September 2, 2025 • 22 mins
In this episode of the HR Mixtape podcast, host Shari Simpson sits down with Dr. Dieter Veldsman, Chief Scientist at AIHR. They delve into the integration of AI in HR practices, emphasizing the importance of managing the narrative around AI to alleviate fears and enhance employee experience. This conversation is particularly timely as organizations navigate the complexities of AI adoption while striving for inclusive leadership and data-driven decision-making.
Listener Takeaways:
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Learn how to effectively introduce AI into your talent strategies without overwhelming your team.
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Discover why understanding the ethical implications of AI is crucial for fostering a diverse and inclusive workplace.
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Explore strategies for enhancing data literacy within HR to leverage analytics for better decision-making.
Hit “Play” to gain insights on transforming HR practices with AI!
Guest(s): Dr. Dieter Veldsman, Chief Scientist, AIHR
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:02):
Joining me today is Dieter Veldsman, Chief scientist from AIHR.
Dieter is a former CHRO and organizational psychologist who leverages data-driven
(00:29):
His expertise spans HR analytics, talent management, and
Dieter, thank you so much for sitting down, coming to
the Paylocity booth here at CIRM and just taking some time to chat with
Looking forward to it and thanks for having me and yeah, fantastic to be here.
Great. And you're here, not from the US, so it was definitely some
(00:52):
travels to get here. So that's super cool. I always love meeting people
from around the world and just hearing their stories and what they're
involved in. And your background just really appealed
Well, look forward to our conversation. And as mentioned, yep, all the way from
Awesome. All right. So from your experience as both a
(01:14):
CHRO and a scientist, how should HR
leaders really start to integrate AI into their talent strategies, ultimately
And I think it's such a relevant question at the moment. And I see a
lot of organizations making the mistake and thinking it's all about which tools should
I be selecting? And how am I going to insert it into my particular process?
(01:34):
And I think specifically in the HR space, there's a starting point around managing the
narrative around AI and what it means for us and helping people understand
the real promise of value that it offers. And looking at it both from,
how are we going to change the mindset of how our people work? And
then on the other side, then start and make it really, really practical. Start
with one use case, get people around the table, get them to experience it,
(01:56):
get them some exposure towards the actual tool sets, and then I think
grow from there. But don't forget the psychological aspect of
it. And that's the big thing that I think currently is a little bit missing when I look at a lot
You know, the psychological aspect is really interesting because
there is this fear, right? Like, hey,
(02:16):
it's gonna replace my job. And I think for a long time,
we kind of talked about like, no, no, it's not gonna replace any jobs. It
is going to replace some jobs. The reality is it is going to replace some jobs. We're
already seeing that, you know, when we look at organizations that have laid
off, you know, more entry-level HR employees because
they're taking this work on through, you know, through AI
(02:37):
tools. How do HR teams start
to get ahead of that and think about potentially
creating that balance where they're adopting AI, but
then they're really kind of leveling up their own function so
that they can drive via data and some of that
And I think HR has got two roles in this transformation, right? I
(02:59):
think there's the first role around how is HR going to equip and
guide the organization. And I think You know, it sometimes still
astounds me. We know so much about change management and all of a sudden now
that AI is here, it's as if we've forgotten all the good principles that
we've tried to incorporate in all our practices over the years.
So I think the first role is how does HR start owning the narrative in their
(03:20):
broader business? And we see all leading companies have got cross-functional teams
that's adopting AI. So it's a HR slash people conversation
with IT, with risk, with operations sitting around the same table
there. Your point, and that kind of leads me towards the second role,
is I think, how do we level up the function as HR? And I think ever since
the 1990s, we've been talking about strategic business partnership and,
(03:41):
you know, this promise that technology is going to take away all transactional administrative
work. If we're honest for a moment, that's never happened. And I
think for the first time we're in a position to really start rethinking
and redesigning how the HR operating model works and
incorporating AI in a meaningful manner, which for me still means that
we augment the human experience that we want to create that works. And
(04:03):
that for me is the real opportunity. It's interesting, the data tells
us at the moment, if people know nothing about AI, they fear it. If
they know a little bit about AI, they kind of get a little bit more comfortable. And
then when they start utilizing it a lot, they get extremely excited, but also aware
of what it can actually accomplish. So you have to give people exposure
(04:23):
How do you think HR teams should think about utilizing
data analytics in this new environment? And what I mean by that is,
you know, I think, you know, if I look through my own career, 20 some
years of HR, it's like, you know, you had spreadsheets, right? And
those people who like could do pivot tables were like, Oh my gosh,
you know what I'm saying? You know what an HR lookup is, right?
