It's not mature yet, but HR's use of artificial intelligence shows promise for both employers and workers.
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[00:00:15] Welcome to PeopleTech, the podcast of WorkforceAI.news. I'm Mark Pfeffer.
[00:00:32] Let's take a step back today and look at how HR is taking advantage of or gearing up for AI.
[00:00:39] I'm joined by Siobhan Fagan, the Editor-in-Chief of Reworked.
[00:00:43] We're going to talk about what's real and what's not, how the workforce will adapt,
[00:00:48] where AI fits into HR, and where HR fits into AI.
[00:00:53] That and more on this edition of PeopleTech.
[00:00:59] Siobhan, thanks for being here today.
[00:01:02] Thanks so much for having me.
[00:01:03] Great to talk with you.
[00:01:05] Let's talk about AI and HR, not surprisingly.
[00:01:12] It seems like in the HR space, there's a lot of hype going on from vendors and consultants about all the wonderful things that AI is going to be able to do for HR.
[00:01:24] If you think about the hype of AI versus the reality of AI, how do those things align?
[00:01:34] Well, I would say that the hype is not relegated only to the HR space.
[00:01:41] The AI conversation has basically sucked all of the air out of the room so that people are fundamentally approaching this from a tech-first question,
[00:01:54] rather than what solutions, like what problems are we trying to solve?
[00:01:59] So it's the old hammer and nail issue, where it's the hammer in search of the nails.
[00:02:06] So I think that that's kind of going across the board and not only specific to HR.
[00:02:13] As we're nearing the end of the year, and we are hearing more and more about agentic AI,
[00:02:20] I think that that is a space now where we are hearing a lot of promises,
[00:02:27] and not necessarily much by way of delivery.
[00:02:30] And I'm assuming your audience knows what is meant by agentic AI,
[00:02:35] although there does not seem to be a common definition.
[00:02:39] But basically, we're talking about those autonomous AI bots that are able to work through entire processes,
[00:02:46] sometimes in coordination with other AI bots,
[00:02:50] to solve complex problems with very little, if any, human oversight.
[00:02:55] And so that's what we're currently being promised.
[00:02:58] And we still are sort of in wait and see mode there.
[00:03:02] You know, it seems to me that throughout the organization,
[00:03:06] there's an awful lot of energy being spent on exploring AI,
[00:03:14] trying it out, you know, implementing it,
[00:03:17] coming up with use cases and all of that kind of thing.
[00:03:20] Do you think that's true, that it's getting an inordinate amount of attention?
[00:03:26] And is that getting in the way first of business in general,
[00:03:31] but secondly, is getting in the way of HR doing its basic job?
[00:03:35] Well, that's an interesting question.
[00:03:39] I do think that to your first point, the hype is definitely getting in the way,
[00:03:46] because I think what it's doing is creating a sense of FOMO,
[00:03:51] where people are scrambling to keep up with what is going on,
[00:03:57] and not necessarily understanding what the realistic things are.
[00:04:03] So if you look at, for example, the promises of AI in recruiting,
[00:04:08] which is obviously one of the big use cases that people are using,
[00:04:12] and you are told that it will simplify and it will streamline
[00:04:18] and it will, you know, help you solve all of your problems.
[00:04:22] But the companies that are doing it well are using it as a assistant
[00:04:30] more than a fully autonomous agent.
[00:04:36] They are using it in areas like for a chatbot.
[00:04:42] I spoke with somebody at a major hospital the other day
[00:04:45] who is using an AI chatbot,
[00:04:47] and it basically streamlined their recruiting process
[00:04:51] by getting all of these nurses and other people
[00:04:54] who were wanting to apply to the hospital system
[00:04:57] to the right jobs quickly,
[00:04:59] which was something that wasn't happening earlier.
[00:05:02] And that was just like this very small,
[00:05:04] very specific use case where they saw real results.
[00:05:08] And I think that we need more case studies like that
[00:05:11] rather than these overblown promises of what it can do
[00:05:16] and where it's going to be seen.
[00:05:19] Well, it does seem like there's a lot of money going into all of this,
[00:05:23] that companies are making a big investment
[00:05:25] in terms of time and dollars.
[00:05:31] Did you think the money that they're spending is well spent,
[00:05:35] or are they just kind of throwing dollars at something
[00:05:39] and hoping they can find a value to it?
[00:05:42] That's a tough one.
[00:05:44] I can't speak to anybody's budgeting process.
