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AI's real impact on jobs - what it means for you, your company and future generations · Radio Davos

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The business models of today, the talent models of today, the jobs of today are all
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going to reforge themselves into new jobs, new tasks.
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So I would say the single biggest shift with AI is that foresight is completely
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fogged.
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Welcome to Radio Davos, the podcast from the World Economic Forum that looks at the
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biggest challenges and how we might solve them.
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This week: what impact will artificial intelligence really have on our jobs and the
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education and training systems needed to prepare current and future generations?
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The jobs of the past are moving out very quickly and the jobs of the future are
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coming at a slower pace.
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There's a minimum of that 11% of the current world's workforce that will need
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support in a managed workforce transition to probably an adjacent industry, if not
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a wholly different industry.
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Ravi Kumar, CEO of technology services company Cognizant, says while money pours
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into AI tech, it also needs to be flowing into education and training.
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Re-skilling should be a part of the infrastructure stack.
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It cannot be done on the side.
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You have to look at it with the same lens as you look at it for compute, for LLM
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access, you have to make reskilling a part of the infrastructure
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stack.
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And World Economic Forum Managing Director Saadia Zahidi says no one should doubt
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that change is happening fast.
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Things are moving perhaps a little bit slower than we might have all thought a
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couple of years ago, but then on the other hand, they're still moving a lot faster
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than our current systems are designed to address.
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Follow Radio Davos wherever you get podcasts.
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I’m Robin Pomeroy, and with this look at the real impact of AI on all our jobs...
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How do you integrate that into everything you do? Robin Pomeroy
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This is Radio Davos.
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Artificial intelligence is already having an impact on the way most of us work -
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but it’s still early days and it's hard to predict exactly which jobs will
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disappear completely, what new ones will be created, and when and where this will
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happen.
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It’s a subject that's important to all of us - so my colleague Gayle Markovitz
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spoke to the head of an AI services firm, Ravi Kumar of Cognizant, and to the head
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of the World Economic Forum’s Centre for the New Economy and Society, Saadia Zahidi.
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Both have unique insights into the impact of AI on the economy, on employment, and
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the implications for education and training.
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Gayle started by asking Ravi Kumar for his assessment of AI’s impact.
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One thing is certain, AI has created a fog on the foresight.
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Modern businesses, modern finance has been built with this assumption that we've
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got to have long, slow bets.
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And with the fog around us, that foresight is broken.
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And those long, slow bets are no longer valid because the models, the business
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models of today, the talent models of today, the jobs of today are all going to
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reforge themselves into new jobs, new tasks, new job families.
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So I would say the single biggest shift with AI is that foresight is completely
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fogged.
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Having said that, I think we have this unique opportunity to re-pivot, a future
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which is more shared prosperity and we could re-pivot in a direction where the
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drift of value coming out of AI goes into the front lines.
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Over the last 50 years, technology has drifted that value upwards, and the
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frontlines have actually been kind of, not actually had that value shift, and
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therefore we have created a level of divide.
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I think we have this unique opportunity to re-pivot and drift that value downwards,
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capability downwards.
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And we hope that value actually moves downwards as well, so that you have more
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wages for the frontline workers.
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That's what I'm hoping so. And that's what we believe.
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We have a very nice opportunity to, you know, reset the whole future of workplaces
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and workforce.
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Saadia, why would you say this is a global problem rather than a company by company
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issue?
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I think what Ravi said is just so spot-on both at a micro level and at a macro
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level.
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And maybe just building on that, maybe there's three big reasons that I guess we
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tend to think of this as something that is a global challenge I'm not sure it's
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fully a problem.
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I think there's a huge opportunity here as well.
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But one element is of course that we found that the bet that's been placed on AI
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and the massive amount of investments, the time to productivity gains is somewhat
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slowing down, which means that there is a very interesting window in which to make
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the equivalent workforce transitions if we want those productivity gains, but also
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be able to do that in a way that people can sort of absorb and manage.
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I think the second element is that there's society level concerns that have to be
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solved for because not every organisation will be able to manage this by themselves.
