Robots that serve dinner, self-driving cars and drone-taxis could be fun and hugely profitable. But don’t hold your breath. They are likely much further off than the hype suggests. It turns out that so much of what appears in mainstream media about self-driving cars being just around the corner is very much overstated. Fully autonomous cars are many years away.

Companies new to the space can learn a great deal from early adopters who have invested billions into AI and are now beginning to reap a range of benefits. After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses—and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. Automakers harness the technology in self-driving cars.

A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI—data. Billions of gigabytes every day, collected by networked devices ranging from web browsers to turbine sensors.

The entrepreneurial activity unleashed by these developments drew three times as much investment in 2016—between $26 billion and $39 billion—as it did three years earlier. Most of the investment in AI consists of internal R&D spending by large, cash-rich digital-native companies like Amazon, Baidu, and Google.

Often there are two sides to high-tech advancements. One side gets a lot of media attention — advances in computing power, software and the like. Here, progress is quick — new apps, new companies and new products sprout up daily. However, the other, often-overlooked side deeply affects many projects — those where the virtual world must connect with the physical or mechanical world in new ways.

At some point, all of that software in autonomous cars meets a hard pavement. In that world, as with other robot applications, progress comes by moving from data to information to knowledge. A fundamental problem is that most observers do not realize just how vast an amount of data is needed to operate in the physical world — ever-increasing amounts or exponential amounts. While it’s understood today that big data are important, the amounts required for many physical operations are far larger than big data imply. The limitations on acquiring such vast amounts of data severely throttle back the speed of advancement for many kinds of projects.

In other words, many optimistic articles about autonomous vehicles overlook the fact that it will take many years to get enough data to make fully self-driving cars work at a large scale — not just a couple of years.

For all of that investment, much of the AI adoption outside of the tech sector is at an early, experimental stage. Few firms have deployed it at scale. AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors, deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership. Companies that have not yet adopted AI technology at scale or in a core part of their business are unsure of a business case for AI or of the returns they can expect on an AI investment.

However, early evidence suggests that there is a business case to be made, and that AI can deliver real value to companies willing to use it across operations and within their core functions. In our survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the next three years.

This adoption pattern is widening a gap between digitized early adopters and others. Sectors at the top of MGI’s Industry Digitization Index, such as high tech and telecoms or financial services, are also leading AI adopters and have the most ambitious AI investment plans. These leaders use multiple technologies across multiple functions or deploy AI at the core of their business. Automakers, for example, use AI to improve their operations as well as develop self-driving vehicles, while financial-services companies use it in customer-experience functions. As these firms expand AI adoption and acquire more data, laggards will find it harder to catch up.

Governments also must get ahead of this change, by adopting regulations to encourage fairness without inhibiting innovation and proactively identifying the jobs that are most likely to be automated and ensuring that retraining programs are available to people whose livelihoods are at risk from AI-powered automation. These individuals need to acquire skills that work with, not compete against, machines.

The future of AI will be innovative, but may not be shared equally. Companies based in the United States absorbed 66 percent of all external investments into AI companies, China was second, at 17 percent, and is growing fast. Both countries have grown AI ecosystems—clusters of entrepreneurs, financiers, and AI users—and have issued national strategic plans in the past 18 months with significant AI dimensions, in some cases backed up by billions of dollars of AI-funding initiatives. South Korea and the United Kingdom have issued similar strategic plans. Other countries that desire to become significant players in AI would be wise to emulate these leaders.

Significant gains are there for the taking. For many companies, this means accelerating the digital-transformation journey. AI is not going to allow companies to leapfrog getting the digital basics right. They will have to get the right digital assets and skills in place to be able to effectively deploy AI.

When we talk to people about artificial intelligence, or AI, they’re often optimistic about how they think AI will improve their lives in the future. What they aren’t thinking about is sweaters. Specifically, how based on previous online behaviors an e-tailer knows that you’re in the market for a warm, blue sweater instead of a long-sleeved gray shirt. Historically, businesses required human interaction in order to understand exactly what a customer needed. Today, that’s no longer the case. Thanks to lots of data and AI, companies now know exactly what you want, when you want it and how you want it.

Getting enough data to be 90% accurate is difficult enough. Some object-recognition software today is 90% accurate, you go to Facebook, there are just so many faces — but there is 90% accuracy in identification. Still, even at 90% your computer-vision colleagues would tell you ‘that’s dumb. But to get from 90% accuracy to 99% accuracy requires a lot more data — exponentially more data. And then to get from 99% accuracy to 99.9% accuracy, guess what? That needs even more data. We compare the exponentially rising data needs to a graph that resembles a hockey stick, with a sudden, sharply rising slope. The problem when it comes to autonomous vehicles, 90% or even 99% accuracy is simply not good enough when human lives are at stake.

To have exponentially more data to get all of the cases right, is extremely hard. And that’s why I think self-driving cars, which involve taking actions based on data, are extremely hard to perfect. Yes, it’s a great concept, and yes, we’re making major strides, but to solve it to the point that we feel absolutely comfortable — it will take a long time.

