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← The human skills we need in an unpredictable world

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Showing Revision 8 created 08/22/2019 by Oliver Friedman.

  1. Recently, the leadership team
    of an American supermarket chain
  2. decided that their business
    needed to get a lot more efficient.
  3. So they embraced their digital
    transformation with zeal.
  4. Out went the teams
    supervising meat, veg, bakery,
  5. and in came an algorithmic task allocator.
  6. Now, instead of people working together,
  7. each employee went, clocked in,
    got assigned a task, did it,
  8. came back for more.
  9. This was scientific
    management on steroids,
  10. standardizing and allocating work.
  11. It was super efficient.
  12. Well, not quite,

  13. because the task allocator didn't know
  14. when a customer was going
    to drop a box of eggs,
  15. couldn't predict when some crazy kid
    was going to knock over a display,
  16. or when the local high school decided
  17. that everybody needed
    to bring in coconuts the next day.
  18. (Laughter)

  19. Efficiency works really well

  20. when you can predict
    exactly what you're going to need.
  21. But when the anomalous
    or unexpected comes along --
  22. kids, customers, coconuts --
  23. well, then efficiency
    is no longer your friend.
  24. This has become a really crucial issue,

  25. this ability to deal with the unexpected,
  26. because the unexpected
    is becoming the norm.
  27. It's why experts and forecasters
    are reluctant to predict anything
  28. more than 400 days out.
  29. Why?
  30. Because over the last 20 or 30 years,
  31. much of the world has gone
    from being complicated
  32. to being complex --
  33. which means that yes, there are patterns,
  34. but they don't repeat
    themselves regularly.
  35. It means that very small changes
    can make a disproportionate impact.
  36. And it means that expertise
    won't always suffice,
  37. because the system
    just keeps changing too fast.
  38. So what that means

  39. is that there's a huge amount in the world
  40. that kind of defies forecasting now.
  41. It's why the Bank of England will say
    yes, there will be another crash,
  42. but we don't know why or when.
  43. We know that climate change is real,
  44. but we can't predict
    where forest fires will break out,
  45. and we don't know which factories
    are going to flood.
  46. It's why companies are blindsided
  47. when plastic straws
    and bags and bottled water
  48. go from staples to rejects overnight,
  49. and baffled when a change in social mores
  50. turns stars into pariahs
    and colleagues into outcasts:
  51. ineradicable uncertainty.
  52. In an environment that defies
    so much forecasting,
  53. efficiency won't just not help us,
  54. it specifically undermines and erodes
    our capacity to adapt and respond.
  55. So if efficiency is no longer
    our guiding principle,

  56. how should we address the future?
  57. What kind of thinking
    is really going to help us?
  58. What sort of talents
    must we be sure to defend?
  59. I think that, where in the past we used to
    think a lot about just in time management,
  60. now we have to start thinking
    about just in case,
  61. preparing for events
    that are generally certain
  62. but specifically remain ambiguous.
  63. One example of this is the Coalition
    for Epidemic Preparedness, CEPI.

  64. We know there will be
    more epidemics in future,
  65. but we don't know where or when or what.
  66. So we can't plan.
  67. But we can prepare.
  68. So CEPI's developing multiple vaccines
    for multiple diseases,
  69. knowing that they can't predict
    which vaccines are going to work
  70. or which diseases will break out.
  71. So some of those vaccines
    will never be used.
  72. That's inefficient.
  73. But it's robust,
  74. because it provides more options,
  75. and it means that we don't depend
    on a single technological solution.
  76. Epidemic responsiveness
    also depends hugely
  77. on people who know and trust each other.
  78. But those relationships
    take time to develop,
  79. time that is always in short supply
    when an epidemic breaks out.
  80. So CEPI is developing relationships,
    friendships, alliances now
  81. knowing that some of those
    may never be used.
  82. That's inefficient,
    a waste of time, perhaps,
  83. but it's robust.
  84. You can see robust thinking
    in financial services, too.

