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"The next job interview you get may already have been decided by an algorithm. AI is transforming recruitment at speed, parsing thousands of résumés in seconds and flagging candidates before any human has weighed in. But this efficiency may come with hidden trade-offs: while automated systems promise to identify the best candidates, they risk codifying historical prejudices if their training sets are flawed. The same technology promising to democratize professional opportunity can quietly gatekeep it along lines nobody chose to draw.


At the same time, AI is redefining what professional competence looks like, and the definition keeps shifting. The skills that secured a job three years ago may already be obsolete; the ones that matter tomorrow are still emerging. For individuals, careers are no longer linear paths but continuous negotiations with a technology that won't stand still. For organizations, the challenge cuts deeper: if the benchmark for talent keeps moving, how do you hire for potential rather than credentials? Which skills allow employees to be adaptable and grow? How can we ensure the ""intelligence"" in HR remains genuinely human-centric, and who is ultimately responsible when a machine makes a career-altering decision?"

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Transcript
00:23Hi everybody, thank you very much for being at Vivitech
00:28on Friday, so you're the brightest one
00:32I'm glad to have all of you on stage with me
00:37so we're going to talk about AI and HR and recruitment
00:42we have a lot of things to talk about
00:45I'm happy to have with me on stage Sue Duke
00:48your Managing Director for EMEA and Latin
00:51MVP Global Public Policy at LinkedIn
00:54and then Ruth Harper, SVP, Chief Marketing and Sustainability Officer at Manpower
01:01and here Claire Lebar, CTO at Malte
01:05which is a marketplace for freelancers
01:09I'm going to start with a question for you Sue
01:13because LinkedIn has just released a report
01:16it was a few weeks ago
01:18and your data shows that the slowdown in hiring
01:21is primarily tied to the macroeconomic context rather than AI
01:27and that the roles most exposed to AI are not really disappearing
01:32so that's quite surprising, no?
01:37First of all, it's great to be here
01:38thank you for having me
01:39I think the data coming through out of a report that we did
01:44looking at hiring over the past number of years across Europe
01:48did come as a surprise for a lot of people
01:50because a lot of the headlines have been focused on
01:54what AI is doing to jobs
01:56what jobs AI is displacing
01:59and I think very much tying some of the headlines
02:02around the slowdown in hiring back to AI
02:05but in fact what we see very clearly in the data on LinkedIn
02:10and LinkedIn has 1.3 billion members all across the world
02:14so when we look at what's happening with hiring on the platform
02:18it's very true that there is a slowdown in hiring
02:20right?
02:21So that slowdown in the labour market
02:23that very competitive labour market
02:25that people are feeling right now
02:27there's no question it is sluggish out there
02:30so when we look at hiring across Europe
02:33over the past number of years
02:35it's down a full 25% since before the pandemic
02:39and it's down a very significant 15% year on year
02:42so it is a very competitive
02:44very fast changing labour market out there, no question
02:47but when we look at what the drivers for that slowdown are
02:51it's very much the macroeconomic context
02:54specifically monetary policy
02:57and that rise in interest rates
02:59that we've seen over the past number of years
03:01the pandemic spillover
03:02and the business uncertainty
03:04that we've seen again over the past number of years
03:07and it's been a tough start to the year more generally
03:09then when we look specifically at the kinds of roles
03:14that are seeing a bigger slowdown versus others
03:17again when we look at the most exposed role
03:19the roles that are most exposed to AI
03:21versus general roles and general hiring
03:24we don't see a difference
03:26and the one bright spot coming through
03:29is that where we see the most resilience in hiring
03:32is in roles that combine AI proficiency
03:36and unique human skills
03:38those augmented AI roles
03:40they're holding up the best
03:42so you want to react maybe?
03:44are you surprised?
03:46I'd love to
03:46and I'd kind of
03:47I love Sue
03:48that you've
03:49thank you too
03:50for being here on a Friday
03:51a very hot Friday
03:53this is a test
03:54maybe it's the most air-conditioned room in here
03:56so thank you
03:57that's why we're all here
03:57Charlie
03:59I just want to kind of reinforce
04:03I'm from Manpower Group
04:04we're in 75 markets around the world
04:06we work with millions of workers
04:09and we are working with hundreds of thousands of clients
04:13we have our own reports
04:14our own data
04:15we are seeing the same thing
04:16the media message is quite a different message
04:20to what we're seeing on paper
04:21I agree with the kind of slowdown
04:23and the sluggishness
04:24but I guess if I kind of consider
04:27how we're looking at the shifts
04:29in the labour market right now
04:30if I look from kind of two angles
04:32we have the luxury of seeing
04:35the organisation angle
04:39and what our clients
04:40or what employers are looking at
04:42and of course we have
04:43very clear indicators
04:46of what the workers are looking for
04:49when they're looking for work
04:51what they feel when they're in work