[00:05:48] I do think that there is a need for companies
[00:05:52] to be experimenting with,
[00:05:54] to be finding good solutions,
[00:05:56] practical solutions of AI in their stacks.
[00:05:59] And I think that since they have already invested
[00:06:03] in so many of these tools already,
[00:06:05] they're already in place
[00:06:06] and their existing vendors are introducing this AI,
[00:06:09] then absolutely play with it.
[00:06:11] Absolutely continue using it.
[00:06:13] When we look at something though,
[00:06:17] like ChatGPT's $200 subscription
[00:06:21] that they announced last week,
[00:06:23] when we're looking at that kind of outlay
[00:06:27] on top of what is already a fairly considerable amount
[00:06:32] of spending per person per month,
[00:06:34] is that practical?
[00:06:36] I would want to see more results
[00:06:38] before I would suggest anybody throw that money down.
[00:06:42] Do you think companies are going to be running
[00:06:45] to do it anyway,
[00:06:46] just because it's sort of a cool thing to do?
[00:06:50] I'm sure some will.
[00:06:51] Yeah, absolutely.
[00:06:52] But I do think that right now,
[00:06:55] the real use cases,
[00:07:00] the practical use cases
[00:07:01] that are helping people do their jobs better
[00:07:04] as opposed to the speculative
[00:07:05] is where people should be putting their money.
[00:07:09] And if possible,
[00:07:12] when they do get those practical use cases running,
[00:07:15] then having that sandbox on the side
[00:07:17] where they can experiment
[00:07:18] and they can find these perhaps moonshot chances
[00:07:23] where it will work in a more innovative way.
[00:07:27] You know,
[00:07:29] in reading and following things about AI and HR,
[00:07:34] hear a lot from CHROs,
[00:07:36] hear a lot from CIOs,
[00:07:39] hear a lot from HR directors,
[00:07:41] but I don't hear a lot from the practitioners.
[00:07:44] What are the people
[00:07:45] who are just kind of frontline HR staff,
[00:07:49] what are they thinking about all this?
[00:07:51] I think in a lot of cases,
[00:07:53] they're trying to balance,
[00:07:55] I mean,
[00:07:55] and it's the role of HR in general.
[00:07:57] They're trying to balance these push and pull
[00:08:00] between their senior leaders
[00:08:01] who are eager to find out
[00:08:05] how AI can create these efficiencies,
[00:08:08] can create this optimization
[00:08:10] that we've all been promised,
[00:08:12] while at the same time
[00:08:13] having to handle the pressure from below
[00:08:17] where employees are both interested in,
[00:08:22] curious of,
[00:08:23] and using AI,
[00:08:25] but at the same time scared of being replaced
[00:08:27] and in some cases
[00:08:29] are needing to be upskilled.
[00:08:31] And so,
[00:08:32] I think the HR people
[00:08:35] are in a tough position between the two,
[00:08:38] but that's sort of the balancing act
[00:08:39] that they do in their roles every day.
[00:08:41] If you like swiping,
[00:08:43] then head over to Substack
[00:08:44] and search up Work Defined,
[00:08:47] WRK Defined,
[00:08:48] and subscribe to the weekly newsletter.
[00:08:51] Hey everybody,
[00:08:51] I'm Lori Rudiman.
[00:08:52] What are you doing?
[00:08:53] Working?
[00:08:54] Nah.
[00:08:54] You're listening to a podcast about work
[00:08:56] and that barely counts.
[00:08:58] So while you're at it,
[00:08:59] check out my show,
[00:09:00] Punk Rock HR,
[00:09:02] now on the Work Defined Network.
[00:09:04] We chat with smart people
[00:09:05] about work,
[00:09:06] power,
[00:09:06] politics,
[00:09:07] and money.
[00:09:08] Are we succeeding?
[00:09:09] Are we fixing work?
[00:09:10] Eh, probably not.
[00:09:11] Work still sucks,
[00:09:12] but tune in for some fun,
[00:09:14] a little nonsense,
[00:09:15] and a fresh take on how to fix work
[00:09:17] once and for all.
[00:09:19] Let me ask you about AI's impact on HR.
[00:09:24] We hear a lot about,
[00:09:25] you know,
[00:09:26] AI improves efficiency,
[00:09:28] lets people spend more time
[00:09:30] on strategic issues
[00:09:32] rather than work-a-day issues.
[00:09:35] Is AI materially changing
[00:09:38] the way HR works,
[00:09:40] do you think?
[00:09:42] Not on any broad scale yet.
[00:09:44] I think there are pockets.
[00:09:46] I think there are success stories.
