CEO SPECIAL... How can business leaders make the new world of work better for people? PART I

How can business leaders make the new world of work better for people? PART I

John Donahoe of ServiceNow and Jeff Weiner of LinkedIn speak about how businesses can play a role in improving work for people in the age of automation and artificial intelligence.

How can CEOs stay ahead of the curve in training and developing their workforces for using automation and artificial intelligence (AI)? Since companies are doing the hiring and creating the jobs, what role do they play in talent and development? How should companies think about hiring as work changes?

In this episode of the New World of Work podcast, McKinsey Global Institute director James Manyika speaks with two leaders on the forefront of applying AI techniques, such as automation and machine learning, in the business world. John Donahoe, president and CEO of ServiceNow, and Jeff Weiner, CEO of LinkedIn, tackle the tough questions facing companies today.

James Manyika: When it comes to the issue of the  future of work and automation, businesses are at the center for several reasons. For one, they are large employers of people and workers, and they are embracing these technologies that are starting to automate work. They play a central role through the choices that they make in using these technologies. Sometimes, they’re also in the business of building products and services that also change and transform how we do work. And then sometimes, you come across rare business leaders who are far-forward-looking, think beyond their own businesses, and think about what these things mean for society.

With that note, I’m quite delighted that we have two business leaders who satisfy all three of those criteria. They are employers, they are innovators building products and services, and they’re also thinking beyond their own businesses to what this means for society. John Donahoe was CEO of eBay and is now president and CEO of ServiceNow. Jeff Weiner is CEO of LinkedIn and has been at LinkedIn for a decade.

I want to start with one issue. Right now, there’s something in the air about the idea that businesses, especially tech businesses like yours, are limiting the opportunities for people to prosper and do well. And so, let’s talk about that quite directly. Jeff, you’ve been talking about an economic graph. What is it? And what does it have to do with LinkedIn?

Jeff Weiner: The economic graph is the manifestation of our vision at LinkedIn to create economic opportunity for every member of the workforce. There are more than three billion people in the global workforce; some would estimate as many as three-and-a-half billion people. 

We draw a clear distinction between vision and mission. The vision is the “true north”—it’s the dream, it inspires us. But the mission is the overarching objective that we measure ourselves against—it’s realizable and, hopefully, inspirational. And the mission is to connect the world’s professionals, make them more productive and successful.

Around six years ago, as we started to recognize that we were on a path to connect the world’s professionals, we started asking ourselves: What’s next? Although there’s plenty of work still to be done regarding the mission, we decided that we should start taking the vision seriously and thinking about how to operationalize it. And so, that’s what the economic graph is all about.

A graph refers to the mechanism through which you map nodes or connections. At LinkedIn, historically, we always had this idea of a professional graph where we connected professionals, and we created value for them through those relationships. The economic graph is digitally mapping the global economy, and we want to do that across six different pillars or dimensions where, ultimately, we’ll have a profile on LinkedIn for every member of the global workforce, all three-billion-plus folks.

And we’ll have a profile for every company in the world. When you include small and medium-size businesses, there are an estimated 60 million companies in the world. We’d like for there to be a digital representation for every available job in the world. There’s roughly 20 million available jobs that can be digitally accessed and made available to people online.

Finally, we’d like for there to be a digital representation for every skill required to obtain the jobs offered by those companies. With the acquisition of Lynda.com several years ago, it’s not only about creating a structured database around these skills and the skills that would be necessary to get those jobs; it also involves providing the coursework that enables people to acquire those skills.

James Manyika: You basically want to be able to connect people to jobs and give them information about the skills that are needed out there. Is that right? 

Jeff Weiner:Yes. The idea is to enable all forms of capital—intellectual capital, working capital, human capital—to float to where it can best be leveraged and, in doing so, help lift and transform the global economy. 

James Manyika:John, in the businesses you’ve been involved in, how have you created opportunities for people? Because you’ve mostly run and created technology-based businesses on some level. 