04:53how they feel about being redeployed
04:55and reassigned when they're in work
04:57and how that relationship begins to kind of
04:59play out over the long term
05:01and we definitely see
05:03from the worker perspective
05:04we're seeing this kind of appetite
05:06four different ways in working actually
05:08and Claire I know you'll reinforce this too
05:12people are choosing more flexibility
05:13people are choosing non-linear career paths
05:18and I use the word career paths
05:20with enormous air quotes
05:22because I think we're talking about
05:24learning journeys
05:25lifelong learning journeys and pathways
05:27and we'll come back to that I'm sure
05:29but people are choosing different things as well
05:32the point too from a client
05:35or a company perspective
05:36they are still hiring
05:38our most recent report said
05:4058% of companies are hiring more
05:44because of AI not less
05:47but the third part of all of that I guess
05:50is that the actual jobs are being redefined
05:55so skill sets are changing
05:57you know we've talked about talent shortages
05:59since you know I was this high
06:01and now I'm this high
06:03but 20 years of kind of structural talent shortages
06:06is still an issue
06:08but it's really changing in terms of what's inside
06:11of that shortage area
06:13and really kind of it's becoming much more nuanced
06:15about that combination of skills
06:17that you talked about
06:18so lots more to get into
06:21do you agree more flexibility of course
06:23absolutely maybe I'll add on top of it
06:26that I think a lot of this narrative
06:28actually has been driven
06:30from California by a few companies
06:32it's been all over your LinkedIn
06:34all over the news
06:35and it's actually influencing sea levels
06:38across the world
06:40and also employees within companies
06:43because it's very
06:44it increases anxiety towards the future
06:46towards job
06:47towards the impact of AI
06:48which sometimes prevents
06:50actually us to think through
06:52what do we want to do with it
06:54what problem do we want to solve
06:55how do we want to leverage this technology
06:58and so this is where actually
07:00we need a lot more independent thinking
07:03in how we approach this transition
07:06and for Europe as well
07:08independence in technology
07:09which comes with independent thinking
07:11and this is something that
07:13you know we see companies realizing more and more
07:15that they need to bring that independent thinking
07:18expertise within their company
07:19via interim executives
07:21where we see a growth of 60%
07:23since last year
07:24and these types of profile
07:25management consultant
07:26to really help them think through
07:28what is it I want to
07:29where do I want to bring my business
07:31how do I want to transition my organization
07:33outside of the noise
07:34that we see online
07:36because we're far from AGI
07:38I think what's the interesting question
07:40is we are we are ahead of like 10 years
07:4315 years of massive technological organizational transformation
07:46and so how do we want to go about that
07:48and come up as winners
07:51in the LinkedIn report
07:53you highlight also a dramatic rise in
07:56people listing themselves as entrepreneurs
08:00founders in Europe
08:02especially in Europe
08:03so thanks to AI of course
08:05because it makes them
08:06they can create
08:08more easily
08:09projects
08:10so is an entrepreneurial mindset
08:13becoming the
08:14yeah the key
08:16if I may say that
08:17that recruiter are searching for
08:19today
08:20even for
08:21salaried positions
08:23yeah you're absolutely right
08:25so one trend
08:26that's coming through
08:27very strongly
08:27over the past couple of years
08:29is this real surge
08:30in people
08:32obviously part one of the drivers
08:34is it is a sluggish
08:35hiring market out there
08:36and so
08:37particularly young people
08:39professionals generally
08:40but particularly
08:41Gen Z
08:43are not sitting back
08:44and waiting for opportunities
08:45to come to them
08:46they're going out
08:47and creating their own opportunities
08:49so globally we've seen
08:50a massive surge
08:51even in the last year
08:52of 60% growth
08:54in founders on our platform
08:56and 30% of Gen Z
08:58are going out
09:00and setting up their own thing
09:01it might be their own company
09:02it might be setting up a portfolio
09:03it might be a side hustle
09:05but there's this huge wave
09:07of entrepreneurship
09:08being unleashed
09:09not least as you say
09:10because the tools enable that
09:12so before
09:12where you may have needed
09:13a marketing department
09:15and a HR department
09:16and a sales department
09:18now the tools can do
09:19an awful lot for you
09:20and you can actually
09:21get something going
09:22as a one person operation
09:24so there's no question
09:25that we're seeing
09:26this real real surge
09:27of entrepreneurship
09:28happening out there
09:29I think for hiring
09:31specifically for companies
09:32and organizations
09:33it's not necessarily
09:35entrepreneurship per se
09:36but that learning mindset
09:39that is crucially
09:41crucially important
09:42I think Claire makes
09:42an excellent point
09:44about the importance
09:44of agency
09:45and that we don't
09:46just sit back
09:47and let this
09:48latest technological
09:49advancement happen to us
09:52but we take control
09:53of it and shape
09:53what it means
09:54for workers
09:54and organizations
09:55and companies
09:56but I think what
09:57companies are specifically
09:59looking for
09:59is people who come in
10:01and be adaptable