(04:45):
Like they were, they had these skills, right? And
then we went through that phase where, remember we talked about big data, like
all the time, big data, it's gonna have this impact. And like, it
didn't really because we didn't know how to utilize it. So now
we're at this interesting, I don't know, pivoting point, I think, where now
we have the data, we have the ability
(05:08):
to use AI to interpret and help us use
that data. How does that change how we think
about data analytics going forward? Because there is
a fear of, hey, I don't want to put all my proprietary
or employee data into these tools, because while you might
say, OK, hey, the data is a U.S.-based company, for
(05:28):
example, But you don't actually know that the data is actually being
sent overseas to get evaluated and then comes back, right?
And that might go against everything that your company stands
Yeah, and I think it speaks to a couple of things, right? I think the first one is, as
you correctly say, in the HR domain, data literacy and
digital skills have always been the ones that's lagged. We've been talking about becoming a
(05:50):
lot more evidence-based, data-driven over the last
10, 15, 20 years already within the HR domain. I think with the adoption of
AI and the opportunities that it does present in the analytics space, we
are finally getting there that it's no longer the skill set that you require to
work with the data. It's a lot more around, do I know which questions to
ask? Can I do it in a particular context? And can I then start infusing
(06:12):
data into the way I make decisions about where we want to go as
an organization? And I think that's a wonderful opportunity because
not everybody needs to be a data scientist. That's not what data-driven HR
means at all. It means that the context within which you apply it
is going to become a lot more important. And that's a very different skill
that I think as HR professionals, we already have. We ask really
(06:33):
good questions. We are able to phrase things as what
is the hypothesis if this happens? And I think if we're able to then leave
each AI for what it's good at, looking at patterns within large data
sets, that opens up a whole new possibility of the way that we partner with
the business. business. Your second point then is you have to know
which models you're using. You have to know how secure they are because
there are safe ways of utilizing this, but it is about being
(06:57):
aware of what can I put in, what should I not be putting into these AI models,
into these large language models. Usually that's where
you do need to partner quite stringently with your IT department to
understand the models that we utilize, how secure are they? What
is the latest legislation telling us about what we can and cannot share?
Because that's also an ever evolving environment that you have to keep an eye on
(07:17):
at the moment. But the myth I want to almost bust a little bit there is
that you can use these tools in a safe way, regardless of
where you're based in the world. But it is to be very aware of what
you're using, what you're putting it in and what you're using it for. And then I think
What ethical considerations have you seen HR start to talk about
(07:38):
in relation to that? And I
think about this from the perspective of, I heard somebody else talking the
other day about using AI to
evaluate like a video that was submitted. like an
interview and, you know, how it can pick
up things like eye contact and speech patterns
(07:58):
and that kind of stuff. And while I thought it was very exciting, the other
side of me goes, yeah, but what about neurodivergent individuals who
don't show up the same way as somebody who's neurotypical? And
that's an ethical concern as you think about having a
And for me, I think the one that everybody's talking
(08:22):
about is obviously how we use AI, right? So in which processes. So
for example, there has been a lot of pushback against video hiring
based utilization of AI for exact reasons that you've mentioned now.
It does not take into account the diversity of approaches that
people have, cultural nuance, et cetera. And I think it's something over time that
we will get better at, but the models are not smart enough yet to
(08:42):
be able to do that in a, can I say in an unbiased manner. On
the other side, I think there's also this question around, but where do we
use AI? Because just because we can does not mean we should. And I
think that's such an important piece to really have that conversation first.
And there's an interesting thing that's starting to happen. And there's a study that came out
in the last week or so that talks about the fact that People almost disconnect
(09:04):
to the work that they're doing if they let AI do the work for them. But
if they use it as a thinking partner, they still hold ownership and engagement of
the pride that I almost have in the work that I've delivered. Now, it's a
silly example, but if you take that into an HR practice and into an
HR process, it means we have to be a lot more intentional about where
we are going to apply AI, because we can apply it across the
(09:24):
whole board, but should we? I don't think so. There's a great example, an
organization we worked with in Singapore, actually said, you know, what's
the most biased process you have in HR? I
said, performance. Right, what we're going to do is we're going to replace the manager
and let AI give feedback to the employees. Completely backfired
because it turns out, as you can imagine, I
(09:44):
might not be doing well, but I still want a manager who might be subjective
to give me that feedback because it's just a much more human experience that
I'm creating. and they rolled it back but then they kind of went
back to the drawing board and then implemented AI in the preparation phase
for both the manager and the employee, leading to a much better conversation.
