned my hand, give me the Styrofoam.” So, we have to be able to communicate the value proposition, and the importance of it across the value chain, not just our own company.

Rachael Kinsella: Yeah, absolutely. Because otherwise, you’re gonna have those behavioural factors, you’re going to have the cost implications, or the potential perceived cost implications, when actually it would be better value to do it in a more sustainable way. So, it’s communicating that, as you say, and educating throughout the supply chain and, and also educating customers on why makes sense, both financially and from a product perspective. So, there’s so many different elements to consider there.

And I think it’s going to be become more and more important for competitiveness as well, across different organizations, being front runners in sustainability, or actually promoting those sustainable supply chains that we’ve discussed, or being able to demonstrate sustainable and socially responsible behaviours.

I’d be interested in your thoughts on how you feel that will impact competitiveness across organizations. And also, it ties in very well with that scientific mindset we were talking about, on how that will affect competitiveness, who are going to be the front runners, who are going to be the ones who are embracing the scientific mindset and the technology in order to enable all of these different initiatives?

Paul Nunes: Well, one of the ways we look at that, in competitiveness is sort of twofold. One is, as consumers become more demanding of sustainable solutions, are you going to be at the forefront of that and available to do that? And then one can think even across industries is becoming sort of we believe in, we’re starting to see some amount of scaling of those winners because if everybody wants, say the plastic replacement cup, or whatever, once that becomes a social imperative, well there may only be one company that can do that, and that should be yours. [Laughs]

So, there’s the whole idea of getting ahead of that curve and being prepared. In some of our old research is this idea of a Big Bang disruption. What happens in the world of perfect information or near perfect information is consumers start to move as a herd, as a group, much more quickly and at a larger scale than in the past. So, customers might like this, but now that you’ve got that, they all move at once, because everybody wants better, cheaper and faster and with technology, you can do all three at the same time. So that’s the big insight that we had in our book, Big Bang Disruption. But that we’re looking for and what I’m excited about is when Big Bang disruption starts to meet sustainability, which says, look, it will simply become so socially unacceptable to do certain behaviours or buy certain things, that we’re going to see huge shifts happening very quickly. And that’s what companies have to be both on top of, so they can capture the opportunity, but aware of in the sense that, you know, if you were the Styrofoam cup maker for, you know, a large national coffee company, almost overnight, right, the orders are cancelled, we’re going away from Styrofoam, you know, and if that’s your business, sorry. So, there’s definitely a lot of opportunity for companies, but they need to be seeing that future and that takes us all the way back to the, you know, learning from the future and predicting the future. The past is not going to tell us anything about sustainable attribute demand.

Rachael Kinsella: Yeah.

Paul Nunes: It’s just not a case of, so, there are certain things that inference engines can tell us about, that we can learn about customers, but we’re not going to learn about those kinds of things, we have to be forward-thinking.

Rachael Kinsella: Absolutely. And that brings us really nicely back full circle almost, to talking about the need to pivot because as you mentioned, if they’re making the Styrofoam cups, and there’s no longer demand for them, and all the orders are cancelled overnight, well, they need to pivot. But at that point, it’s too late. So yeah, how companies can predict when they need to pivot. And in one of the books that you’ve co-written “Pivot to the Future”, you talk about the strategic approach to business value creation, which you refer to as the wise pivot. I’m really interested to hear more about this and why you believe it’s the only solution to continuous and potentially devastating change of that nature. within organizations, and indeed, looking from a sustainability perspective.

Paul Nunes: Yeah, the key insight to Pivot to the Future, and there are a couple of them. And the first one starts with the insight that every business is actually, for most companies, three businesses. And I don’t mean portfolio, but what I mean is in the business, say you’re in soft drinks, there’s yesterday’s products, today’s products and tomorrow’s products: the old, the now and the new. And traditionally, by every textbook and business school training, the idea has been, you get out of the old when it matures, and you can no longer make an abnormal return. You exploit the now, you milk the cash cow, and then you get to the future as fast as you can. What our research in the book has found is that approach doesn’t work anymore, if it ever did. Particularly with technology today, the old can be revived to last longer, and the old is necessary because the old actually fuels the now and the new.

