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The New Human Age
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00:00Now, there is a big issue here.
00:02With so much technology, we may even forget about people skills.
00:08So underrated, but so necessary.
00:12Now, one of them may be the ability to connect and communicate in person
00:17like we're doing right now.
00:18So, when having platforms like ChatGPT
00:21and with so many generative AI telling us what to say or do behind our screens,
00:26how are we supposed to think on our feet when the time comes,
00:31when you have somebody in person in front of you.
00:34Now, here to talk about the importance of strengthening,
00:38of strengthening, sorry, human skills in a tech era,
00:43we have Bob Moritz, CEO of PwC,
00:47Jonas Pricing, CEO of Manpower,
00:49and Ana Paula de Jesus Asis, Chair and General Manager of EMEA of IBM.
00:57The session will be moderated by Karen Tissot, journalist from CNBC.
01:02Karen, the floor is now yours.
01:27Well, good afternoon, everyone.
01:28Thank you so much for having us back at Viva Tech.
01:31I think most of us have all been here a number of times over.
01:34But what a session we've got for you.
01:36I've got to say from the lens over at CNBC,
01:39we have been watching this gold rush into artificial intelligence,
01:43in particular generative AI, since late last year.
01:47Stocks, a double upgrade on NVIDIA stock,
01:49now a trillion dollar market cap, other big names.
01:53Alphabet also floated.
01:55Microsoft.
01:56But already questions around extinction risk.
01:59What happens from here if computers become smarter than humans?
02:03What is the impact on the workforce?
02:05So I'm going to pick up on that conversation now with our terrific panel
02:08and just pivot and ask first up, Jonas,
02:11what is the risk here for workers?
02:14Blue, white-collar jobs, what are most in danger now because of AI?
02:18You know, Karen, this is a conversation we come to, you know, on a regular basis.
02:23And every time there is a big technological innovation,
02:25the first place we go to is how many jobs are going to be eliminated.
02:29Disregarding, in large part, the history that says technological innovation drives way many more jobs
02:38in expected professions and unexpected professions in different ways.
02:44So we think it's going to be the same with this next evolution.
02:49But the biggest opportunity and challenge will not be the alienation of jobs
02:54or the creation of new jobs.
02:57The biggest challenge is going to be it will require different skill sets.
03:00So that means the workforce needs to be transformed at scale in a way
03:05and at a speed we've never seen before.
03:08And I come in on this because some of the stats that I've seen,
03:11I interviewed the CEO of Stingularity Net the other week.
03:14This is the man who was behind the creator of Sophia, the robot, the brain in Sophia.
03:20And he said AI could take 80% of human careers in the next few years.
03:25So listen, I think that Jonas said it very, very precisely.
03:30I mean, every era, every evolution represented a shift of skills and capabilities.
03:36So it's our responsibility now to enable the workforce of the future.
03:42And of course, there are elements of risk that we will need to address
03:46that we will have to work with the regulatory bodies in order to focus on.
03:50But from an enterprise standpoint, from a leadership standpoint,
03:55it is really our responsibility to ensure that we enable
03:58and we bring people together in this revolution.
04:01You sort of tiptoed around this, though.
04:04Are jobs at risk?
04:06Sorry?
04:06Are jobs at risk, though?
04:08Our current jobs as they are, yes.
04:12But what we'll need to do is reshape the nature of jobs
04:15and make sure that they are prepared to take advantage of the benefits of the new technologies.
04:21Bob, you work across multiple sectors with the clients that you've got.
04:25What do you think, as you weigh in, do you think there are jobs at risk,
04:29whether they're blue-collar jobs, white-collar jobs, high-end jobs?
04:32Jobs are clearly at risk.
04:35And for the first time, probably in a long time in scale, as Jonas and Ana Paula said,
04:39white-collar jobs are equally at risk compared to blue-collar jobs.
04:44But the reality is, as both of my fellow panelists have said,
04:47there's an opportunity on the other side in terms of what I'll call new jobs, new forms of work.
04:51So you have to break this down into a couple of pieces.
04:55First, let's talk about the redesign of work.
04:58And, oh, by the way, the workers, if they're skilled, should be part of that process.
05:03Think about it from years ago, right?
05:04We had a number of accounting people put together what I'll call manual spreadsheets.
05:10Then automation came.
05:11Those accounting clerks had to go away.
05:13But the amount of time people spent on the analysis from those spreadsheets now
05:17were actually replacing those that got eliminated.
05:21With AI, those spreadsheets now no longer need to be created by the technology.