So I think ethics is not just about is it biased, is the data diverse
(10:07):
enough that we're utilizing, that for me is kind of table stakes. On
the other side, I think we have to go a step further and really give some thought to
where are we inserting AI into the processes and does it make sense to
Yeah, I had a conversation with somebody on a webinar recently,
and she talked about a similar
concept, but her kind of bet was when
(10:28):
you look at your organization, you might not
say every role 100% should have AI involved in
it. You might say, hey, you know what, this group right here,
let's implement AI at 80% there, but this group over
here, maybe only 20%, and really getting, like you
said, granular and understanding, like, what actually is
(10:49):
going to help us. And I love that idea of, instead of letting
the bot, right, give the feedback, right, give it the resources to
the humans so that they can be more effective. And
you're totally right. The times that I've been like, hey, write me an email from scratch
compared to, Here's my draft. Help me
think through, this is what I'm trying to accomplish. This is how I'm trying
(11:10):
to accomplish it, how I want to come across. Not only do I get a better product
from the AI tool, but I do feel ownership
because it's like, well, it didn't write it for me. I wrote it. It just didn't
Somebody made a comment to me once, which I thought was very appropriate. They said, remember, AI
is like having 50 interns. They're all willing, but you need to give them guidance.
You're still accountable for whatever they deliver, and you have to engage with
(11:33):
them in a direct, assertive manner around what it is that you want.
And I think we fall into the trap of throwing around the word adoption broadly.
But adoption happens at different levels. So individual adoption, you know, it's
what you and I do when we write an email, summarize a data set, look
at sentiment data to gather some themes. I think that
is where most roles will have an element of that because it just improves individual
(11:55):
productivity. I think one level up, and that's where organizations are
struggling, is at that team process practice level. Because
to collaborate through AI is much more difficult. And there I agree with you. There
I think you have to be a lot more intentional about what's the use cases
that's going to work in your context for your organization. Otherwise,
AI becomes a novel thing, but not a very valuable thing. And
(12:16):
then the last level is, you know, at the enterprise level, and I don't even think we're
there yet. I think that's something that over the next couple of years, we'll start
understanding how do we really utilize AI almost, and that's a
little bit more in the agentic space around really driving certain
One of the things that we've done recently in that kind
of enterprise space for the team that I sit on is
(12:39):
we've used an AI tool and we've loaded
in our brand voice into it so that it can help
us with our communication, which seems like very simple, but
from a team perspective, if you have all these people, different people
in in this space writing content, whether it's a blog or
a webinar presentation or whatever, like having
(13:01):
that tool, be able to say, hey, like this actually doesn't quite match
your brand voice. Let's try tweaking it. It's
been really, really helpful for us. As you as you think
about the larger HR group, and I
might have shared this on the podcast, but I'm working on my doctoral degree,
and part of it is around HR competencies. And so it's top
(13:22):
of mind all the time for me. So as I think about this group, how
do we start thinking about our own skill set? And
I don't know, beyond just like learn AI, like that's great,
yeah, we should all be learning AI, but like what really do I need
So something we're talking a lot about at the moment is how do
(13:42):
you learn yourself AI fluency, right? And AI fluency means
almost the adoption of a new language to incorporate so that you can
utilize these tools to its fullest extent. But underneath that, there's
a whole bunch of different skill sets that you're going to have to master over time.
And for me, the first one is, and for lack of a better word, call it
AI awareness. You need to know what it is and where I can utilize it
(14:03):
and how I actually leverage it for value. Otherwise it's forever going to
be a tool that you utilize to calculate the
fastest way to get to whichever destination you want to go to. I
think the second one is a little bit more in the tool space, which
is around understanding what different tools do, how you can utilize them,
their pros and their cons and their limitations. Because I think as these different
(14:24):
models learn, they will be utilized in vastly different ways. So your example
of some of them are great for brand voice and for writing. Others
are fantastic for coding. Others are fantastic, again, for data analysis.
Know what these things are for and how you can leverage and utilize them. We've spoken
quite a lot about the ethical use, how I apply them ethically, how do
you ingrain that into your own thinking whenever you adopt it. And
(14:46):
I think there's also a skill set there around what we just call human-AI collaboration.
