[Photograph: Erik Roth, Director at McKinsey & Co]
Erik Roth heads the global innovation practice at McKinsey & Co and is based in their Shanghai office. He is the firm’s leading expert on matters related to innovation strategy, business building, idea generation, portfolio and pipeline management, open innovation, R&D organization and footprint optimization. He serves as advisor to numerous CEOs and senior executives on innovation and R&D related topics. Roth’s work spans the globe, with significant projects across the US, Europe and in Asia for both Asia-based companies as well as leading multi-national corporations. In 2004, along with Clayton Christensen and Scott Anthony, he co-authored Seeing What’s Next: Using the Theories of Innovation to Predict Industry Change. Prior to McKinsey, he was a core member of the team at GM that created OnStar and the Chief Innovation Officer at LG. Erik holds a MBA from the Harvard Business School.
Roth spoke to Indrajit Gupta on the sidelines of a Nasscom summit on technology in New Delhi earlier this month.
Edited excerpts from a two-part interview:
Q. What’s changed about what we know about innovation since the time you wrote Seeing What’s Next with Clayton Christensen back in 2004?
A. Seeing What’s Next was very much an extension of the disruption theory, of what causes disruption. And that’s today well-known. What we were trying to figure out was whether the theories have predictive value. And while [we] haven’t totally back-tested it, most of what we suggested has come through. One industry we got slightly off was education. We under-estimated the political complexity. We thought it would shift a lot faster. Healthcare was in the same direction. There were a lot of factors that restrain the pace of change. Directionally, it felt more or less right. But at its core, it was about small companies attacking large ones. And since that experience, I’ve had the opportunity to work with a lot of large companies, who are acting small. The unique set of experiences that I have had have been working with and working inside large companies going through this transition.
First, it may be important for me to first define what I see as innovation. Innovation, for me, is about products, services, processes and business models. It is all of those things. And any organisation’s ability to think holistically about all of them, rather than just about products. And that’s what has changed: that message has started to come out. And certainly today, digital business models get a lot more time and attention than back then when it was largely about innovation product.
Also, innovation has to be sustainable over time. Any company can have one-off wins, but the real challenge is how do you do it on a continual basis? And the third element that is tied to that is how do you incrementally create value? This is where the differentiation between invention and innovation lies. Invention is about creating technology, but if you can commercialise that at scale and over time, that is the Holy Grail. So when I think about innovation, I think of that lens. And you can argue that a large company should have an advantage doing that. After all, it has the resources, the investment potential, and breadth. But for a lot of the reasons that have been well-studied before, it struggles.
We recently interviewed CEOs around the world, and we asked them this question: is innovation important? As many as 85% of CEOs said it was top of their agenda. What is even more interesting is that almost 80% of them admitted they were concerned that their business models were at risk. So they connected the idea that innovation and business model disruption are related. This is different from what it was ten years ago.
We then asked them: how well prepared are you? Only 6% said they had the capability required to survive. That’s shocking. Only 23% said they were directly involved in making this change happen. Which is also interesting given what it tells us: that it is a top CEO level issue, there is no real good solution, but only a quarter of CEOs are taking action. Our interpretation is that they don’t know what to do. Also, based on the hundreds of conversations I’ve had with CEOs, I know this to be true. They really don’t know what to do. Innovation is a cross-functional, multi-disciplinary, very squishy problem, which no one really owns. So a couple of years ago, especially after my experience at LG, driving a large-scale transformation, there is no playbook.
Innovation is a cross-functional, multi-disciplinary, very squishy problem, which no one really owns.
Look at the lists of the most innovative companies in the world, the same companies rise to the top. However, these companies innovate in fundamentally different ways. Google, Amazon and Apple, usually on top of most lists, have wholly different models. Google is a serial acquirer. Apple is fully integrated and Amazon is customer-centric, but it does a lot of incubation, small-scale experiments. What makes these companies successful? Why do they rise to the top? And what lessons exist? In the last few years, we’ve done a study where we interviewed over 3,000 executives and studied 500 companies looking inside out. The thing with the media lists is that they are outside in. If you look inside out, what are the markers of a successful innovator? Our article The Eight Essentials of Innovation is a high-level summary of what we found.