10:03be agile
10:04and always be up
10:06for learning
10:06so when we look
10:07at the average skill set
10:09for the average job
10:10we expect it to change
10:1270% by 2030
10:13so the person you hire
10:16today
10:17for the job
10:18today
10:19needs to fundamentally
10:21transform their skill set
10:22don't mind
10:23in the next six years
10:24but in the next two years
10:26and so adaptability
10:28that learning mindset
10:30is absolutely top of the list
10:31for employers out there
10:32and I don't expect
10:33that to change
10:34maybe I can add
10:35a few statistics
10:36from the freelancer
10:37data standpoint
10:39so we definitely
10:40have seen
10:41that due to the
10:41macroeconomic context
10:42there is a slowdown
10:44in full-time hiring
10:45and due to that uncertainty
10:47actually companies
10:48are more likely
10:48to start
10:50a lot of their initiative
10:51and transformation
10:52with freelancer
10:53which are typically
10:53very adaptable
10:56resilience
10:57and are going to
10:58help shape
10:59the organization
11:00of tomorrow
11:00and learn
11:01with the organization
11:02so we see
11:03the most advanced
11:04large companies
11:05actually accelerating
11:07their freelancing program
11:08currently
11:09not only with
11:10the roles I mentioned
11:11but also in technology
11:13biggest acceleration
11:15is plus 600%
11:16on the AI
11:18and automation
11:19engineers
11:20just in the last year
11:22because I'm not
11:23quite sure
11:24how the role
11:25is going to land
11:26there's all sorts
11:27of profile
11:28behind that role
11:28but leveraging
11:30external expertise
11:31is helping them
11:32shape the organization
11:32of tomorrow
11:33at lower risk
11:36so I'm going to ask
11:37you a question
11:38of course
11:39about you know
11:39we always have
11:40the debate
11:41of hard skills
11:42soft skills
11:43so what is
11:44the right combination
11:45today
11:46in this world
11:48where we're
11:49surrounding
11:49by AI
11:49we have been
11:50having this debate
11:51for a long time
11:51it feels like
11:52now it's coming
11:53to life
11:53right
11:54yeah the combination
11:57I can't reiterate
11:58enough
11:58we would talk
11:59about curiosity
12:00and how we
12:01you know
12:01how are we
12:02unleashing curiosity
12:04encouraging curiosity
12:05in our children
12:07in Gen Z
12:10but let's not forget
12:11as leaders
12:11that we need to be
12:12demonstrating
12:13that curiosity too
12:14and I think
12:15you know
12:15that's a whole other
12:16conversation
12:17because people are
12:18following and fitting
12:19with the culture
12:19of the organization
12:21you know
12:23we've talked
12:24for a number of years
12:25actually about
12:25the concept
12:26of intrapreneurship
12:27which is
12:28to your point
12:29bringing that
12:29kind of founder mindset
12:31into the organization
12:33we were just
12:34having a conversation
12:35with Emmanuel
12:36of Sanofi
12:37yesterday
12:38and we talked
12:38about
12:39we have to be
12:40thinking about
12:40how we're reinventing
12:42how we run
12:43our organizations
12:44and how we do work
12:45not where are we
12:46dropping technology
12:47or automation
12:48or AI
12:49into easier
12:51even dull tasks
12:52but how are we
12:53reinventing
12:54how we get stuff
12:55done
12:55and to be able
12:57to do that
12:57so this is where
12:58I think
12:58wow we've been
12:59talking about this
13:00for so many years
13:01learnability
13:02the ability
13:03and the appetite
13:04to carry on learning
13:05is a massively
13:07the massively
13:08most valuable
13:09skill
13:10in terms of
13:11kind of adaptability
13:12curiosity
13:14even comfort level
13:16with the anxiety
13:17that fast change
13:19and unpredictable
13:20destinations
13:21and I don't know
13:22what my job
13:23my next job
13:23will be
13:24that kind of adaptability
13:26is the strongest thing
13:28that we can nurture
13:29if I speak a little
13:31more specifically
13:31about roles
13:33and why I get excited
13:34you know
13:34we've had so much
13:35discussion this week
13:36about what are the new
13:38roles of the future
13:39well I don't know yet
13:41I don't know yet
13:42is a really important
13:43is a really important
13:44message
13:45you know
13:46but some of our
13:46kind of
13:47some of the new
13:47engineering roles
13:49the kind of
13:50the roles that are
13:51coming into engineering
13:52that once upon a time
13:54were software developers
13:55that really do have
13:575, 10, 15
13:59different key criteria
14:00to them
14:01from everything
14:02from kind of
14:02Python and API knowledge
14:04and all of that
14:05right through to
14:06you're going to be
14:07the person that goes in
14:08talks to the client
14:09tells them everything's wrong
14:11and this is what
14:12they need to do
14:13oh and bring your team
14:14with you actually
14:14because you need
14:15those kind of
14:16human skills
14:17at Manpower Group
14:18we talk about
14:19human first
14:20digital always
14:21because one is not
14:22coming without the other
14:23but a lot of these
14:25these new roles
14:26are really beginning
14:27to emphasize the criteria
14:28that is required
14:29on both of those sides
14:30super exciting
14:32and you know
14:33if you have the curiosity
14:37putting your hand up
14:38and even kind of
14:39practicing at the weekends
14:40again we were talking about
14:41well what should I be doing
14:42to kind of practice
14:43vibe coding
14:44how do I do that
14:45in my own life
14:46so that I get comfortable
14:47talking about
14:48I'm learning something
14:49that I never knew
14:50I would need to learn before
14:53I couldn't agree more
