The challenge is changing how organizations make important
decisions, from front-line employees to the top of the organization.
People get to make decisions, but computers get to be part of it to
let them know if they seem to be heading in the wrong direction.
— ANDREW McAFEE
and test hypotheses. You can run experiments. You
can adopt a much more scientific mind-set.
I think if you don’t try to migrate your company
and your decision making in that direction, you’re
missing out on a huge opportunity, and you had
better hope your competition is also not moving in
that direction. Because when you compare scientific to pre-scientific approaches, there’s one clear
winner over and over.
Can you give an example?
Here’s one. If you build a pretty simple model of a
lot of medical, clinical decisions, then run a bunch
of patients through that model and also run them
by an experienced clinician, the model’s going to do
a better job of diagnosing them and improving
health outcomes.
It’s really intriguing. The pattern appears to be
that if you have to make a choice between complete
reliance on human intuition and turning things
over to a computer to spit out an answer, you might
want to turn things over to the computer.
But I set up a false choice. You actually don’t have to
choose exclusively between human intuition and push
the button and run with what answer comes out of
the computer. You can blend the two. What the machine’s not going to be really good at is what some
people have termed complex communication.
The challenge is changing how organizations
make important decisions, from front-line employees to middle-level managers to the top of the
organization. People get to make decisions, but
computers get to be part of it to let them know if
they seem to be heading in the wrong direction.
But it’s easy to imagine a lot of impediments to
meaningful change in how companies actually
make decisions…
Well, this transformation toward a more “scientific
organization” is a long, slow, uphill battle. It’s un-
comfortable in a lot of ways for people to be
second-guessed, to be put into a process in tandem
with a machine. It’s not easy to become more data
reliant, to become more enumerate, to become
more quantitatively oriented, to understand what
an experiment is and what a control group is and
what’s a significant difference. We’re not terribly
well trained for it, most of us. And so instilling this
philosophy inside an organization is a long, slow
transition.
Right, people in business talk all the time about
“experimenting.” But they don’t mean experimenting; they mean “trying stuff.”
Or they mean, “Let’s design something that’s going
to confirm what I really want to have happen here.”
An eight-month process to spit out exactly the result that they want.
When we look at what real experimenting organizations do, they approach this in an open spirit
of, “I don’t know what the answer is, and that’s
what’s really exciting. I’m going to throw something
out to see if this heads us in more the correct direction or the wrong one. Based on what I learn, I’m
then going to do a subsequent trial.”
Are there particular characteristics companies
will need to cultivate in order to make this “
scientific” transition?
F. Scott Fitzgerald has a fantastic quote, I think in
his book The Crack-Up. I’m going to mangle it, but
he talks about how one of the characteristics of a
first-rate mind is the ability to hold two opposing
viewpoints at the same time and not go crazy. That’s
really becoming important in organizations today.
The two opposing viewpoints are, first of all,
that in a lot of ways companies have the opportunity to become even more tightly orchestrated,
regimented, regulated via technology. We have all
this amazing business-process technology that
