Learning Agility: Teams, Tools, and Systems

How do we survive and thrive in an era of AI, roboticsand accelerated change? Is it with individual cleverness, or with a combinationof teams, tools, and systems?

That’s one of the questions that has driven my ongoing exploration into learning agility. But, to answer it, there’s another obvious questionto address first—and that question is, how did our ancestors hunt mammoths?

Seriously, that’s the question—keep reading, it willbe worth the trip…

Themammoth question 

Picture a mammoth, lumbering through a cold,inhospitable land. As a healthy adult, it has few predators to fear—dinosaursdied millions of years previously, so its main threat is in the form of twospecies: humans and Neanderthals.

Mammoths were crucial to the survival of bothgroups—providing meat, fat, hides, bones, and ivory for food, shelter, weaponsand even art.

There’s a lot we don’t know about that period. Someacademics believe that Neanderthals used short-ranged weapons to hunt mammothswhile humans made the shift to throwing spears. One hypothesis, although it’s recently been questioned, was that mammoths were herded off cliffs, similar tohow Native Americans in later times killed bison.

There is convincing evidence of orchestrated attacksby groups of humans in various locations. In addition to working together, a recent theory suggests that some humans trained wolves, or early dogs, to help find,trap, kill, and transport mammoths. One anthropologist went so far as to refer to this combination as the rise of a “newcombined dog-and-human predator.”

Through the debates, most of the studies I’ve reviewedimplicitly agree on one answer to the question how did humans hunt mammoths?

The answer: in teams, withtools, using systems.

Meanwhile,in New York City 

Hold that thought, and let’s fast forward to a chessmatch in New York City during May 1997.

The player moving black, a grandmaster at the top ofhis game, makes an early mistake. His opponent, playing white, is quick tocapitalize, and after 19 moves the grandmaster concedes defeat.

The grandmaster was Garry Kasparov, often cited as thebest chess player the world has seen. Kasparov’s losses were rare enough, butwhat made this defeat unique was that his opponent was IBM’s Deep Blue computer.

Deep Blue went on to win the series, the first suchvictory for a computer over a grandmaster. Today, it seems a given thatcomputers are better than humans at chess but, back then, few expected IBM’svictory.

Enterthe age of centaurs

There are three key takeaways that unfolded afterKasparov’s defeat. The first is promising, the second interesting, and, for meat least, the third was mind-blowing.

The promising? Despite being outclassed by a computer,Kasparov and other chess players did not lose their jobs. It seems like a smallpoint, but we still occasionally value humans in some roles, even where theymight be outclassed.

The interesting? The computer vs. human discussion wassuperseded in 2005, when the chess world found itself host to the first ever “freestyle”tournament. This tournament allowed mixed teams of humans and computers tocompete. Known as “centaurs,” the human-computer combinations have generallybeaten computers alone.

As Kasparov himself noted, “The teams of human plus machine dominated even thestrongest computers. The chess machine Hydra, which is a chess-specificsupercomputer like Deep Blue, was no match for a strong human player using arelatively weak laptop. Human strategic guidance combined with the tacticalacuity of a computer was overwhelming.”

Surprised? Well, I promised mind-blowing and, for me,that came when I read about the winners of that first freestyle tournament.

It wasn’t a team with a grandmaster, nor one with apowerful supercomputer. Instead, the prize went to two relative chess amateursusing three basic computers.

Their skill wasn’t in chess per se; it was in the artand systems of collaborating and making the most of their tools. Kasparov summarized this: “A weak human player plus a machine plus a betterprocess is superior to a very powerful machine alone, but more remarkably, issuperior to a strong human player plus machine and an inferior process.”

So how do you win at chessin an era of computers and AI?

The answer: in teams, withtools, using systems.

Augmentedworkers

For those of you playing at home, you might havenoticed that the story has remained the same, yet everything has changed.

Performance support and the use of tools “to get stuffdone” has defined humanity. However, the nature of those tools is growing incomplexity and impact.

The most obvious areas are the applications of robotic exoskeletons that support everything from factory workers becomingstronger to military operatives becoming safer (or deadlier, depending on yourperspective), and even to surgeons to becoming more precise.

This already has massive implications for L&D.Take Amazon’s shift from a six-week training program of new holiday factory workers to atwo-day program. It was achieved with the help of packing robots who gatheredrelevant items and provided just-in-time touch screen support for workers tofinish the job.

The less obvious but more pervasive version ofaugmentation is happening in our workplaces and homes. Take Google’s recent demonstration of Duplex, an AI-driven assistant that books reservations for haircutsand restaurants in conversational language.

Google is also ramping up its integration of AI into GSuite. For example, over 10 percent of Gmail replies are now “machine written and human accepted.” GoogleCloud CEO Dianne Greene recently commented that “We are now working to make it easy for you. We are incorporatingAI into everything you do.”

Not to be outdone, Microsoft is releasing Dynamics 365, a bundle of AI-driven applications to assist with siftingdata and providing insights in sales, customer service, and other areas. They’realso working on CAPERS,an AI function built into Outlook that identifies the most relevant emails toread so you can efficiently prepare for upcoming meetings.

Wheredoes that leave humans?

When I speak to large audiences, I often ask, “Whatpart of your work do you want to lose to robots?”

So far, everyone has wanted to lose something. For me,it’s sifting through the mountains of articles in the early stages ofresearching a new topic; bookkeeping; the daily email battle; and even aspectsof project management.

My next question is, “How would having that workautomated help you bring more of your humanity—your empathy, communication,creativity, and strategic thinking—into your work?”

I’m not alone in noting that, as technology continuesto develop, it’s opening more opportunities and need for us to embrace thosehuman aspects. And that means embracing the idea of learning and performing inteams, with tools, using systems.

Learningagility is working smart

In my experience, learning is too often seen assomething that occurs in a vacuum, in isolation from tools and people. I lovelearning for learning’s sake as much as the next geek, but in a work context,the main game is performance.

Does Figure 1 seem familiar?

Figure 1: Six alternatives and one solution for a performance problem

Rather than jump to learning, ask, “What can youdelegate? What tools and performance support can you leverage? How else can youget the job done?” Don’t just develop your way to performance outcomes, hackyour way to them first.

Learning agility, then, is as much about working smartas it is about learning fast.

I previously outlined five key areas for learning agility. My latest iteration has expanded slightly to becomeFigure 2.

Figure 2: Areas for learning agility, updated

I hope it will help serve as a reminder to start byasking, “How can we augment before we invest in developing?” Doing so can helpto free up learning to focus on the important stuff—extending our creativeproblem solving and complex human skills. Essentially it’s the stuff thatallows us to pull our weight as the fleshy side of human-robot centaurs!

On the augmented side of the equation, Figure 2emphasizes the need to establish systems and habits to amplify the impact ofthe tools, technology, and people.

So, whether you’re hunting a mammoth, winningfreestyle chess tournaments, or about to embark on a leadershipprogram—consider how you are making themost of your work in teams, with tools, using systems.

As always, I’d love to hear your experiences andthoughts on this topic either via the comments below or Linkedin and Twitter.