1 00:00:00,973 --> 00:00:04,107 2014 is a very special year for me: 2 00:00:04,107 --> 00:00:06,034 20 years as a consultant, 3 00:00:06,034 --> 00:00:07,706 20 years of marriage, 4 00:00:07,706 --> 00:00:10,887 and I'm turning 50 in one month. 5 00:00:10,887 --> 00:00:16,437 That means I was born in 1964 in a small town in Germany. 6 00:00:16,437 --> 00:00:18,155 It was a Grey November day, 7 00:00:18,155 --> 00:00:20,035 and I was overdue. 8 00:00:20,035 --> 00:00:23,518 The hospital's maternity ward was really stressed out 9 00:00:23,518 --> 00:00:28,023 because a lot of babies were born on this Grey November day. 10 00:00:28,023 --> 00:00:30,995 As a matter of fact, 11 00:00:30,995 --> 00:00:34,571 1964 was the year with the highest birth rate ever in Germany: 12 00:00:34,571 --> 00:00:36,591 more than 1.3 million. 13 00:00:36,591 --> 00:00:39,725 Last year, we just hit over 600,000, 14 00:00:39,725 --> 00:00:40,947 so half of my number. 15 00:00:41,490 --> 00:00:45,321 What you can see here is the German age pyramid, 16 00:00:45,321 --> 00:00:48,130 and there, the small black point at the top, that's me. 17 00:00:48,130 --> 00:00:52,124 (Laughter) (Applause) 18 00:00:54,307 --> 00:00:58,440 In red, you can see the potential working-age population, 19 00:00:58,440 --> 00:01:02,689 so people over 15 and under 65, 20 00:01:02,689 --> 00:01:06,404 and I'm actually only interested in this red area. 21 00:01:06,404 --> 00:01:08,447 Now, let's do a simple simulation 22 00:01:08,447 --> 00:01:12,975 of how this age structure will develop over the next couple of years. 23 00:01:12,975 --> 00:01:14,414 As you can see, 24 00:01:14,414 --> 00:01:16,713 the peak is moving to the right, 25 00:01:16,713 --> 00:01:23,238 and I, with many other baby boomers, will retire in 2030. 26 00:01:23,238 --> 00:01:25,513 By the way, I don't need any forecasts 27 00:01:25,513 --> 00:01:28,382 of birth rates for predicting this red area. 28 00:01:28,406 --> 00:01:29,654 The red area, 29 00:01:29,678 --> 00:01:33,290 so the potential working-age population in 2030, 30 00:01:33,291 --> 00:01:36,589 is already set in stone today, 31 00:01:36,589 --> 00:01:40,141 except for much higher migration rates. 32 00:01:40,141 --> 00:01:46,271 And if you compare this red area in 2030 with the red area in 2040, 33 00:01:46,271 --> 00:01:49,382 it is much, much smaller. 34 00:01:49,382 --> 00:01:51,820 So before I show you the rest of the world, 35 00:01:51,820 --> 00:01:55,117 what does this mean for Germany? 36 00:01:55,117 --> 00:01:59,204 So what we know from this picture is that labor supply, 37 00:01:59,204 --> 00:02:01,131 so people who provide labor, 38 00:02:01,131 --> 00:02:04,800 will go down in Germany, and will go down significantly. 39 00:02:04,800 --> 00:02:07,400 Now, what about labor demand? 40 00:02:07,400 --> 00:02:09,211 That's where it gets tricky. 41 00:02:10,163 --> 00:02:14,970 As you might know, the consultant's favorite answer to any question is, 42 00:02:14,970 --> 00:02:16,711 "It depends." 43 00:02:16,711 --> 00:02:19,079 So I would say it depends. 44 00:02:19,079 --> 00:02:21,448 We didn't want to forecast the future. 45 00:02:21,448 --> 00:02:22,957 Highly speculative. 46 00:02:22,957 --> 00:02:24,513 We did something else. 47 00:02:24,513 --> 00:02:27,903 We looked at the GDP and productivity growth of Germany 48 00:02:27,903 --> 00:02:29,598 over the last 20 years, 49 00:02:29,598 --> 00:02:32,082 and calculated the following scenario: 50 00:02:32,082 --> 00:02:36,563 if Germany wants to continue this GDP and productivity growth, 51 00:02:36,563 --> 00:02:38,676 we could directly calculate 52 00:02:38,676 --> 00:02:42,647 how many people Germany would need to support this growth. 53 00:02:42,647 --> 00:02:45,619 And this is the green line: labor demand. 54 00:02:45,619 --> 00:02:51,331 So Germany will run into a major talent shortage very quickly. 55 00:02:51,331 --> 00:02:53,165 Eight million people are missing, 56 00:02:53,165 --> 00:02:55,928 which is more than 20 percent of our current work force, 57 00:02:55,928 --> 00:02:58,598 so big numbers, really big numbers. 