At a high level, none of what we found is surprising. But at a granular level, underneath those insights, there are nearly 120 practices we identified that are crucially important, things that do make a difference. We talk about eight essentials: aspire, choose, discover, involve, extend, accelerate, scale and mobilise. And each one has a serious question which is difficult to answer, yet is seemingly intuitive.
Let me take one: Is innovation absolutely critical to future growth? The term ‘absolutely critical’ is the most important part of the question. We then translate that down to: How do we know whether or not it is absolutely critical inside your company? Is there a clear objective for inspirational goals? And this is where most CEOs tend to fail: the CEO might get very excited about innovation, talk about it, have the words to describe it, but it doesn’t go a lot further than that.
Then we ask a couple of more questions about your innovation objectives. How clear is it? If your aspiration is translated down to the average employee who is supposed to do something different, do they know what they’re supposed to do differently tomorrow? More often than not, the connections between the aspiration and what needs to be done differently to get to that, is missing. If you have an aspiration, you’ve got to ensure that there is a clear end-point, clearly articulated and measurable. An aspiration which doesn’t have an end point and isn’t measurable, which can be translated into a bunch of KPIs that can be cascaded down the organisation, you’ve already hurt your chances of success. I particularly like how [Procter & Gamble CEO] AG Lafley articulated his aspiration at P&G. He said three things: consumer is king, the next billion consumers and 50% of innovation should come from external sources. I am sure he spent a lot of time thinking about that in crafting aspirations that focus on what we need to do now—but also what it should be like when we get there.
The connections between the aspiration and what needs to be done differently to get to that, is missing.
Most often, I believe innovation is more a resource allocation problem rather than an idea problem. Ideas are nice and important, but honestly, I have never seen a company without ideas. They may not know which ones are good ones and which ones are bad ones, but they’re there. And the key question is whether you can mobilise resources effectively to reallocate time, dollars and people to go develop them. The trouble is that in so many companies, the best predictor of next year’s allocation is this year’s allocation, within roughly 5%. In a world where budgets don’t move, organisations don’t move, the key point is to ensure that innovation is designed into the strategic processes.
Innovation is more a resource allocation problem rather than an idea problem.
Q. If we agree that bottlenecks are usually at the top of the bottle, how do we look at the specific leadership challenges involved in innovation?
A. I like to do calendar analysis to see where do leaders spend their time. How many hours or days in a month are spent on innovation? Is the company even set up for success? Have they imagined being set up for success? Have they got themselves into the mind-set to perform? We now understand what drives innovation at a much deeper level. And particularly for a large company, what can be done to innovate at scale. It doesn’t mean that your journey will be easy. It is a minimum two year journey. But if we can apply the science we now know around innovation and demystify it, with the agility that organisations need to succeed in innovation, particularly, in a disruptive world, the prognosis for large companies is a lot better than it was ten years ago, when they were given up as dead from disruption.
Q. If we agree that only business models die, and not businesses, are there ways to help prevent companies from dying?
A. It is true that business models disrupt, not businesses. Companies that die, their business models die. There could be a lot of reasons why companies die. But the ones that are truly well-managed and making the right kind of decisions, they are pursuing their success and that success takes them to a place that is no longer the primary lever of value creation, they were myopic or they didn’t realise the business model. That’s the classic disruption model.
That said, I think there is hope for a lot of companies. We did this study earlier on just how many business models are out there. There are fewer out there than what most people would like to believe. Disruption happens in waves. And when a business model comes to the forefront in a given industry, it tends to get re-applied many, many times over. For instance, is Netflix a new business model? No. Companies that don’t have the ability to do pattern recognition are the ones that are at most risk. Companies that are able to see change and recognise the implications, it is probably not the only time it has occurred, are the ones that have a greater chance of success. Of course, they have to have the agility to reallocate resources to co-opt, combat or move. But you have to pattern recognise first to see what’s happening and understand the likely implications.