John Donahoe: I think these technology platforms can create opportunities. I got to see this firsthand at eBay, which used technology to help people, entrepreneurs, and small businesses earn their living. 

About 1.4 million people make their primary or secondary living on eBay. When you get to see who these people are, they are sometimes unemployed when they started their businesses. They’re often people who were laid off. They’re not coming from privileged circumstances. What technology has done is to augment their ability to compete on their creativity and their hard work. What the technology did was take what is complex and mundane about building a business and take that part out so that they could compete. It was a fascinating use of technology to enable people to create economic opportunity for themselves in the work they loved and create a job out of it. No one was thinking about creating a job out of collar stays, or selling collar stays, or selling all the hundreds of thousands of different items people sell on eBay.

James Manyika: Is the analogy in your case that we shouldn’t think of wages but of incomes? Because you provided a way for people to get incomes in a much wider range of things beyond just their wages. 

John Donahoe: The broader  income-inequality issues are very complicated. Minds far bigger than mine can solve those. But I do think technology can help people do higher-value-added work and bring the very best out in themselves so that they can create jobs.

James Manyika: Right. Well, take that to what you’re doing now, with ServiceNow. 

John Donahoe: It was a similar thing that led me to ServiceNow, in an unanticipated way. We all know how technology and cloud-based applications have transformed our lives as consumers at home. They’ve taken what used to be complex, tedious, or mundane and made it easy and intuitive. They’ve added value to our lives. But as I reflected about technology in the workplace, no one would say technology in the workplace is easy, intuitive, or adds value. It’s frustrating. It’s complicated. We spend huge amounts of our time at work dealing with the complexity of technology. When I got introduced to ServiceNow, I realized that cloud-based platforms have the power to transform our experiences at work in the same way that this technology has done at home. And increasingly, millennials are demanding the same experiences at work as they’re getting at home. 

That’s what ServiceNow does—we make the world of work better for people. I’ll just give a small example that’s mundane, but I often think these big topics like automation and AI get down to our own mundane lives. Say you have your money in PayPal. If you can’t get into PayPal, you can reset your password in about one minute, safely, anywhere in the world, from your mobile phone. And you do it safely. That’s convenience. That’s adding value to your life.

How many people here have had trouble resetting their email password at work? Why is it that at work, if we can’t get into our email, we have to call the IT person? It’s a frustrating experience. The IT professional hates it. We hate it. It’s frustrating because it’s not our money, it’s our email. What ServiceNow does, in simple terms, is to provide the same kind of self-help automation to reset your email password as you would be able to use to reset your banking or PayPal password at home. Just look at the amount of work that goes into menial, redundant, frustrating tasks and how technology can help simplify those, automate them, so that you can spend more time at work on value-added activities, creative activities, much like eBay sellers could spend more time selling.

James Manyika: Both of you are CEOs; both of you employ lots of people; but at the same time, you’re also clearly embracing these technologies. You’re probably embracing  machine learning. You’re probably embracing automation in one form or another. Let’s talk about that and the choices you’re making. Jeff, where are you embracing these tools in your business, and how are you using them? And what does it mean for the people who work for you?

Jeff Weiner: So, we can segment it across at least three different constituents: members, customers, and our employees. 

Regarding members, machine learning has always been a foundational part of LinkedIn. And we’re trying to make the best recommendations we can to create the most relevant experience our members, whether that’s in the feed or whether that’s regarding a skill that you should be learning or a job that we think is going to be well-suited for you based on your experience, your background, or who you know. For members, we can make better matches by virtue of leveraging that technology. We can also better understand where skills gaps exist. So, if a member is interested in a particular job, by virtue of their profile, we can see what skills they have and what experiences they have. When we think there’s a gap, we can make a recommendation in terms of the kinds of skills they should be picking up—which, by the way, are not necessarily relegated exclusively to technology. Interpersonal skills, leadership, and some of the softer skills that were mentioned earlier will continue to be essential.

