WEBVTT

00:00:00.973 --> 00:00:04.107
2014 is a very special year for me:

00:00:04.107 --> 00:00:06.034
20 years as a consultant,

00:00:06.034 --> 00:00:07.706
20 years of marriage,

00:00:07.706 --> 00:00:10.887
and I'm turning 50 in one month.

00:00:10.887 --> 00:00:14.208
That means I was born in 1964

00:00:14.208 --> 00:00:16.437
in a small town in Germany.

NOTE Paragraph

00:00:16.437 --> 00:00:18.155
It was a grey November day,

00:00:18.155 --> 00:00:20.035
and I was overdue.

00:00:20.035 --> 00:00:23.518
The hospital's maternity ward
was really stressed out

00:00:23.518 --> 00:00:25.840
because a lot of babies were born

00:00:25.840 --> 00:00:28.023
on this grey November day.

00:00:28.023 --> 00:00:30.995
As a matter of fact, 1964

00:00:30.995 --> 00:00:34.571
was the year with the highest
birth rate ever in Germany:

00:00:34.571 --> 00:00:36.591
more than 1.3 million.

00:00:36.591 --> 00:00:39.725
Last year, we just hit over 600,000,

00:00:39.725 --> 00:00:41.490
so half of my number.

NOTE Paragraph

00:00:41.490 --> 00:00:43.324
What you can see here

00:00:43.324 --> 00:00:45.321
is the German age pyramid,

00:00:45.321 --> 00:00:48.130
and there, the small black point
at the top, that's me.

00:00:48.130 --> 00:00:52.124
(Laughter) (Applause)

00:00:54.307 --> 00:00:58.440
In red, you can see the potential
working age population,

00:00:58.440 --> 00:01:02.689
so people over 15 and under 65,

00:01:02.689 --> 00:01:04.361
and I'm actually only interested

00:01:04.361 --> 00:01:06.404
in this red area.

NOTE Paragraph

00:01:06.404 --> 00:01:08.447
Now, let's do a simple simulation

00:01:08.447 --> 00:01:10.189
of how this age structure will develop

00:01:10.189 --> 00:01:12.975
over the next couple of years.

00:01:12.975 --> 00:01:14.414
As you can see,

00:01:14.414 --> 00:01:16.713
the peak is moving to the right,

00:01:16.713 --> 00:01:19.941
and I, with many other baby boomers,

00:01:19.941 --> 00:01:23.238
will retire in 2030.

00:01:23.238 --> 00:01:25.513
By the way, I don't need any forecasts

00:01:25.513 --> 00:01:28.648
of birth rates for
predicting this red area.

00:01:28.648 --> 00:01:31.132
The red area, so the potential working age

00:01:31.132 --> 00:01:33.291
population in 2030

00:01:33.291 --> 00:01:36.589
is already set in stone today,

00:01:36.589 --> 00:01:40.141
except for much higher migration rates.

00:01:40.141 --> 00:01:43.160
And if you compare this red area in 2030

00:01:43.160 --> 00:01:46.271
with red area in 2040,

00:01:46.271 --> 00:01:49.382
it is much, much smaller.

NOTE Paragraph

00:01:49.382 --> 00:01:51.820
So before I show you
the rest of the world,

00:01:51.820 --> 00:01:55.117
what does this mean for Germany?

00:01:55.117 --> 00:01:59.204
So what we know from
this picture is that labor supply,

00:01:59.204 --> 00:02:01.131
so people who provide labor,

00:02:01.131 --> 00:02:04.800
will go down in Germany,
and will go down significantly.

00:02:04.800 --> 00:02:07.400
Now, what about labor demand?

00:02:07.400 --> 00:02:10.372
That's where it gets tricky.

00:02:10.372 --> 00:02:12.253
As you might know, the consultant's

00:02:12.253 --> 00:02:14.970
favorite answer to any question is,

00:02:14.970 --> 00:02:16.711
"It depends."

00:02:16.711 --> 00:02:19.079
So I would say it depends.

00:02:19.079 --> 00:02:21.448
We didn't want to forecast the future.

00:02:21.448 --> 00:02:22.957
Highly speculative.

00:02:22.957 --> 00:02:24.513
We did something else.