[00:09:49] I can think of one
[00:09:51] in the form of MasterCard,
[00:09:53] who was an early adopter of AI
[00:09:56] in their internal talent marketplace,
[00:10:00] and they were using the AI
[00:10:02] in that case
[00:10:03] to connect employees
[00:10:06] with potential part-time work,
[00:10:10] potential mentors,
[00:10:11] and all sorts of other things.
[00:10:13] And in that case,
[00:10:13] it was quite successful
[00:10:14] and it was a very specific use case.
[00:10:18] But I don't think
[00:10:20] that we are seeing
[00:10:21] the efficiencies yet
[00:10:24] at any scale.
[00:10:25] And I think that
[00:10:26] there is a fundamental
[00:10:29] talent gap
[00:10:30] both in the employees
[00:10:33] but also in the HR field
[00:10:35] where we all need to be upskilled.
[00:10:37] We all need to have
[00:10:38] that understanding
[00:10:41] specifically
[00:10:42] of when to use AI
[00:10:44] and when not to use AI
[00:10:45] and what practical use cases
[00:10:48] will deliver the most value.
[00:10:51] Well, you know,
[00:10:53] I hear a lot about that.
[00:10:54] People needing to be upskilled
[00:10:56] in order to make
[00:10:57] the best use of AI.
[00:10:58] And I wonder about,
[00:11:00] for example,
[00:11:01] people like us.
[00:11:02] We're both journalists.
[00:11:04] We both spend a lot of time
[00:11:06] thinking about AI.
[00:11:08] But what kind of upskilling
[00:11:10] do I need, for example?
[00:11:13] That's a great question.
[00:11:15] I mean, there's the use case
[00:11:17] of a specific tool.
[00:11:18] So if you're introducing
[00:11:20] a specific tool
[00:11:21] in your company,
[00:11:22] then the person needs
[00:11:23] to very, very specifically
[00:11:25] be upskilled on that.
[00:11:27] I think on a broader sense,
[00:11:29] it is more the
[00:11:31] sort of sandbox
[00:11:33] what I was talking about before
[00:11:34] where people have
[00:11:35] that safe place
[00:11:37] where they feel comfortable
[00:11:40] exploring,
[00:11:41] experimenting with,
[00:11:43] and also
[00:11:45] have a place
[00:11:46] where they can actually
[00:11:47] ask questions
[00:11:48] where they don't feel stupid.
[00:11:50] And I think that that's
[00:11:50] actually a really
[00:11:51] important part of it
[00:11:52] because everybody's
[00:11:54] learning at different paces
[00:11:56] and we all learn better
[00:11:58] when we're actually
[00:11:59] comfortable sharing
[00:12:00] our success stories
[00:12:01] and our failures together.
[00:12:03] Do you think that HR
[00:12:05] is providing
[00:12:08] any kind of,
[00:12:09] I guess, test case?
[00:12:11] You know,
[00:12:12] HR is funny
[00:12:13] because
[00:12:13] its impact
[00:12:15] is felt
[00:12:15] throughout the organization.
[00:12:17] it's not like
[00:12:18] sales
[00:12:19] who worry about sales
[00:12:21] or, you know,
[00:12:23] development
[00:12:23] who worry about development.
[00:12:25] HR is kind of everywhere.
[00:12:28] It would seem to me
[00:12:29] that
[00:12:30] that makes a good place
[00:12:32] for companies
[00:12:33] to start
[00:12:34] with AI.
[00:12:37] Do you think
[00:12:38] companies are doing that
[00:12:39] or are they just
[00:12:40] sort of everyone's
[00:12:41] picking their own way in?
[00:12:43] Yeah, I don't see
[00:12:47] HR being
[00:12:48] the testing ground
[00:12:49] necessarily.
[00:12:51] Not that companies
[00:12:52] aren't introducing it
[00:12:54] in HR
[00:12:54] but as being
[00:12:55] like the starting place
[00:12:56] for a company
[00:12:57] in part
[00:12:57] because
[00:12:58] it's a little bit
[00:12:59] more fraught
[00:13:00] than other use cases.
[00:13:02] So,
[00:13:03] if you're looking
[00:13:04] at AI,
[00:13:05] say,
[00:13:05] for document management
[00:13:07] and it's
[00:13:09] for search,
[00:13:10] in that case
[00:13:11] you are working
[00:13:12] within this contained thing.
[00:13:15] You are in
[00:13:16] your own environment
[00:13:17] within the enterprise
[00:13:19] and
[00:13:20] the results
[00:13:21] are actually codified.