How do do I know where my earlier point, do I actually want
to go and insert this into the way that I, you know, engage
with these tools and my processes and my practices and where should I
avoid it? Where is it okay to go slower? Because it's more meaningful to
do that in that particular way. And then I think maybe the big one then
(15:07):
is also the experimentation and learning piece is around how
do you continuously in a, in a space that's ever evolving, continue
learning and upskilling yourself pertaining to these different elements. And
for me around the skills that I've mentioned is the human skills. Do not forget
the human skill sets that's going to make you win in this market. And that's for me, critical
thinking, problem solving, and the ability to deal with the
(15:30):
What about transparency to the organization? It's
funny now because I use AI every day. I spend a
lot of time with it. We have lots of conversations about lots of different things.
And what's funny now is I notice now on
social media or sometimes I'll get an email back and I was like, oh,
(15:50):
you clearly did not write that. I
mean, M-dashes give it away every time. Nobody needs
that many M-dashes in a communication. But that being said, I
don't think it's bad that people are using these tools. But
there's this transparency component. How do
we as organizations communicate out to our employees, hey,
here's how we're using AI with your information. And,
(16:14):
I guess and, should we give
them an opt-out option, and how do we even tackle that
if we're going to start using, you know, lots of data into
Yeah, and I mean, it boils down to trust on the one side, right? In
terms of what is the culture that you are creating in your organization around the
(16:34):
adoption of these different elements. And I want to almost equate it back to
when everybody started working remotely and there was this massive surveillance productivity,
are you working, is your little green light on movement? And I think
we're slightly in a similar situation there. I think organizations, and
I'm sounding like a broken record player, but own your narratives around AI
internally and be transparent about where you're experimenting with
(16:56):
it, what data are you utilizing as part of that. firm
believer, and I know not everybody shares this view, that employees should
be able to opt out if they don't want to be able for their data to be
utilized in certain things. However, I think before that happens, we
need to educate them what's the benefit for them in an individual sense
of being part of these AI experiments. So, for example, if
I give my data as part of career and internal mobility practices. There's
(17:20):
massive impact for me as an individual, for me to be able to
start understanding what do different career paths look like for me, individualized
skills development, et cetera. But if I don't know what it's being used for,
my first initial reaction is going to be, no, don't
talk to me about that at all. So I think it does start with a bit of education,
continuous communication, transparency, and be able to answer the questions
(17:41):
around where you're using it and where you're not using it. Last point I
wanna make on this, which I ponder about the future, is I wonder
if there's not going to be this big moment towards authenticity, where
people want to understand what was actually created by
human beings without the use of AI or augmented by AI. And
think about the learning space, which we play in quite a bit. I think there
(18:02):
will be a drive there where people actually say, but I want to be taught by a
real person. I don't mind that they use AI to help them write their script,
but I want to be taught by a real person with real experience. Where's
the stamp of authenticity on that? And I wonder if that's not going to happen
It's really interesting. I think about that. I saw a presentation, this
was a couple of months ago now. And the person was like, yeah, I did
(18:24):
the whole thing through AI. And part of it was like, yeah, I love that. But
the reality was this, this person was still delivering the content and
like, they still were bringing in their own stories. If
they had just gone up and read the script. I
think I would agree with that. I would feel like, why am
I listening to you when I could adjust, you know, ask AI
(18:46):
What's unique about this? What is different about this? What's the context
pertaining to it? And I agree with you. I think the augmentation piece,
I think people will still value, but the completely replaced, it wasn't
a real individual that I can kind of feel and attach to. I think
Okay, I want to switch gears kind of as we wrap
up our conversation. You know, I've had the fortunate opportunity
(19:09):
to work with data scientists over the years. And usually
they're not embedded in HR, they're embedded somewhere in the business, right?
How do HR practitioners start
to see that person in their org as like their BFF?