05:25Well, the technology will do it.
05:26But nobody needs to be involved in those anymore.
05:28And now we're really into the higher-end thinking.
05:30And this goes back to Jonas Zanopoulos' point, skills and enhancement, job re-engineering,
05:37and support from a policy perspective in government is going to be needed
05:40to actually make this all come to life.
05:42I'm hearing you because in 2023, we have so much data now, so many emails.
05:47There's so much to process.
05:49And in a way, we're getting overloaded by the amount of data coming our way.
05:53So you can see the relevance of AI in terms of just cutting through all the numbers.
05:57The one thing we do have to be mindful of is you think about AI.
06:01There's a big terminology of AI, but you've got to break it down into some pieces, right?
06:06There's augmented intelligence coming from the technology and the mass amount of data
06:10that will help people make better decisions.
06:13Their jobs don't go away.
06:15There's autonomous AI, which is it will take over what humans now can do.
06:20And so you've got to actually differentiate where we are in the cycle.
06:23And right now, we're in that augmented piece.
06:25We're not yet at scale in an autonomous piece.
06:29That's yet to come, and it's coming pretty quickly.
06:32But you've got to be thinking about this in stages and segmentations.
06:34I think we're probably all loving the optimism on stage that we can augment
06:39and change and shift with this new technology.
06:42But it could actually go very badly wrong if we don't have buy-in across corporates,
06:47across institutions in terms of re-skilling.
06:49And if you think about some of the early conversations about whether we need
06:53a universal basic wage, if that happens, how do we not get to that point?
06:57How do we actually think about re-skilling and changing human intelligence
07:01so it's relevant in the workforce?
07:02Jonas.
07:03You know, as we have been tracking employers' intent to re-skill
07:08and up-skill their own workforces, we've been serving employers
07:11for the better part of 15 years.
07:15You know, 10 years ago, maybe 20% of employers said,
07:18no, no, training and re-skilling is really important to us in our strategy.
07:23The same questions today, 80% to 90% of organizations are fully aware of the need
07:28and they state we need to re-skill and up-skill our workforce,
07:32fully aware of the rapid evolution of technology,
07:36but also the evolution of demographics, the tight labor markets,
07:40the structural changes that are coming into labor markets.
07:42So employers are more willing and are investing more in training and development today
07:47than they've done in decades because they can see that they need to grow the skills of their workforce
07:53and they need to provide this notion of continuous learning within the organization
07:59as they're transforming through and with the help of new technologies.
08:04You know, you have a vested interest here to work with employers to make sure they're still hiring.
08:09Anna, by comparison, in a way, you don't. You want to sell the new technology.
08:13You want as many people as possible to be buying some of this new AI so the business does well.
08:17How do you, though, think about ensuring that the workforce is still relevant?
08:21So, we have so many problems to solve yet, right?
08:26Let's think about what we have to do in terms of climate change, sustainability,
08:31how to make, you know, the world more inclusive.
08:34Technology is going to be supporting the resolution and the solution of those complex problems.
08:41So what we want to do is make sure that people that today are spending their time doing repetitive, boring
08:48tasks,
08:49start to devote their intelligence, their capabilities to solving those most complex problems.
08:54So there will be need for talented people and, of course, it is going to be our responsibility
09:01to make sure that we are bringing those talented people to our organizations.
09:06Bob, do you want to weigh in on this first?
09:08Do you want to weigh in on how we reshape the labor force?
09:11How do we think about it in the future with this technology?
09:13Yeah, there's a couple of things that we have to think about here.
09:15First is, as Jonas and Ana Paula said, there's basic skill set enhancement.
09:20And that goes in two ways.
09:21One is the creation of AI and the skills needed to do that and do it ethically and responsibly.
09:27The other one is the adoption of AI.
09:30Let's just be really pragmatic for a second.
09:32There's lots of people in the audience.
09:33How do I appropriately prompt the questions to actually get the information I need?
09:38And if you don't do that right, you're wasting a lot of time.
09:41So there's a big productivity point here on both of those elements.
09:43So skill set enhancement is usually important.
09:46We would also say, and I think all three of us would agree with this,
09:49getting the employees from the bottom up to re-engineer the work they do so there's more buy-in,
09:54creates loyalty, creates an improved morale,
09:57creates a sense of accomplishment and values and purpose.
10:01So the redesign of that work, bottoms up, is a better way to go than the top down.
10:05Third element, government.
10:07Our education system right now is ill-equipped.