[00:13:24] HR,
[00:13:25] because of
[00:13:26] its reach
[00:13:27] throughout
[00:13:27] the
[00:13:28] workplace
[00:13:29] and because
[00:13:31] it's dealing
[00:13:31] with humans,
[00:13:34] it has
[00:13:35] that extra
[00:13:36] layer
[00:13:36] of complexity
[00:13:37] that I think
[00:13:38] makes it
[00:13:39] a more
[00:13:41] challenging
[00:13:42] case scenario
[00:13:43] than in other areas.
[00:13:45] So,
[00:13:46] that's
[00:13:47] kind of
[00:13:47] where I see it.
[00:13:49] I do think
[00:13:50] that there are
[00:13:51] a lot of different
[00:13:51] areas
[00:13:52] where it can be
[00:13:53] applied,
[00:13:53] where it's sort
[00:13:54] of the lower level
[00:13:55] like the chatbot
[00:13:56] that I mentioned
[00:13:56] earlier.
[00:13:58] But I think
[00:13:59] that it's
[00:14:00] complicated
[00:14:00] and I think
[00:14:01] it also
[00:14:02] involves
[00:14:02] some
[00:14:03] fundamental
[00:14:04] rethinking
[00:14:04] of
[00:14:05] jobs,
[00:14:07] breaking
[00:14:08] down
[00:14:08] tasks
[00:14:08] and
[00:14:09] other
[00:14:11] work
[00:14:11] that
[00:14:11] has to
[00:14:12] go on
[00:14:12] externally
[00:14:13] before you
[00:14:14] can introduce
[00:14:14] the AI.
[00:14:16] It does
[00:14:17] seem like
[00:14:17] there's this
[00:14:19] ground
[00:14:20] shifting
[00:14:20] going on
[00:14:21] where
[00:14:23] AI
[00:14:23] is making
[00:14:24] the
[00:14:25] technology
[00:14:26] smarter
[00:14:27] or more
[00:14:28] responsive,
[00:14:29] let's say.
[00:14:32] there's
[00:14:32] more
[00:14:33] integration
[00:14:33] going
[00:14:34] on
[00:14:35] where
[00:14:36] employees
[00:14:37] might
[00:14:38] access
[00:14:39] their
[00:14:39] HR
[00:14:40] system
[00:14:40] through
[00:14:40] Slack
[00:14:41] say
[00:14:41] or
[00:14:42] Teams.
[00:14:44] People
[00:14:45] are relying
[00:14:45] more and
[00:14:46] more on
[00:14:46] chatbots
[00:14:47] as a way
[00:14:48] to interact
[00:14:49] with the
[00:14:49] technology.
[00:14:50] Those
[00:14:51] are three
[00:14:52] pretty
[00:14:52] big
[00:14:53] changes
[00:14:53] in the
[00:14:55] tech world.
[00:14:56] Do you
[00:14:57] see the
[00:14:57] same thing?
[00:14:58] If you
[00:14:59] do,
[00:14:59] where do
[00:14:59] you think
[00:15:00] that's
[00:15:00] all
[00:15:00] going?
[00:15:02] I think
[00:15:03] part of
[00:15:03] that is
[00:15:04] a direct
[00:15:09] response
[00:15:09] to the
[00:15:10] proliferation
[00:15:11] of tools
[00:15:12] that we've
[00:15:12] seen in
[00:15:13] the workplace
[00:15:14] over the
[00:15:14] last three
[00:15:14] or four
[00:15:15] years.
[00:15:15] I think
[00:15:17] what happened
[00:15:18] was
[00:15:18] companies
[00:15:20] very
[00:15:20] successfully
[00:15:21] made that
[00:15:21] transition
[00:15:22] to working
[00:15:23] remotely,
[00:15:25] which was
[00:15:26] fantastic,
[00:15:27] but due
[00:15:28] to the
[00:15:28] time
[00:15:28] constraints,
[00:15:29] didn't
[00:15:29] necessarily
[00:15:30] have the
[00:15:30] governance
[00:15:31] in place
[00:15:32] for the
[00:15:33] rollout of
[00:15:34] all those
[00:15:34] tools.
