Conversations about the future of work often highlight the increasing intelligence of technology—innovations like big data analytics, artificial intelligence, machine learning, the Internet of Things, and so on. There’s no questioning the profound impact and transformative business implications of these developments. But just as much as we focus on the innovations themselves, Citrix has always focused at least as much on what the future of work means for people—the way we work and live, the way our teams and organizations operate, the problems we can solve.
Now, in the digital era, it’s worth considering one of the most human questions of all: as machines become smarter and smarter, what will be left for smart people to do? Will machine intelligence replace human expertise?
Malcolm Gladwell doesn’t think so. In his keynote address at our recent Citrix Synergy event in Orlando, he explored what it means to be an expert in the age of machine learning, AI, and big data. In his view, innovations like these don’t obviate the need for human expertise. In fact, they actually underscore the importance of the human intuition and insight in the future of work. The key is to understand the evolving nature of our role and the complementary relationship between human and machine intelligence in solving different types of problems.
When more data isn’t more helpful
Malcolm’s discussion of the future of work revolves around the difference between puzzles and mysteries, building on a theory first developed by a former U.S. intelligence expert named Gregory Treverton. Put simply, a puzzle is a problem that you solve by acquiring and processing additional information. A mystery is a problem that you solve by making sense of the information that you already have.
This distinction is absolutely crucial to understand, so you can determine what kind of problem you’re facing, and thus, how to approach its solution. If you treat every problem like a puzzle and throw more data at it, you may solve the problems that actually are puzzles, but you’ll only make the mysteries harder to solve, as needles of insight are buried under more and more hay. As Malcolm points out, the reason the intelligence community was unable to prevent the Sept. 11 attacks wasn’t a lack of data; there was more than enough information available. So much more, in fact, that analysts were overwhelmed, unable to see the big picture or draw the necessary inferences.
Today, many of our institutions, from government to industry to education, operate on the assumption that every problem is a puzzle. All we have to do is gather enough information—performance metrics, patterns of behavior, historical trends and so on—and the solution will become evident. But that’s not the world we’re living in. For every puzzle that can be solved with data, there’s a mystery that calls for deeper understanding—for seeing things that no machine can perceive.
Mysteries beyond machine understanding
How can you tell a mystery from a puzzle? An example of each may be helpful. Let’s say a retailer needs to figure out how much to scale its operations for the coming holiday season. To begin with, you’d consider how much your own sales volume had increased during the previous year’s holiday peak. Now look at comparable figures for your market as a whole. Layer on the potential impact of current economic conditions compared to last year. Factor in any changes in your distribution networks and retail footprint. Make apples-to-apples comparisons for the performance of individual products this year and last year. With each additional data source, your forecast becomes more accurate. It’ll never be perfect, but there’s a direct correlation between data volume and insight. This is a puzzle, and it should be solved as one.
Now, a mystery. In his Synergy keynote, Malcolm donned the hat of an NBA general manager to consider the prospects of top draft picks over the first few years of their career. The data shows a clear trend: for a variety of reasons, players tend to peak statistically in their fourth year. Viewing this as a puzzle, you’d collect more and more data to confirm this trend, gain confidence in its reliability, and make a practice of viewing a player’s fourth-year performance as his high-water mark.
But—what about Gordon Hayward of the Utah Jazz? Although his performance rose during his first three seasons as predicted, followed by a slump in his fourth season, it soared to new heights in his seventh season, at a time when the model would indicate likely complacency and gradual decline. Why? All the data in the world won’t help you solve this mystery. Instead, you’d need to go inside Hayward’s head and discover his renewed determination to raise his game. Unsatisfied with his performance to date, he changed his approach to preparation and competition and became one of the top players in the league.
We all want a Gordon Hayward on our team, but you’re not going to find him with an algorithm. Only an expert in the psychology of sports—a human one—would be able to perceive the unique passion and grit that set him apart from his peers.
Become the expert your organization needs now
Yes, machine intelligence will play a central role in the future of work. But what role will you play? What we need now are experts who can perceive the nuances that machines miss. After all, the challenge facing most organizations now isn’t a shortage of data—we’re swimming in it. What’s needed are thoughtful people who can see the reality beyond the numbers. Any good doctor knows that there’s no one right answer for every patient; decisions about testing and treatment must factor in myriad considerations, such as the individual’s tolerance for risk, desired quality of life or lifespan, available resources, physical condition and more. A strategy for market expansion must take into account not just the new geography’s demographic and economic profile, but also its history, culture and language. An executive search can never be conducted solely on paper; eye contact, tone of voice, and an empathic bearing can be as telling as any item on the resume.
Your role in the future of work is no longer to solve puzzles. Machines can now do it better and faster. Instead, it’s to recognize mysteries as they arrive, and apply the human perception and reasoning essential to solve them. Only a human expert can act socially, not just operationally, guided by intuitive understanding and a tolerance for uncertainty and subjectivity. Only a human expert can have the kind of complicated, intimate, sensitive, real conversations needed to solve human problems, whether for the patient on the table, the student in the classroom, or the business partner across the globe.
That’s not necessarily an easy transition to make. Many of our professions and organizations are built around solving puzzles, not mysteries, and it can be difficult to move beyond the more-data-please mentality. But it begins with the recognition that future of work will revolve around the human element no less than technology. Just as machines grow more intelligent in their own way, we need to keep developing our own human expertise—our mystery-solving skills.
Although Citrix has always had technological innovation deep in our DNA, we’ve never lost our focus on the people who use that technology. As we enable the future of work, we build big data analytics, AI, and machine learning into our solutions—but we also strengthen and streamline the connections people need to apply their own intelligence. We empower people to access tools, resources and fellow humans more easily, anywhere, at any time, to learn, collaborate, brainstorm, decide and solve. Within every mystery is a new beginning, a new way forward—a new future. It’s in our hands.