  85. In the past, banks used to hold
    much less capital
  86. than they're required to today,
  87. because holding so little capital,
    being too efficient with it,
  88. is what made the banks
    so fragile in the first place.
  89. Now, holding more capital
    looks and is inefficient.
  90. But it's robust, because it protects
    the financial system against surprises.
  91. Countries that are really serious
    about climate change

  92. know that they have to adopt
    multiple solutions,
  93. multiple forms of renewable energy,
  94. not just one.
  95. The countries that are most advanced
    have been working for years now,
  96. changing their water and food supply
    and healthcare systems,
  97. because they recognize that by the time
    they have certain prediction,
  98. that information may very well
    come too late.
  99. You can take the same approach
    to trade wars, and many countries do.

  100. Instead of depending on a single
    huge trading partner,
  101. they try to be everybody's friends,
  102. because they know they can't predict
  103. which markets might
    suddenly become unstable.
  104. It's time-consuming and expensive,
    negotiating all these deals,
  105. but it's robust
  106. because it makes their whole economy
    better defended against shocks.
  107. It's particularly a strategy
    adopted by small countries
  108. that know they'll never have
    the market muscle to call the shots,
  109. so it's just better to have
    too many friends.
  110. But if you're stuck
    in one of these organizations
  111. that's still kind of captured
    by the efficiency myth,
  112. how do you start to change it?
  113. Try some experiments.
  114. In the Netherlands,

  115. home care nursing used to be run
    pretty much like the supermarket:
  116. standardized and prescribed work
  117. to the minute:
  118. nine minutes on Monday,
    seven minutes on Wednesday,
  119. eight minutes on Friday.
  120. The nurses hated it.
  121. So one of them, Jos de Blok,
  122. proposed an experiment.
  123. Since every patient is different,
  124. and we don't quite know
    exactly what they'll need,
  125. why don't we just leave it
    to the nurses to decide?
  126. Sound reckless?

  127. (Laughter)

  128. (Applause)

  129. In his experiment, Jos found
    the patients got better

  130. in half the time,
  131. and costs fell by 30 percent.
  132. When I asked Jos what had surprised him
    about his experiment,
  133. he just kind of laughed and he said,
  134. "Well, I had no idea it could be so easy
  135. to find such a huge improvement,
  136. because this isn't the kind of thing
    you can know or predict
  137. sitting at a desk
    or staring at a computer screen."
  138. So now this form of nursing
    has proliferated across the Netherlands
  139. and around the world.
  140. But in every new country
    it still starts with experiments,
  141. because each place is slightly
    and unpredictably different.
  142. Of course, not all experiments work.

  143. Jos tried a similar approach
    to the fire service
  144. and found it didn't work because
    the service is just too centralized.
  145. Failed experiments look inefficient,
  146. but they're often the only way
    you can figure out
  147. how the real world works.
  148. So now he's trying teachers.
  149. Experiments like that require creativity
  150. and not a little bravery.
  151. In England --

  152. I was about to say in the UK,
    but in England --
  153. (Laughter)

  154. (Applause)

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

  156. is Saracens.
  157. The manager and the coach there realized
    that all the physical training they do
  158. and the data-driven
    conditioning that they do
  159. has become generic;
  160. really, all the teams
    do exactly the same thing.
  161. So they risked an experiment.
  162. They took the whole team away,
    even in match season,
  163. on ski trips
  164. and to look at social projects in Chicago.
  165. This was expensive,
  166. it was time-consuming,
  167. and it could be a little risky
  168. putting a whole bunch of rugby players
    on a ski slope, right?
  169. (Laughter)

  170. But what they found was that
    the players came back

  171. with renewed bonds
    of loyalty and solidarity.
  172. And now when they're on the pitch
    under incredible pressure,
  173. they manifest what the manager
    calls "poise" --
  174. an unflinching, unwavering dedication
  175. to each other.
  176. Their opponents are in awe of this,
  177. but still too in thrall
    to efficiency to try it.
  178. At a London tech company, Verve,