Regarding customers, all of our business lines are oriented around making our customers more productive, more efficient, and more effective across multiple value propositions. We just announced a new product suite called Talent Intelligence. In use cases for the economic graph, we would talk about it in the context of locality and geography. You could pick any place in the world and understand skills gaps within that locality. You could understand the fastest-growing jobs and the skills required to obtain those jobs. You could understand the skills of the aggregate workforce within that locality. You could measure the gap. And then, you could equip those that could make use of it—vocation-training facilities, junior colleges, four-year universities—with data that demonstrates where those gaps exist. They could close the gaps, create just-in-time curriculum, and make sure that they’re training the workforce for the jobs that are and will be, and not just the jobs that once were. We can now do that for customers. Within any company anywhere in the world, we can help them develop a workforce strategy that is going to better position them considering all the changes that are taking place based on our infrastructure, our data, et cetera.

Finally, for employees, it’s similar to what we’re doing for our customers, to the extent that we can find these repeatable, high-volume tasks that don’t necessarily require the kinds of talent that we have within the organization. If we can take the robot out of the role and leave them to the parts that are higher value added—that are uniquely suited for our kind of talent and our team—then that makes them more efficient and more effective.

James Manyika: Let’s just imagine you’re the CEO of a large retailer or the CEO of a large manufacturing company—a large, very people-intensive business. And, suddenly, these technologies come along, and you can automate things. How would you think about that? How should CEOs think about that question, when there clearly are business benefits to using these technologies, but, at the same time, you’ve got these large workforces? 

John Donahoe: The word “automation” and the word “AI” evoke a very binary, almost emotional, reaction: it’s going to be the takeover of the ma chines, and humans will be gone.

Jeff Weiner: Makes for better movies, John. 

John Donahoe:  It makes for better movies—yeah, exactly. And then, to be honest, I think the other side of the equation is that there are Silicon Valley companies with their heads in the sand saying, “Well, technology’s great. There’ll be no second-order effects.” And neither of these is true.

The way automation really has the biggest impact is in what you said in your report, Jobs lost, jobs gained: Workforce transitions in a time of automation. It’s taking pieces of jobs. It’s taking the parts, often the redundant, the mundane, the not very exciting parts of a job, and simplifying them and automating them. I’ll give two examples.

At ServiceNow, we make automation software for customer-support operations. On our own customer-support operation, we have 400 engineers that solve our customers’ problems when those customers call. About 10 percent of those engineers’ time is spent trying to figure out—categorizing—what the problem is and getting it to the right person to fix it. So, we turn the machine learning on in our platform. And within a week, the machine was more accurately categorizing what the inbound customer problem was and getting it to the right person so it could be solved more quickly and solved the right way the first time. So, you could say, “Oh my God, that took away 10 percent of the jobs of the 400 engineers.” Of course, that’s not what they felt. They felt like it was the bottom 10 percent of what they hated to do. And now they took that 10 percent, and they applied it to solving customers’ real problems. And that’s a case where automation—in this case, machine learning—is taking a piece of a job or role—and often the lowest-value-added piece—and freeing them up from it.

Second example. We had the privilege of having dinner with Doug McMillon, the CEO of Walmart, and his team about a month ago, talking about this very issue. And Walmart’s the largest private-sector employer in the world, and he was so articulate about saying that Walmart is a people company, not a technology company. And yet, their store associates spend a reasonable amount of time restocking shelves and doing tasks that they don’t really like to do and are not particularly creative, customer-serving tasks. So, they’re now trying to use automation to simplify and automate some of the restocking tasks so that their store associates can spend more time with customers, calling upon their customer-facing skills and their creativity. And what I thought was so nice about that is Doug has declared, “We are a people company, first and foremost. And technology is in service to our people,” not vice versa. By the way, Pierre Omidyar said that at eBay, Fred Luddy said that at ServiceNow, Reid Hoffman said that at LinkedIn—technology is in service to people, not the other way around.

CONTINUES IN PART II