14:55and we see exactly
14:57the same thing
14:58in hiring
14:59as you say
14:59there is hiring out there
15:01and where hiring
15:03is holding up strongest
15:04is in where
15:05it's not an either or
15:07it's both
15:07where that AI literacy
15:10and capability
15:10is present
15:11but it's paired
15:13with those
15:14unique
15:15irreplaceable
15:16human skills
15:17and to pick up
15:18on your specific example
15:19of software engineers
15:21that's a great example
15:22not least because
15:23it's one of those roles
15:23that people talk about
15:25that's going to get
15:26automated away
15:26when we break down
15:28the skills required
15:29to be a software engineer
15:31it is such a brilliant example
15:33of how this change
15:34is manifesting
15:35so if you looked back
15:37at the top 15 skills
15:39required to be a software engineer
15:41four years ago
15:42which is typically
15:43the length of a degree
15:44for software engineering
15:46zero Gen AI skills
15:48in that top 15 list
15:49today Gen AI skills
15:51make up over half
15:52of the most in demand
15:54top 15 skills
15:56for software engineer
15:57and secondly
15:58what's absolutely
16:00shot up that demand list
16:01are those human skills
16:03team coordination
16:05super important
16:06team leadership
16:07cross functional capabilities
16:09organization
16:10getting people excited
16:11about what
16:12you don't even know
16:13the answer to yet
16:14and bringing other functions
16:15with you
16:16thinking about the organization
16:17and the systems level
16:18exactly as you say
16:19so we are seeing
16:21it manifest
16:21specifically
16:22in the makeup
16:24of the skills
16:24of these roles
16:25and specifically
16:26in the makeup
16:27of the skills
16:28of those roles
16:29that people fear
16:30are going to be
16:30automated away
16:32I agree
16:33yet
16:34not fully
16:35because hard skills
16:36are not going away
16:37engineering
16:38is not going away
16:39it's actually a job
16:41that's been transforming
16:42for the last
16:4340-50 years
16:44you know
16:45it started
16:45as like punching holes
16:47in a card
16:47that was what
16:48software engineering was
16:50then coding on C
16:53compiler
16:54Python
16:55was not on the map
16:5610 years ago
16:56not as much
16:58it's been kind of
16:59the top coding language
17:00now
17:01of what we see
17:02on Mount
17:02right up there
17:03with Java
17:04and so it's actually
17:05a function
17:06it's been just
17:07the cloud
17:08you know
17:08all of that
17:09has been evolving
17:10constantly
17:10and so engineering
17:11the fact that
17:12you know
17:12you're someone
17:13who's solving problem
17:14with technology
17:15that's going to remain
17:17vibe coding
17:18is amazing
17:19but
17:21what we see
17:22actually
17:22is that
17:22not everybody
17:23is actually
17:24a product manager
17:26in the sense
17:27that they're going
17:27to be able
17:27to build products
17:29anybody can cook
17:30who cooks
17:33today
17:34right
17:35not everybody
17:35I think it's the same
17:37you know
17:37with creating website
17:38apps
17:39there was
17:39local no code
17:40before
17:40the tools are going
17:41to be easier
17:42and easier
17:42yes
17:43the engineering job
17:44is going to be
17:44more and more
17:45about architecting
17:46a system of agents
17:47but I'm convinced
17:48it's not going away
17:49and actually
17:50I think
17:50the talk about
17:52automating
17:53software engineering
17:54it's preventing
17:55some organization
17:55to fully experiment
17:57with the power of AI
17:58and so that's why
17:59we're seeing
18:00companies
18:01which are name
18:02jumping from
18:02token maximizing
18:03to token minimizing
18:04and I think
18:05this is just
18:06slowing down
18:06the productive
18:08adoption of AI
18:09I mean
18:10I'd reiterate
18:11that
18:11I don't want
18:12anybody to think
18:12that we say
18:13it's all about
18:13the soft skills
18:14the combination
18:16is what's going
18:17to be absolutely
18:17critical
18:18the technical skills
18:20it's a given
18:20but if you come
18:22with technical
18:22skills only
18:24then you know
18:25somebody else
18:26is going to get
18:26to the top
18:26of the list
18:27first
18:27because there
18:28are so many
18:28other things
18:29you know
18:29if I think
18:30about kind
18:30of transformation
18:32partnering with
18:33technology companies
18:34to go in
18:34and say
18:35how do you
18:35drop AI
18:36into the workflow
18:37how do you
18:38help your
18:39organization
18:40get comfortable
18:40with agents
18:41working next
18:43to your humans
18:44for your humans
18:45how are they
18:46part of the team
18:47that is not
18:48a technology job
18:49if you leave
18:50that to be
18:51a technology job
18:52you've got
18:52a massive
18:53culture problem
18:53either happening
18:54right now
18:55or it will happen
18:56how you bring
18:57people along
18:58with where you're
18:59dropping AI
18:59bringing AI
19:00and technology
19:01and reinventing
19:03what your
19:04organization does
19:05right through
19:06to reinventing
19:07individuals
19:07jobs
19:08is a hugely
19:11soft skill
19:12next to that
19:13technical skill
19:13and it's super
19:15exciting to see
19:16the kind of
19:17fusion of some
19:18of that coming
19:18together
19:18that perhaps
19:19has not been
19:19recognized so
19:20clearly before
19:21I think we would
19:22have said
19:22exactly the same
19:23thing 15 years
19:24ago here
19:24talking about
19:25I don't know
19:26if Ivetek
19:26was 15 years
19:27ago
19:28do we need
19:28to rename
19:29soft skills
19:29because it's
19:30getting a bit
19:30tired
19:31talking about
19:32digital