  179. the CEO measures just about
    everything that moves,
  180. but she couldn't find anything
    that made any difference
  181. to the company's productivity.
  182. So she devised an experiment
    that she calls "Love Week":
  183. a whole week where each employee
    has to look for really clever,
  184. helpful, imaginative things
  185. that a counterpart does,
  186. call it out and celebrate it.
  187. It takes a huge amount of time and effort;
  188. lots of people would call it distracting.
  189. But it really energizes the business
  190. and makes the whole company
    more productive.
  191. Preparedness, coalition-building,

  192. imagination, experiments,
  193. bravery --
  194. in an unpredictable age,
  195. these are tremendous sources
    of resilience and strength.
  196. They aren't efficient,
  197. but they give us limitless capacity
  198. for adaptation, variation and invention.
  199. And the less we know about the future,
  200. the more we're going to need
    these tremendous sources
  201. of human, messy, unpredictable skills.
  202. But in our growing
    dependence on technology,

  203. we're asset-stripping those skills.
  204. Every time we use technology
  205. to nudge us through a decision or a choice
  206. or to interpret how somebody's feeling
  207. or to guide us through a conversation,
  208. we outsource to a machine
    what we could, can do ourselves,
  209. and it's an expensive trade-off.
  210. The more we let machines think for us,
  211. the less we can think for ourselves.
  212. The more --
  213. (Applause)

  214. The more time doctors spend
    staring at digital medical records,

  215. the less time they spend
    looking at their patients.
  216. The more we use parenting apps,
  217. the less we know our kids.
  218. The more time we spend with people that
    we're predicted and programmed to like,
  219. the less we can connect with people
    who are different from ourselves.
  220. And the less compassion we need,
    the less compassion we have.
  221. What all of these
    technologies attempt to do

  222. is to force-fit a standardized model
    of a predictable reality
  223. onto a world that is
    infinitely surprising.
  224. What gets left out?
  225. Anything that can't be measured --
  226. which is just about
    everything that counts.
  227. (Applause)

  228. Our growing dependence on technology

  229. risks us becoming less skilled,
  230. more vulnerable
  231. to the deep and growing complexity
  232. of the real world.
  233. Now, as I was thinking about
    the extremes of stress and turbulence

  234. that we know we will have to confront,
  235. I went and I talked to
    a number of chief executives
  236. whose own businesses had gone
    through existential crises,
  237. when they teetered
    on the brink of collapse.
  238. These were frank,
    gut-wrenching conversations.
  239. Many men wept just remembering.
  240. So I asked them:
  241. "What kept you going through this?"
  242. And they all had exactly the same answer.

  243. "It wasn't data or technology," they said.
  244. "It was my friends and my colleagues
  245. who kept me going."
  246. One added, "It was pretty much
    the opposite of the gig economy."

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

  248. and I asked them,
  249. "Who are your friends at work?"
  250. And they just looked blank.
  251. "There's no time."

  252. "They're too busy."

  253. "It's not efficient."

  254. Who, I wondered, is going to give them

  255. imagination and stamina and bravery
  256. when the storms come?
  257. Anyone who tries to tell you
    that they know the future

  258. is just trying to own it,
  259. a spurious kind of manifest destiny.
  260. The harder, deeper truth is
  261. that the future is uncharted,
  262. that we can't map it till we get there.
  263. But that's OK,

  264. because we have so much imagination --
  265. if we use it.
  266. We have deep talents
    of inventiveness and exploration --
  267. if we apply them.
  268. We are brave enough to invent things
    we've never seen before.
  269. Lose those skills,
  270. and we are adrift.
  271. But hone and develop them,
  272. we can make any future we choose.
  273. Thank you.

  274. (Applause)