00:02:24.513 --> 00:02:27.903
We looked at the GDP and
productivity growth of Germany

00:02:27.903 --> 00:02:29.598
over the last 20 years

00:02:29.598 --> 00:02:32.082
and calculated the following scenario:

00:02:32.082 --> 00:02:36.563
if Germany wants to continue
this GDP and productivity growth,

00:02:36.563 --> 00:02:38.676
we could directly calculate

00:02:38.676 --> 00:02:40.580
how many people Germany would need

00:02:40.580 --> 00:02:42.647
to support this growth.

00:02:42.647 --> 00:02:45.619
And this is the green line: labor demand.

00:02:45.619 --> 00:02:48.614
So Germany will run into a major

00:02:48.614 --> 00:02:51.331
talent shortage very quickly.

00:02:51.331 --> 00:02:53.165
Eight million people are missing,

00:02:53.165 --> 00:02:55.928
which is more than 20 percent
of our current work force,

00:02:55.928 --> 00:02:58.598
so big numbers, really big numbers.

00:02:58.598 --> 00:03:00.711
And we calculated several scenarios,

00:03:00.711 --> 00:03:04.565
and the picture always looked like this.

NOTE Paragraph

00:03:04.565 --> 00:03:06.446
Now, to close the gap,

00:03:06.446 --> 00:03:08.722
Germany has to significantly

00:03:08.722 --> 00:03:10.509
increase migration,

00:03:10.509 --> 00:03:12.715
get many more women in the work force,

00:03:12.715 --> 00:03:14.480
increase retirement age.

00:03:14.480 --> 00:03:16.895
— by the way, we just
lowered it this year —

00:03:16.895 --> 00:03:19.658
and all these measures at once.

00:03:19.658 --> 00:03:21.817
If Germany fails here,

00:03:21.817 --> 00:03:23.721
Germany will stagnate.

00:03:23.721 --> 00:03:26.043
We won't grow anymore. Why?

00:03:26.043 --> 00:03:29.456
Because the workers are not there
who can generate this growth.

00:03:29.456 --> 00:03:31.616
And companies will look

00:03:31.616 --> 00:03:34.123
for talents somewhere else.

00:03:34.123 --> 00:03:36.143
But where?

NOTE Paragraph

00:03:36.143 --> 00:03:39.556
Now, we simulated labor supply

00:03:39.556 --> 00:03:40.996
and labor demand

00:03:40.996 --> 00:03:43.945
for the largest 15 economies in the world,

00:03:43.945 --> 00:03:46.104
representing more than 70 percent

00:03:46.104 --> 00:03:47.637
of world GDP,

00:03:47.637 --> 00:03:49.935
and the overall picture looks like this

00:03:49.935 --> 00:03:51.584
by 2020.

00:03:51.584 --> 00:03:54.277
Blue indicates a labor surplus,

00:03:54.277 --> 00:03:56.762
red indicates a labor shortfall,

00:03:56.762 --> 00:04:00.454
and grey are those countries
which are borderline.

00:04:00.454 --> 00:04:04.540
So by 2020, we still see a labor surplus

00:04:04.540 --> 00:04:06.560
in some countries,

00:04:06.560 --> 00:04:08.743
like Italy, France, the U.S.,

00:04:08.743 --> 00:04:10.252
but this picture will change

00:04:10.252 --> 00:04:13.340
dramatically by 2030.

00:04:13.340 --> 00:04:17.961
By 2030, we will face
a global work force crisis

00:04:17.961 --> 00:04:20.956
in most of our largest economies,

00:04:20.956 --> 00:04:23.371
including three out of
the four BRIC countries.

00:04:23.371 --> 00:04:26.529
China, with its former
one-child policy, will be it,

00:04:26.529 --> 00:04:30.453
as well as Brazil and Russia.

NOTE Paragraph

00:04:30.453 --> 00:04:34.168
Now, to tell the truth,

00:04:34.168 --> 00:04:36.815
in reality, the situation

00:04:36.815 --> 00:04:38.974
will be even more challenging.

00:04:38.974 --> 00:04:42.317
What you can see here
are average numbers.

00:04:42.317 --> 00:04:44.013
We de-averaged them

00:04:44.013 --> 00:04:45.777
and broke them down

00:04:45.777 --> 00:04:47.240
to different skill levels,

00:04:47.240 --> 00:04:48.633
and what we found

00:04:48.633 --> 00:04:50.305
were even higher shortfalls

00:04:50.305 --> 00:04:51.884
for high-skilled people

00:04:51.884 --> 00:04:53.764
and a partial surplus

00:04:53.764 --> 00:04:56.063
for low-skilled workers.