19:32transformation
19:33right
19:34and when
19:36we talk
19:36about tech
19:36companies
19:39before AI
19:40I think this
19:41is what it
19:41was about
19:42it was less
19:42about technology
19:43it was about
19:44trying to
19:44organize differently
19:46and going
19:46for these
19:47soft skills
19:47also in
19:48engineering
19:49right
19:49if you look
19:49at a product
19:50engineer
19:50in a tech
19:51company
19:52very business
19:53oriented
19:53very product
19:54oriented
19:55I think
19:55just now
19:55it's spreading
19:56it's being
19:57more and more
19:57common
19:57but I don't
19:58think it's
19:58new
19:59so I'm
20:00sure you
20:01can debate
20:01this for
20:03hours
20:03come back
20:04in 10
20:04years time
20:05see if we're
20:05still talking
20:06about it
20:06but well
20:07other topics
20:09to talk about
20:10maybe Ruth
20:11question for
20:12you because
20:13you know
20:14AI is
20:14embedded
20:15through
20:16much of
20:17your hiring
20:18workflow
20:18today
20:19so how
20:20can AI
20:20improve
20:21job access
20:22and responsiveness
20:23while still
20:24preserving
20:25you know
20:25the human
20:27moments
20:28that matters
20:28most to
20:29candidates
20:30thank you
20:31for the
20:32question
20:32and
20:34this is
20:35an enormous
20:35one
20:36and I
20:36think
20:36even
20:37kind of
20:37probably
20:38our
20:38perspectives
20:39have all
20:39changed
20:40it feels
20:40like we've
20:41been here
20:41at VivaTech
20:42if you've
20:42been here
20:42for three
20:43days
20:43it feels
20:43like maybe
20:44we've been
20:44here for
20:45a couple
20:45of weeks
20:45but I
20:46love
20:47the learning
20:48that takes
20:49place
20:49when we're
20:50here
20:50and I
20:51guess
20:51I'm
20:52learning
20:52too
20:53that
20:53you know
20:54AI is
20:56embedded
20:56we've
20:57talked
20:57about
20:57too
20:57much
20:58of the
20:58conversation
20:58I guess
20:59is around
20:59are we
21:00replacing
21:01the human
21:02interaction
21:03at the
21:03front end
21:04of the
21:04candidate
21:05job seeker
21:06journey
21:07are we
21:08using too
21:09much algorithm
21:09in the
21:10process
21:10to not
21:11treat
21:12the job
21:12seeker
21:13like a
21:13human
21:13oh my
21:15gosh
21:15the debate
21:16has moved
21:16on
21:16I think
21:17the discussion
21:18has moved
21:18on so much
21:19further
21:19than some
21:20of that
21:22because we
21:23need to
21:23meet people
21:24where they're
21:24at
21:25and that
21:25includes
21:25the
21:26recruiters
21:26the
21:26hundreds
21:27of
21:27thousands
21:27that work
21:28for
21:29manpower
21:29group
21:30and others
21:30in the
21:30industry
21:31and within
21:31organizations
21:32how do we
21:33make
21:33recruiters
21:34lives
21:34better
21:34how do we
21:35make
21:35job seekers
21:36lives
21:36better
21:37how do we
21:37improve
21:38the
21:38experience
21:39so that
21:40we're
21:40finding the
21:41right talent
21:41and matching
21:42the right
21:42talent
21:43what is it
21:44that job seekers
21:44want
21:45we survey
21:45job seekers
21:46all of the
21:46time
21:47and the
21:49biggest
21:49frustration
21:50that they
21:51have
21:51is the
21:52time
21:52it takes
21:53for any
21:53organization
21:54to respond
21:55to them
21:55so how
21:56are we
21:56beginning
21:57to leverage
21:57AI to
21:58actually solve
21:59the problems
21:59that exist
22:00for organizations
22:01yes
22:02for specific
22:03sectors
22:03yes
22:04and for
22:04job seekers
22:05so how do we
22:06get faster
22:06on responsiveness
22:07by screening
22:08by leveraging
22:09AI
22:10by actually
22:11having
22:11by having
22:13up front
22:15conversational
22:16AI that allows
22:17somebody to
22:17not just search
22:19a job board
22:19but to be able
22:20to say
22:21but I live
22:22an hour
22:22from the
22:23bus
22:23station
22:24and I
22:25have to
22:25be back
22:26by 5pm
22:27and I
22:28only like
22:28working in
22:29small groups
22:30or whatever
22:30that kind
22:31of
22:31that
22:32enabling
22:32of that
22:33personalization
22:34right up
22:34front
22:35in the
22:35chat
22:36enables
22:37even a
22:38kind of
22:39more
22:39specificity
22:40around the
22:41kind of
22:42roles that
22:43the AI
22:43is then
22:44presenting
22:44to the
22:45individual
22:45so you
22:46can go
22:47through
22:47these
22:47rounds
22:47of
22:49personalization
22:49at my
22:50time
22:51in my
22:51pajamas
22:52in bed
22:53when the
22:53children
22:53have gone
22:54to bed
22:54instead of
22:55in the
22:559 to 5
22:56of the
22:56day
22:56too
22:57so how
22:57you enable
22:58access
22:59at different
23:00times
23:00in different
23:01ways
23:01to different
23:01kinds
23:02of people
23:02is massively
23:03influencing
23:04the very
23:05up front
23:06slowness
23:06of the
23:07kind of
23:07whole
23:08jobs
23:08process
23:09we see
23:10we're
23:10seeing
23:10kind of
23:1190%
23:11satisfaction
23:12rate
23:12on some
23:13of those
23:14tools
23:14that we're
23:15using
23:15that are
23:15AI
23:16generated
23:16but super
23:17personalized
23:17and then
23:19from a
23:20recruiter
23:20perspective
23:21too
23:22I was
23:22just
23:22listening
23:22to
23:23somebody
23:23yesterday
23:23who said
23:24oh
23:24everybody's
23:25writing
23:26CVs
23:27but
23:27through
23:28AI
23:28and saying
23:29how
23:30can you
23:31write
23:31here's
23:31my CV
23:32write me
23:32my CV
23:33so that
23:34I get
23:34two rounds
23:35to the
23:36interview
23:36process
23:37I don't
23:38want
23:38the back
23:39and forth
23:39and it
23:41is working
23:41the AI
23:42is working
23:42in getting
23:43people to
23:43interview
23:44process
23:44how
23:45disappointing
23:46when you
23:46shouldn't
23:46be there
23:47and so