58 00:02:58,598 --> 00:03:00,711 And we calculated several scenarios, 59 00:03:00,711 --> 00:03:03,173 and the picture always looked like this. 60 00:03:04,565 --> 00:03:06,446 Now, to close the gap, 61 00:03:06,446 --> 00:03:10,509 Germany has to significantly increase migration, 62 00:03:10,509 --> 00:03:12,715 get many more women in the work force, 63 00:03:12,715 --> 00:03:14,480 increase retirement age. 64 00:03:14,480 --> 00:03:16,895 — by the way, we just lowered it this year — 65 00:03:16,895 --> 00:03:19,658 and all these measures at once. 66 00:03:19,658 --> 00:03:23,721 If Germany fails here, Germany will stagnate. 67 00:03:23,721 --> 00:03:26,043 We won't grow anymore. Why? 68 00:03:26,043 --> 00:03:29,456 Because the workers are not there who can generate this growth. 69 00:03:29,456 --> 00:03:34,123 And companies will look for talents somewhere else. 70 00:03:34,123 --> 00:03:35,289 But where? 71 00:03:36,730 --> 00:03:40,996 Now, we simulated labor supply and labor demand 72 00:03:40,996 --> 00:03:43,945 for the largest 15 economies in the world, 73 00:03:43,945 --> 00:03:47,637 representing more than 70 percent of world GDP, 74 00:03:47,637 --> 00:03:51,584 and the overall picture looks like this by 2020. 75 00:03:51,584 --> 00:03:54,277 Blue indicates a labor surplus, 76 00:03:54,277 --> 00:03:56,762 red indicates a labor shortfall, 77 00:03:56,762 --> 00:04:00,454 and Grey are those countries which are borderline. 78 00:04:00,454 --> 00:04:06,560 So by 2020, we still see a labor surplus in some countries, 79 00:04:06,560 --> 00:04:08,743 like Italy, France, the U.S., 80 00:04:08,743 --> 00:04:13,340 but this picture will change dramatically by 2030. 81 00:04:13,340 --> 00:04:17,961 By 2030, we will face a global work-force crisis 82 00:04:17,961 --> 00:04:20,956 in most of our largest economies, 83 00:04:20,956 --> 00:04:23,371 including three out of the four BRIC countries. 84 00:04:23,371 --> 00:04:26,529 China, with its former one-child policy, will be it, 85 00:04:26,529 --> 00:04:30,453 as well as Brazil and Russia. 86 00:04:30,453 --> 00:04:34,168 Now, to tell the truth, 87 00:04:34,168 --> 00:04:38,974 in reality, the situation will be even more challenging. 88 00:04:38,974 --> 00:04:42,317 What you can see here are average numbers. 89 00:04:42,317 --> 00:04:44,013 We De-averaged them 90 00:04:44,013 --> 00:04:46,659 and broke them down to different skill levels, 91 00:04:46,683 --> 00:04:47,920 and what we found 92 00:04:47,944 --> 00:04:51,884 were even higher shortfalls for high-skilled people 93 00:04:51,884 --> 00:04:56,063 and a partial surplus for low-skilled workers. 94 00:04:56,063 --> 00:04:59,221 So on top of an overall labor shortage, 95 00:04:59,221 --> 00:05:03,453 we will face a big skill mismatch in the future, 96 00:05:03,477 --> 00:05:05,176 and this means huge challenges 97 00:05:05,200 --> 00:05:07,265 in terms of education, qualification, 98 00:05:07,289 --> 00:05:10,314 upskilling for governments and companies. 99 00:05:12,397 --> 00:05:18,375 Now, the next thing we looked into was robots, automation, technology. 100 00:05:18,375 --> 00:05:22,389 Will technology change this picture and boost productivity? 101 00:05:23,728 --> 00:05:25,798 Now, the short answer would be 102 00:05:25,798 --> 00:05:30,359 that our numbers already include a significant growth in productivity 103 00:05:30,359 --> 00:05:31,942 driven by technology. 104 00:05:33,093 --> 00:05:36,628 A long answer would go like this. 105 00:05:36,628 --> 00:05:39,113 Let's take Germany again. 106 00:05:39,113 --> 00:05:41,667 The Germans have a certain reputation in the world 107 00:05:41,667 --> 00:05:44,244 when it comes to productivity. 108 00:05:44,244 --> 00:05:48,841 In the '90s, I worked in our Boston office for almost two years, 109 00:05:48,841 --> 00:05:52,998 and when I left, an old senior partner told me literally, 110 00:05:52,998 --> 00:05:56,480 "Send me more of these Germans, they work like machines." 