00:04:56.063 --> 00:04:59.221
So on top of an overall labor shortage,

00:04:59.221 --> 00:05:02.652
we will face a big skill
mismatch in the future,

00:05:02.652 --> 00:05:05.156
and this means huge challenges

00:05:05.076 --> 00:05:07.289
in terms of education, qualification,

00:05:07.289 --> 00:05:12.013
upskilling for governments and companies.

NOTE Paragraph

00:05:12.013 --> 00:05:14.673
Now, the next thing we looked into

00:05:14.673 --> 00:05:18.375
was robots, automation, technology.

00:05:18.375 --> 00:05:21.057
Will technology change this picture

00:05:21.057 --> 00:05:23.728
and boost productivity?

00:05:23.728 --> 00:05:25.798
Now, the short answer would be

00:05:25.798 --> 00:05:27.988
that our numbers already include

00:05:27.988 --> 00:05:30.359
a significant growth in productivity

00:05:30.359 --> 00:05:33.308
driven by technology.

00:05:33.308 --> 00:05:36.628
A long answer would go like this.

00:05:36.628 --> 00:05:39.113
Let's take Germany.

00:05:39.113 --> 00:05:41.667
The Germans have a certain
reputation in the world

00:05:41.667 --> 00:05:44.244
when it comes to productivity.

00:05:44.244 --> 00:05:47.077
In the '90s, I worked in our Boston office

00:05:47.077 --> 00:05:48.841
for almost two years,

00:05:48.841 --> 00:05:51.488
and when I left, an old senior partner

00:05:51.488 --> 00:05:52.998
told me literally,

00:05:52.998 --> 00:05:54.600
"Send me more of these Germans,

00:05:54.600 --> 00:05:56.480
they work like machines."

00:05:56.480 --> 00:06:01.287
(Laughter)

00:06:01.287 --> 00:06:04.259
That was 1998.

00:06:04.259 --> 00:06:07.742
Sixteen years later, you'd
probably say the opposite.

00:06:07.742 --> 00:06:09.553
"Send me more of these machines.

00:06:09.553 --> 00:06:11.410
They work like Germans."

00:06:11.410 --> 00:06:15.566
(Laughter) (Applause)

NOTE Paragraph

00:06:18.353 --> 00:06:20.768
Technology will replace

00:06:20.768 --> 00:06:22.811
a lot of jobs, regular jobs.

00:06:22.811 --> 00:06:24.808
Not only in the production industry,

00:06:24.808 --> 00:06:26.619
but even office workers are in jeopardy

00:06:26.619 --> 00:06:29.521
and might be replaced by robots,

00:06:29.521 --> 00:06:33.120
artificial intelligence,
big data, or automation.

00:06:33.120 --> 00:06:34.954
So the key question is not

00:06:34.954 --> 00:06:38.042
if technology replaces some of these jobs,

00:06:38.042 --> 00:06:41.688
but when, how fast, and to what extent,

00:06:41.688 --> 00:06:43.383
or in other words,

00:06:43.383 --> 00:06:45.194
will technology help us

00:06:45.194 --> 00:06:48.839
to solve this global work force crisis?

00:06:48.839 --> 00:06:51.254
Yes and no.

00:06:51.254 --> 00:06:55.317
This is a more sophisticated
version of "it depends."

NOTE Paragraph

00:06:55.317 --> 00:06:58.684
Let's take the automotive industry

00:06:58.684 --> 00:07:00.310
as an example,

00:07:00.310 --> 00:07:02.213
because there, more than 40 percent

00:07:02.213 --> 00:07:04.814
of industrial robots are already working

00:07:04.814 --> 00:07:08.761
and automation has already taken place.

00:07:08.761 --> 00:07:12.360
In 1980, less than 10 percent

00:07:12.360 --> 00:07:14.659
of the production cost of a car

00:07:14.659 --> 00:07:17.329
was caused by electronic parts.

00:07:17.329 --> 00:07:20.580
Today, this number is more than 30 percent

00:07:20.580 --> 00:07:23.621
and it will grow to more than 50 percent

00:07:23.621 --> 00:07:25.618
by 2030.