23:48that kind
23:48of playing
23:49the AI
23:50and I
23:50definitely
23:51don't see
23:51it as
23:52cheating
23:52I see
23:53it as
23:53using
23:54the AI
23:54playing
23:55with AI
23:55to work
23:56for me
23:56how do
23:57we meet
23:58them
23:58partway
23:58to make
23:59sure
23:59that the
23:59match
24:00is closer
24:00and that's
24:01how we're
24:02looking at
24:02not just
24:03the application
24:03part of
24:04the process
24:05but the
24:08personalization
24:08of the
24:09match
24:09the precision
24:10of the
24:10match
24:11the timing
24:12of how
24:13AI can
24:14support
24:15true
24:15diversity
24:16true
24:17access
24:17to
24:18looking
24:19for
24:19new
24:19jobs
24:20specificity
24:21around
24:21the
24:22job
24:22and
24:23then
24:23once we
24:24have a
24:24relationship
24:24with those
24:25people
24:25how are we
24:26managing
24:27that relationship
24:27how are we
24:28understanding
24:28how they're
24:29progressing
24:29in the role
24:30and how are we
24:31understanding
24:32when they are
24:33ready
24:33to be able
24:34to move
24:34to the
24:34next role
24:35and then
24:36we can
24:36be figuring
24:36out how
24:37we can
24:37redeploy
24:38reassign
24:39at the
24:39time
24:39and in
24:40the way
24:40and to
24:42encourage
24:42their own
24:42learning path
24:43to as we
24:44learn how
24:45their capability
24:46is increased
24:46it is all
24:47the way
24:48through the
24:48workflow
24:49and it's
24:50good for
24:50recruiters
24:51because it
24:51means they're
24:51spending more
24:52time with
24:53humans
24:54understanding
24:54those specific
24:55needs
24:57and much
24:58greater
24:58customer
24:59satisfaction
24:59from a
25:01worker
25:01perspective
25:02and generally
25:03a much
25:04stronger
25:04match
25:04and greater
25:05longevity
25:07of the
25:08relationship
25:08when we
25:09place them
25:09into work
25:10too
25:11I think
25:12when we
25:13think about
25:13AI in
25:14the hiring
25:14process
25:15there's
25:15probably
25:16three facts
25:17we need
25:17to juggle
25:17with
25:18reconcile
25:18I think
25:20the first
25:20one
25:20the job
25:20market
25:21is very
25:21biased
25:22it's not
25:22new
25:24and so
25:25when we
25:25leverage
25:26past data
25:26in AI
25:27in the
25:28recruitment
25:28process
25:29this is
25:29really
25:30something
25:30we have
25:30to
25:31watch out
25:31for
25:31measure
25:32and
25:32mitigate
25:35now
25:36when we
25:37talk about
25:38this risk
25:38what did
25:39people do
25:39when they
25:40were
25:40receiving
25:40thousands
25:41of
25:41applications
25:41before
25:42pre-AI
25:43actually
25:43they
25:43use
25:44filters
25:44like
25:45your
25:45schools
25:45your
25:46diploma
25:46previous
25:47company
25:47which
25:48are
25:48heavily
25:49biased
25:49with
25:50large
25:51language
25:52models
25:53whether
25:53they are
25:53generative
25:54or not
25:54you don't
25:54need a
25:55generative
25:55model
25:55for that
25:56you can
25:56go in
25:57the space
25:57of
25:57semantic
25:58matching
25:58which
25:58opens
25:59the
25:59floor
26:00for
26:00skills
26:01based
26:02matching
26:02which
26:03is
26:03much
26:03less
26:04biased
26:04than
26:04filters
26:05like
26:05diploma
26:06school
26:06and
26:07whatnot
26:07so
26:07I think
26:08this is
26:08a very
26:08interesting
26:08application
26:09of a
26:10small
26:10or large
26:11language
26:11model
26:11that
26:12don't
26:12have
26:12to be
26:12large
26:12actually
26:13to
26:13work
26:13properly
26:14this is
26:14something
26:14we've
26:14integrated
26:15in our
26:15matching
26:16process
26:16and we've
26:17seen
26:17as you
26:17said
26:18getting
26:18conversion
26:19mitigation
26:21of
26:21some
26:22of the
26:22issues
26:23that
26:23we would
26:24see
26:24in
26:24typical
26:25filtering
26:25the second
26:26fact
26:27though
26:28surprising
26:29to me
26:29I think
26:30there's
26:30no
26:30research
26:31no survey
26:32yet
26:32in Europe
26:32but there's
26:33been a few
26:34in the
26:34US
26:34and in
26:35Asia
26:35we see
26:36that when
26:36you ask
26:37people
26:37do you want
26:37to be
26:37interviewed
26:38by human
26:39or AI
26:40the majority
26:41prefers AI
26:42which is
26:43really surprising
26:44especially women
26:45because there's a
26:46perception that
26:47AI is going to be
26:48less biased
26:48than human
26:50so for me
26:51it's been
26:51really puzzling
26:51but I think
26:53it's an
26:53interesting fact
26:54and there's
26:54as you said
26:55there's demand
26:56actually for people
26:57I definitely
26:57I definitely
26:57think that's
26:58beginning to
26:58shift
26:59isn't it
26:59whether it's
27:00bias
27:00or whether
27:00it's
27:01convenience
27:01there's
27:02definitely
27:02a much
27:03greater
27:04comfort
27:05level
27:05at the
27:07interaction
27:07from an
27:08AI
27:08perspective
27:09as well
27:10as human
27:10yeah
27:11exactly
27:11so this
27:12is something
27:12we have
27:13to consider
27:13as we
27:13build
27:14these
27:14products
27:14and the
27:15third
27:15fact
27:16which is
27:16more
27:16concerning
27:17to me
27:17it's
27:17when we
27:17look at
27:18new
27:18graduates
27:19the
27:20experience
27:20of the
27:21job
27:21market
27:21is
27:21very
27:22different
27:22from
27:23the
27:24one
27:24we've
27:24had
27:24several
27:25years
27:25ago
27:25right
27:26now
27:27they
27:27have
27:27to
27:27interview
27:28to
27:28a
27:28lot
27:28more
27:28jobs
27:30and
27:31often
27:32actually
27:32they get
27:33rejected
27:33without
27:34talking
27:34to
27:34any
27:34human
27:35and
27:36or
27:36they
27:36get
27:37to
27:37talk
27:37to
27:37a
27:37human
27:37after
27:38three
27:38or
27:39four
27:39rounds
27:39sometimes
27:40and
27:41this
27:41is
