Vishal Sikka: Why AI Needs a Broader, More Realistic Approach 02-16

The concept of artificial intelligence (AI), or the ability of machines to perform tasks that typically require human-like understanding, has been around for more than 60 years. But the buzz around AI now is louder and shriller than ever. With the computing power of machines increasing exponentially and staggering amounts of data available, AI seems to be on the brink of revolutionizing various industries and, indeed, the way we lead our lives.

Vishal Sikka until last summer was the CEO of Infosys, an Indian information technology services firm, and before that a member of the executive board at SAP, a German software firm, where he led all products and drove innovation for the firm. India Today magazine named him among the top 50 most powerful Indians in 2017. Sikka is now working on his next venture exploring the breakthroughs that AI can bring and ways in which AI can help elevate humanity.

Sikka says he is passionate about building technology that amplifies human potential. He expects that the current wave of AI will “produce a tremendous number of applications and have a huge impact.” He also believes that this “hype cycle will die” and “make way for a more thoughtful, broader approach.”

In a conversation with Knowledge@Wharton, Sikka, who describes himself as a “lifelong student of AI,” discusses the current hype around AI, the bottlenecks it faces, and other nuances.

Knowledge@Wharton: Artificial intelligence (AI) has been around for more than 60 years. Why has interest in the field picked up in the last few years?

 Vishal Sikka: I have been a lifelong student of AI. I met [AI pioneer and cognitive scientist] Marvin Minsky when I was about 20 years old. I’ve been studying this field ever since. I did my Ph.D. in AI. John McCarthy, the father of AI, was the head of my qualifying exam committee.

The field of AI goes back to 1956 when John, Marvin, Allen Newell, Herbert Simon and a few others organized a summer workshop at Dartmouth. John came up with the name “AI” and Marvin gave its first definition. Over the first 50 years, there were hills and valleys in the AI journey. The progress was multifaceted. It was multidimensional. Marvin wrote a wonderful book in 1986 called The Society of Mind. What has happened in the last 10 years, especially since 2012, is that there has been a tremendous interest in one particular set of techniques. These are based on what are called “deep neural networks.”

Neural networks themselves have been around for a long time. In fact, Marvin’s thesis was on a part of neural networks in the early 1950s. But in the last 20 years or so, these neural network-based techniques have become extraordinarily popular and powerful for a couple of reasons.
First, if I can step back for a second, the idea of neural networks is that you create a network that resembles the human or the biological neural networks.

This idea has been around for more than 70 years. However, in 1986 a breakthrough happened thanks to a professor in Canada, Geoff Hinton. His technique of backpropagation (a supervised learning method used to train neural networks by adjusting the weights and the biases of each neuron) created a lot of excitement, and a great book, Parallel Distributed Processing, by David Rumelhart and James McClelland, together with Hinton, moved the field of neural net-related “connectionist” AI forward. But still, back then, AI was quite multifaceted.

Second, in the last five years, one of Hinton’s groups invented a technique called “deep learning” or “deep neural networks.” There isn’t anything particularly deep about it other than the fact that the networks have many layers, and they are massive. This has happened because of two things. One, computers have become extraordinarily powerful. With Moore’s law, every two years, more or less, we have seen doubling of price performance in computing. Those effects are becoming dramatic and much more visible now. Computers today are tens of thousands of times more powerful than they were when I first worked on neural networks in the early 1990s.

“The hype we see around AI today will pass and make way for a more thoughtful and realistic approach.”

The second thing is that big cloud companies like Google, Facebook, Alibaba, Baidu and others have massive amounts of data, absolutely staggering amounts of data, that they can use to train neural networks. The combination of deep learning, together with these two phenomena, has created this new hype cycle, this new interest in AI.

But AI has seen many hype cycles over the last six decades. This time around, there is a lot of excitement, but the progress is still very narrow and asymmetric. It’s not multifaceted. My feeling is that this hype cycle will produce great applications and have a big impact and wonderful things will be done. But this hype cycle will die and a few years later another hype cycle will come along, and then we’ll have more breakthroughs around broader kinds of AI and more general approaches. The hype we see around AI today will pass and make way for a more thoughtful and realistic approach.

Knowledge@Wharton: What do you see as the most significant breakthroughs in AI? How far along are we in AI development?

Sikka: If you look at the success of deep neural networks or of reinforcement learning, we have produced some amazing applications. My friend [and computer science professor] Stuart Russell characterizes these as “one-second tasks.” These are tasks that people can perform in one second. For instance, identifying a cat in an image, checking if there’s an obstacle on the road, confirming if the information in a credit or loan application is correct, and so on.

With the advances in techniques — the neural network-based techniques, the reinforcement learning techniques — as well as the advances in computing and the availability of large amounts of data, computers can already do many one-second tasks better than people. We get alarmed by this because AI systems are superseding human behavior even in sophisticated jobs like radiology or legal — jobs that we typically associate with large amounts of human training. But I don’t see it as alarming at all. It will have an impact in different ways on the workforce, but I see that as a kind of great awakening.

But, to answer your question, we already have the ability to apply these techniques and build applications where a system can learn to conduct tasks in a well-defined domain. When you think about the enterprise in the business world, these applications will have tremendous impact and value.

Knowledge@Wharton: In one of your talks, you referred to new ways that fraud could be detected by using AI. Could you explain that?

Sikka: You find fraud by connecting the dots across many dimensions. Already we can build systems that can identify fraud far better than people by themselves can. Depending on the risk tolerance of the enterprise, these systems can either assist senior people whose judgment ultimately prevails, or, the systems just take over the task. Either way, fraud detection is a great example of the kinds of things that we can do with reinforcement learning, with deep neural networks, and so on.

Another example is anything that requires visual identification. For instance, looking at pictures and identifying damages, or identifying intrusions. In the medical domain, it could be looking at radiology, looking at skin cancer identifications, things like that. There are some amazing examples of systems that have done way better than people at many of these tasks. Other examples include security surveillance, or analyzing damage for insurance companies, or conducting specific tasks like processing loans, job applications or account openings. All these are areas where we can apply these techniques. Of course, these applications still have to be built. We are in the early stages of building these kinds of applications, but the technology is already there, in these narrow domains, to have a great impact.

Knowledge@Wharton: What do you expect will be the most significant trends in AI technology and fundamental research in the next 10 years? What will drive these developments?

Sikka: It is human nature to continue what has worked, so lots of money is flowing into ongoing aspects of AI. From chips, in addition to NVidia, Intel, Qualcomm etc., Google, Huawei and others are building their own AI processors and many startups are as well, and all this is becoming available in cloud platforms.  There is tons of work happening in incrementally advancing the core software technologies that sit on top of this infrastructure, like TensorFlow, Caffe, etc., which are still in the early stages of maturity. And this will of course continue.

But beyond this, my sense is that there are going to be three different fronts of development. One will be in building applications of these technologies. There is going to be a massive set of opportunities around bringing different applications in different domains to the businesses and to consumers, to help improve things. We are still woefully early on this front. That is going to be one big thing that will happen in the next five to 10 years. We will see applications in all kinds of areas, and there will be application-oriented breakthroughs.

“The development of AI is asymmetric.”

Two, from a technology perspective, there will be a realization that while the technology that we have currently is exciting, there is still a long way to go in building more sophisticated behavior, building more general behavior. We are nowhere close to building what Marvin [Minsky] called the “society of mind.” In 1991, he said in a paper that these symbolic techniques will come together with the connectionist techniques, and we would see the benefits of both. That has not happened yet.
John [McCarthy] used to say that machine learning systems should understand the reality behind the appearance, not just the appearance.

I expect that more general kinds of techniques will be developed and we will see progress towards more ensemble approaches, broader, more resilient, more general-purpose approaches. My own Ph.D. thesis was along these lines, on integrating many specialists/narrow experts into a symbolic general-purpose reasoning system. I am thinking about and working on these ideas and am very excited about it.

