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Recently, the leadership team
of an American supermarket chain

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decided that their business
needed to get a lot more efficient.

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So they embraced their digital
transformation with zeal.

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Out went the teams
supervising meat, veg, bakery,

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and in came an algorithmic task allocator.

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Now, instead of people working together,

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each employee went, clocked in,
got assigned a task, did it,

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came back for more.

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This was scientific
management on steroids,

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standardizing and allocating work.

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It was super efficient.

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Well, not quite,

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because the task allocator didn't know

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when a customer was going
to drop a box of eggs,

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couldn't predict when some crazy kid
was going to knock over a display,

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or when the local high school decided

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that everybody needed
to bring in coconuts the next day.

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(Laughter)

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Efficiency works really well

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when you can predict
exactly what you're going to need.

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But when the anomalous
or unexpected comes along --

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kids, customers, coconuts --

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well, then efficiency
is no longer your friend.

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This has become a really crucial issue,

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this ability to deal with the unexpected,

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because the unexpected
is becoming the norm.

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It's why experts and forecasters
are reluctant to predict anything

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more than 400 days out.

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Why?

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Because over the last 20 or 30 years,

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much of the world has gone
from being complicated

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to being complex --

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which means that yes, there are patterns,

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but they don't repeat
themselves regularly.

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It means that very small changes
can make a disproportionate impact.

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And it means that expertise
won't always suffice,

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because the system
just keeps changing too fast.

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So what that means

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is that there's a huge amount in the world

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that kind of defies forecasting now.

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It's why the Bank of England will say
yes, there will be another crash,

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but we don't know why or when.

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We know that climate change is real,

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but we can't predict
where forest fires will break out,

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and we don't know which factories
are going to flood.

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It's why companies are blindsided

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when plastic straws
and bags and bottled water

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go from staples to rejects overnight,

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and baffled when a change in social mores

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turns stars into pariahs
and colleagues into outcasts:

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ineradicable uncertainty.

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In an environment that defies
so much forecasting,

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efficiency won't just not help us,

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it specifically undermines and erodes
our capacity to adapt and respond.

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So if efficiency is no longer
our guiding principle,

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how should we address the future?

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What kind of thinking
is really going to help us?

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What sort of talents
must we be sure to defend?

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I think that, where in the past we used to
think a lot about just in time management,

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now we have to start thinking
about just in case,

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preparing for events
that are generally certain

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but specifically remain ambiguous.

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One example of this is the Coalition
for Epidemic Preparedness, CEPI.

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We know there will be
more epidemics in future,

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but we don't know where or when or what.

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So we can't plan.

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But we can prepare.

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So CEPI's developing multiple vaccines
for multiple diseases,

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knowing that they can't predict
which vaccines are going to work

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or which diseases will break out.

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So some of those vaccines
will never be used.

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That's inefficient.

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But it's robust,

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because it provides more options,

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and it means that we don't depend
on a single technological solution.

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Epidemic responsiveness
also depends hugely

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on people who know and trust each other.

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But those relationships
take time to develop,

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time that is always in short supply
when an epidemic breaks out.

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So CEPI is developing relationships,
friendships, alliances now

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knowing that some of those
may never be used.

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That's inefficient,
a waste of time, perhaps,

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but it's robust.

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You can see robust thinking
in financial services, too.

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In the past, banks used to hold
much less capital

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than they're required to today,

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because holding so little capital,
being too efficient with it,

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is what made the banks
so fragile in the first place.

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Now, holding more capital
looks and is inefficient.

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But it's robust, because it protects
the financial system against surprises.

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Countries that are really serious
about climate change

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know that they have to adopt
multiple solutions,

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multiple forms of renewable energy,

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not just one.

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The countries that are most advanced
have been working for years now,

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changing their water and food supply
and healthcare systems,

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because they recognize that by the time
they have certain prediction,

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that information may very well
come too late.

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You can take the same approach
to trade wars, and many countries do.

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Instead of depending on a single
huge trading partner,

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they try to be everybody's friends,

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because they know they can't predict

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which markets might
suddenly become unstable.

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It's time-consuming and expensive,
negotiating all these deals,

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but it's robust

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because it makes their whole economy
better defended against shocks.

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It's particularly a strategy
adopted by small countries

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that know they'll never have
the market muscle to call the shots,

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so it's just better to have
too many friends.

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But if you're stuck
in one of these organizations

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that's still kind of captured
by the efficiency myth,

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how do you start to change it?

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Try some experiments.

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In the Netherlands,

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home care nursing used to be run
pretty much like the supermarket:

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standardized and prescribed work

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to the minute:

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nine minutes on Monday,
seven minutes on Wednesday,

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eight minutes on Friday.

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The nurses hated it.

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So one of them, Jos de Blok,

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proposed an experiment.

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Since every patient is different,

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and we don't quite know
exactly what they'll need,

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why don't we just leave it
to the nurses to decide?

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Sound reckless?

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(Laughter)

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(Applause)

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In his experiment, Jos found
the patients got better

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in half the time,

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and costs fell by 30 percent.

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When I asked Jos what had surprised him
about his experiment,

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he just kind of laughed and he said,

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"Well, I had no idea it could be so easy

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to find such a huge improvement,

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because this isn't the kind of thing
you can know or predict

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sitting at a desk
or staring at a computer screen."

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So now this form of nursing
has proliferated across the Netherlands

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and around the world.

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But in every new country
it still starts with experiments,

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because each place is slightly
and unpredictably different.

