5 big questions on innovation

This technological and business model transformation we are living through actually requires companies to innovate differently. The set of innovation practices companies could rely on to stay in the game – even recently – are no longer sufficient. In the past, and even the most recent past, the main philosophy was that innovation is a science – that you can go through a number of predictable steps: create new ideas; decide which ones to invest in; have an efficient development engine that gets those ideas to market; and manage your portfolio in the marketplace.

What’s happening now is that the level of disruption is such – and the level of fluidity is such – that this is no longer enough: you now need to plug into the ecosystem of these new technologies, and monitor your broader role in the marketplace. I explain it to companies this way, especially for larger enterprises: you need to play the long game fast.

As we know, the capabilities deployed from new technologies allow you to do amazing things. But out of those 50 amazing things you can do, we assess which of those will actually deliver the most value within your timeframe, and what you need to do within that timeframe to take maximum advantage. Some of these technologies, and corresponding opportunities, are mature enough that you actually need to jump on the bandwagon and start developing a product right away, or start implementing a feature. Some technologies (and business models) are farther out, and therefore you shouldn’t do that – but you do need to engage with the ecosystem and monitor the development of those technologies so you are in the flow, and are not left behind.

Whether you look at IoT, or AI, or Big Data, or any one of those fundamental tech shifts – and even the business model shifts – we see that they are affecting the majority of the industries in a way those industries are not used to. Companies are either seeing real benefits or are suffering.

Take IoT, for example. If you think about being able to source granular data in real time, process it and effect change back in the physical environment, you can think about all kinds of examples of solutions. Look at LA, where drivers lose millions of hours a year just looking for a parking space.

If I can instrument my parking garages so that they can show the absence and presence of cars in specific parking spaces, and if I can couple it with positioning sensors in the cars so I can understand where they are going, I can actually create predictive models where I can direct drivers to the places which most likely have open spaces at the time of their arrival. I can even predict when a space will likely become available, or at least predict turnover on average stays. I’ll know that 15 cars are leaving every 5 minutes. If I install sensors in various places in this whole use case – then think of it as a use case with multiple players – parking garage; traffic controls; individual drivers. I can actually collect and connect that information to optimize the whole use case and deliver value to multiple players at the same time.

At a strategic level, we’re helping companies figure out how technologies like IoT present a potential for disruption: in some cases, fairly minor, which can be dealt with in the normal course of business, but in other cases, the disruption will be fundamental. And the challenge is not just that they will be fundamental, the challenge is that they will sneak up on you. These start as an interesting curiosity, but before you know it, you are behind the curve.

Industries with significant capital assets like oil & gas are not going to be displaced by the next Internet company, but they can be materially affected from a competitiveness point of view. If some competitors start instrumenting their assets and increasing their asset utilizations – e.g., predictive maintenance based on predicted wear – that will be a competitive game changer that will demand a response.

Let me give you another example. Let’s say you have a logistics company with warehouses, which you might think is not a very cutting edge business. You will find that as simple a thing as a garage door can be the focus point for a significant opportunity. Residential garage doors in the U.S. have some level of automation at around 80%, but only about 30% of commercial doors are instrumented in any way. By 'simply' installing automation and the sensors on the garage door, you can significantly reduce energy consumption, because you’re otherwise losing all that heat or AC to the environment – and who thinks about keeping the door closed? There is nobody in charge of closing the door within 10 seconds of a truck coming in. Then you go a step beyond that – with algorithms that allow me optimize goods and truck flow through those doors, and that creates additional opportunities from the same technological solution.

At A.T. Kearney, when it comes to innovation and disruption our differentiators are three-fold.

First, we have a very deep understanding and knowledge of the industries in which we operate. So, for a project we did recently for an insurance company, we brought our financial services group and our high-tech group together, both to the pitch and the project.