The third area — and I wish that there is more progress on this front — is a broader awareness, broader education around AI. I see that as a tremendous challenge facing us. The development of AI is asymmetric. A few companies have disproportionate access to data and to the AI experts. There is just a massive amount of hype, myth and noise around AI. We need to broaden the base, to bring the awareness of AI and the awareness of technology to large numbers of people. This is a problem of scaling the educational infrastructure.

Knowledge@Wharton: Picking up on what you said about AI development being asymmetric, which industries do you think are best positioned for AI adoption over the next decade?

Sikka: Manufacturing is an obvious example because of the great advances in robotics, in advancing how robots perceive their environments, reason about these, and affect increasingly finer control over it. There is going to be a great amount of progress in anything that involves transportation, though I don’t think we are still close to autonomy in driving because there are some structural problems that have to be solved.

Health care is going to be transformed because of AI, both the practice of health care as well as the quality of health care, the way we build medicines, protein-binding is a great case for deep learning, personalize medicines, personalization of care, and so on. There will be tremendous improvement in financial services, where in addition to AI, decentralized/p2p technologies like blockchain will have a huge impact. Education, as an industry, will go through another round of significant change.

There are many industries that will go through a massive transformation because of AI. In any business there will be areas where AI will help to renew the existing business, improve efficiency, improve productivity, dramatically improve agility and the speed at which we can conduct our business, connect the dots, and so forth. But there will also be opportunities around completely new breakthrough technologies that are possible because of these applications — things that we currently can’t foresee.

The point about asymmetry is a broader issue; the fact that a relatively small number of companies have access to the relatively small talent of people and to massive amounts of data and computing, and therefore, development of AI is very disproportionate. I think that is something that needs to be addressed seriously.

Knowledge@Wharton: How do you address that? Education is one way, of course. Beyond that, is there anything else that can be done?

Sikka: I find it extraordinary that in the traditional industries, for example in construction, you can walk into any building and see the plans of that building, see how the building is constructed and what the structure is like. If there is a problem, if something goes wrong in a building, we know exactly how to diagnose it, how to identify what went wrong. It’s the same with airplanes, with cars, with most complex systems.

“The compartmentalization of data and broader access to it has to be fixed.”

But when it comes to AI, when it comes to software systems, we are woefully behind. I find it astounding that we have extremely critical and extremely important services in our lives where we seem to be okay with not being able to tell what happened when the service fails or betrays our trust in some way. This is something that has to be fixed. The compartmentalization of data and broader access to it has to be fixed. This is something that the government will have to step in and address. The European governments are further ahead on this than other countries. I was surprised to see that the EU’s decision on demanding explainability of AI systems has seen some resistance, including here in the valley.

I think it behooves us to improve the state of the art, develop better technologies, more articulate technologies, and even look back on history to see work that has already been done, to see how we can build explainable and articulate AI, make technology work together with people, to share contexts and information between machines and people, to enable a great synthesis, and not impenetrable black boxes.

But the point on accessibility goes beyond this. There simply aren’t enough people who know these techniques. China’s Tencent sponsored some research recently which showed that there are basically some 300,000 machine learning engineers worldwide, whereas millions are needed. And how are we addressing this? Of course there is good work going on in online education and classes on Udacity, Coursera, and others.  My friend [Udacity co-founder] Sebastian Thrun started a wonderful class on autonomous driving that has thousands of students. But it is not nearly enough.

And so the big tech companies are building “AutoML” tools, or machine learning for machine learning, to make the underlying techniques more accessible. But we have to see that in doing so, we don’t make them even more opaque to people. Simplifying the use of systems should lead to more tinkering, more making and experimentation. Marvin [Minsky] used to say that we don’t really learn something until we’ve learnt it in more than one way. I think we need to do much more on both making the technology easier to access, so more people have access to it, and we demystify it, but also in making the systems built with these technologies more articulate and more transparent.

Knowledge@Wharton: What do you believe are some of the biggest bottlenecks hampering the growth of AI, and in what fields do you expect there will be breakthroughs?

Sikka: As I mentioned earlier, research and availability of talent is still quite lopsided. But there is another way in which the current state of AI is lopsided or bottlenecked. If you look at the way our brains are constructed, they are highly resilient. We are not only fraud identification machines. We are not only obstacle detection and avoidance machines. We are much broader machines. I can have this conversation with you while also driving a car and thinking about what I have to do next and whether I’m feeling thirsty or not, and so forth.

This requires certain fundamental breakthroughs that still have not been happened. The state of AI today is such that there is a gold rush around a particular set of techniques. We need to develop some of the more broad-based, more general techniques as well, more ensemble techniques, which bring in reasoning, articulation, etc.

For example, if you go to Google or [Amazon’s virtual assistant] Alexa or any one of these services out there and ask them, “How tall was the President of the United States when Barack Obama was born?” None of these services can answer this, even though they all know the answers to the three underlying questions. But a 5-year-old can. The basic ability to explicitly reason about things is an area where tremendous work has been done for the last many decades, but it seems largely lost on the AI research today. There are some signs that this area is developing, but it is still very early. There is a lot more work that needs to be done. I, myself, am working on some of these fundamental problems.

Knowledge@Wharton: You talked about the disproportionate and lopsided nature of resource allocation. Which sectors of AI are getting the most investment today? How do you expect that to evolve over the next decade? What do traditional industries need to do to exploit these trends and adapt to transformation?

Sikka: There’s a lot of interest in autonomous driving. There is also a lot of interest in health care. Enterprise AI should start to pick up. So there are several areas of interest but they are quite lumpy and clustered in a few areas. It reminds me of the parable of the guy who lost his keys in the dark and looks for them underneath a lamp because that’s where the light was.

But I don’t want to make light of what is happening. There are a large number of very serious people also working in these areas, but generally it is quite lopsided. From an investment point of view, it is all around automating and simplifying and improving existing processes. There are a few developments around bringing AI to completely new things, or doing things in new ways, breakthrough ways, but there is a disproportionate usage of AI for efficiency improvements and automation of existing businesses and we need to do more on the human-AI experience, of AI amplifying people’s work.

“There simply aren’t enough people who know these techniques.”

If you look at companies like Uber or Didi [China’s ride-sharing service] or Apple and Google, they are aware of what is going on with their consumers more or less in real time. For instance, Didi knows every meter of every car ride done by every consumer in real time. It’s the same with Uber and in China, even in physical retail as I mentioned earlier, Alibaba is showing that real-time connection to customers and integration of physical and digital experiences can be done very well.
But in the traditional world, in the consumer packaged goods (CPG) industry or in banking, telecom or retail, where customer contact is necessary, businesses are quite disconnected from what the true end-user is doing. It is not real time. It is not large-scale. Typically, CPG companies still analyze data that is several months old. Some CPG companies still get DVDs from behavioral aggregators three months later.

I think an awareness of that [lag] is building in businesses. Many of my friends who are CEOs of large companies in the CPG world, in banking, pharmaceuticals and telecom, are trying to now embrace new technology platforms that bring these next generation technologies to life.  But beyond embracing technology, and deploying a few next-generation applications, my sense is, the traditional companies really need to think of themselves as technology companies.

My wife Vandana started and built up the Infosys Foundation in the U.S., and her main passion is computer science education. [She left the foundation in 2017.] She found this amazing statistic that in the dark ages some 6% of the world’s population could read and write, but if you think about computing as the new literacy, today some half a percent of the world’s population can program a computer.

We are finally approaching 90% literacy in the world, and of course we are not all writers or poets or journalists, but we all know how to write and to read, and it has to be the same way with computing and digital technologies, and especially now with AI, which is as big a shift for us as computing itself.