When a business model comes to the forefront in a given industry, it tends to get re-applied many, many times over
Q. Very often, much of the disruption is caused by cross-industry competitors, rather than direct industry players. The new payment banks are a good example. Many of them aren’t traditional banks. How does that impact the innovation process inside a company?
A. Technology has made it easier. Banking is a good example. The cost of software has gone down. So has the cost of processes. The ability for someone with a strong customer relationship around transactions allows them to become a bank, almost overnight, thanks largely to IT. That’s a unique scenario. That type of scenario won’t be common across all industries. Take P&G. Are they susceptible to disruptors nibbling off bits of its business? Some of them are technology enabled. But it is more around a fundamental disruption of the value chain, rather than just the technology per se. There are some players who are coming direct to consumer. Is that new? I think not. Technology has enabled a better ability to reach that consumer. The same is true for the distribution infrastructure. Some people say that the web has transformed the landscape. But getting something from the point of production to the person’s home is real tough. In India, that’s one of the greatest challenges here. I live in China and they’re rapidly solving that problem, but it isn’t completely solved. You need the whole operating model. So more modular the operating system is, the more likely it will be [for] more new entrants to change the dynamics of the supply chain.
But it isn’t just technology. Everyone gets hung up on disruptive technologies. Technologies do not disrupt, business models disrupt. It is about the commercialisation and application of the technology that makes all the difference. I have seen some mind-bending technologies in the labs, which have never seen the light of day. I can tell you about a technology in the heating space. I took the entire team at LG to Israel to demo this technology. And they were blown away. It was a game-changing technology, but has never been commercialised because there was never a business model around it that made sense. And it has been five years.
It is about the commercialisation and application of the technology that makes all the difference.
Q. It would seem as if there are two different approaches to innovation: one that is led by large amounts of behavioural data that companies have access to. And two, the traditional qualitative methods of understanding consumers. Will these two worlds coalesce?
A. Successful innovation lies at the centre of three circles: one, a valuable consumer problem solved; two, the technology that enables the solution, and three, a business model that monetises it. The ability of a company to define a valuable customer problem to solve, with the advent of Big Data and data mining, should be a lot better. I don’t think it will be either or. I’ve been doing a lot of work on Big Data recently. And I’ve [been] getting increasingly involved in machine learning. So the ability for machine data to predict what’s going to happen will get a lot better as large amounts of behavioural data gets acquired, which will allow for better predictive outcomes. In the current state, that will not replace the historical, qualitative research techniques because most data still sits in an unstructured environment. And a lot needs to be done to refine the ability to collect right pieces of data.
In large companies, it is often not customer data, behavioural data or consumer data. It is operations data, like time stamps. It is usually data required to operate the business, not learn from it. So there needs to be a meta-layer that has to be applied to that data in most cases that has dynamic variables that allows for understanding of the data. If you have a bunch of data with time stamps, it doesn’t tell you anything. What you want to know is the distance between the time stamps, how many time stamps there are, which will tell you about behaviour and the interaction with your business. That’s not there in the data set. That has to be built on top of the data. So the art of data sciences, or creating the meta-layers, that allow for the interpretation of the data, is going to take some time. As the unstructured data grows, there will be greater need to structure it, test it and learn from it. Most companies’ metabolic rate in testing isn’t high enough. The qualitative techniques are going to be needed, at least in the short term to interpret, until data science catches up to allow for marketers or product developers to understand what actions they are to take based on the insights. That said, the probability that machine learning and data science will inform more heavily what kind of products and services get created is very high. And we are already starting to see evidence of that.
Next and concluding part: The role of Design Thinking, the role of the chief innovation officer, the role of the mobile phone in digital disruption, what it takes to innovate in India and China.