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What we found in one of our last reports, and we'll do an update later this year,
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is that if the world's workforce was 100 people, 11 of them would not be able to
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re-skill and redeploy in their current organisation.
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Which means there's a minimum of that 11% of the current world's workforce that
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will need support in a managed workforce transition to probably an adjacent
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industry, if not a wholly different industry.
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So there's a set of support that is needed between the public and the private
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sector there.
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And then finally, the third element, just again at that very big picture level, is
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that AI is not the only trend and it is interacting with a number of other trends
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that include all of the geopolitical and geoeconomic shifts we see, and that
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include the demographic shift.
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So let's take the demographics piece.
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There is that opportunity here for many of the world's workforces where there
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actually is a reduction in talent over the long-term due to ageing or shrinking
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workforce, there's an opportunity here to integrate artificial intelligence in a
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very different way.
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And equally so, there's an opportunity for AI tools to actually support and
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up-skill talent in many parts of the world where education systems have not been
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able to keep up with the needs of many young populations.
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So again, it's a complex picture, but I think there are some broad global
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implications that go beyond what is happening inside each organisation.
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So Ravi, I know that Cognizant has recently conducted some new research on AI and
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jobs.
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And it's revealed some changes that you hadn't predicted.
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What was the statistic that stood out that really surprised you?
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We did a research in 2023, and we did another research in 2026, early 2026.
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We also partnered with the World Economic Forum to do some joint research as well.
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You know, when we did this in 2023 we expected impact on every job, and when I mean
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impact on every job, the job by itself changes in form, the tasks underneath
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change, some of those jobs will remain, but they will reforge in a different
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direction, and there'll be new jobs.
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90% of the jobs we actually in 2023 said will actually get impacted by AI, at least
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10% of those tasks by 2030.
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And here we are in 2026, 93% of these jobs when we did the second survey in 2026,
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we realised that they have actually got impacted.
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We took almost 18,000 tasks, a thousand occupations, and we kind of, you know,
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conducted that research on those occupations and on those tasks.
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What is fascinating is the velocity of change has significantly changed.
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You know, what was supposed to happen in 2030 is now happening in 2026.
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We saw the velocity of changes at around 9% every year.
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50% exposure to jobs, where there is, you know, you would actually believe that the
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jobs have changed tectonically, 50% of exposure, 30% of the occupations actually
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have gone through that.
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25% exposure, where you actually have those jobs reforged in a different direction,
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we almost, we almost saw 56% of the jobs going through that kind of change.
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So most of these jobs we have today, those occupations are going to reforge in a
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different direction.
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Some are jobs which are going to be futuristic.
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In fact, we believe AI is going to be in the middle of a flow.
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They're going to be a lot of jobs on the front and a lot jobs on back.
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The ones on the are related to authentication, problem finding, creativity.
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The ones in the back are going to the validation, verification, judgement,
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accountability, outcomes.
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So you're going to see a lot of those jobs.
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So the asymmetry is not going to be about expertise and intelligence.
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The asymmetry is going to come from applying that intelligence and including that
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intelligence as throughput into your input factors on a job.
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I'm actually fascinated by the opportunities which this uniquely presents to us.
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You know, look at it this way.
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If you're going to create more throughput, more productivity on the front lines,
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you're going to actually shower more wages.
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Per capita wages have not gone up for the last 20 years if you if you adjust it to
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real inflation.
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Now, wages go up because throughput goes up and you're going to get significantly
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higher output without inflation, it's a good thing for economies.
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The point is, how do you drip that capability downwards?
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How do you push more throughput downwards so that there is an incremental wage and
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higher throughput?
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That's what we are looking for.
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So this research tells us that it's coming at rapid pace.
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The capability is right out there. The production value in enterprises is way below.
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And this is what Saadia was referring to.
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The production value is way below the capabilities out there. There is a big bridge.
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So we have a unique opportunity on that bridge to build, you know, workforce skills
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and re-skill our workforce so that the production that you can go up.
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And we need the production very to go up because the technology, you now, you know
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this, in the last 12 months, a trillion dollars has been invested into AI
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infrastructure.
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The scaling loss for this infrastructure is only six months, which means in six
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months it goes obsolete.