So businesses need to reorient themselves from “I am an X company,” to “I am a technology company that happens to be in X.” Because if we don’t, we may be vulnerable to a tech company that better sees and executes and scales on that X, as we have already seen in many industries. The iPhone wasn’t so much as a phone, as it is a computer in the shape of a phone. The Apple Watch isn’t a watch, but a computer, a smart computing service, in the shape of a watch. The Tesla is not so much an electric car, but rather a computer, an intelligent, connected, computing service, in the shape of a car. So if you are simply making your car an electric one, this is not enough.

“The iPhone isn’t so much a phone as it is a computer in the shape of a phone.”

Too often companies don’t transform, and they become irrelevant. They may not die immediately. Indeed large, successful, complex structures often outlive us humans, and die long slow deaths, but they lose their relevance to the new very quickly. Transformations are difficult. One has to let go of the past, of what we have known, and embrace something completely new, alien to us. As my friend and teacher [renowned computer scientist] Alan Kay said, “We only make progress by going differently than we believe.” And of course we have to do this as individuals as well. We have to continually learn and renew our skills, our perspectives on the world.

Knowledge@Wharton: How should companies measure the return on investment (ROI) in AI? Should they think about these investments in the same way as other IT investments or is there a difference?

Sikka: First of all, it is good that we are applying AI to things where we already know the ROI. I was talking to a friend recently, and he said, “In this particular part of my business, I have 50,000 people. I could do this work with one-fourth the people, at even better efficiency.” In such a situation, the ROI is clear. In financial services, one area that has become exciting is active trading of asset management. People have started applying AI here. One hedge fund wrote about the remarkable results it got by applying AI.

A start-up in China does the entire management of investments through AI. There are no people involved and the company delivers breakthrough results.

So, that’s one way. Applying AI to areas where the ROI is clear, where we know how much better the process can become, how much cheaper, how much faster, how much better throughput, how much more accurate, and so on. But again this is all based on the known, the past. We have to think beyond that, more broadly than that. We have to think about AI as becoming an augmentation for every one of our decisions, every one of the questions that we ask, and have that fed by data and analyzed in real time. Instead of doing generalizations or approximations, we must insist on AI amplifying all of our decisions. We must bring AI to areas where we don’t yet have ROIs clearly identified or clearly understood. We must build ROIs on the fly.

Knowledge@Wharton: How does investment in AI in the U.S. compare with China and other parts of the world? What are the relative strengths and weaknesses of the U.S. and Chinese approaches to AI development?

Sikka: I’m very impressed by how China is approaching this. It is a national priority for the country. The government is very serious about broad-based AI development, skill development and building AI applications. They have defined clear goals in terms of the size of the economy, the number of people, and the leadership position. They actively recruit [AI experts]. The big Chinese technology companies are [attracting] U.S.-based, Chinese-origin scientists, researchers and experts who are moving back there.

In many ways, they are the leaders already in building applications of AI technology, and are doing leading work in technology as well. When you think about AI technology or research, the U.S. and many European universities and countries are still ahead. But in terms of large-scale applications of AI, I would argue that China is already ahead of everybody else in the world. The sophistication of their applications, the scale, the complex conditions in which they apply these, is simply extraordinary. Another dimension of that is the adoption. The adoption of AI technology and modern technology in China, especially in rural areas, is staggering.

Knowledge@Wharton: Could you give a couple of examples of what impressed you most?

Sikka: Look at the payments space — at Alipay, WeChat Pay or other forms of payments from companies like Ping An Insurance, as well as Alibaba and Tencent. It’s amazing. Shops in rural China don’t take cash. They don’t take credit cards. They only do payments on WeChat Pay or on Alipay or others like that. You don’t see this anywhere else in the world at nearly the same scale.
Bike rentals are another example. In the past year, there has been an extraordinary development in China around bicycles.

When you walk into a Chinese city, you see tens of thousands of bicycles across the landscape — yellow ones, orange ones, blue ones. When you look at these bicycles, you think, “This is a smart bicycle.” It is another example of an intelligent, connected computing service in the shape of a bicycle. You just have to wave your phone at it with your Baidu account or your Alibaba account or something like that and you can ride the bike. It has GPS. It is fully connected. It has all kinds of sensors inside it. When you get to your destination, you can leave the bike there and carry on with whatever you need to do. Already in the last nine months, this has had a huge impact on traffic.

“The adoption of AI technology and modern technology in China, especially in rural areas, is staggering.”

If you walk into any of Alibaba’s Hema supermarkets in Beijing and Shanghai, I think they have around 20 of these already, teeming with people, they are far ahead of any retail experiences we see today in the US, including at Whole Foods. The entire store is integrated into mobile experiences, so you can wave your phone at any product on the shelf and get a complete online experience. There is no checkout, the whole experience is on mobile and automated, although there are lots of folks there to help customers. The store is also a warehouse, in fact it serves some 70% of demand from local online customers, and fulfills that demand in less than an hour.

My friend ordered a live fish from the store for dinner and it, that particular fish that he had picked on his phone, was delivered 39 minutes later. Tencent has now invested in a supermarket company. And JD has its own stores. So this is rapidly evolving.  It would be wonderful to see convenience like this in every supermarket around the world in the next few years.

A more recent example is battery chargers. All across China, there are little kiosks with chargers inside. You can open the kiosk by waving your phone at it, pick up a charger, charge your phone for a couple of hours, and then drop it off at another kiosk wherever you are. What I find impressive is not that somebody came up with the idea of sharing based on connected phone chargers, but how rapidly the idea has been adopted in the country and how quickly the landscape has adapted itself to assimilate this new idea. The rate at which the generation [of ideas] happens, gets diffused into the society, matures and becomes a part of the fabric is astounding. I don’t think people outside of China appreciate the magnitude of what is going on.

When you walk around Shenzhen, you can see the incredible advances in manufacturing, electronic device manufacturing, drones and things like that. I was there a few weeks ago. I saw a drone that is smaller than the tip of your finger. At the same time, I saw a demo of a swarm of a thousand or so drones which can carry massive loads collectively. So it is quite impressive how broadly the advance of AI is being embraced in China.

“The act of innovating is the act of seeing something that is not there.”

At the other end of the spectrum, I would say that in Europe, especially in Germany, the government is much more rigorous and thoughtful about the implications of these technologies. From a broader, regulatory and governmental perspective, they seem to be doing a wonderful job. Henning Kagermann, who used to be my boss at SAP for many years, recently shared with me a report from the ethics commission on automated and connected driving. The thoughtfulness and the rigor with which they are thinking about this is worth emulating. Many countries, especially the U.S., will be well served to embrace those ideas.

Knowledge@Wharton: How does the approach of companies like Apple, Facebook, Google, Microsoft and Amazon towards AI differ from that of Chinese companies like Alibaba, Baidu, or Tencent?

Sikka: I think there is a lot of similarity, and the similarities outweigh the differences. And of course, they’re all connected with each other. Tencent and Baidu both have advanced labs in Silicon Valley. And so does Alibaba. JD, which is a large e-commerce company in China, recently announced a partnership around AI with Stanford. There’s a lot of sharing and also competitive aspects within these companies.

There are some differences. The U.S. companies are interested in certain U.S.-specific or more international aspects of things. The Chinese companies focus a lot on the domestic market within China. In many ways, the Chinese market offers challenges and circumstances that are even more sophisticated than the ones in the U.S. But I wouldn’t say that there is anything particularly different between these companies.

If you look at Amazon and Microsoft and Google, their advances, when it comes to bringing their platforms to the enterprise, are further ahead than the Chinese companies. Alibaba and Tencent have both announced ambitions to bring their platform to the enterprise. I would say that in this regard, the U.S. companies are further ahead. But otherwise, they are all doing extraordinary work. The bigger issue in my mind is the gap between all of them and the rest of the companies.