27:41exacerbating
27:42actually
27:43the
27:43feeling
27:44of loneliness
27:45and rejection
27:46of that
27:46generation
27:47which
27:48feeds
27:48into
27:49some
27:49of the
27:49fear
27:50and
27:50rejection
27:50of
27:50AI
27:51as
27:51well
27:51and
27:52so
27:52I
27:53think
27:53when
27:53we
27:54look
27:54at
27:54interviewing
27:54and
27:55the
27:55job
27:55market
27:55let's
27:55be
27:56extra
27:56careful
27:56about
27:57the
27:57experience
27:57of
27:57these
27:57young
27:58people
27:59and
28:00this
28:00generation
28:02yeah
28:03maybe
28:03just to
28:03build
28:04I agree
28:04with a ton
28:05of what's
28:05been said
28:06I think
28:06we have
28:06this paradox
28:07right now
28:08in part
28:08because it
28:09is such
28:09a tough
28:10labor market
28:10out there
28:11where job
28:11applicants
28:12I think
28:13particularly
28:13new
28:13entrants
28:14to the
28:15job
28:15market
28:15and younger
28:16workers
28:16feel like
28:17they're
28:17applying
28:17to just
28:18thousands
28:18and thousands
28:19of jobs
28:19and actually
28:20what I think
28:21is the most
28:23you just
28:24hear nothing
28:25I've applied
28:25to all of
28:26these different
28:26organizations
28:27I've literally
28:27heard
28:27don't mind
28:28heard
28:28nothing
28:29at all
28:30don't mind
28:30I heard
28:31I had an
28:32interview
28:32with AI
28:33or I got to
28:34the second round
28:35like nothing
28:35at all
28:35it's just
28:36so destroying
28:37but then
28:38on the other
28:38side
28:39we continuously
28:40hear from
28:40organizations
28:40that they
28:41just can't
28:41get the
28:42skills
28:42that they
28:43need
28:43and can't
28:43find
28:44the right
28:44candidates
28:45for the
28:45right role
28:46and I
28:47think both
28:47are really
28:48struggling
28:49with
28:49what AI
28:50should
28:51and can
28:51be doing
28:52in this
28:52space
28:53and I
28:54think going
28:54back to
28:54your point
28:55Ruth
28:55about on
28:56the recruiter
28:57side
28:57a well
28:58designed
28:58well applied
28:59AI
29:00should be
29:02making it
29:03significantly
29:04easier
29:05on both
29:05sides of
29:06that marketplace
29:06so those
29:07recruiters
29:07that you
29:08mentioned
29:08increasing
29:08the time
29:09speeding up
29:10the time
29:11that kind
29:11of anxiety
29:12that frustration
29:13that soul
29:14destroyingness
29:14I would rather
29:15hear no
29:16100%
29:17I think
29:18if we
29:18ask
29:18most people
29:19in this
29:19room
29:20would I
29:20prefer to
29:21hear nothing
29:21or would I
29:22prefer to
29:23hear no
29:23I would at
29:23least like
29:24to know
29:24ideally I'd
29:25get some
29:26feedback as
29:26well
29:26so that can
29:27help me
29:27and improve
29:28the next
29:29application
29:29that I do
29:30I think
29:31if you were
29:31to ask
29:32those
29:32hundreds
29:32of
29:32thousands
29:33of
29:33recruiters
29:33they would
29:34all say
29:34to you
29:34I did
29:35not get
29:35into recruitment
29:37to set up
29:38meetings
29:38and take
29:39meeting notes
29:40and to do
29:41the scheduling
29:41and all
29:42of the stuff
29:42that just
29:43all the emails
29:44just takes
29:44hours and hours
29:45out of my week
29:45every single
29:48week
29:48I got in
29:49to recruitment
29:50to find the
29:51right person
29:51for the right
29:52role
29:52and if the
29:53AI is applied
29:54in the right
29:54way where it's
29:55taking that
29:56drudgery
29:56and those
29:57more routine
29:57tasks out
29:58it means
29:58I as a
29:59recruiter
29:59can spend
29:59more time
30:00with the
30:00hiring manager
30:01to deeply
30:01understand
30:02what this
30:02person needs
30:03and what
30:04the right
30:04fit would
30:04be
30:04and I
30:05can spend
30:06more time
30:06with the
30:08individual
30:08candidates
30:09understanding
30:09are we the
30:10right fit
30:11for them
30:11and are they
30:11the right
30:11fit for me
30:12and I
30:13think the
30:13exact same
30:14thing on
30:16the job
30:16seeking side
30:17which is
30:17if the
30:18AI is
30:18designed
30:19and applied
30:19in the
30:20right way
30:20it should
30:21be helping
30:22job seekers
30:23not only
30:25apply for
30:26the roles
30:26do that
30:27first draft
30:27of the CV
30:28help get
30:28them ready
30:29but I
30:30think Claire's
30:30point is
30:30extremely well
30:31made
30:31it will
30:32also help
30:33candidates to
30:34find jobs
30:35that they don't
30:36necessarily know
30:37that they're
30:37currently qualified
30:38so we have
30:39an AI powered
30:40search which
30:41we're seeing
30:41very very strong
30:42results of
30:43candidates being
30:44able to apply
30:45for fewer
30:46roles but more
30:47relevant roles
30:47and it's
30:48precisely based
30:49on not the
30:50job title
30:50but on the
30:51skills
30:52and understanding
30:53at that atomic
30:54level what are the
30:55individual skills
30:56that I have
30:57what are the
30:58individual skills
30:59that make up
30:59these roles
31:00and what's
31:01the bridge
31:02for me from
31:02where I am
31:03to where I
31:04need to get
31:04to is exactly
31:05one of the
31:06huge advantages
31:08of AI
31:08but it needs
31:09to be applied
31:10and designed
31:11in the right way
31:12so it works
31:12for both
31:13parts of the
31:14marketplace
31:15and fundamentally
31:16has people
31:17at the centre
31:18people hire
31:19people
31:19and it's
31:20crucially important
31:21that that remains
31:21and Sue I think
31:22what you're
31:23describing
31:23and maybe
31:24the title
31:25of the event
31:26was a teaser
31:27and maybe
31:28a little misleading
31:28because I don't
31:29think this is
31:30about the resume
31:30anymore
31:31I think how
31:32are we leveraging