111 00:05:56,480 --> 00:06:01,287 (Laughter) 112 00:06:01,287 --> 00:06:04,259 That was 1998. 113 00:06:04,259 --> 00:06:07,742 Sixteen years later, you'd probably say the opposite. 114 00:06:07,742 --> 00:06:11,410 "Send me more of these machines. They work like Germans." 115 00:06:11,410 --> 00:06:15,566 (Laughter) (Applause) 116 00:06:18,108 --> 00:06:22,811 Technology will replace a lot of jobs, regular jobs. 117 00:06:22,811 --> 00:06:24,808 Not only in the production industry, 118 00:06:24,808 --> 00:06:26,666 but even office workers are in jeopardy 119 00:06:26,690 --> 00:06:29,521 and might be replaced by robots, 120 00:06:29,521 --> 00:06:32,036 artificial intelligence, big data, or automation. 121 00:06:33,120 --> 00:06:38,042 So the key question is not if technology replaces some of these jobs, 122 00:06:38,042 --> 00:06:41,688 but when, how fast, and to what extent, 123 00:06:41,688 --> 00:06:43,383 or in other words, 124 00:06:43,383 --> 00:06:47,549 will technology help us to solve this global work-force crisis? 125 00:06:49,334 --> 00:06:51,254 Yes and no. 126 00:06:51,254 --> 00:06:54,156 This is a more sophisticated version of "it depends." 127 00:06:54,156 --> 00:06:55,203 (Laughter) 128 00:06:55,227 --> 00:07:00,310 Let's take the automotive industry as an example, 129 00:07:00,310 --> 00:07:04,814 because there, more than 40 percent of industrial robots are already working 130 00:07:04,814 --> 00:07:07,546 and automation has already taken place. 131 00:07:09,332 --> 00:07:14,659 In 1980, less than 10 percent of the production cost of a car 132 00:07:14,659 --> 00:07:17,329 was caused by electronic parts. 133 00:07:17,329 --> 00:07:20,580 Today, this number is more than 30 percent 134 00:07:20,580 --> 00:07:25,403 and it will grow to more than 50 percent by 2030. 135 00:07:25,427 --> 00:07:29,608 And these new electronic parts and applications 136 00:07:29,632 --> 00:07:33,652 require new skills and have created a lot of new jobs, 137 00:07:33,652 --> 00:07:36,199 like the cognitive systems engineer 138 00:07:36,223 --> 00:07:40,688 who optimizes the interaction between driver and electronic system. 139 00:07:42,081 --> 00:07:48,668 In 1980, no one had the slightest clue that such a job would ever exist. 140 00:07:49,534 --> 00:07:51,027 As a matter of fact, 141 00:07:51,051 --> 00:07:55,246 the overall number of people involved in the production of a car 142 00:07:55,246 --> 00:07:58,659 has only changed slightly in the last decades, 143 00:07:58,659 --> 00:08:01,677 in spite of robots and automation. 144 00:08:01,677 --> 00:08:03,443 So what does this mean? 145 00:08:03,443 --> 00:08:05,856 Yes, technology will replace a lot of jobs, 146 00:08:05,880 --> 00:08:11,707 but we will also see a lot of new jobs and new skills on the horizon, 147 00:08:11,731 --> 00:08:17,768 and that means technology will worsen our overall skill mismatch. 148 00:08:17,768 --> 00:08:19,510 And this kind of De-averaging 149 00:08:19,510 --> 00:08:23,111 reveals the crucial challenge for governments and businesses. 150 00:08:25,175 --> 00:08:29,006 So people, high-skilled people, 151 00:08:29,006 --> 00:08:33,046 talents, will be the big thing in the next decade. 152 00:08:33,046 --> 00:08:38,688 If they are the scarce resource, we have to understand them much better. 153 00:08:38,688 --> 00:08:41,637 Are they actually willing to work abroad? 154 00:08:41,637 --> 00:08:43,402 What are their job preferences? 155 00:08:44,552 --> 00:08:49,593 To find out, this year we conducted a global survey 156 00:08:49,617 --> 00:08:54,870 among more than 200,000 job seekers from 189 countries. 157 00:08:56,021 --> 00:09:01,449 Migration is certainly one key measure to close a gap, 158 00:09:01,469 --> 00:09:03,013 at least in the short term, 159 00:09:03,013 --> 00:09:05,613 so we asked about mobility. 160 00:09:05,613 --> 00:09:10,234 More than 60 percent of these 200,000 job seekers 161 00:09:10,234 --> 00:09:12,834 are willing to work abroad. 162 00:09:12,834 --> 00:09:14,994 For me, a surprisingly high number. 