Knowledge@Wharton: Where does India stand in all of this? India has quite a lot of strengths in the IT area, and because of demonetization there has been a strong push towards digitization. Do you see India playing any significant role here?

Sikka: India is at a critical juncture, a unique juncture. If you look at it from the perspective of the big U.S. companies or the big Chinese companies, India is by far their largest market. We have a massive population and a relatively large amount of wealth. So, there is a lot of interest in all these companies, and consequently their countries, towards India and developing the market there. If that happens, then of course the companies will benefit. But it’s also a loss of opportunity for India to do its own development through educating its workforce on these areas.

One of the largest populations that could be affected by the impact of AI in the near-term is going to be in India. The impact of automation in the IT services world, or broadly in the services world, will be huge from an employment perspective. If you look at the growth that is happening everywhere, especially in India, some people call it “jobless growth.” It’s not jobless. It’s that companies grow their revenues disproportionately compared to the growth in the number of employees.

“Finding the problem, identifying the innovation — that will be the human frontier.”

There is a gap that is emerging in the employment world. Unless we fix the education problem it’s going to have a huge impact on the workforce. Some of this is already happening. One of the things I used to find astounding in Bangalore was that a lot of people with engineering degrees do freelance jobs like driving Uber and Ola cabs. And yet we have tremendous potential.

The value of education is central to us in India, and we have a large, young, generation of highly inspired youngsters ready to embrace and shape the future, who are increasingly entrepreneurial in their outlook. So we have to build on foundations like the “India stack,” we have to build our own technological strengths, from research and core technology to applications and services. And a redoubling of the focus on education, on training massive numbers of people on technologies of the future, is absolutely critical.

So, in India, we are at this critical juncture, where on one hand there is a massive opportunity to show a great way forward, and help AI be a great amplifier for our creativity, imagination, productivity, indeed for our humanity. On the other hand, if we don’t do these things, we could be victims of these disruptions.

Knowledge@Wharton: How should countries reform their education programs to prepare young people for a future shift by AI?

Sikka: India’s Prime Minister Narendra Modi has talked about this a lot. He is passionate about this idea of job creators, not just job seekers, and about a broad culture of entrepreneurship.

I’m an optimist. I’m an entrepreneur. I like to see the opportunity in what we have, even though there are some serious issues when it comes to the future of the workforce. My own sense is that in the time of AI, the right way forward for us is to become more evolved, more enlightened, more aware, more educated, and to unleash our imagination, to unleash our creativity.

John McCarthy was a great teacher in my life. He used to say that articulating a problem is half its solution. I believe that in our lifetime, certainly in our children’s lifetime, we will see AI technology advance to the point where any task, any activity, any job, any work that can be precisely formulated and precisely articulated, will be done automatically, far better than we can do with our senses and our muscles. However, articulating the problem, finding the problem, identifying the innovation — that will be the human frontier. It is the act of seeing something that is not there. The act of exercising our creativity. And then, using AI to become a great amplifier, to help us achieve our imagination, our vision. I think that is the great calling of our time. That is my great calling.

Five or six hundred million years ago, there was this unusual event that happened geologically. It was called the Cambrian explosion. It was the greatest creation of life in the history of our planet. Before that, the Earth was basically covered by water. Land had started to emerge, and oxygen had started to emerge. Life, as it existed at that point, was very primitive. People wondered, “How did the Cambrian explosion happen? How did all these different life forms show up in a relatively small period of time?”

What happened was that the availability of oxygen, the availability of land, and the availability of light as a provider of life, as a provider of living, created a situation which formed all these species that had the ability to see. They all came out of the dark, out of the water, onto the land, into the air, where opportunities were much more plentiful, where they could all grow, they could all thrive. People wonder, “What were they looking for?” It turns out they were looking for light. The Cambrian explosion was about all these species looking for light.

When I think about the future, about the time in front of us, I see another Cambrian explosion. The act of innovating is the act of seeing something that is not there. Our eyes are programmed by nature to see what is there. We are not programmed to see what is not there. But when you think about innovation, when you think about making something new, everything that has ever been innovated was somebody seeing something that was not there.

I think the act of seeing something that is not there is in all of us. We can all be trained to see what is not there. It is not only a Steve Jobs or a Mark Zuckerberg or a Thomas Edison or an Albert Einstein who can see something that is not there. I think we can all see some things that are not there. To Vandana’s statistic, we should strive to see a billion entrepreneurs out there. A billion-plus computer literate people who can work with, even build, systems that use AI techniques, and who can switch their perspective from making a living to making a life.

When I was at Infosys, we trained 150,000 people on design thinking for this reason: To get people to become innovators. In our lifetime, all the mechanical, mechanizable, repeatable things are going to be done way better by machines. Therefore, the great frontier for us will be to innovate, to find things that are not there. I think that will be a new kind of Cambrian explosion. If we don’t do that, humanity will probably end.

Paul MacCready, one of my heroes and a pioneer in aerospace engineering, once said that if we don’t become creative, a silicon life form will likely succeed us. I believe that it is in us to refer back to our spirituality, to refer back to our creativity, our imagination, and to have AI amplify that. I think this is what Marvin [Minsky] and John [McCarthy] were after and it behooves us to transcend the technology. And we can do that. It is going to be tough. It is going to require a lot of work. But it can be done. As I look at the future, I am personally extremely excited about doing something in that area, something that fundamentally improves the world.

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Reshaping Business With Artificial Intelligence 10-01

 CLOSING THE GAP BETWEEN AMBITION AND ACTION......

Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business.

Executive Summary

1. Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts. Building on data rather than conjecture, the research is based on a global survey of more than 3,000 executives, managers, and analysts across industries and in-depth interviews with more than 30 technology experts and executives. (See “About the Research.”) 

The gap between ambition and execution is large at most companies. Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensively incorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in place. The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one.

Our research reveals large gaps between today’s leaders — companies that already understand and have adopted AI — and laggards. One sizeable difference is their approach to data. AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. While most leaders are investing in AI talent and have built robust information infrastructures, other companies lack analytics expertise and easy access to their data. Our research surfaced several misunderstandings about the resources needed to train AI. The leaders not only have a much deeper appreciation about what’s required to produce AI than laggards, they are also more likely to have senior leadership support and have developed a business case for AI initiatives.

AI has implications for management and organizational practices. While there are already multiple models for organizing for AI, organizational flexibility is a centerpiece of all of them. For large companies, the culture change required to implement AI will be daunting, according to several executives with whom we spoke.

Our survey respondents and interviewees are more sanguine than conventional wisdom on job loss. Most managers we surveyed do not expect that AI will lead to staff reductions at their organization within the next five years. Rather, they hope that AI will take over some of their more boring and unpleasant current tasks.

AI at Work

2. As Airbus started to ramp up production of its new A350 aircraft, the company faced a multibillion-euro challenge. In the words of Matthew Evans, vice president of digital transformation at the Toulouse, France-based company, “Our plan was to increase the production rate of that aircraft faster than ever before. To do that, we needed to address issues like responding quickly to disruptions in the factory. Because they will happen.”

Airbus turned to artificial intelligence. It combined data from past production programs, continuing input from the A350 program, fuzzy matching, and a self-learning algorithm to identify patterns in production problems. In some areas, the system matches about 70% of the production disruptions to solutions used previously — in near real time. Evans describes how AI enables the entire Airbus production line to learn quickly and meet its business challenge:

What the system does is essentially look at a problem description, taking in all of the contextual information, and then it matches that with the description of the issue itself and gives the person on the floor an immediate recommendation. The problem might be new to them, but in fact, we’ve seen something very similar in the production line the weekend before, or on a different shift, or on a different section of the line. This has allowed us to shorten the amount of time it takes us to deal with disruptions by more than a third.

