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Build Structures Around Autonomy
In chapter 3, we tackled a key question: If the hyperactive hive mind
is so ineffective, why is it so popular? Ironically, a major part of the
answer I provided concerned the very same person who identified
knowledge worker productivity as the central challenge of the
twenty-first century: Peter Drucker. As detailed earlier, during the
1950s and 1960s, Drucker helped the business world understand the
emergence of knowledge work as a major economic sector. One of his
central messages was the importance of autonomy. “The knowledge
worker cannot be supervised closely or in detail,” he wrote in 1967.
“He must direct himself.”
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What Drucker realized was that knowledge work was too skilled
and creative to be broken down into a series of repetitive tasks that
could be prescribed to workers by managers, as was the case with
manual labor. There was simply no easy way to take something as
abstract as coming up with a new business strategy, or innovating a
new scientific industrial process, and reduce it down to a clear series
of optimized steps to be followed unthinkingly.
This emphasis on autonomy proved influential, and it goes a long
way toward explaining the stubborn persistence of the hive mind. As
I argued, when you delegate productivity decisions to the individual,
it’s not surprising that you end up stuck with a simple, flexible,
lowest common denominator–style workflow like the hyperactive
hive mind.
It’s here that we arrive at an impasse. On the one hand,
autonomy is unavoidable in knowledge work due to the complexity of
these efforts. On the other hand, autonomy entrenches hive mind–
style workflows. To succeed with applying the attention capital
principle, we must somehow escape this trap. To do so, we’ll have to
pick up where Drucker left off and clarify exactly where autonomy
really matters.
—
Knowledge work is better understood as the combination of two
components: work execution and workflow. The first component,
work execution, describes the act of actually executing the underlying
value-producing activities of knowledge work—the programmer
coding, the publicist writing the press release. It’s how you generate
value from attention capital.
The second component, workflow, is one we defined in the
introduction of this book. It describes how these fundamental
activities are identified, assigned, coordinated, and reviewed. The
hyperactive hive mind is a workflow, as is Devesh’s project board
system. If work execution is what generates value, then workflows
are what structure these efforts.
Once we understand that these components describe two
different things, we find a way to escape the autonomy trap. When
Drucker emphasized autonomy, he was thinking about work
execution, as these activities are often too complicated to be
decomposed into rote procedures. Workflows, on the other hand,
should not be left to individuals to figure out on their own, as the
most effective systems are unlikely to arise naturally. They need
instead to be explicitly identified as part of an organization’s
operating procedures.
If I manage a development team, I shouldn’t tell my computer
programmers how to write specific routines. I should, however, think
a lot about how many routines they’re asked to write, how these tasks
are tracked, how we manage the code base, and even who else in the
organization is allowed to bother them, and so on. (For more on
radically original workflows for software developers, see the case
study on extreme programming in chapter 7.)
We see this division in action in Devesh’s marketing firm. By
moving project management to Trello boards, Devesh didn’t
constrain how his team actually executed the core activities of
designing and deploying marketing campaigns. What he did change,
however, was the workflows that supported these activities—
including how information about these projects was tracked, and
how relevant information and questions were communicated. He
innovated workflows but left the details of work execution up to his
skilled employees. You’ll see this same division in most of the case
studies and examples you’ll encounter in the chapters ahead.
To be fair, we cannot blame Drucker for not making this
distinction in his original research on knowledge work. In the 1950s
and 1960s, when he first began tackling this topic, the notion of any
employee autonomy was so radical that there wasn’t much room left
for nuance. It was hard enough to simply convince people that the
authoritarian approach that had produced such miraculous growth
in the industrial sector might not apply to this new type of brain-
centric activity.
Drucker succeeded in his efforts to preach the gospel of
autonomy to a skeptical audience, and those of us who participate in
the knowledge sector today are beneficiaries of these clear-sighted
arguments. We cannot, however, stop here. To deliver the great
promise of the attention capital principle, we must stand on
Drucker’s shoulders and push these theories toward their next
evolution in complexity. Differentiating workflows and work
execution is crucial if we’re going to continue to improve knowledge
sector productivity. To get the full value of attention capital, we must
start taking seriously the way we structure work. This doesn’t stifle
the autonomy of knowledge workers, but instead sets them up to
make even more out of their skill and creativity.
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