163 00:09:14,994 --> 00:09:18,407 If you look at the employees aged 21 to 30, 164 00:09:18,407 --> 00:09:20,706 this number is even higher. 165 00:09:20,706 --> 00:09:24,026 If you split this number up by country, 166 00:09:24,026 --> 00:09:29,227 yes, the world is mobile, but only partly. 167 00:09:29,227 --> 00:09:33,279 The least mobile countries are Russia, Germany, and the U.S. 168 00:09:34,358 --> 00:09:37,632 Now, where would these people like to move to? 169 00:09:37,632 --> 00:09:42,462 Number seven is Australia, where 28 percent could imagine moving to. 170 00:09:42,462 --> 00:09:46,780 Then France, Switzerland, Germany, Canada, U.K., 171 00:09:46,804 --> 00:09:50,109 and the top choice worldwide is the U.S. 172 00:09:50,744 --> 00:09:54,239 Now, what are the job preferences of these 200,000 people? 173 00:09:54,263 --> 00:09:55,746 So, what are they looking for? 174 00:09:57,043 --> 00:10:03,475 Out of a list of 26 topics, salary is only number eight. 175 00:10:03,475 --> 00:10:08,003 The top four topics are all around culture. 176 00:10:08,003 --> 00:10:09,558 Number four, 177 00:10:09,558 --> 00:10:12,554 having a great relationship with the boss. 178 00:10:12,554 --> 00:10:16,571 Three, enjoying a great work-life balance. 179 00:10:16,571 --> 00:10:20,332 Two, having a great relationship with colleagues; 180 00:10:20,332 --> 00:10:23,727 and the top priority worldwide 181 00:10:23,751 --> 00:10:27,340 is being appreciated for your work. 182 00:10:28,321 --> 00:10:31,152 So, do I get a thank you? 183 00:10:31,152 --> 00:10:34,496 Not only once a year with the annual bonus payment, 184 00:10:34,496 --> 00:10:36,701 but every day. 185 00:10:36,701 --> 00:10:41,926 And now, our global work-force crisis becomes very personal. 186 00:10:41,926 --> 00:10:44,967 People are looking for recognition. 187 00:10:44,967 --> 00:10:48,267 Aren't we all looking for recognition in our jobs? 188 00:10:51,302 --> 00:10:54,882 Now, let me connect the dots. 189 00:10:54,882 --> 00:10:57,250 We will face a global work-force crisis 190 00:10:57,250 --> 00:11:00,222 which consists of an overall labor shortage 191 00:11:00,222 --> 00:11:02,173 plus a huge skill mismatch, 192 00:11:02,173 --> 00:11:05,307 plus a big cultural challenge. 193 00:11:05,307 --> 00:11:09,231 And this global work-force crisis is approaching very fast. 194 00:11:09,231 --> 00:11:11,971 Right now, we are just at the turning point. 195 00:11:11,971 --> 00:11:16,359 So what can we, what can governments, what can companies do? 196 00:11:16,359 --> 00:11:17,962 Every company, 197 00:11:17,962 --> 00:11:19,749 but also every country, 198 00:11:19,749 --> 00:11:21,677 needs a people strategy, 199 00:11:21,677 --> 00:11:24,649 and act on it immediately, 200 00:11:24,649 --> 00:11:28,596 and such a people strategy consists of four parts. 201 00:11:28,596 --> 00:11:30,407 Number one, a plan 202 00:11:30,407 --> 00:11:36,537 for how to forecast supply and demand for different jobs and different skills. 203 00:11:36,537 --> 00:11:38,232 Work-force planning 204 00:11:38,232 --> 00:11:41,014 will become more important than financial planning. 205 00:11:42,109 --> 00:11:45,732 Two, a plan for how to attract great people: 206 00:11:45,732 --> 00:11:48,831 generation Y, women, but also retirees. 207 00:11:49,865 --> 00:11:53,854 Three, a plan for how to educate and upskill them. 208 00:11:53,878 --> 00:11:56,433 There's a huge upskilling challenge ahead of us. 209 00:11:57,666 --> 00:11:59,500 And four, 210 00:11:59,500 --> 00:12:02,029 for how to retain the best people, 211 00:12:02,053 --> 00:12:03,399 or in other words, 212 00:12:03,423 --> 00:12:08,298 how to realize an appreciation and relationship culture. 213 00:12:11,643 --> 00:12:17,675 However, one crucial underlying factor is to change our attitudes. 214 00:12:18,424 --> 00:12:22,627 Employees are resources, are assets, 215 00:12:22,627 --> 00:12:25,072 not costs, not head counts, 216 00:12:25,096 --> 00:12:26,481 not machines, 217 00:12:26,505 --> 00:12:28,046 not even the Germans. 218 00:12:28,176 --> 00:12:29,278 Thank you. 219 00:12:29,302 --> 00:12:33,189 (Applause)