AI empowered Airbus to solve a business problem more quickly and efficiently than prior approaches (such as root-cause analysis based on manual analysis of hundreds or thousands of cases).
Just as it is enabling speed and efficiency at Airbus, AI capabilities are leading directly to new, better processes and results at other pioneering organizations. Other large companies, such as BP, Infosys, Wells Fargo, and Ping An Insurance, are already solving important business problems with AI. Many others, however, have yet to get started.

High Expectations Amid Diverse Applications

3. Expectations for AI run high across industries, company sizes, and geography. While most executives have not yet seen substantial effects from AI, they clearly expect to in the next five years. Across all organizations, only 14% of respondents believe that AI is currently having a large effect (a lot or to a great extent) on their organization’s offerings. However, 63% expect to see these effects within just five years.

Expectations for Change Across Industries and Within Organizations

Expectations for AI’s effects on companies’ offerings are consistently high across industry sectors. (See Figure 1.) Within the technology, media, and telecommunications industry, 72% of respondents expect large effects from AI in five years, a 52-percentage-point increase from the number of respondents currently reporting large effects. However, even in the public sector — the industry with the lowest overall expectations for AI’s effects — 41% of respondents expect large effects from AI within five years, an increase of 30 percentage points from current levels. This bullishness is apparent regardless of the size or geography of the organization.

Figure 1 Expectations for AI’s effect on businesses’ offerings in five years are consistently high across industries. 

Within organizations, respondents report similarly high expectations for the large effects of AI on processes. While 15% of respondents reported a large effect of AI on current processes, over 59% expect to see large effects within five years. (See Figure 2.) Most organizations foresee sizable effects on information technology, operations and manufacturing, supply chain management, and customer-facing activities. (See Figure 3.) For example:

Figure 2 As with offerings, organizations expect AI to have a great impact on processes within the next five years.

Information technology: Business process outsourcing providers serve as an example of the potential of AI. “IT services, where Infosys plays a big role, has seen tremendous growth in the last 20 or so years,” says Infosys Ltd. CEO and managing director Vishal Sikka.1 “Many jobs that moved to low labor-cost countries were the ones that were more mechanical: system administration, IT administration, business operations, verification. With AI techniques, we now have systems that can do more and more of those kinds of jobs. We are still in the early stages and portions of these activities can be automated, but we will get to the point in the next few years where the majority if not all of these jobs will be automated. However, just as AI technologies automate existing, well-defined activities, they also create opportunities for new, breakthrough kinds of activities that did not exist.” 

Figure 3

Most organizations foresee a sizable effect on IT, operations, and customer-facing activities.

Operations and manufacturing: Executives at industrial companies expect the largest effect in operations and manufacturing. BP plc, for example, augments human skills with AI in order to improve operations in the field. “We have something called the BP well advisor,” says Ahmed Hashmi, global head of upstream technology, “that takes all of the data that’s coming off of the drilling systems and creates advice for the engineers to adjust their drilling parameters to remain in the optimum zone and alerts them to potential operational upsets and risks down the road. We are also trying to automate root-cause failure analysis to where the system trains itself over time and it has the intelligence to rapidly assess and move from description to prediction to prescription.”
Customer-facing activities: Ping An Insurance Co. of China Ltd., the second-largest insurer in China, with a market capitalization of $120 billion, is improving customer service across its insurance and financial services portfolio with AI. For example, it now offers an online loan in three minutes, thanks in part to a customer scoring tool that uses an internally developed AI-based face-recognition capability that is more accurate than humans. The tool has verified more than 300 million faces in various uses and now complements Ping An's cognitive AI capabilities including voice and imaging recognition.

Adoption as Opportunity and Risk

While expectations for AI run high, executives recognize its potential risks. Sikka is optimistic but cautions against hyping AI’s imminent triumph: “If you look at the history of AI since its origin in 1956, it has been a story of peaks and valleys, and right now we are in a particularly exuberant time where everything looks like there is one magnificent peak in front of us.” More than 80% of the executives surveyed are eyeing the peaks and view AI as a strategic opportunity. (See Figure 4.) In fact, the largest group of respondents, 50%, consider AI to be only an opportunity. Some see risks and the potential for increased competition from AI as well as benefits. Almost 40% of managers see AI as a strategic risk as well. A much smaller group (13%) does not view AI as either an opportunity or risk.

Figure 4

More than 80% of organizations see AI as a strategic opportunity, while almost 40% also see strategic risks.  

What is behind these high expectations and business interest in AI? There is no single explanation. (See Figure 5.) Most respondents believe that AI will benefit their organization, such as through new business or reduced costs; 84% believe Al will allow their organization to obtain or sustain a competitive advantage. Three in four managers think AI will allow them to move into new businesses.

Continued 2  3

Leading to Become Obsolete 07-07

Image credit : Shyam's Imagination Library

Haier CEO Zhang Ruimin is transforming a manufacturing giant into a platform for entrepreneurship — and his employees into self-governing entrepreneurs.We live and work in an age when the need for corporate reinvention is treated almost as a given. Countless CEOs talk about reducing hierarchy and increasing agility, flexibility, and connectedness to the market, and virtually every large company is “transforming for digital.” Yet in most organizations, lip service to change remains more the order of the day than real change itself.

Then again, you might work with Zhang Ruimin. The CEO and chairman of the white goods giant Haier Group Corp., based in Qingdao, China, has done what most chief executives dare not even dream about. He blew up much of the administrative structure of a global manufacturing enterprise, eliminating 10,000 management jobs that once held it together. And he has guided the organization to reemerge as a network of entrepreneurial ventures run by employees, whose compensation is based on the success of their products in the market.

In its transformed state, Haier is no longer a traditional manufacturer corporation so much as a platform that provides financing, support, and coordination for microenterprises all focused on developing products and services for the “smart home,” the internet of things (IoT)-based concept of a fully connected and networked household.

Haier calls its management model Rendanheyi, a term that refers to connecting employees with users. The company sees it as a “win-win” model for reducing the distance between the organization and its end users to zero and moving as close as possible to a state of co-creation with the customer.
This isn’t the first organizational innovation Zhang has led at Haier during his three decades with the company, but it is certainly the most profound. The 68-year-old executive, who has been named to a number of “most admired” and “top thinker” lists, received the Legend in Leadership Award from the Yale School of Management’s Chief Executive Leadership Institute in 2016. In noting the honor, Jeffrey Sonnenfeld, a senior associate dean of leadership programs at Yale, called Zhang “a genuine global business giant who inspires mythic awe in his competitors, his peers, and his fellow Chinese business leaders.”

During a spring 2017 trip to Washington, D.C., Zhang sat down with MIT Sloan Management Review editor in chief Paul Michelman to discuss Haier’s latest reinvention. The interview was conducted through a translator, and a further exchange took place via email. What follows is an edited and condensed version of the conversation.

MIT Sloan Management Review: The strategic transformation that you are undertaking right now is unprecedented in many ways. I’d like to begin by asking: Why now?

Two things make us believe now is the time. One is the internet, and the other is the internet of things. The internet has closed the distance between parts of the organization and between the organization and its customers to zero. This means that traditional management models — like Taylorism and bureaucracy as proposed by Max Weber — are no longer relevant. Then there is the internet of things, which represents the next generation of the internet. Despite a dozen years of development, the idea of IoT has not taken off — or as we like to say, it has not been ignited. We are undertaking this fundamental transformation of our corporate structure using the internet in the hope of becoming a leader in IoT.

Do you believe that this is the only viable path to lead in IoT? Did you consider other possible organizational forms?

We looked at this question from two different angles. First, we have been coming to the U.S. for years. We’ve talked to many corporations in the hope of finding a management model from which we can learn. But we have failed to identify the right one. So we decided to explore on our own. And we have come to believe that the traditional corporate model has to be upended and disrupted to survive in the internet era.