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So the faster you could actually drip that value to enterprise production value,
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the better it is.
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And to drift that production value and bring it to the same level as capability,
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you will have to re-skill the workforce.
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I've actually said this in the WEF research we jointly did that re-skilling should
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be a part of the infrastructure stack.
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It cannot be done on the site. It has to be a power of the infrastructure stack.
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You have to look at it with the same lens as you look at it for compute, for LLM
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access, you have to make reskilling a part of the infrastructure stack.
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What does that look like, especially for say a non-tech organisation, what does
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that mean in practise?
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And I'm interested also that you use language like bridge, because it really is
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like building something.
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The
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reason why this production value is significantly lower than the capability, I
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mean, the capability will keep going up.
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So the bridge is actually going to be broadened.
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And you know you have to keep bridging it and I call it the velocity gap.
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And the reason is very simple.
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You know, we can't apply this technology on old stuff in businesses.
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You have to reinvent flows.
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You have to re-invent the ability to integrate digital labour with human labour and
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to reinvent and reimagine business models, operating models and business flows.
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This technology is very contextual.
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The classical software we wrote in the last 50 years was very deterministic.
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We codified it.
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A lot of the balanced things we in a workplace, in the flows of a business, are
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very contextual, they're judgement oriented.
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You have to ground this technology into the hustle of the company.
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We call it a science called context engineering where we put the guardrails, we,
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you now, we put the harnesses needed for this technology to be productive and it
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has to work in sync with human effort and it to be integrated.
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So all that work really means you have to reinvent those businesses.
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And therefore, I believe that bridge is very important.
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And you will then have to redesign jobs.
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You will have to redesign the flows of jobs.
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You have redesign tasks which are done by people and how they're integrated with
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what is done by digital labour.
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We did a good, nice landing spot on this and the bridge, I feel, has to be
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smoothened a little bit because the jobs of the past are moving out very quickly
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and the jobs in the future are coming at a slower pace.
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So in between the two, we have to create a bridge.
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In fact, I wrote this thesis that you should tax to a large extent digital labour
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which is eliminating tasks versus amplifying human potential.
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And that's a temporary bridge, the temporary bridge of taxing the capital so that
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there is a little bit of a level playing field between human effort and digital
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effort.
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I mean, remember, capital got a free runway because it actually created more wages
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and more jobs.
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Now if capital is creating digital labour and human effort, and human labour, and
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work, and jobs of the future are coming at a slower pace, you need a nice landing
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spot.
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So, you know, one of the suggestions I had is to look at capital taxation in a
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slightly different way in a short term and use it as a smooth landing spot
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It sounds like a huge challenge for human beings to kind of come up to speed with
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that gap.
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Is this is there some good news? Is there something?
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I know there's been research, Saadia, there have been some misconceptions about the
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time it takes, for example, to upskill.
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Yeah, so actually in the research that where we partnered with Cognizant there we
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found that it is not quite as prohibitive as people may think to have a base level
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of understanding when it comes to AI and big data, so roughly through 30 hours of
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study.
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But of course, to become more proficient, then you're talking about 137 hours or
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obviously a lot more depending on the level of depth that you want to get into.
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But I say that to note that, again, in some of the cross-industry surveys that
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we've done, one of the fastest rising in demand skills is AI and big data.
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But no one is suggesting that that needs to be at a level of depth.
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It's simply the ability to be able to work with and understand technology.
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And what also comes through over and over is that the organisations that are likely
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to be the most successful are the ones that will be able to combine artificial
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intelligence with human judgement.
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And that means there's still a huge premium on creativity and collaboration and
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interpersonal dynamics and leadership skills and social influence skills.
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So all of that combined with then the ability to understand.
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So I think it's increasingly that you won't see two completely different tracks.
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You will need to bring some of that together.
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So I think, just adding to what Ravi has said, that's what's going to be needed,
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but this is where some of the pain points come in.
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So things are moving perhaps a little bit slower than we might have all thought a
16:24
couple of years ago, but then on the other hand, they're still moving a lot faster
16:29
than our current systems are designed to address.