10:10And as Jonas rightly said, corporates are picking up the slack because they're giving upskilling or reskilling
10:16almost as a benefit like insurance for medical or life or health or otherwise.
10:22And that's got to be mandated.
10:23But the government education here and the dedication of capital to that so they can actually do it in the
10:29right way is going to be important.
10:30And why?
10:30Because those countries that don't have the skills of the people are yet again potentially left behind.
10:37That's a big societal challenge we've got to be mindful of as well.
10:40Yeah, no, I think the point that Bob is making is extremely relevant, right?
10:44That we need to shift from being a mere AI user to an AI value creator.
10:50And that is where companies are really going to use the technology to create competitive advantage.
10:55So our recommendation is that you don't outsource the development of AI in your organization.
11:01You don't reduce your AI strategy to just call in an API.
11:06That you develop your models, that you really invest in organizing the data,
11:11and then you put AI to work for you.
11:13One problem that I have now is when I look at the economic cycle, it is challenging.
11:18You've just come out of an economy that has been growing.
11:21Now it's dipping.
11:22We've had monetary policy, interest rates tightened and tightened by a lot of different central banks.
11:27Employers have had the challenge of a very tight labor force.
11:30They can't get the skills and the workers that they want.
11:32They've had to pay through the nose for those particular workers.
11:36Bring in productivity gains from AI.
11:39Why? Surely this is the moment when employers want to maximize those benefits,
11:43cut back on the workforce, cut back on the upskilling.
11:48How do we think about the challenge of the economic cycle, Bob,
11:51in terms of stopping that investment from corporates?
11:55So you have two things you've got to be thinking about.
11:57One is the investment needed to transform the organizations.
12:00And that's a pretty big investment in terms of the CapEx and the OpEx that's got to be applied to
12:04that.
12:04But you're absolutely right.
12:05The incentive for the C-suite right now, and I don't care if you're a startup, a mid-sized company,
12:10or a large company,
12:11is how do I get costs out of my system right now in a slowing economy?
12:15That's going to speed up the change curve in a big-time way.
12:18And that's why there's going to be a rush to try to bring this stuff in as best as they
12:21can.
12:21But there is a cost.
12:22The one thing we're not talking about at times is there's this euphoria right now of AI.
12:27There's a cost to using it.
12:29The technology company is going to charge for this.
12:31There's going to be a huge energy cost associating with the computing power that comes from that.
12:36So we haven't really figured out the equilibrium on the cost side of the employee labor versus the AI aspects
12:42associated with it.
12:43The second thing I would say is this is where C-suite individuals are going to have to be resilient,
12:50agile, and learn along the way.
12:52Because you're not going to be able to plot a linear path forward.
12:55You're going to have to pilot a lot, learn a lot, adjust a lot.
12:58And that's the whole concept when you're using AI.
13:00It's going to be the concepts they have to apply in their own change management as an organization as well.
13:04Well, we assume that CEOs are strategic, Jonas, and they think into the future about the skills that they're going
13:08to need.
13:09But often, they're really looking at quarter-by-quarter numbers.
13:12And as you look at the economy now, do you really see CEOs trying to upskill their workforce so they're
13:18resilient around AI?
13:20Yeah, no, we see them continue to upskill their workforce.
13:23We see them continue to invest in technology.
13:27And we see them continue to think about, with even greater attention, how can we drive greater productivity?
13:35Because for all of the technological evolution that we've seen, you know, the sectors, there are some sectors that have
13:41seen very significant productivity improvements over the last three decades.
13:45But in aggregate, we've not seen a step change in productivity for the last two to three decades.
13:52And the hope is that the regenerative AI can be that next step change in productivity.
14:01So clearly, just as Bob and Anna are describing, more cautious, very specific use cases for where it can help.
14:08And that's where the money is going to because they understand what will generate the benefit as opposed to a
14:14general tell me things that I don't know about.
14:17And hopefully, it creates value.
14:19But this is an area where companies and organizations are laser focused.
14:24And if they keep on doing one thing, this is what they will keep on doing.
14:29And they know that human capital augmented by technology is what's going to accelerate their progress.
14:37They are not thinking, my exhalation of progress is going to be without people.
14:42It's with people and technology together.
14:44Anna, Bob just made a point before about the education system not being fit for purpose effectively in this day
14:52and age.
14:52But, you know, we've been talking about STEM everywhere.
14:55You know, a lot of schools, they're pushing forward their level of expertise in STEM.
15:00What comes next?
15:01If we're already focused on STEM, it's not enough.