Alan Greenspan famously said there was irrational exuberance in the stock market not long before the crash of the huge tech stock bubble in the early 2000s. A similar kind of exaggeration is true for today for self-driving cars. That’s where the irrational exuberance comes in. It’s a technology that is almost there, but it’s going to take a long time to finally assimilate.

Tesla head Elon Musk claims all of the technology to allow new cars to drive themselves already exists (though not necessarily without a human aboard to take over in an emergency) and that the main problem is human acceptance of the technology. Musk will also tell you that batteries are improving and getting better and better. Actually, it’s the same battery that existed five or 10 years ago. What is different is that batteries have become smaller and less expensive, because more of us are buying batteries. But fundamentally it’s the same thing.

Progress has been slow elsewhere, too. In the physical domain, not much has changed when it comes to energy and power, either. You look at electric motors, it’s World War II technology. So, on the physical side we are not making the same progress we are on the information side. And guess what? In USA, 2% of all of electricity consumption is through data centers. If you really want that much more data, if you want to confront the hockey stick, you are going to burn a lot of power just getting the data centers to work. At some point it gets harder and harder and harder.

Similar constraints apply to drone technology. Here’s a simple fact. To fly a drone requires about 200 watts per kilo. So, if you want to lift a 75-kilo individual into the air, that’s a lot of power. Where are you going to get the batteries to do that? The only power source with enough power density to lift such heavy payloads is fossil fuels. You could get small jet turbines to power drones. But to have electric power and motors and batteries to power drones that can lift people in the air — this is a pipe dream. That is not to say one “can’t do interesting things with drones, but whatever you do — you have to think of payloads that are commensurate what you want to do.

In other areas, like electric cars, progress is moving along smartly and there is lots of potential. The Chinese have shown that, they are leading the world. The number of electric cars in China on an annual basis that are being produced is three times that of the USA. Electric cars are here to stay, but not so sure about drones using electric power.

Some may fear AI, but we think of it as doing for minds what the Industrial Revolution did for muscles: machines taking on difficult and repetitive tasks and improving output. As in the case of an online retailer, AI will change the way marketing is done by turning to machine learning in order to discover patterns in data on human behavior. We don’t have to look far into the future to see how AI will make every marketing campaign smarter and more personalized; we’re already there.

To understand how AI will change the role of the marketer, we have to first look at what it does well. AI finds patterns in data, which we humans do poorly by comparison. Here’s why: first, there’s too much data to pour through, and second, we’re biased. We only ask what we know to ask for. By contrast, machines aren’t necessarily looking for something—they’re just looking.

By looking through massive amounts of information, AI discovers its own design as it goes. The machine finds surprising information in patterns, and not just in individual shopping patterns. It’s able to capture large and impactful trends like social patterns that define a community’s habits.

So why is the time for AI now? Only in recent years do we have the volume of data needed to find these patterns, and the economies of scale to be able to store it.

Marketing has traditionally been linear and deterministic. But with AI, journeys become intelligent and dynamic, and marketing can be predictive for each customer. The ability to capture, store and retrieve a boundless amount of data opens up avenues to reach customers with personalized messages and experiences. And, since the system learns, it gets smarter and more accurate with further iterations.

Let’s take a look at the famous auto company Peugeot and how it has been able to use AI to drive impressive marketing ROI. Of the millions of people who come through its doors, visit its websites, even look at an ad on the open web, only thousands of them buy a car. We can ask what sequences of events people followed to buy, or not. There are thousands of ways in, from downloadable content to walk-ins. Using data and AI, Peugeot created 800 micro-segments that enabled content personalization across 2,200 microsites. By following the buying journey of those who did buy, Peugeot saw how each path was different. With the power of AI, companies like Peugeot can tease out the many different variations to sell and market accordingly.

What about the creativity and art in marketing and advertising? Design remains a huge component of successful marketing. AI certainly has the power to help determine what sorts of digital ads, for example, a consumer is likely to click on—from color preferences to style and price. Many experiments with AI-created trailers, tweetbots and other interaction experiments have taught us that we still need both human and machine. The AI algorithm can interact, iterate and optimize for success, but design still lies in the art of human creativity.

AI removes the tedium and guesswork of running a marketing campaign, which includes creative, email, social media and more, by generating insights from haystacks of data. But the marketer still designs and runs the campaign. What I see is people and AI working together to be more efficient than either one alone.

Creativity aside, the beauty of AI is that even if marketers don’t have specialized IT skills, they can still act on high-level insights. For example, iteration cycles start to become much faster as AI kicks in, so we think of this process as a layer cake.

The first layer is a huge quantity of data that AI uses to figure out what to probe for insight. If you perform a large-scale data-driven experiment with a piece of content to test engagement (like A/B testing), you’re going to get a lot of data back. This is the second layer. AI can then process that information quickly, and the iteration cycle gets shorter. In the third layer, AI works through nuances in the data so that it’s not just A or B, but a mix of both. By the fourth layer, AI has optimized the system, working out inefficiencies, maximizing speed and providing a clearer picture of customer preference and behavior.