Also, because many of us here are engineers or technologists by training, we come into all of that with a very pragmatic lens. Its very easy to get completely absorbed into the hype: “Okay, robots and Big Data are taking over the world; AI is the next best thing since sliced bread; IoT is nirvana.“ And to present big messages and great forecasts.  But where it becomes valuable for our clients is our ability to actually translate that into what it means for their business, now and in the medium and long term, and how they need to respond to it in very pragmatic terms. Very few companies have unlimited money – therefore, while you can have the best strategy, you still need to be able to make the right choices.

A third differentiator is that we are truly a single global partnership; there is no friction in bringing together our capabilities in the Silicon Valley and in Korea or Germany. Many of our competitors are regionally or even locally structured, so they have a more difficult time in bringing a global perspective to bear. What we can help companies in a very collaborative, open way, is to really drive to a better set of choices that will allow them to do what they need to be successful in this ever-changing landscape.

Beyond the right choices, companies are faced with the need to incubate things that have a much longer gestation period. If you are an automotive insurance company, for instance, instrumenting all the cars in order to take all their information will change your business model, from a statistical, actuarial model to an individual use model. But you’re not going to instrument old cars en masse, only new cars, and you’ll need to achieve fleet thresholds  – so its a 5 or 7-year horizon. Look at what’s happening at Intel – the pre-eminent chip company is laying off 12,000 people, and its because they were not fast enough; not agile within the ecosystem when it came to mobile. With the new technologies, that agility will be even more important.

I have looked at how innovation has evolved, going way back. In the Middle Ages innovation was mostly considered magic, and the winner became a tribal chief and the loser possibly was burned at the stake. In later centuries it was then driven as art, and it evolved to a more or less a science by the end of the 20^th century. We are in transition right now, and it is now fundamentally culture driven – a culture that requires rapid experimentation and constant involvement in the ecosystem.

Even the recent past, when you were innovative, your prize was your position of dominance in the market.  What you have now as a prize is simply an opportunity to innovate again; that’s all you actually get. Just science isn’t enough; you actually have to change the DNA of the company to innovate differently.

Different industries will move at different speeds. The number of technologies that are now available for a broader set of industries and how those technologies interplay with each other is fundamentally changing the business environment.

Before, you had communications and computing at certain speeds, and sensors at a certain cost – when you put them together, each of those things was useful, but they were not reinforcing each other. Now they are all at the level of cost/performance where you can put them together in a way that creates completely novel outcomes. They are no longer additive; they are multiplicative.

In a major presentation to executives in Silicon Valley last year, entitled “Rethinking the 'How' of enterprise innovation,” Blanter listed some key obstacles to innovation within large organizations, including:

• Stable – and somewhat frozen – business models;
• A complex regulatory environment, and resulting risk aversion;
• Culture of large organizations – with its premium on current performance and business stability;
• Legacy infrastructure – and significant costs to re--platform.

He added: “Addressing these challenges one-by-one is a losing proposition. A change in DNA is required.”

Blanter told BPI:

“The incumbency curse is real. And old innovation methods will deliver old innovation results." Organizational inertia is a real challenge; every large enterprise is struggling with the notion of delivering value today, with adapting and adjusting to delivering value tomorrow. And the metrics are built for the very short term.

Playing the long game fast. The majority of innovations, when they are just starting out, are not material enough to a big company to matter. Therefore, one of the biggest challenges to innovation is to figure out how to stay the course with these small but very new projects, while at the same time executing on the business plan for revenue. You simply don’t know which of the twenty small projects are going to be the next great thing.

Look at Amazon – one reason they are so successful is that, for many years, they told a story to the street that they were not expecting to be profitable in near future. That allowed them to plough money back into the business – like echo device or cloud services. Most regular companies would never have had the foresight to do that.

Culture isn’t an objective; it's a means to something bigger – growth or profitability or competitive advantage. We don’t want to destroy value – there are countless examples of new CEOs turning companies upside down just for the sake of change, and destroying value.

But many large enterprises are clearly not evolving fast enough, with average tenure of companies in the S&P500 dropping by 80%. So we look at the culture through the lens of the journey the company has taken; where the CEO and the executive team wants to take the firm, and what things need to change, and for what purpose.