00:07:25.618 --> 00:07:27.731
And these new electronic parts

00:07:27.731 --> 00:07:31.005
and applications require new skills

00:07:31.005 --> 00:07:33.652
and have created a lot of new jobs,

00:07:33.652 --> 00:07:35.720
like the cognitive systems engineer

00:07:35.720 --> 00:07:38.365
who optimizes the interaction

00:07:38.365 --> 00:07:42.081
between driver and electronic system.

00:07:42.081 --> 00:07:46.446
In 1980, no one had the slightest clue

00:07:46.446 --> 00:07:49.534
that such a job would ever exist.

00:07:49.534 --> 00:07:53.063
As a matter of fact,
the overall number of people

00:07:53.063 --> 00:07:55.246
involved in the production of a car

00:07:55.246 --> 00:07:57.103
has only changed slightly

00:07:57.103 --> 00:07:58.659
in the last decades,

00:07:58.659 --> 00:08:01.677
in spite of robots and automation.

NOTE Paragraph

00:08:01.677 --> 00:08:03.443
So what does this mean?

00:08:03.443 --> 00:08:06.135
Yes, technology will
replace a lot of jobs,

00:08:06.135 --> 00:08:08.806
but we will also see a lot of new jobs

00:08:08.806 --> 00:08:11.731
and new skills on the horizon,

00:08:11.731 --> 00:08:14.935
and that means technology will worsen

00:08:14.935 --> 00:08:17.768
our overall skill mismatch.

00:08:17.768 --> 00:08:19.510
And this kind of de-averaging

00:08:19.510 --> 00:08:21.274
reveals the crucial challenge

00:08:21.274 --> 00:08:24.478
for governments and businesses.

NOTE Paragraph

00:08:24.478 --> 00:08:29.006
So people, high-skilled people,

00:08:29.006 --> 00:08:31.096
talents, will be the big thing

00:08:31.096 --> 00:08:33.046
in the next decade.

00:08:33.046 --> 00:08:35.670
If they are the scarce resource,

00:08:35.670 --> 00:08:38.688
we have to understand them much better.

00:08:38.688 --> 00:08:41.637
Are they actually willing to work abroad?

00:08:41.637 --> 00:08:44.261
What are their job preferences?

NOTE Paragraph

00:08:44.261 --> 00:08:47.651
To find out, this year we conducted

00:08:47.651 --> 00:08:49.973
a global survey

00:08:49.973 --> 00:08:52.564
among more than 200,000 job seekers

00:08:52.564 --> 00:08:55.722
from 189 countries.

00:08:55.722 --> 00:09:00.157
Migration is certainly one key measure

00:09:00.157 --> 00:09:03.013
to close a gap, at least
in the short term,

00:09:03.013 --> 00:09:05.613
so we asked about mobility.

00:09:05.613 --> 00:09:07.680
More than 60 percent

00:09:07.680 --> 00:09:10.234
of these 200,000 job seekers

00:09:10.234 --> 00:09:12.834
are willing to work abroad,

00:09:12.834 --> 00:09:14.994
for me a surprisingly high number.

00:09:14.994 --> 00:09:18.407
If you look at the employees aged 21 to 30

00:09:18.407 --> 00:09:20.706
this number is even higher.

00:09:20.706 --> 00:09:24.026
If you split this number up by country,

00:09:24.026 --> 00:09:26.185
yes, the world is mobile,

00:09:26.185 --> 00:09:29.227
but only partly.

00:09:29.227 --> 00:09:30.713
The least mobile countries

00:09:30.713 --> 00:09:34.358
are Russia, Germany, and the U.S.

NOTE Paragraph

00:09:34.358 --> 00:09:37.632
Now, where would these
people like to move to?

00:09:37.632 --> 00:09:39.490
Number seven is Australia,

00:09:39.490 --> 00:09:42.462
where 28 percent could imagine moving to.

00:09:42.462 --> 00:09:45.387
Then France, Switzerland, Germany,

00:09:45.387 --> 00:09:49.126
Canada, U.K., and top choice worldwide

00:09:49.126 --> 00:09:50.937
is the U.S.

NOTE Paragraph

00:09:50.937 --> 00:09:54.605
Now, what are the job preferences
of these 200,000 people?