15:03And now we have this new technology on top.
15:05How do we change the schooling, the education level so it is right for the workforce of the future?
15:12Yeah, no, I think the major shift we need to see is that we have a more skills-oriented education
15:18system, right?
15:19So, what we are seeing, for example, in the U.S. today, IBM hires 50% of its workforce of
15:27people that don't have formal degrees.
15:30They don't have university graduation.
15:32And that is what we call a skills-first approach.
15:35It's what the person, what that individual can contribute to the business.
15:39And not spend, you know, four or five years in a formal education that, quite frankly, might not be applicable
15:46or necessary in the current requirements of the workforce today.
15:50So, I think this skills-first approach is going to be absolutely vital so that we can provide a more
15:56inclusive and accessible work environment.
15:59Bob, let me get you back on this.
16:01Was it more STEM that you wanted or was it other skills around the STEM that's necessary now?
16:06It's a bit of both.
16:08The reality is you don't have enough people with the skills to build and create.
16:11You still don't have enough people that actually know how to adopt and use it, appropriately so.
16:15And you don't have people in the middle that know how to regulate or govern over this either.
16:20So, you actually have all three that need that help on both the STEM side and the non-STEM side
16:24to move forward.
16:25The other thing I would say, picking up on Ana Paula's point, is that as you look at this, this
16:30concept of not degrees, but credentials.
16:35Credentials that are mobile, that can go from company to company, place to place, country to country, are going to
16:40be really important.
16:40And we're starting to see that both in the recruiting side of the equation as much as you are starting
16:45to see that in the non-formal education system right now.
16:48So, that's one of these changes that will be coming relatively soon.
16:50And that is a tremendous step forward in terms of including a broader spectrum of talent that traditionally wouldn't have
17:00been considered by companies.
17:01So, for you now to be able to have 50% tapping into that talent pool opens the aperture to
17:08a much greater diversity and inclusion.
17:11I can probably go a little bit deeper with this question with you because you've seen where there have been
17:16pockets of skill set around tech where you've had, effectively, companies pivot to those countries because of the skill set.
17:24So, who's doing well on this front in terms of having the right education system even for today's technology?
17:30Who's doing badly as well?
17:32Well, there are some countries and geographies that are very strong in terms of their capability to produce, you know,
17:38higher skill sets within certain technology fields at scale.
17:43But I think this pandemic gave us an opportunity to see that we can work in different ways and tap
17:50into those talent pools a little bit everywhere.
17:53You know, a lot of companies migrated to Asia in terms of where they pull talent from because the scale
17:59and dimension is so big.
18:00But I think today we see companies really, you know, leveraging a multipolar world also in terms of talent and
18:07tapping into various areas because with technology, you can access the talent wherever it resides.
18:14You don't have to move talent to work.
18:16You can move work to talent to a much greater degree than you ever could in the past.
18:21Let me bring up a different point.
18:23What is very evident after conversations on the ground is that there is a huge move towards generative AI.
18:30Big companies are looking at how they can use the capabilities today.
18:34Based on that, Bob, should employees be allowed to use these tools that are publicly available?
18:41To what extent should they be able to use it in their current workflow today?
18:44And what guardrails need to be put in place very quickly for employees so they're not accused of using tools
18:50that they shouldn't be?
18:52There's two things you got to think about when dealing with do the employees have access.
18:57First, you got to get them to actually learn.
18:59And that could be on the personal side or on the professional side, but on the professional side with limitations.
19:03Because the question is going to be, what information and data are they having access to to use the generative
19:11AI?
19:11Which is really important as you think about data sets and data privacy and other aspects like that.
19:16And second, is the AI been built for purpose?
19:21And that's going to be really important as you think about the bias that's in the coding.
19:24So organizations are thinking right now of, yeah, you can go pilot and pilot on the consumer side.
19:30Pilot on maybe some small, narrow, functional areas so we can get the skill sets up and the learning.
19:35We can get some use cases adopted.
19:37But large scale, probably not yet until you deal with those two issues.
19:41The second thing I would say is for the regulated industries, you have to be really careful in terms of
19:47how far and how fast you go.
19:48Because you've got a whole bunch of compliance requirements in terms of what you can do, what the technology can
19:53do,
19:54and how do you prove contemporaneously you've done all the right stuff in accordance with the rules and regulations.
19:58And before, you know, there's any real guidelines here, it could almost feel like you're cheating on an exam.
20:05You know, that you're using something that you shouldn't be, right?