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And so, that is where most organisations will need to move forward very quickly in
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thinking about, as Ravi said, workflow redesign, but then very quickly thinking
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about what are then the consequences for the people that are currently attached to
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a set of occupations that will go through a lot of change.
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And then at a policymaker level, I think something very similar does need to be
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done as well.
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And that's where the piece comes in where there is simply no way for each
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organisation to handle this separately.
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And then there's the other pieces around, how do we fund this?
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And that where there are ideas such as what Ravi has mentioned, and there's also
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some other ways in thinking about how to do this.
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Because of course, the costs of re-skilling and up-skilling are also going down
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because of artificial intelligence.
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And the ability to personalise that learning and re- skilling and upskilling.
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Are also, that ability is just so much higher with artificial intelligence.
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So there are some ways to turn this technology and its disruptions into an
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advantage when it comes to speeding up re-skilling and up-skilling.
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A follow-on question
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from that is, given it's so much more fluid to upskill and re-skill, do you think
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expertise will be in some ways democratised, as a question to Ravi, does it mean
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that we're all going to become
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generalists?
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I think what's going to certainly happen is there's going be diffusion of this
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technology much deeper downwards.
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That's because the interface is natural language, unlike in the past when you
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needed digital skills to access technology.
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This is kind of democratising that process.
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You know, we had this distinction of a producer of software and a consumer of
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software.
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That line is blurring.
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Everybody can be a producer and a customer, which means you could build your
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technology and allow it to amplify yourself.
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So expertise in some ways is going to be on your fingertips, which means the
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asymmetry we created over the last 50 years based on expertise.
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We created asymmetry with individuals. We created asymmetry with organisations.
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That isn't asymmetry anymore. The asymmetry will come from interdisciplinary skills.
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You should be a biologist with the ability to use agentic to improve your
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throughput, improve your output.
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You should a historian to have four clock terminals around you to be a futurist.
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You could be a chartered accountant having a bunch of AI agentic work around you to
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power your insights.
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That is the future we are all looking for, which means the ability to absorb this
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as an interdisciplinary skill is much, much easier as Saadia pointed this out.
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It's much relatively easier in comparison to what we did in the past because
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expertise was really the symmetry.
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The symmetry now is interdisciplinary skills.
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We need this intersection between a domain, business operations, and technology.
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And I think that is much relatively easier, including the fact that you would also
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use AI to create a personalised, micro-personalised tutor.
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I mean, we now have this unique opportunity to have a tutor and a nurse for each
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individual, each person on the planet at a throwaway price.
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That's the power of this technology.
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I think we have to pivot this to these meaningful, purposeful use cases, which will
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support this process.
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You know, over the last 50 years, the drift of value went upwards.
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We created layers of white-collar jobs.
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We captured value there, and we created premium on wages.
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If you're pushing that downwards, a nurse in a hospital, a frontline worker in
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manufacturing, they would have this capability.
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But the way you have to design the workflows, the way you design organisational
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structures is you have drifting the capability downwards.
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It doesn't necessarily increase wages.
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You have to drift the value downwards as well, because value actually follows
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controls.
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It doesn't follow access.
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Once you do that redesign, then the asymmetry will shift to judgement,
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accountability, and outcomes.
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And once you have judgement, accountability, and outcomes on the front lines,
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you're obviously going to pay more wages.
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And so there's going to be more distributed wages in the process.
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So I think this is the re-pivot we have to do.
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We have got this unique opportunity to reset our workforce and the work we do and
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the way we actually distribute value.
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And if we can design this well, this is a unique opportunity for that reset.
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So in that scenario, what happens to the kind of traditional talent pyramid?
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Is that no longer that relevant?
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Gayle, I've been a big believer.
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I've written quite a bit about this extensively, it's a contrarian view.
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I think the pyramids are going to be broader.
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They're not going to be, you know, the pyramids were like this.
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They're going to be a broader.
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You'll have more early careers and shorter path to expertise.
22:12
The entry barriers on the pyramid are going to to be disappearing.
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I've been a big believer of this at Cognizant.
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We hired 20,000 school graduates last year.
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We're going to hire more than 20,00 this year. The year before we hired 12,000.