To measure innovation the right way, you are actually introducing a level of discomfort into the organization, because that’s a mechanism to change culture. You can’t just delegate innovation to people in the corner, you actually need to change the culture – and that is probably the most difficult thing to do; certainly, more difficult than changing strategy. You want to amplify the right messages and behaviors, and to create barriers for the wrong ones. One interesting metric for innovation is revenue from new products. But what is a new product? – that’s actually not a trivial determination.

Removing organizational barriers and silos, and all the normal problems enterprises grow up to have – difficulty in collaborating across regions; difficulties in taking risk; difficulty in connecting engineering with manufacturing; difficulty in driving revenue from new products; it is all a challenge for changing culture.

On metrics: We worked with a semiconductor equipment company, whose main business is equipment, but they also have a significant services and spares businesses. We found that the innovation measure for their equipment business needed to be very different from their innovation measure for their services business.

Conversations with our clients start at the business problem level – some of those are actually big enough to require culture change.  Within Kearney itself, we try very hard to practice what we preach in terms of an innovative culture. We have put in place mechanisms for collecting ideas; we have a global innovator competition, where winners get real resources to implement those ideas. We look across our client portfolio, and look at which project was most innovative, and what lessons can be learned from that.

But we found that the infectious impetus of personal passion can also drive culture change. We do a lot of work in the digital space, but we started with a number of partners and staff who were simply passionate about innovations in that space, personally, and that had its own momentum. 

The transition we are in is being triggered by a collection of new technologies, not a single one. But, okay, take 3-D printing.  Some people believe that this will disrupt manufacturing overall in the near term. Yes, it will materially impact how manufacturing is done, and, yes, it will allow people to make things locally, versus globally. But I don’t think it will change manufacturing in the near term; the installed base is just too great.

That said, I would argue that the impact of 3-D printing will be much greater on design: it will allow design engineers to design parts that could not be made now, and especially in designing simpler, more reliable products with fewer parts. Its value is disruptive in nature in the short term, rather than a huge monetary impact. Cars are not going to be 3-D printed in 3 or 5 years, but the design of cars will start changing because of it.

Big data and analytics will bring a level of insight that was never possible before, for a broad range of industries, and in ways those companies can start taking advantage of it in more explicit ways. I think the number and variety if business models companies will be able to deploy will increase significantly. Insurance is a good example – when companies start to charge on actual use, rather than on statistical models, that’s a very different business model, leading to a very different relationship with the customer.

If I look at large industrial equipment: at the moment I make it, I sell it; you pay me to use it. What if, now, I can instrument that equipment to better manage it, so that I can guarantee things to you that I could not guarantee before?

The Nest Thermostat started as a replacement for individual consumers to regulate their private heating and cooling. Yet it is emerging as a remarkable tool for power utilities – because it has information from individual homes, the company can go to the power utility and say: “in this part of the grid, by making very minor automatic adjustment to the temperature, we can reduce your peak load requirements, so you don’t have to bring additional power plants online, or build them.”

Business models based on a combination of integrated technologies – that’s the game of the future. Its not jumping on the bandwagon of a single technology, partly because none will deploy fast enough, especially in the industrial space. A lot of companies underestimate this potential.

The whole proliferation of sharing business models, enabled by technology, is something no one saw coming at this level.  You have a set of underutilized assets – like homes or cars – and what is fundamental about this is that a set of technologies and business models increase utilization of physical assets, and that is profound. Because if you can do it with cars and homes, you can do it with anything.

The whole notion of AI is exciting, in terms of the convergence of technologies. Look at what Kaiser is doing in Healthcare; they are at at forefront making patient records not only electronic, but minable.

With a good enough data structure, you can start to look at prevention, not just  treatment. A patient was recently diagnosed in an emergency room by referring to data from the person’s FitBit.

Novel ways to use technologies; that’s what is exciting for me.