20:07Versus for another company, you're on the front foot because you're adopting this technology.
20:11So how do we think about employees using this technology here and today?
20:15So I think it's every employer's responsibility to enable their teams to use, you know, and adopt the current technologies.
20:24So the access and the ability to experiment with those technologies should be part of the company's strategy.
20:32Now, you don't want to see your sensitive data or your clients' sensitive data sitting on platforms that you don't
20:38control.
20:39You certainly don't want your reputation to be at risk by people doing, you know,
20:45experimentations with chatbots that can come up with some strange answers, right?
20:48Or hallucinate, as is the term that we use in the industry.
20:53So you have to make sure that you have the guardrails in place on what is possible to be done
20:59and what is going to be ethical to be done and what is not.
21:02But blocking the access to the technology is really preventing you to evolve
21:08and to gain competitive advantage in the future.
21:11Karen, if I can add on to Ana Paula's point,
21:13people need to understand that when we're talking AI,
21:17there's standalone platforms to leverage and apply to your organization.
21:21There's already AI being built into what IBM offers you,
21:25what SAP offers you, what Microsoft offers you.
21:28There's going to be AI embedded in every single email platform
21:32to automate some things there.
21:34So again, dissecting what we mean by the AI and what's permissible
21:38is really important in terms of the playbook for employees to learn,
21:41to grow and do it in a responsible way.
21:44But Jonas, this is where it gets tricky.
21:45So you can say to your boss,
21:47well, hey, you're already using this AI technology in the business.
21:50Why can't I use something that's publicly available
21:53to enhance my workflow at this point?
21:55So where do you see the lines at this stage
21:57and how do companies very, very clear with their employees
22:00about what's okay at this stage?
22:02Well, I think Ana and Bob have said it very well.
22:05And to put it into a pragmatic, you know, context,
22:09in our company, the adoption of ChatGPT was way ahead of our ability to govern it.
22:17So what we've had to establish is the framework.
22:20And to your point, you shouldn't stop the usage,
22:23but you have to govern the framework between do's and don'ts.
22:27Because if you don't govern it,
22:29you could be using it for ways that are going to be harmful to the individual,
22:33harmful around privacy, harmful around, you know,
22:37all kinds of aspects of your business
22:39because you're putting potentially your know-how out into the wider world.
22:44So we've had to establish a framework with do's and don'ts
22:48that we've spread and provided to all of our employees.
22:51So please go ahead, use it like this, don't use it for this,
22:55and always use caution when you assess
22:59whether the answers you're getting make sense or not.
23:02Because even at this stage, in many cases,
23:05whatever you are receiving is not 100% correct.
23:08Unfortunately, it also carries a lot of our human bias
23:12full on into the artificial intelligence world as well.
23:18So you have to be quite careful when you interpret the results
23:21and, you know, not rely all of it onto what you're reading.
23:25We've got about just over a minute left
23:27and all three of you have sight lines across so many sectors and businesses.
23:32Everybody wants to back a winner.
23:34Bob, who's going to win from AI?
23:36Which sectors, which industries?
23:38If you're thinking about positioning your career for the future,
23:41who do you go with?
23:42So a couple of things here.
23:43First, AI is going to impact every sector.
23:46The question is, what are you going to do in packaging the AI
23:49with other forms of technologies
23:50to actually get the productivity that you talked about?
23:53Good example, our own study from two years ago.
23:55China, because of the manufacturing,
23:57the AI coupled with robotics,
23:59huge productivity benefit coming from that.
24:01If you look at the automotive industry,
24:03the transportation industry, the entertainment industry,
24:05you're going to see huge transformational changes there as well.
24:08Anything that requires significant data on the AI side,
24:11but also other forms of automation
24:13is really going to be where the upside is going to be,
24:15and it's going to be the advanced economies
24:17that are more labor-intensive
24:18that are going to be the beneficiaries
24:20or, if not managed correctly, the losers in this case.
24:24Yeah, listen, data is our most valuable asset right now, right?
24:28So the winners are the ones that are going to be bold,
24:31that are going to act with a sense of urgency,
24:33but do it in a responsible way.
24:35Jonas?
24:35Access to data, and we believe the services sector
24:40disproportionately benefits from these changes.
24:43We've got to wrap up the conversation.
24:45I hope there are plenty of takeaways there for you here at Viva Tech.
24:48Thank you very much to Anna, Bob, and Jonas.
24:51Thank you, Kevin.
24:52Thanks, Anna.
24:53Thank you.
24:54Thank you.
24:56Thank you.
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