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Of course, not all experiments work.

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Jos tried a similar approach
to the fire service

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and found it didn't work because
the service is just too centralized.

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Failed experiments look inefficient,

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but they're often the only way
you can figure out

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how the real world works.

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So now he's trying teachers.

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Experiments like that require creativity

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and not a little bravery.

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In England --

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I was about to say in the UK,
but in England --

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(Laughter)

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(Applause)

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In England, the leading rugby team,
or one of the leading rugby teams,

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is Saracens.

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The manager and the coach there realized
that all the physical training they do

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and the data-driven
conditioning that they do

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has become generic;

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really, all the teams
do exactly the same thing.

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So they risked an experiment.

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They took the whole team away,
even in match season,

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on ski trips

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and to look at social projects in Chicago.

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This was expensive,

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it was time-consuming,

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and it could be a little risky

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putting a whole bunch of rugby players
on a ski slope, right?

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(Laughter)

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But what they found was that
the players came back

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with renewed bonds
of loyalty and solidarity.

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And now when they're on the pitch
under incredible pressure,

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they manifest what the manager
calls "poise" --

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an unflinching, unwavering dedication

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to each other.

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Their opponents are in awe of this,

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but still too in thrall
to efficiency to try it.

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At a London tech company, Verve,

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the CEO measures just about
everything that moves,

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but she couldn't find anything
that made any difference

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to the company's productivity.

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So she devised an experiment
that she calls "Love Week":

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a whole week where each employee
has to look for really clever,

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helpful, imaginative things

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that a counterpart does,

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call it out and celebrate it.

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It takes a huge amount of time and effort;

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lots of people would call it distracting.

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But it really energizes the business

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and makes the whole company
more productive.

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Preparedness, coalition-building,

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imagination, experiments,

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bravery --

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in an unpredictable age,

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these are tremendous sources
of resilience and strength.

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They aren't efficient,

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but they give us limitless capacity

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for adaptation, variation and invention.

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And the less we know about the future,

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the more we're going to need
these tremendous sources

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of human, messy, unpredictable skills.

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But in our growing
dependence on technology,

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we're asset-stripping those skills.

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Every time we use technology

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to nudge us through a decision or a choice

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or to interpret how somebody's feeling

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or to guide us through a conversation,

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we outsource to a machine
what we could, can do ourselves,

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and it's an expensive trade-off.

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The more we let machines think for us,

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the less we can think for ourselves.

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The more --

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(Applause)

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The more time doctors spend
staring at digital medical records,

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the less time they spend
looking at their patients.

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The more we use parenting apps,

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the less we know our kids.

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The more time we spend with people that
we're predicted and programmed to like,

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the less we can connect with people
who are different from ourselves.

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And the less compassion we need,
the less compassion we have.

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What all of these
technologies attempt to do

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is to force-fit a standardized model
of a predictable reality

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onto a world that is
infinitely surprising.

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What gets left out?

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Anything that can't be measured --

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which is just about
everything that counts.

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(Applause)

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Our growing dependence on technology

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risks us becoming less skilled,

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more vulnerable

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to the deep and growing complexity

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of the real world.

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Now, as I was thinking about
the extremes of stress and turbulence

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that we know we will have to confront,

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I went and I talked to
a number of chief executives

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whose own businesses had gone
through existential crises,

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when they teetered
on the brink of collapse.

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These were frank,
gut-wrenching conversations.

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Many men wept just remembering.

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So I asked them:

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"What kept you going through this?"

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And they all had exactly the same answer.

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"It wasn't data or technology," they said.

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"It was my friends and my colleagues

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who kept me going."

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One added, "It was pretty much
the opposite of the gig economy."

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But then I went and I talked to a group
of young, rising executives,

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and I asked them,

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"Who are your friends at work?"

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And they just looked blank.

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"There's no time."

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"They're too busy."

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"It's not efficient."

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Who, I wondered, is going to give them

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imagination and stamina and bravery

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00:14:36,065 --> 00:14:37,581
when the storms come?

257
00:14:39,694 --> 00:14:43,337
Anyone who tries to tell you
that they know the future

258
00:14:43,361 --> 00:14:45,559
is just trying to own it,

259
00:14:45,583 --> 00:14:48,891
a spurious kind of manifest destiny.

260
00:14:49,794 --> 00:14:52,115
The harder, deeper truth is

261
00:14:53,126 --> 00:14:55,535
that the future is uncharted,

262
00:14:55,559 --> 00:14:57,803
that we can't map it till we get there.

263
00:14:58,734 --> 00:15:00,797
But that's OK,

264
00:15:00,821 --> 00:15:03,838
because we have so much imagination --

265
00:15:03,862 --> 00:15:05,309
if we use it.

266
00:15:05,333 --> 00:15:10,810
We have deep talents
of inventiveness and exploration --

267
00:15:10,834 --> 00:15:12,611
if we apply them.

268
00:15:12,635 --> 00:15:18,152
We are brave enough to invent things
we've never seen before.

269
00:15:19,175 --> 00:15:20,790
Lose those skills,

270
00:15:21,810 --> 00:15:23,532
and we are adrift.

271
00:15:24,384 --> 00:15:27,109
But hone and develop them,

272
00:15:28,498 --> 00:15:30,956
we can make any future we choose.

273
00:15:32,382 --> 00:15:33,556
Thank you.

274
00:15:33,580 --> 00:15:39,666
(Applause)