Secondly, what’s called for in the IoT age? It’s a direct interaction with users and a focus on creating the best user experience. However, in the traditional economy, there are no “users,” there are only “customers.” Customers are anonymous; users are real people who are directly involved in the process of creation.

Why hasn’t IoT been ignited? Because an interactive platform for users — where companies can take direction from the people who will buy their products — has yet to be created. We need to establish a “community economy” with zero distance between customers and companies. Our end goal is to have a true connection with our users and to create legitimate lifetime value for them via the internet of things.

Will every Haier business run on the platform? Will anything be carved off and managed in a more traditional way?

The platform is the only place for a business to go to. By eradicating our middle management layer — and laying off more than 10,000 middle-level managers — we have destroyed the original hierarchical structure. So we are merely a platform for entrepreneurs. All the businesses have to succeed as innovative entrepreneurial enterprises, or they will be kicked off the platform. The platform is also accessible to entrepreneurial projects from outside Haier. Today, we have more than 3,000 microenterprises operating on it.

What has surprised you the most along this transformative journey?

Three things. The first is our transformation from a traditional hierarchical organization to one with more than 200 different entrepreneurial teams operating thousands of microenterprises on our platform. This was totally unimaginable back in 2005, when the idea for this strategic transformation was proposed. The structure we have now is totally different.

The second thing is the variety of markets the entrepreneurial teams can enter. For example, our gaming laptop has grown to become the No. 1 market player in China in the short span of two to three years since the laptop team became entrepreneurial. The team did not come to me for approval. All the decisions were made by the [team].

But what has surprised me most is that employees have accepted the radical compensation change. Previously, we used IBM’s broadbanding model, where pay was determined based on an employee’s position and contribution. Now, compensation is determined by how much value is created for the user. When employees create value, they get paid. If they don’t create measurable value, they don’t get paid. Ultimately, if they don’t create value, they have to leave.

As we think about the Haier platform as a place where entrepreneurship occurs, many of us will draw on what we’ve learned and witnessed about successful entrepreneurs — that they possess a set of skills and characteristics that differ significantly from people who succeed in more directed environments.

We don’t require employees to possess certain skills. We don’t impose a training system or coach employees on how to be entrepreneurial. I don’t believe there is any training that is so effective as to transform people into entrepreneurs overnight. If someone can meet the requirements — if they can help start up a business — then they will prosper on the platform. If they cannot, they probably have to leave.

At the same time, we have external IoT entrepreneurs joining our platform because they believe it offers resources and support that other platforms do not. We have developed a networked organization that attracts the most capable people. We often say that the whole world is now our human resources department.

Was there anything done to support employees’ transition?

What we do is help people form communities of interest so that they can work together as entrepreneurs.

The process begins with an objective. For instance, someone comes up with an idea for a product targeting a certain niche of the market. And then people from different departments or disciplines — research and development [R&D], sales, manufacturing, marketing — will sit down and analyze its viability across all the relevant dimensions. If they believe it is viable, they will form a community to bring it forward as a new microenterprise.

Then they need to attach their plan to their compensation. We call it a predefined value adjustment mechanism, or VAM, which defines what goal the plan has to realize and how the members of the community will be paid if the goal is achieved. This is a signed agreement between Haier and its microenterprises.

We also have microenterprises that focus on more cutting-edge projects. These teams may not plan to achieve revenue for a couple of years. Here, we set different targets and schedules. For example, at a certain point of this endeavor, they must be able to attract external venture capital. If they can’t achieve the investment by an agreed-upon time, then they have to let it [the project] go, or we might invite another entrepreneurial team to work on the project.

Many leaders have a vision for the way people in their organizations will act. I’m curious to know if you’ve imagined certain core behaviors that indicate whether an individual will be successful?
I think most business leaders tend to view their employees as passive performers who take orders from their superiors. According to traditional management philosophy, there are managers and those to be managed. But in my opinion, everyone is capable of leadership — or in our words, “Everyone can be their own CEO.”

The reason why a company’s employees are not leaders is that they have not had the soil or platform to grow upon. With access to such a platform and with entrepreneurial competence, anyone can prosper.

In our model we have delegated the major powers of corporate executives to the employees — or at least to the microenterprises — including the power of decision-making, the power of selecting and appointing personnel, and the power of financial allocation. Other companies would not do that. They believe that if these powers are delegated, managers will lose control. Our goal is different: We are trying to motivate employees to unleash their potential and realize their own value. We don’t want to control them.

How does this transformation affect frontline employees, particularly in the manufacturing area? What has changed with respect to the factories themselves, such as how they’re run and how individuals in manufacturing jobs are compensated?

That’s a very important question, and one of our biggest challenges. It’s true that manufacturing workers do not typically face the market directly, but we can create a connection to the market by allowing our different production lines to compete with one another.

We have 108 factories all around the world, each possessing many production lines; every production line is a microenterprise. We evaluate the performance of these microenterprises based on cost, delivery and service quality, and market response to the products they make. This evaluation determines how they are qualified to get subsequent orders. Some production lines are able to acquire many orders. Some get fewer — and as a result employees on those lines are not paid as well. Lines gaining more orders can merge with those having fewer.

In this way the production lines are organically connected with the market. Moving forward, we are forging an even tighter connection by allowing users to work directly with the factory to place, customize, monitor, and take delivery straight from the production line. We have eight of these “interconnected factories” operating now.

As a fully realized open platform for entrepreneurship, what will Haier provide or enable that can’t be replicated? Thinking ahead, what will Haier be good for?

This is a question we are constantly reflecting upon, and it guides our direction. Though we have turned Haier into an entrepreneurial platform, we are not an investment company. The goal of an investment company is to put in money and take out profit. After an IPO, the goal is fulfilled — that is not our aim.

Our primary aim is to ignite the internet of things. All the entrepreneurial teams on the platform — even though they cross industries — focus on the smart home in some way. This is also why so many teams outside of Haier are willing to start up smart-home businesses on our platform. If they turn to venture capitalists, they will get money but not coordination. On Haier’s platform, businesses gain access to our sales network, logistics operation, and R&D system. Haier’s platform offers the help to IoT businesses that other platforms or funds cannot.

Today you’re working within a certain construct: the smart home. As you explore the potential of a truly open platform for entrepreneurship, how far will you allow yourselves to stray from this focus?
The smart home is already encompassing and covering many different entrepreneurial ventures. If we cannot succeed in this very broad construct, other goals are undoubtedly out of reach.

What’s most important is our resolute aim to be the enterprise that can truly ignite the whole idea of IoT. And this requires evolving from stand-alone products to products connected to the internet and on to a network of products and services all connected to each other.

So, what has become of the electric refrigerator in this scenario? It has transformed from a single appliance into the hub of a network connected to 400 organic food suppliers that monitor inventory levels and keep the refrigerator stocked. This model — and this level of interconnectedness — is really difficult to achieve in terms of both technology and business. For companies, revenue no longer comes solely from selling refrigerators but also from sales of organic food. These are two different concepts. When you’re taking into account this kind of transformation, a true ignition for IoT becomes very hard to reach.

What facets of the transformation have been enabled by Chinese organizational tradition? And what elements, if any, have been made more challenging by the same tradition?

China doesn’t have any well-established corporate models. When it comes to business, Chinese companies basically replicate Western management. So, it’s not as difficult for us to disrupt the model because it’s not Chinese in the first place.

But I do think Chinese traditional culture can aid this transformation. Western culture mainly focuses on dichotomy and atomism. In a typical Western company, activities are siloed by departments and then further cut up into more detailed tasks.

In China, we tend to look at things from the holistic perspective. Consider the difference between traditional Chinese medicine and Western medicine, which tends to focus on the cellular level of the human body. If something is wrong with your stomach, then something is wrong with your stomach.