Everything described here—the massive data gathering, the importance of trust, the ability to analyze, iterate and predict, and intelligent journeys—are separate pieces of what is already shaping up to be a seamless experience.

As marketers work hard to get ever closer to the customer at every touch point, AI gives them the total view of what’s possible and what makes sense, reducing the friction to customer satisfaction even as their expectations are constantly rising.

While AI may perform many impressive and valuable tasks, once again physical limitations remain almost fixed. A deep-learning algorithm that than can do just speech recognition, which is translating what you are saying, has to be trained on millions of hours of data and uses huge data farms. And while a deep-learning network might have hundreds of thousands of neurons, the human brain has trillions. Humans, for the time being, are much more energy-efficient. They can work all day on a tiny slice of pizza!


Today, the effort is to reverse the teacher and pupil relationship so that, instead, machines begin to learn to communicate with humans. The research and development, and application of AI algorithms and machines that will work for us, cater to us, is underway. They will understand our meaning, our emotion, our personality, our affect and all that. The goal is for AI to account for the different layers of human-to-human communication.

We look at each other, we engage each other’s emotion and intent. We use body language. It’s not just words. That’s why we prefer face-to-face meetings, and we prefer even Skype to just talking on the phone. There is a need to teach robots to understand and mimic human emotion. Basically, it is making machines that understand our feelings and intent, more than just what we say, and respond to us in a more human way.

Such affective computing means machines will ultimately show affect recognition picked up from our voices, texts, facial expressions and body language. Future human-robot communication must have that layer of communication. But capturing intent as well as emotion is an extremely difficult challenge. Natural language is very hard to understand by machines — and by humans. We often misunderstand each other.


In the near future, no one needs to worry because machines are pretty dumb. We could make a robot today capable of doing some simple household chores, but, it’s still cheaper for me to do it, or to teach others to do it. So, for the near future there are tons of jobs where it would be too expensive to replace them with machines. Fifty to 100 years from now, that’s likely to change, just as today’s world is different from 50 years ago.

But even as new tech arrives it is not always clear what the effect will be ultimately. For example, after the banking industry first introduced automatic teller machines, instead of having fewer tellers we had more tellers. ATMs made it cheaper to have a branch, and then we had more branches, and therefore we had more tellers in the end.

On the other hand, introducing blockchain technology as a ledger system into banking will likely eliminate the need for a third-party to double-check the accounting. Anything requiring reconciliation can be done instantly, with no need for confirmation. Eventually the cost of doing a transaction will be like sending an email, it will be like zero, without any possibility of confusion, there’s no cost. Imagine if you apply that to trade finance, etc.

New jobs will be created in the wake of new technologies, as was the case following ATMs. We area  bit concerned about the speed of change, which may cause us to be careful, but there will be new things coming out. 

Even in fintech, progress will be throttled by the available data. In certain areas, you have a lot of data, in others you don’t. Financial executives have have huge databases, but not nearly large enough to accomplish many of their goals.

Today we are creating more jobs for robots than humans, a cause for concern for the future of jobs for humans. AI and robotics will work best in applications where they work with humans. It’s going to take a long time before we build machines with the kind of intelligence associated with humans. When it comes to going from information to knowledge, we have no clue. We don’t know how the human brain works.

Jobs most likely to be eliminated could surprise people. What is the one thing that computers are really good at? They are good at taking exams. So, this expectation of, oh, I got a 4.0 from this very well-known university, I will have a job in the future — this is not true. At the same time, for robots cleaning up a room after your three-year old is just very, very hard. Serving dinner is very, very hard. Cleaning up after dinner is even harder. Those jobs are secure.

The jobs safest from robot replacement will be those at the top and the bottom, not those in the middle. What about many years down the road, when robots become advanced enough and cheap enough to take over more and more human activities. What’s to become of human work? For one thing, there will be a lot more AI engineers and people who have to regulate machines, maintain machines, and somehow design them until the machines can reproduce themselves.

But also, many jobs will begin to adapt to the new world. Suppose, for example, at some point in the distant future many restaurants have robot servers and waiters. People will pay a lot more money to go to a restaurant where the chef is a human and the waiter is a human, so human labor would then become very valuable.

Many people might become artists and chefs, and performing artists, because you still want to listen to a concert performed by humans, don’t you, rather than just robots playing a concerto for you. And you will still want to read a novel written by a human even though it’s no different from a novel written by a machine someday. You still appreciate that human touch.

What’s more, creativity already is becoming increasingly important. So, it’s not whether AI engineers or business people will be calling the shots in the future. It’s really creative people versus non-creative people. There is more and more demand for creative people. Already, it appears more difficult for engineering students to compete with the best compared to the old days.

In the past, for engineers, a good academic record guaranteed a good job. Today, tech companies interview applicants in so many different areas. They look beyond technical skills. They look for creativity. Engineers have to learn more non-engineering skills, and then the non-engineers will be learning more of the engineering skills, including scientific thinking, including some coding. The idea of a well-rounded graduate, the idea of liberal education today, includes engineering and includes business. 


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