00:09:54.605 --> 00:09:57.043
So what are they looking for?

00:09:57.043 --> 00:09:59.644
Out of a list of 26 topics,

00:09:59.644 --> 00:10:03.475
salary is only number eight.

00:10:03.475 --> 00:10:08.003
The top four topics
are all around culture:

00:10:08.003 --> 00:10:09.558
number four,

00:10:09.558 --> 00:10:12.554
having a great relationship with the boss;

00:10:12.554 --> 00:10:16.571
three, enjoying a great work-life balance;

00:10:16.571 --> 00:10:20.332
two, having a great
relationship with colleagues;

00:10:20.332 --> 00:10:22.329
and top priority worldwide

00:10:22.329 --> 00:10:28.528
is being appreciated for your work.

00:10:28.528 --> 00:10:31.152
So do I get a thank you?

00:10:31.152 --> 00:10:32.801
Not only once a year

00:10:32.801 --> 00:10:34.496
with the annual bonus payment,

00:10:34.496 --> 00:10:36.701
but every day.

00:10:36.701 --> 00:10:39.720
And now, our global work force crisis

00:10:39.720 --> 00:10:41.926
becomes very personal.

00:10:41.926 --> 00:10:44.967
People are looking for recognition.

00:10:44.967 --> 00:10:47.429
Aren't we all looking for recognition

00:10:47.429 --> 00:10:49.959
in our jobs?

NOTE Paragraph

00:10:49.959 --> 00:10:54.882
Now, let me connect the dots.

00:10:54.882 --> 00:10:57.250
We will face a global work force crisis

00:10:57.250 --> 00:11:00.222
which consists of
an overall labor shortage

00:11:00.222 --> 00:11:02.173
plus a huge skill mismatch

00:11:02.173 --> 00:11:05.307
plus a big cultural challenge.

00:11:05.307 --> 00:11:06.886
And this global work force crisis

00:11:06.886 --> 00:11:09.231
is approaching very fast.

00:11:09.231 --> 00:11:11.971
Right now, we are
just at the turning point.

00:11:11.971 --> 00:11:13.689
So what can we, what can governments,

00:11:13.689 --> 00:11:16.359
what can companies do?

00:11:16.359 --> 00:11:17.962
Every company,

00:11:17.962 --> 00:11:19.749
but also every country,

00:11:19.749 --> 00:11:21.677
needs a people strategy,

00:11:21.677 --> 00:11:24.649
and act on it immediately,

00:11:24.649 --> 00:11:26.762
and such a people strategy consists

00:11:26.762 --> 00:11:28.596
out of four parts.

00:11:28.596 --> 00:11:30.407
Number one, a plan

00:11:30.407 --> 00:11:33.565
for how to forecast supply and demand

00:11:33.565 --> 00:11:36.537
for different jobs and different skills.

00:11:36.537 --> 00:11:38.232
Work force planning

00:11:38.232 --> 00:11:42.109
will become more important
than financial planning.

00:11:42.109 --> 00:11:45.732
Two, a plan for how to
attract great people:

00:11:45.732 --> 00:11:49.865
generation Y, women, but also retirees.

00:11:49.865 --> 00:11:52.837
Three, a plan for how to educate

00:11:52.837 --> 00:11:54.207
and upskill them.

00:11:54.207 --> 00:11:55.925
There's a huge upskilling challenge

00:11:55.925 --> 00:11:57.666
ahead of us.

00:11:57.666 --> 00:11:59.500
And four,

00:11:59.500 --> 00:12:02.333
for how to retain the best people,

00:12:02.333 --> 00:12:03.726
or in other words,

00:12:03.726 --> 00:12:06.698
how to realize an appreciation

00:12:06.698 --> 00:12:11.215
and relationship culture.

NOTE Paragraph

00:12:11.215 --> 00:12:15.382
However, one crucial underlying factor

00:12:15.382 --> 00:12:18.424
is to change our attitudes.

00:12:18.424 --> 00:12:22.627
Employees are resources, are assets,

00:12:22.627 --> 00:12:25.250
not costs, not head counts,

00:12:25.250 --> 00:12:28.176
not machines, not even the Germans.

NOTE Paragraph

00:12:28.176 --> 00:12:30.544
Thank you.

NOTE Paragraph

00:12:30.544 --> 00:12:34.431
(Applause)