So, the West will look more closely: What part of the stomach is wrong? Whereas in Chinese traditional medicine, we will not just look at your stomach. We will consider the connection between your stomach and other organs of your body, and we will look at your body as a whole before providing a cure.

So we are applying this traditional holistic thinking to our management transformation. The internet and IoT require enterprises to see things from the whole and systemic perspectives and to stop dividing everything into tiny parts.

That might suggest that the open platform model could find some challenges in scaling across geographies. Do you think that Western companies will have a hard time following suit?

This is a big challenge for us. We are a global business and must be able to globalize Rendanheyi. We acquired a consumer appliance business from Sanyo Electric Co. of Japan and used this model to transform it. We also acquired Fisher & Paykel Appliances of New Zealand, and they too have gradually accepted Rendanheyi. So it’s working, although at present [it is] applied only in the Asia-Pacific region.

The biggest challenge at the moment is GE Appliances [which Haier bought in 2016]. It’s a very large American company with a standard linear management model, where every action has a basis. In its hierarchy, there are protocols that direct employee behaviors at each step.

Rendanheyi is a nonlinear management model in which employees must be able to answer the question, “What do I do next?” for themselves. There is no one for you to ask — and that’s a challenging transformation.

Since we acquired GE Appliances, we have not sent a single executive over to the U.S. to implement our model. Instead, we have focused on communication and education with the existing executive team to make sure they understand and are willing to accept this philosophy. And they are coming around.

You are a student of Western management and familiar with the idea of corporate culture as an adhesive framework that helps ensure that people are all moving in the same direction. But as we think about an organization that is self-organizing, that is freely incorporating internal and external resources, do we have reason to question whether culture remains a significant factor?

I think an organization’s values are very important. The core value of Haier is self-negation. When most companies achieve success, they tend to fall into states of self-satisfaction and complacence, celebrating and falling in love with their achievements. That is not us. Even when we have a great success, we question where we can improve. Instead of being proud, we realize our own defects and mistakes. We challenge ourselves to reach another height.

This core value was essential in our own transformation from an execution culture to an entrepreneurial culture. Because we have a DNA of self-negation, it is easier for us to disrupt ourselves and to accept the need for change. We keep saying internally to our employees that there’s no such thing as a successful business. There’s only a business that is compatible with the task at hand.

So, if you are doing well right now, don’t be conceited. You’re just doing the right thing at the right time. Things change all the time. The only thing that doesn’t change is time itself. So, if you don’t keep up with changes, you’ll be quickly made obsolete.

I have met with many companies all around the world, but few of them possess this virtue. Usually they are arrogant.

Even as you create a business that aims to transcend traditional management and become self-perpetuating, your personal leadership of Haier demonstrates the value of a strategic visionary. How will the organization survive you? I can’t help but think that you may be Haier’s Steve Jobs.
(Laughing) This question has been raised by many people. I often ask it myself. I’ve been working at Haier for more than 30 years, but even if I can keep working and keep leading the organization, it doesn’t guarantee future success. My task is not to cultivate a replacement but to cultivate many people who are willing to challenge both themselves and the status quo.

That’s the reason why we have installed Rendanheyi. We are developing a multitude of microenterprises and entrepreneurial teams with the goal of dispensing with my authority. Rather than listening to my orders, my instructions — which might turn out to be erroneous — our teams follow the demands of the market and of our users. This will lower the failure of the individual microenterprises and the probability of failure for Haier as a whole.

Nowadays, the management model in many enterprises is “empowerment,” but we are not empowering; we are returning all the power to the employees.

You just mentioned Jobs. There is a book about him titled To Live Is to Change the World. That is the organization we are designing — one meant to keep changing both ourselves and the world.

What is implicit in your answer is that Haier is on this new path permanently. And if it is, then perhaps traditional leadership will not become necessary. Maybe you don’t even need a single CEO or chairman.

Among so many foreigners I have met, you are the only one who truly understands me.

Reproduced from MITSLOAN Management Review

Choose staff wisely when planning a digital transformation 06-13

Plenty of large businesses are, justifiably, embracing innovation of all kinds. But, cautions HPE's Craig Partridge, consider whether IT staff from old-school backgrounds (and their "think conservatively" cultural values) are the right people for a successful digital transition.

Every business wants to enhance what it does to make its products more valuable to customers (and thus more profitable to the company) and work more efficiently (that is, save money). So just about every enterprise organization is motivated to augment or create a digital strategy.

It’s one thing for a business to say, “Let’s exploit new technologies to gain competitive advantage.” Reaching that goal—or at least avoiding being left behind—takes a strategic plan, a dose of shiny new technology, and most important, attention to the human beings who create and implement the plan

In a Hewlett Packard Enterprise Discover presentation, “Thriving in the Age of Digital Disruption,” HPE’s Craig Partridge, worldwide director of data center platforms consulting, shared real-life lessons of digital transformation based on customer use cases and successful projects. In the one-hour, high-speed session, Partridge detailed a blueprint highlighting the elements needed for success.

And regardless of the many technologies and business processes that may be involved, there’s one key lesson to take away from the exercise: Choose the right people for the job, and value your staff for their diverse abilities. Doing so creates tension, Partridge said. But that isn’t a bad thing.

Digital disruption is about data

Disruption might take the form of a car manufacturer that wants to build out a connected car. It may be a bank aiming to give customers a good mobile digital experience. Perhaps it’s a sports stadium that recognizes that attending a game now includes mobility and Wi-Fi, not just a hot dog. Or the Rio airport, which during the Olympics had to digitize its services to accommodate an extra 2 million passengers.

Most of these projects are powered by emerging technologies like the Internet of Things, cloud, machine learning, and data analytics.

Technologically speaking, the “edge” is about data: how you collect it, how you analyze it, and how you use it for competitive advantage. Each of us generates a huge amount of unstructured data, especially with our mobile devices. Nowadays, the "machine edge" (smart sensors and machine-to-machine communication) is adding even more data. “Going forward, I see people combining those two data sets to create a good experience,” Partridge said.

In the past, cloud computing discussions have focused on core-out issues: What should IT move out of the data center? Today, the conversation is about what data to bring in and how best to do so. That encourages a different viewpoint. “Hybrid IT is what powers that new experience at the edge,” Partridge said. And IT has to change the operating model to work in that new way.  

As organizations put together software-defined agendas to accelerate how and where they deliver services, the first step is recognizing that not every traditional business application needs to be changed or disrupted. Some big transactional systems don't need to be mobile. Other systems need to be bulldozed and replaced.

The drive to improve digital experiences is also forcing organizations to work with partners in the value chain (especially with API-based tools). It means adopting concepts like continuous integration and the DevOps agenda, cloud management tool sets, and open cloud stacks, all with quick feedback and quick iteration. This kind of thinking does not come naturally to many large IT shops.

Yet “new” often translates into “We haven’t figured this out yet.” (If it were otherwise, it wouldn’t be much of a disruption, right?) HPE has created blueprints for the business process to help organizations succeed—after all, you’d rather learn from others’ mistakes than your own, right?

Foster the people

“The No. 1 reason projects succeed or fail is people,” said Partridge, echoing sentiments long understood by developers and IT professionals, if not their managers. People processes, politics, and governance have a huge effect on project outcomes, even when you don’t think you are dealing with a so-called peopleware problem.

“Brokering the supply chain sounds like a technical issue,” Partridge noted. “What people miss is that it requires an organization shift.” A business’s CIO now has to place demand appropriately across the supply chain, which sometimes is in other parts of the organization.

Less obvious to many enterprise development teams are cultural issues. They spent years creating an organization based on repeatable processes and infrastructure, such as reliability, approval-based plans, and a waterfall development model that’s measured in months.