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So entry barriers to jobs are going to be in some ways disappearing.
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You know, a lot of jobs were STEM related.
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Now you're going to see STEM and non-STEM because effectively you could be a
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producer and a consumer and you could intertwine technology in your daily flows,
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which means you know, you need a lawyer with agentic skills.
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You need a biologist with agentics skills to do life sciences, drug development
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kind of a thing.
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So you have broader pyramids, shorter pyramids.
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The middle layers in every company are going to be player coaches.
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We also had roles for coordination, orchestration. Those roles will disappear.
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So there'll be more player coaches roles in the middle.
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And those nodes are going to to be very real and agentic.
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So you're going to see digital labour, doing things which were in the past related
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to coordination, orchestration, and moving information up and down, as I call it,
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those roles will disappear.
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They will get transitioned to digital labour.
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The new roles are going to be player coaches in the middle, and you're going to see
23:31
much broader pyramids.
23:33
That's a phenomenal thing.
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I mean, if you have much broader pyramids and shorter path to expertise, you're
23:39
going to see more modular teams, more singular pods.
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Or singular squads.
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I mean, this is brilliant because to express yourself, you don't need large teams,
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you need small teams.
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In fact, to express yourselves over the last 50 years, we used institutions to
23:55
deliver our mission and work with companies who actually have shared mission.
24:00
Now you could do that in a much more modular democratised way.
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So I think that's the future of how organisational structures are going to be.
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They're going to be more network versus hierarchical.
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There is this rumbling negative narrative around job losses.
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And we even saw recently with commencements some of the graduates booing tech
24:22
leaders, for example.
24:24
What does real augmentation look like?
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And how can we persuade those graduates that actually this is an exciting time to
24:33
be entering
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the workforce?
24:34
I think in terms of what does real augmentation look like, it is something around
24:39
that player-coach model that Ravi's just mentioned, but many organisations haven't
24:44
quite made that bridge yet.
24:46
I'll then step back and just refer to what we've found so far.
24:50
There is an overall net positive.
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We have found that it is very likely that there is, likely to be overall job growth
24:57
rather than overall job displacement or reduction over time.
25:00
And that would point to that healthy, growing, bigger base pyramid that Ravi is
25:05
referring to.
25:06
That is likely based on everything we've heard so far.
25:09
At the same time, though, there's probably sort of three ways that people are
25:14
thinking about this.
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There's a set of people that believe this is sort of an early canary in the coal
25:20
mine situation.
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You're going to have these large urban rust belts because a lot of entry-level and
25:26
middle-level roles are going to get wiped out.
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There's a set of people that I think, again, we just discussed this piece,
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broadening pyramids.
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Actually, we're going to need so much more talent, not just because of the
25:39
augmentation piece, but because of wholly new roles and new value-add that people
25:44
can bring as some of this workflow redesign takes place where essentially wholly
25:49
new products and services are possible to create, some things that are not possible
25:54
to imagine right now, because we're still thinking in the domain of current jobs.
25:58
But if we think four or five years from now, this would just be a wholly new set of
26:04
jobs.
26:04
And then there's the set of folks that I think would probably say, actually, none
26:09
of this is true.
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And we're essentially looking at a number of organisations that are tightening
26:14
their belts due to the current economic situation.
26:17
And that is why you're seeing a reduction in some of that entry-level work and
26:22
actually has nothing to do with artificial intelligence.
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I guess it's really going to depend on industry and organisational readiness and
26:30
what they're actually absorbing in terms of technology.
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And not every industry is making this leap at the same pace as others.
26:38
So I think that pyramid and how things go is going to look very different across
26:43
different organisations.
26:44
But one point that I think is probably consistently true for over the last 10
26:49
years, we have found business leaders telling us in one form or the other that for
26:54
about 60 or 70 percent of them, the lack of skilled entry-level workers is one of
26:59
the major things holding back the transformation of their organisations, which
27:04
means that with or without artificial intelligence what the education and
27:08
university systems have been producing in terms of talent, while it may do it that
27:13
while there are many good things about it, it doesn't always equip young people
27:18
with the new economy skills that they need today.