So, a great example, there’s one that we use in the book is beer, AB InBev. Budweiser could be considered a mass-market beer that was losing profitability. It was an old product. And by traditional Peter Drucker thinking, when the margins go away, when it’s commoditized, you get rid of those capabilities, those assets and you let somebody else do something better with them.

What AB InBev did actually is they reinvested in new technologies that dramatically lowered the cost, they used zero-based budgeting and brought the costs of mass-market beer down tremendously so they could compete with the new microbrews and in fact could create a lower cost to serve and generated so much new money out of that, that they had the ability to buy 26 new microbrews that then got folded into their portfolio. And part of it was the technology. So old businesses, and we have lots of examples, that’s just one, of old businesses that had been prematurely thrown away because companies were using bad logic. The now, is the idea that, you know, we overturn the idea of cash cows, milk the cash cow. Well, in short, there is no such thing as a cash cow anymore, because the technology changes so quickly, that you need constant reinvention, you can’t rest on your laurels and hope to, you know, cut all your investment spending, your innovation spending, say, I’m going to save that, and I’m going to put that to the new. And that’s the example we use, there’s Google, which makes enormous investments still in search engine. Because it recognizes that you know, there are, there are competitors out there, Microsoft’s Bing, for example, is still a very viable and strong option for those and so the competitiveness and the innovation in that business has never gone away.

So, there is no milking the cash cow. You have to use technology and be thinking about how technology is going to preserve that business in new tradition ways. And then there’s the new and in our take on the new and the pivoting to the new is counterintuitive as well, in that you don’t just automatically go to the new, you need to pivot to the new with the intent of scale profitability. The example I love to use there is Tesla versus Prius. So, you think about the Toyota Prius, and how long did it take before it was created before it was profitable before it was as profitable as a gas car. That was four years, two years and four years or something like that, basically, in seven years, it was as profitable – they went from being nothing, the dream of somebody’s mind, to being an actual product that was as profitable as what they call an ice: an internal combustion engine car. But you look at companies like Tesla, and others in the electric car market, right. But Tesla’s been at it for 17 years, 2003 18 years, you know, just beginning to get to the edges. Now, sometimes things take longer, it’s not a knock on Tesla. But it’s the idea of the necessity of profitability and scale can often drive companies to do better decision making and profitable decision making, if they wanted needed, as opposed to simply getting to the future, and not really knowing what to do there, because the future is unprofitable. And we have lots more examples in the book. So that’s what we talked about. It’s a three-unit pivot, it’s pivoting on the past, pivoting on the today, and pivoting through technology with different kinds of future.

Rachael Kinsella: Alright, I think that’s the ideal point to leave our listeners with, in how to pivot successfully. The wise pivot, and it’s all about thinking about things differently. It’s adopting this different mindset and not just going along with the past, because that’s the way it’s always been done, or you know, those obstacles have always been there. Finding different viewpoints, and then different ways to innovate and then to pivot, which I think is so useful and really fascinating. I’ve read the book, so I recommend it to anyone who’s listening. [Laughs].

Paul, are there any final points that you’d like to leave us with? We’ve covered a lot of ground, I’m aware. Really interested to hear from you today and your vast experience, and all the exciting projects and research that you’re involved in at the moment. Are there any key takeaways that you would like to emphasize before we finish?

Paul Nunes: No, we’ve had a great discussion, I really enjoyed it. So, thank you, Rachael, I think we’ve covered a lot of points. I can only encourage people to continue to read and look and see. I’ve been assessing the future of technology, almost literally, for 35 years now, when I was at the technology assessment group at Accenture and all through the think tanks and the research and writing I’ve done and I can tell you I’m as excited today as I was 30 years ago about what technology is promising and the new things to come out. So, you would think, I would think after 30 years I’d seen it all. Maybe be kind of over it. But the future’s as bright today, for a better technology enabled world, as it was in the last 30 years, and that’s really exciting to me.

Rachael Kinsella: That is really exciting and your enthusiasm shines through. So, it’s really good to see and I hope we can spread some of that enthusiasm and positive thinking about the future of a technology enabled world to everyone who’s listening. Thank you so much, Paul. It’s been a real delight talking with you today. And I hope you’ve enjoyed it. And we’ll be in touch again soon, hopefully to talk some more about some other topics and potentially create some content together.

Paul Nunes: Hope so. Thank you, Rachael it’s been great.

Rachael Kinsella: Thank you, Paul. Really appreciate it.