That predictability and resilience are strengths. “These are big deals to IT,” Partridge said. “We can’t lose that DNA. These systems of record need to maintain that integrity.”

But the new systems that are part of the digital disruption move a lot faster. Innovation-optimized projects emphasize flexibility, working on small teams that are business-centric and close to the customer, with short-term goals and a willingness to embrace uncertainty. “That technical documentation is six months old, so it’s out of date,” one DevOps consultant said to me during the conference, just in passing.

The development process for imagining disruption requires a different mind-set. Central IT pros can generally learn new tech, but learning new values and mind-sets can be much more challenging. “We can be retrained, but we have habits ingrained from years of work,” Partridge said.

For example, when the automobile manufacturer launched its digital transformation project, it initially staffed the team from its central IT department, whose "cadence didn't lend itself to rapid iterative development,” Partridge said.

The company ended up starting over with a new IT group that operated in parallel with the existing central IT team. Although that might seem like a recipe for bickering and dysfunction, Partridge characterized the relationship as one of “creative tension,” because the friction led both teams to come up with ideas that helped one another. 

Digital transformation: Lessons for leaders

• “New” often translates into “We haven’t figured this out yet.”
• No matter how brilliant the idea is, success depends on putting the right personnel in place and supporting them properly. 
• Value existing systems, and recognize what doesn’t benefit from changing. 

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Survey for scaling up Digital Technology 06-07

Indian PSUs, still far from a digital deluge in Technology. 06-01

Indian PSUs, still far from a digital deluge in Technology

Public sector undertakings (PSUs) are the nation builders of India. Over the past couple of decades, they’ve catapulted the country onto the world stage in sectors from energy and finance to agriculture and transportation. Now they face a new challenge: digital, a force that’s impacting PSUs from the corner office to the factory floor. New digitally savvy rivals are gaining on traditional turf. The question becomes: Are PSUs ready to build the workforce of the future?

Maharatna. Navratna. Miniratna. The jewels of India’s public sector undertakings continue to shape the competitive landscape of India, contributing an impressive 25 percent of the overall gross domestic product.1 They represent some of the most trusted brands for consumers, and coveted employers for workers. Like other leading companies around the world, PSUs are investing in technology. Particularly digital innovation that will put them ahead of competitors, making them more agile and competitive.

But to date, one critical element of PSUs’ digital strategy has been overlooked: the workforce. It’s as if the prevailing thought is, “We’ll invest in the technology and our people will be digital by default.” But gaining the agility required to compete in the age of disruption goes beyond systems. It requires a deep shift for PSUs: in leadership, recruitment and organization. The current PSU culture is not well suited to such sweeping changes. Accenture Strategy research has identified the top ten attributes that correlate to successful culture change.

PSUs are on par with non-PSUs in only half of those attributes.2 PSUs rank in the bottom quartile for the remaining attributes, including talent management, adaptability and confronting conflict. In addition, current PSU employees are more skeptical about their organization’s readiness to leverage digital advances. Fewer than half (47 percent) of PSU employees, versus 56 percent of non-PSU employees, expect to derive productivity improvements or drive innovation from digital transformation.3 It’s a challenging starting point, but the direction is clear. PSUs in India need to embrace digital or witness their long-held national dominance quickly evaporate.

Accenture Strategy research has identified the top ten attributes that correlate to successful culture change. PSUs are on par with non-PSUs in only half of those attributes.

Digital has upended many traditional business models, philosophies and processes. One of those concerns the idea that leadership is practiced only at the top levels of the organization, by leaders who advanced through the ranks of an organization based largely on seniority. It was a system that worked well in an environment that was much less volatile and more predictable. Where skills like organization and delegation were paramount. While those skills are still important, there are other, more critical ones in the digital age.

 Leaders today need to thrive at building crossorganization and industry connections that lead to new sources of innovation. They need to influence all levels of the organization but without the authoritarian approach that marks traditional management. They also need to grasp new technologies and their impact on growth and gain the ability to experiment quickly and move on if the desired results aren’t achieved. PSUs need to open their organizations to feedback and ideas that lead to innovation. And flatten hierarchies, cutting out the layers and processes that impede agility. An influx of young talent signals a truth about PSUs in the digital age: old school leaders cannot lead digital transformation alone. They need to build mentors throughout the organization. And push out decision making to the edges by developing a pool of leaders with high digital quotient. Leading at the edge

The requirements of digital—to tap sources of innovation across functional boundaries and industries—means a change in the way PSUs are managed. Digital is horizontal. Traditional is vertical. While PSUs have invested in new systems and hardware to connect their operations, they have overlooked a critical element: the workforce. 73 percent of PSU employees recognize that digital will seriously transform the nature of their work over the next three years.4 They won’t be “digital by default.” Employers must rise to the challenge and change their current talent pools. Retraining them to handle new challenges and attracting a much more diverse new team. Let’s take energy as an example, an industry that is increasingly deregulated and privatized. As the industry shifts to a profit-driven business model, companies will need to recruit new skills like analytics, and sophisticated customer relationship capabilities. This will require expanding beyond the usual degrees in fields like IT and engineering, to backgrounds in statistics and internet marketing. In some instances, PSUs may want to tap into non-traditional sources of specialized skills such as on-line talent exchanges, or third-party partnerships.

Your workforce is not “digital by default”

73 percent of PSU employees recognize that digital will seriously transform the nature of their work over the next three years.

4 | Indian PSUs

To keep the sparkle in Maharatna, Navratna and Miniratna there are three things PSUs can do now to lead in digital: Define a disruptive business model Because of their traditional reach, and long-established marketplace presence, it used to be that no other rival could match the strength of a PSU. But digital allows startups to forgo brick and mortar infrastructure and rapidly achieve new levels of scale unheard of even a handful of years ago. Consider the Bank of India and ICICI Bank. In under two decades time, ICICI Bank has catapulted from startup status to competing neckand-neck with the PSU.5 How? By early on developing an aggressive online strategy that includes virtual banking and next generation mobile banking apps, among other digital moves. The message: it’s time for PSUs to get serious about digital investment. They need to take an ‘equity investor’ approach and incubate digital plays. That requires redesigning the organization for speed to leap ahead of competition instead of treating digital as adjunct to their current strategies. Leaders should learn from juniors With the long tradition of command-and-control leadership style, this flies in the face of management wisdom for most PSUs. But to survive in a digital world, senior leadership needs to turn to younger counterparts to gain a digital edge. This requires “reverse mentoring” where senior leaders learn from their younger counterparts. This learning goes beyond the basic skills like using apps and internet devices to learning about the internet of things, leveraging social networks for employee / customer engagement, and seeing opportunities where earlier none were apparent. PSUs are taking heed: in the general insurance sector, a raft of young officers were fast-tracked to the position of general manager in companies including National Insurance, New India Assurance and United India, among others. As a result, PSUs are starting to infuse new blood into their highest echelons.6 It’s a shot across the bows to PSUs that aren’t moving far, or fast enough.

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Rewrite the value proposition to create competitive advantage To attract and retain the kind of talent digital requires, PSUs need to craft value propositions that include benefits to society as well as opportunities for personal growth. Previous generations of Indians were focused on climbing the socioeconomic ladder of success. Now that many have “arrived,” the focus of newer generations is giving back. PSUs, by the nature of their services, are about contributing to the good of the country. Companies need only to underscore that in their value propositions to attract new pools of talent looking for reward through social contribution. PSUs also need to emulate the offerings of their non-PSU competitors. Consider National Thermal Power Corporation.7 This PSU launched an innovative plan to attract and retain young talent at their often remote locations. They developed the concept of “PUPS,” which stands for providing urban facilities at projects. Included in the new facilities are cafes, libraries, Wi-Fi hotspots and other trappings of city living.

Brilliance of the jewels

Thanks to Accenture for the content.