[00:15:35] What we're
[00:15:36] seeing now
[00:15:37] is the
[00:15:38] overlap
[00:15:39] of a lot
[00:15:40] of tools,
[00:15:40] the excess
[00:15:41] of tools
[00:15:42] where people
[00:15:42] don't
[00:15:43] necessarily
[00:15:43] know
[00:15:44] where
[00:15:44] they
[00:15:44] should
[00:15:44] be
[00:15:46] sharing
[00:15:46] a
[00:15:46] document,
[00:15:47] and so
[00:15:47] they
[00:15:47] share
[00:15:48] it in
[00:15:48] five
[00:15:48] places
[00:15:48] at
[00:15:49] once.
[00:15:50] They
[00:15:50] don't
[00:15:51] know
[00:15:51] where
[00:15:51] they
[00:15:51] should
[00:15:51] be
[00:15:51] communicating
[00:15:52] with
[00:15:52] their
[00:15:52] team
[00:15:52] at
[00:15:53] any
[00:15:53] given
[00:15:53] time.
[00:15:54] So
[00:15:54] that
[00:15:55] kind
[00:15:55] of
[00:15:56] integration
[00:15:56] I
[00:15:57] think
[00:15:57] we'll
[00:15:58] see
[00:15:58] more
[00:15:59] of,
[00:15:59] but I
[00:15:59] think
[00:16:00] that
[00:16:00] at
[00:16:00] the
[00:16:00] same
[00:16:01] time
[00:16:01] where
[00:16:01] AI
[00:16:01] could
[00:16:02] be
[00:16:02] potentially
[00:16:03] helping
[00:16:03] is
[00:16:03] identifying
[00:16:04] those
[00:16:05] overlaps,
[00:16:06] identifying
[00:16:06] where
[00:16:07] those
[00:16:07] tools
[00:16:08] can
[00:16:08] be
[00:16:08] streamlined,
[00:16:09] and
[00:16:10] identifying
[00:16:10] how to
[00:16:12] get rid
[00:16:12] of those
[00:16:12] redundancies.
[00:16:15] I keep
[00:16:16] hearing
[00:16:16] the same
[00:16:17] thing about
[00:16:18] where
[00:16:18] HR is
[00:16:19] being
[00:16:19] used.
[00:16:20] You know,
[00:16:21] it's being
[00:16:22] used to
[00:16:23] increase
[00:16:24] efficiency,
[00:16:25] it's being
[00:16:26] used to
[00:16:27] streamline
[00:16:28] communications
[00:16:29] by drafting
[00:16:30] job
[00:16:31] descriptions
[00:16:32] or what
[00:16:33] have you.
[00:16:33] It makes
[00:16:34] me wonder
[00:16:35] where else
[00:16:36] should
[00:16:37] these
[00:16:39] talents,
[00:16:39] if you
[00:16:40] will,
[00:16:40] but where
[00:16:41] else
[00:16:41] should
[00:16:41] these
[00:16:42] capabilities
[00:16:42] be
[00:16:43] applied?
[00:16:43] It
[00:16:44] seems
[00:16:44] like
[00:16:45] there's
[00:16:46] more
[00:16:46] to
[00:16:46] AI
[00:16:46] than
[00:16:48] sifting
[00:16:49] through
[00:16:49] job
[00:16:50] applications
[00:16:50] or
[00:16:51] writing
[00:16:52] job
[00:16:52] descriptions
[00:16:52] or
[00:16:53] all
[00:16:54] of
[00:16:54] that.
[00:16:55] Is
[00:16:56] there
[00:16:56] a place
[00:16:57] where it
[00:16:57] should
[00:16:57] be
[00:16:57] used
[00:16:57] to
[00:16:58] be
[00:16:58] used
[00:16:59] yet?
[00:17:00] I
[00:17:01] can't
[00:17:01] speak
[00:17:01] to
[00:17:02] not
[00:17:02] being
[00:17:02] used.
[00:17:02] I
[00:17:03] do
[00:17:03] think
[00:17:03] that
[00:17:03] the
[00:17:04] MasterCard
[00:17:04] example
[00:17:05] that
[00:17:05] I
[00:17:05] shared
[00:17:05] earlier
[00:17:06] with
[00:17:07] the
[00:17:07] personalized
[00:17:09] learning
[00:17:11] journeys
[00:17:12] that they're
[00:17:12] providing
[00:17:13] their employees
[00:17:13] is a
[00:17:14] very
[00:17:15] compelling
[00:17:16] use case.