31:33back to kind
31:33of what's the
31:34problem we're
31:34trying to solve
31:35how can we
31:36leverage AI
31:37more, better,
31:38faster
31:39to begin to
31:40predict performance
31:41to begin to
31:42predict not just
31:43can I match you
31:44to this job
31:45what you're
31:45describing is
31:46we're using it
31:47to match to the
31:48job much more
31:49or to match to
31:50the job based
31:50on the skills
31:51that you have
31:52maybe even
31:52versus the skills
31:53that were asked
31:54for as well
31:55so this kind
31:56of skills based
31:57approach
31:57which will also
31:59enable us
32:00to begin to say
32:01I can see
32:02where you're going
32:03to be in three
32:03months time
32:04I can see
32:05what's going
32:05to suit you
32:06in six months
32:06time
32:07I can see
32:07the speed
32:08with which
32:08you can move
32:09to earn
32:10this amount
32:10and here's
32:11how you need
32:12to do it
32:12which is exactly
32:13right
32:14recruiters
32:15want to be
32:15advising
32:15and talking
32:16to humans
32:17to really bring
32:18the value
32:18and remove
32:20a lot of
32:20the drudgery
32:21and I think
32:21that is
32:22what we're
32:22beginning to
32:23see
32:23and that's
32:24what I think
32:24we'd be talking
32:25about in a
32:25year's time
32:26to say
32:26wow
32:27now we really
32:28are predicting
32:29potential
32:31maybe one last
32:33question
32:33and maybe one
32:34takeaway
32:35from each
32:36each of you
32:38what mindset
32:39should
32:40maybe
32:41yeah
32:41for the audience
32:42we have
32:43talents
32:44we have executives
32:45a lot of people
32:46but can you tell us
32:47what mindset
32:48they should
32:48adapt
32:49adopt
32:50to ensure
32:51that they're
32:51not really
32:52enduring AI
32:53but actually
32:54more winning
32:55with it
32:57happy to go
32:58first
32:59I will go back
33:00to that learning
33:01mindset
33:01I think that
33:02is the most
33:03important
33:04skill
33:05or approach
33:06to really
33:07hardwire
33:08into ourselves
33:09and into
33:10organizations
33:11right now
33:11not least
33:12because everything
33:13is changing
33:13so quickly
33:14and we're
33:15going to have
33:15to continue
33:16I think
33:16exactly as you
33:17said Ruth
33:17we've been
33:18talking about
33:18lifelong learning
33:19for all my
33:21lifelong
33:21but actually
33:24what meaningful
33:25progress
33:26like do we
33:27really internalize
33:28it ourselves
33:28and within
33:29organizations
33:29like now is
33:30the time we
33:31really need
33:31to do that
33:32and again
33:32we see this
33:33in our data
33:35very very clearly
33:36that for all
33:37of the
33:37and again
33:38we are seeing
33:38enormous surge
33:40in the demand
33:40for those AI
33:41literacy skills
33:42the demand
33:43for those skills
33:43are up 70%
33:45remember the backdrop
33:46of hiring
33:46down 15%
33:4870% uplift
33:50in the demand
33:50for AI literacy
33:51skills
33:52over the past
33:53year alone
33:53and those AI
33:54technical skills
33:55absolutely continue
33:56to surge well
33:57into double digit
33:58increase year on
33:59year as well
34:00but what remains
34:01rock solid
34:03constant
34:03are those human
34:05skills
34:05that will be
34:06right at the
34:07center of this
34:08transition
34:08and adaptability
34:10is in the top
34:11three
34:12always
34:12when we look
34:13at markets
34:13right across
34:14the world
34:14right now
34:15that is what
34:16organizations
34:17are looking for
34:18because they know
34:19the job I hire
34:20for you today
34:21will certainly
34:22change in the
34:23next year
34:23and could change
34:24in the next
34:24couple of months
34:25so that to me
34:26is the key
34:27takeaway
34:27and the key
34:28area to focus
34:29on for us
34:29as individuals
34:30and collectively
34:31as well
34:32I'll be a little
34:34bit
34:34because I agree
34:35I'll be a little
34:36bit briefer
34:37you know
34:38it might be
34:39it might be
34:40driving fear
34:41and uncertainty
34:42how do we
34:43help people
34:44embrace that
34:44we like to
34:45think that AI
34:46is coming to
34:47our jobs
34:48not coming
34:48for our jobs
34:49and that goes
34:50right back to
34:51the curiosity
34:51the adaptability
34:52and the learnability
34:53to be able to
34:54wrap your arms
34:55around it
34:57I think the
34:58winning organizations
34:59are the ones
34:59who understand
35:01the importance
35:01of psychological
35:02safety
35:03right now
35:04for the right
35:04experimentation
35:05and the right
35:07innovation to
35:07happen
35:08building in
35:09intelligence
35:10liquidity
35:10meaning it's
35:11less about
35:12the size
35:12of your team
35:13and more about
35:13being able to
35:14leverage the right
35:14skill
35:15the right
35:15expertise
35:16the right
35:16person at
35:16the right
35:17time
35:17and the last
35:18one is
35:18independent
35:19thinking
35:19because you
35:20really need
35:20to bring
35:21you know
35:22move away
35:23from like
35:23the old ways
35:24or your
35:25mainstream
35:26LinkedIn feed
35:27and go seek
35:27people who
35:28are not
35:28attached to
35:29one brand
35:30one technology
35:31one model
35:32and are going
35:32to be able
35:33to try it
35:33for your
35:33company
35:34so I would
35:34say these
35:34three things
35:35are I think
35:36critical for
35:36the organization
35:37to succeed
35:39okay so
35:40thank you
35:40very much
35:41thank you
35:42Sue
35:42thank you
35:43Ruth
35:43thank you
35:43Claire
35:44for being
35:44with us
35:45at Vivacek
35:46today
35:46thank you
35:47very much
35:47thank you
35:47thank you
35:47everybody
35:48thank you
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