27:21
And so if that is the case, then a lot more effort needs to go into building simply
27:26
those new economy's skills inside education systems.
27:29
And as they enter the workforce.
27:31
And I think that's where we have to build the bridge.
27:34
That's where a lot of the Forum's time and effort is going to be going, ensuring
27:39
that those cross-cutting new economy skills are built up through education systems
27:44
and as they enter the workforce, because that's going to necessary regardless of
27:49
the particular shape of an org structure across any industry.
27:53
Do you feel like traditional four-year, three, four-year degrees are still relevant?
27:58
And do you think education is keeping up?
28:01
That's a great question.
28:02
You know the current system of going to an academic, full-time academic
28:08
intervention for the first 25 years of a life, working for the next 50 years and
28:14
then retiring is a linear template from the industrial revolution, where the world
28:20
was running at a much slower pace.
28:22
With the clock speed we have, I think we have to revisit that template where the
28:28
K-12 schools should kind of focus on building lifelong learners.
28:33
And then you have partnerships, industry partnerships for I would call it digital
28:39
apprenticeships or AI apprenticeships or whatever you like.
28:43
And then we draw learning resources all our life on a continual basis.
28:48
I mean, today the alumni associations of schools are actually for networks, not
28:54
really to draw resources all your life.
28:57
I would think that template should be revisited.
29:01
You should intertwine work and learning resources all of your life because the
29:07
change is happening in the middle.
29:09
It's not happening on the front and at the back.
29:13
It's happening in middle when you need it the most. So there is a certain revisit.
29:19
Every institution is doing some experiments, but it's not as mainstream.
29:24
I wish we could you know, we could intertwine a few years of that undergrad
29:30
education into apprenticeships in a different form.
29:34
This is AI-led apprenticeships or digital apprenticeships.
29:38
And then we draw learning resources all our life.
29:42
When we look at sort of K through 12 education, let's also not forget that that is
29:47
the place where young people learn how to be members of society.
29:51
There's so many other sort of skill sets and traits and characteristics that are
29:56
built up during that time that are incredibly important.
29:59
But in many parts of the world, that K through twelve education system is designed
30:04
for competition and for rank-ordering students by the end of a school year, which
30:09
is very different from the skills that are going to be needed in the future.
30:14
Very few of those systems actually teach some of those interpersonal dynamics that
30:19
teach what is needed in terms of collaboration.
30:22
So the earlier that can begin, I think the healthier it is for societies as a
30:27
whole, much less for businesses and the economy.
30:30
And then on the university point, absolutely, and this is why we've set up at the
30:35
Forum the First Mile Sandbox, which is all about creating those industry
30:40
partnerships with universities, including in the digital apprenticeship space that
30:45
Ravi was mentioning.
30:46
And we're beginning with five industries, and we plan to roll that out across all
30:51
the various industry groupings that the forum works with exactly for this reason,
30:56
because just this entire methodology has to change.
31:00
And to Ravi's point, some of this is right now about that sort of first mile
31:04
sandbox.
31:05
And it's really focused on that early part of the career.
31:08
But this needs to be continuous across the entire life cycle.
31:12
And there is an interesting stat where something like 0.11% of the GDP of OECD
31:17
countries is spent on the lifelong learning piece, the post-university learning,
31:22
retraining, and upscaling.
31:24
Many large businesses that can afford it spend thousands and thousands per employee
31:29
in terms of retraining and reskilling.
31:31
If that entire system, which does have enough funding in it, could just be better
31:36
connected into institutions who job it is and who have really the expertise,
31:41
universities and colleges and community colleges, that can really do this at scale,
31:46
and if we could do that throughout entire life cycles, that would really pay off.
31:51
And that's going to be necessary.
31:54
I think, you know, going back to the point that I was making earlier around
31:58
policymakers, that is essentially how policymakers will have to rethink the
32:03
incentives they create for collaboration between private sector and the education
32:08
sector.
32:09
I have a teen.
32:10
And I wonder, so asking for a friend, Ravi, if there was one piece of advice that
32:16
you would give her if she were graduating this year, 2026, now as she's looking for
32:22
a job, and then perhaps once she's started that job, what would the advice be?