[00:17:17] I
[00:17:18] think
[00:17:18] that
[00:17:18] being
[00:17:19] able
[00:17:19] to
[00:17:20] better
[00:17:20] personalize
[00:17:21] the
[00:17:22] employee
[00:17:22] journey
[00:17:23] so you
[00:17:24] are
[00:17:25] starting
[00:17:25] to see
[00:17:25] these
[00:17:26] larger
[00:17:26] platforms
[00:17:27] I'm
[00:17:28] thinking
[00:17:28] of
[00:17:29] SAP
[00:17:30] success
[00:17:30] factors
[00:17:31] or
[00:17:31] Workday
[00:17:31] rolling
[00:17:32] out
[00:17:32] these
[00:17:32] entire
[00:17:34] platforms
[00:17:35] that are
[00:17:35] going to
[00:17:36] bring you
[00:17:36] along
[00:17:36] the
[00:17:37] entire
[00:17:37] employee
[00:17:37] journey
[00:17:38] and
[00:17:38] showing
[00:17:38] these
[00:17:39] capabilities
[00:17:39] throughout
[00:17:40] so that
[00:17:40] it becomes
[00:17:41] more
[00:17:41] seamless
[00:17:42] that shows
[00:17:43] great
[00:17:43] promise.
[00:17:44] I
[00:17:44] can't
[00:17:45] speak
[00:17:45] to
[00:17:45] whether
[00:17:45] it's
[00:17:46] delivering
[00:17:46] yet
[00:17:46] but it
[00:17:47] shows
[00:17:47] great
[00:17:47] promise
[00:17:48] but
[00:17:49] anything
[00:17:50] that is
[00:17:50] going
[00:17:51] to
[00:17:51] better
[00:17:51] reduce
[00:17:52] the
[00:17:52] friction
[00:17:53] for
[00:17:53] employees
[00:17:54] in
[00:17:54] their
[00:17:55] day-to-day
[00:17:55] work
[00:17:55] is
[00:17:56] someplace
[00:17:57] where
[00:17:57] I
[00:17:57] think
[00:17:57] HR
[00:17:58] should
[00:17:58] be
[00:17:58] focusing
[00:18:00] and
[00:18:00] I
[00:18:01] also
[00:18:01] think
[00:18:01] that
[00:18:03] anything
[00:18:03] that
[00:18:04] they
[00:18:04] can
[00:18:04] be
[00:18:04] doing
[00:18:05] to
[00:18:05] better
[00:18:07] identify
[00:18:08] the
[00:18:08] skill
[00:18:09] sets
[00:18:09] of
[00:18:10] their
[00:18:10] employees
[00:18:11] and
[00:18:12] that
[00:18:12] is
[00:18:12] an
[00:18:13] area
[00:18:13] where
[00:18:13] AI
[00:18:13] can
[00:18:14] help
[00:18:15] will
[00:18:15] be
[00:18:16] good
[00:18:16] because
[00:18:16] I
[00:18:16] think
[00:18:17] that's
[00:18:17] where
[00:18:17] jobs
[00:18:18] are
[00:18:18] going
[00:18:18] to
[00:18:18] be
[00:18:18] moving
[00:18:18] in
[00:18:18] the
[00:18:18] future
[00:18:19] and
[00:18:19] we've
[00:18:19] been
[00:18:19] hearing
[00:18:20] about
[00:18:20] skills
[00:18:20] based
[00:18:21] hiring
[00:18:21] for
[00:18:21] ages
[00:18:22] but
[00:18:22] we
[00:18:22] haven't
[00:18:22] seen
[00:18:23] it
[00:18:23] come
[00:18:23] true
[00:18:24] but
[00:18:25] I
[00:18:25] do
[00:18:27] AI's
[00:18:28] automation
[00:18:29] capabilities
[00:18:29] jobs
[00:18:30] will be
[00:18:31] broken
[00:18:31] down
[00:18:32] into
[00:18:32] more
[00:18:33] distinct
[00:18:33] discrete
[00:18:34] parts
[00:18:34] and
[00:18:35] so
[00:18:35] identifying
[00:18:36] those
[00:18:36] specific
[00:18:37] skills
[00:18:37] of
[00:18:37] people
[00:18:37] and
[00:18:38] where
[00:18:38] they
[00:18:38] can
[00:18:38] be
[00:18:38] applied
[00:18:39] and
[00:18:39] how
[00:18:40] they
[00:18:40] can
[00:18:40] actually
[00:18:42] not
[00:18:42] only
[00:18:43] broaden
[00:18:44] but
[00:18:45] also
[00:18:46] apply
[00:18:46] their
[00:18:46] skills
[00:18:47] in
[00:18:47] other
[00:18:47] areas
[00:18:47] is
[00:18:47] going
[00:18:48] to
[00:18:48] be
[00:18:48] very
[00:18:48] important
[00:18:50] Do
[00:18:51] you
[00:18:51] think
[00:18:51] the
[00:18:51] capabilities
[00:18:52] of
[00:18:52] AI
[00:18:52] are
[00:18:53] going
[00:18:54] to
[00:18:55] encourage
[00:18:56] executives
[00:18:56] to look