32:29
I have two toddlers at home. I wish I could tell them this.
32:33
I would say the future is going to be much more interdisciplinary.
32:38
You don't need to be a computer science graduate to thrive in the AI era.
32:44
You need to figure out a way to apply the technology, this extraordinary technology.
32:51
You know, it's a significant shift in terms of capability from the technologies of
32:57
the past.
32:58
How do you integrate that into everything you do at a workplace in your
33:04
professional life?
33:05
O ne of the policymakers asked me this question saying, and what should K-12
33:11
schools and undergrad schools do?
33:14
And you know with related to AI.
33:16
I mean it's funny we tell students if you use AI at your work at your class we're
33:23
going to fire you and we're telling employees if you don't use we'll fire you and
33:29
you know the dichotomy of dealing with that is you should build native skills, a
33:36
class without AI, and you should build, you should do your homework and your
33:42
evaluations with AI.
33:43
What then happens is you power your native skills with an amplification with AI, so
33:50
effectively you have the native skills to do the judgement, which Saadia was
33:56
referring to outcomes, accountability, and intellectual curiosity and everything
34:02
else, but you then amplify yourself with AI.
34:06
So I would actually believe build the native scales without it and use it to
34:12
amplify it and you know try to power this with interdisciplinary opportunities.
34:18
You could, be a journalist you could be a biologist you be a chemist, you could be
34:25
a lawyer.
34:25
You just have to look at this technology and say, you know, it's available on your
34:32
fingertips.
34:33
How do I integrate it into everything I do and create more productivity, new
34:39
products, new services?
34:41
How would the economies who are doing this right look different to those who get it
34:49
wrong?
34:50
Let me maybe just give a quick overview of a scenarios piece that we did, and just
34:55
in very simplistic terms, think of one vector where it's about how quickly
34:59
technology is moving forward and being integrated across an economy, and think of
35:04
the other vector as how quickly people are being skilled, re-skilled, up-skill.
35:09
And essentially, the only no-regret move available to policymakers, is to combine
35:14
that technology investment with the people-based investment.
35:17
There is essentially no such thing as getting the returns from the technology
35:22
investment without the equivalent people investment, because these two things have
35:27
to work together.
35:28
There is no other way.
35:30
And so I'd say the first thing is, economies that understand that, that do not
35:34
think that the people related investments are an afterthought.
35:38
As Ravi said, they have to be integrated into that stack to begin with, that has to
35:43
be number one, just that basic understanding.
35:46
I think the second piece is around raising the digital floor for everybody.
35:51
Because I think a lot of organisations, businesses, governments are thinking about
35:56
that sort of top end.
35:57
But what we still have to remember is there's nearly 3 billion people across the
36:02
planet that still don't have basic digital connectivity.
36:05
And so this is just an extremely fast-growing chasm between the haves and the
36:10
have-nots.
36:10
And so the digital floor does have to be raised for everybody.
36:14
And then the third element is the public-private collaboration that is going to be
36:19
needed to manage this well.
36:21
And I don't want to boil it down to sort of, you know, just a basic term like that.
36:26
It's everything that we've just been talking about.
36:29
It is that element of, yes, businesses have to do a lot within.
36:33
Yes, governments have to think a lot about policymaking on their own.
36:37
But those two sectors will have to talk much more to each other when it comes to
36:42
managing this workforce transformation.
36:45
Saadia Zahidi, Managing Director of the World Economic Forum.
36:46
You also heard Ravi Kumar, CEO of Cognizant.
36:47
They were speaking to my colleague Gayle Markovitz.
36:48
The World Economic Forum and its partners do lots of research into the future of
36:50
jobs and skills, and on artificial intelligence more widely.
36:52
Find that on our website, links in the show notes.
36:53
And it’s one of the subjects we watch closely on Radio Davos - make sure you are
36:55
following us wherever you get podcasts and you can find the Forum’s three weekly
36:57
podcasts at wef.ch/podcasts.
36:57
Radio Davos will be back next week, but for now thanks to you for listening and
36:59
goodbye.