[00:18:57] at
[00:18:57] HR
[00:18:57] in a
[00:18:58] different
[00:18:58] way
[00:18:59] meaning
[00:19:01] looking
[00:19:02] for
[00:19:02] different
[00:19:03] ways
[00:19:03] that
[00:19:03] HR
[00:19:04] can
[00:19:04] bring
[00:19:04] value
[00:19:04] to
[00:19:05] the
[00:19:05] business
[00:19:05] or
[00:19:06] different
[00:19:06] ways
[00:19:07] that
[00:19:07] it's
[00:19:07] contributing
[00:19:08] in
[00:19:09] some
[00:19:09] way
[00:19:09] shape
[00:19:09] or
[00:19:09] form
[00:19:10] to
[00:19:10] the
[00:19:11] business
[00:19:11] financials
[00:19:12] in
[00:19:12] the
[00:19:12] bottom
[00:19:12] line
[00:19:13] is
[00:19:13] it
[00:19:14] setting
[00:19:15] things
[00:19:15] up
[00:19:16] for
[00:19:16] that
[00:19:17] big
[00:19:17] of
[00:19:17] a
[00:19:17] landscape
[00:19:18] shift
[00:19:19] is
[00:19:20] I
[00:19:20] hope
[00:19:21] so
[00:19:21] a
[00:19:21] good
[00:19:21] answer
[00:19:21] I
[00:19:26] mean
[00:19:26] that's
[00:19:27] what
[00:19:27] I
[00:19:27] think
[00:19:27] I
[00:19:28] hope
[00:19:28] so
[00:19:30] but
[00:19:30] we've
[00:19:31] been
[00:19:31] hearing
[00:19:31] about
[00:19:32] HR's
[00:19:33] seat
[00:19:34] at
[00:19:34] the
[00:19:34] proverbial
[00:19:35] table
[00:19:35] for
[00:19:36] years
[00:19:37] now
[00:19:37] and
[00:19:37] it
[00:19:37] keeps
[00:19:38] kind
[00:19:38] of
[00:19:38] manifesting
[00:19:39] and
[00:19:39] being
[00:19:40] taken
[00:19:40] away
[00:19:40] and
[00:19:41] being
[00:19:41] taken
[00:19:42] away
[00:19:42] so
[00:19:44] yeah
[00:19:44] I keep
[00:19:45] thinking
[00:19:45] that
[00:19:46] we're
[00:19:46] in a
[00:19:46] period
[00:19:47] that's
[00:19:47] a
[00:19:48] lot
[00:19:48] like
[00:19:48] it
[00:19:48] was
[00:19:48] say
[00:19:49] six
[00:19:49] or
[00:19:49] seven
[00:19:49] years
[00:19:50] ago
[00:19:50] when
[00:19:51] data
[00:19:51] was
[00:19:52] the
[00:19:52] big
[00:19:52] thing
[00:19:52] and
[00:19:53] everybody
[00:19:54] was
[00:19:54] writing
[00:19:54] articles
[00:19:55] about
[00:19:55] how
[00:19:55] if
[00:19:56] you
[00:19:56] were
[00:19:56] going
[00:19:56] to be
[00:19:57] in
[00:19:57] HR
[00:19:57] you
[00:19:57] really
[00:19:57] had
[00:19:58] to
[00:19:58] know
[00:19:58] data
[00:19:59] you
[00:19:59] didn't
[00:19:59] have
[00:20:00] to
[00:20:00] quite
[00:20:00] be
[00:20:00] a
[00:20:18] the
[00:20:18] technology
[00:20:19] go
[00:20:19] to
[00:20:20] drive
[00:20:20] to
[00:20:21] go
[00:20:23] to
[00:20:23] go
[00:20:24] to
[00:20:24] and
[00:20:24] go
[00:20:25] to
[00:20:25] change
[00:20:29] the
[00:20:29] environment
[00:20:30] for
[00:20:30] HR
[00:20:30] I
[00:20:33] think
[00:20:33] it
[00:20:33] will
[00:20:33] change
[00:20:34] the
[00:20:34] environment
[00:20:34] for
[00:20:35] HR
[00:20:36] but I don't know that it's comparable to the data example in part because
[00:20:47] the data is a specific skill set that you could hire for and you could have somebody on your team
[00:20:55] who could handle that data analysis. The difference with generative AI in my opinion is that it's going
[00:21:04] to permeate every aspect of the business and it's also going to fundamentally change how we work
[00:21:11] if it lives up to its promise. And when we think about fundamentally changing how we work,
[00:21:20] that's where HR needs to be involved. So it's not just that they need to have those skills in AI
[00:21:28] and the ability. I think they need a basic understanding and I think they need
[00:21:34] to, as much as possible, use their imagination about how it can potentially improve work.
[00:21:42] But I think that it's also where they really need to double down on what it is that they do best,
[00:21:48] which is understanding people, understanding organizational development,
[00:21:54] and leaning in and strengthening those areas. Now, if you were a CHRO or if you were asked by a CHRO
[00:22:03] to sketch out the best way to handle all of this, the change that AI is bringing to HR, the
[00:22:14] advance of technology in general, the workplace and the way people approach work is changing.
[00:22:21] It's a lot going on for an executive to try to keep track of and manage. What do CHROs think of all of
[00:22:30] this right now? I can give you anecdotal things, but CHROs as a whole, I don't know that there is
[00:22:38] some kind of unanimous feeling. One thing that I do think has been manifesting repeatedly,
[00:22:48] and this is even before generative AI, is that CHROs cannot be working in isolation, that they need
[00:22:57] to be working in cross-collaboration with their colleagues across multiple departments, and that
[00:23:02] it's only through that kind of collaborative work that where to use AI, where to use any tool,
[00:23:12] how to design the workplace is going to become clearer, because it can't be working in isolation
[00:23:18] anymore.
[00:23:20] I guess my last question is really kind of general and maybe even a little vague, but
[00:23:27] when you look at all the things that are going on,
[00:23:33] AI, advanced technology, changes in the workplace,
[00:23:38] where do you think it's all going? I mean, is this going to really change work or is this just
[00:23:44] basically new tools and new technology being applied to the same old things?
[00:23:50] It's a good question. It's one that I ask myself frequently. I don't know. I genuinely don't know.
[00:24:00] I do think that the level of hype right now is not helpful, but I do think given the amount of
[00:24:13] investment in these tools, we're going to see businesses continue to try and find that work,
[00:24:23] you know, find that success case. And when I say businesses, I mean the vendors,
[00:24:28] they're going to be able to do that. I think that's what I'm going to do.
[00:24:32] Will it change workplaces? I imagine yes. Will it change how we work? I do see
[00:24:43] that jobs will probably have to be reimagined to a certain extent in that they will have to,
[00:24:49] if only to support the automation, be broken down into more discrete areas, like I said before.
[00:24:55] The level of it, I don't know. When we'll get there, I don't know. I mean, artificial general
[00:25:03] intelligence is something that I find hard to believe that we're there imminently, as Sam Altman
[00:25:13] says. But it's very difficult. And I think that the most helpful thing right now, rather than sort of
[00:25:22] speculating where we're going to be a few years down the line, is focusing on separating those helpful
[00:25:30] use cases out from the hype, sharing the success stories, sharing the failures. I find that a lot of
[00:25:40] companies will talk off the record about their attempts and how they have not come through,
[00:25:49] but will not necessarily speak on the record. So I think we need to not only hear about those success
[00:25:55] stories, which definitely exist, but also about those cases where it's not necessarily delivering,
[00:26:01] so that people can have perhaps a little bit more calmness, a little bit less FOMO,
[00:26:09] so that they can approach it with a clear mind and hopefully be able to find some value.
[00:26:18] Thank you very much. It's great to talk with you. And it's an interesting collection of topics,
[00:26:24] I think.
[00:26:25] Yeah, thanks for having me. I hope this is helpful. And I hope that I didn't say I don't know too much.
[00:26:44] My guest today has been Siobhan Fagan, the editor in chief of reworked.co.
[00:26:51] And this has been People Tech, the podcast of workforceai.news.
[00:26:55] We're a part of the Work Defined Podcast Network. Find them at www.wrkdefined.com.
[00:27:05] And to keep up with AI technology and HR, subscribe to Workforce AI today. We're the
[00:27:11] most trusted source of news in the HR tech industry. Find us at www.workforceai.news.
[00:27:20] I'm Mark Feffer.
[00:27:27] I get it. The podcast just isn't enough. That's all right. Head over to your favorite social app,
[00:27:33] search up Work Defined, WRK Defined, and connect with us.
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