Because there's so much data scientists can teach us about understanding our
(19:30):
And there's two parts to it, right? I think the first one is going to sound a little
strange, but I think first we need to help manage the
reputation a little bit of HR because I also know a lot of data scientists
that said, there's no interesting things in HR to look at. So how
do you manage that expectation around the vast amount of longitudinal
data sets that we actually sit on and how interesting that can be when you
(19:50):
start connecting it to market data and labor trends and
those types of things, et cetera. And then I think there's a common language
that we need to learn between the two. So I think as an HR professional, it's
not expected that you need to talk deep data science language to be
able to utilize data to its fullest extent. But you do need to
know, what is it that I need to ask? What is possible? What is
(20:11):
it that we can explore? And go and build that trust and partner with
somebody and say, hey, I need your skills. I can bring you context. I
can tell you where this fits into the business. And I can tell you what difference it's going
to make if we get the right answer in terms of where this is going to
take us. very different conversation there. And I do think
that that is starting to change where a lot of HR teams have pulled people
(20:31):
analytics teams much closer to them. And I think kind
of the AI movement, the analytics movement of a couple of years ago, that's
helping a lot for these individuals to see their part of being part of
the HR organization. And I think it's that shift that we just need to
I love that. All right, as we kind of wrap up our conversation and
you look forward, I even hate saying this,
(20:52):
five years, because in the AI world, that seems like a trillion years
in advance. But what are some of the trends in
AI and analytics that you're watching that you think are really going to
the, and maybe a big one that we are watching at the moment is
workforce transformation. So what is it going to look like when
we realize that not all work is going to be done by human beings and it's a
(21:13):
blended workforce of humans, AI agents, who
manages that? Is that an HR conversation? Is that an HR IT
conversation? Is it a joint conversation? So I think how we
think about the workforce is a big one that's going to change enough with
AI and beyond. I think the second one is
what we've spoken about around AI fluency will become a ticket
(21:34):
to the game for people working in various environments. It's
the same where 30 years ago, you had to learn how to use MS
Office. And if you couldn't, you were kind of a little bit dead in the water. I
think that's also going to start shifting quite a bit more. And we'll see
that I think just the natural part of how people learn to work will become
the norm in terms of AI fluency. And the one I'm keeping an eye
(21:55):
on personally is, I think there's a massive opportunity in the HR
operating model space. When we start shifting towards AI
driven, AI led HR operating models, what does that look like? You
know, we've had similar type of models and I'm not saying they no longer work. I'm saying we
should just ask the question, the last 30 years or so, is
it not time to actually start reshaping our mandate, but
(22:15):
then having the tech and the models that's able to support that in the way that we
Yeah, so exciting what AI and data is doing in
our organizations, as well as transforming HR and
the competencies that we're going to have to have as we come to the table. So
Dieter, I'm so glad that you were able to sit down and chat with me. Wonderful
(22:38):
I hope you enjoyed today's episode. You can find show notes
and links at thehrmixtape.com. Come back
Joining me today is Dieter Veldsman, Chief scientist from AIHR.
Dieter is a former CHRO and organizational psychologist who leverages data-driven
(00:29):
His expertise spans HR analytics, talent management, and
Dieter, thank you so much for sitting down, coming to
the Paylocity booth here at CIRM and just taking some time to chat with
Looking forward to it and thanks for having me and yeah, fantastic to be here.
Great. And you're here, not from the US, so it was definitely some
(00:52):
travels to get here. So that's super cool. I always love meeting people
from around the world and just hearing their stories and what they're
involved in. And your background just really appealed
Well, look forward to our conversation. And as mentioned, yep, all the way from
Awesome. All right. So from your experience as both a
(01:14):
CHRO and a scientist, how should HR
leaders really start to integrate AI into their talent strategies, ultimately
And I think it's such a relevant question at the moment. And I see a
lot of organizations making the mistake and thinking it's all about which tools should
I be selecting? And how am I going to insert it into my particular process?
(01:34):
And I think specifically in the HR space, there's a starting point around managing the
narrative around AI and what it means for us and helping people understand
the real promise of value that it offers. And looking at it both from,
how are we going to change the mindset of how our people work? And
then on the other side, then start and make it really, really practical. Start
with one use case, get people around the table, get them to experience it,
(01:56):
get them some exposure towards the actual tool sets, and then I think
grow from there. But don't forget the psychological aspect of
it. And that's the big thing that I think currently is a little bit missing when I look at a lot
You know, the psychological aspect is really interesting because
there is this fear, right? Like, hey,
(02:16):
it's gonna replace my job. And I think for a long time,
we kind of talked about like, no, no, it's not gonna replace any jobs. It
is going to replace some jobs. The reality is it is going to replace some jobs. We're
already seeing that, you know, when we look at organizations that have laid
off, you know, more entry-level HR employees because
they're taking this work on through, you know, through AI
(02:37):
tools. How do HR teams start
to get ahead of that and think about potentially
creating that balance where they're adopting AI, but
then they're really kind of leveling up their own function so
that they can drive via data and some of that
And I think HR has got two roles in this transformation, right? I
(02:59):
think there's the first role around how is HR going to equip and
guide the organization. And I think You know, it sometimes still
astounds me. We know so much about change management and all of a sudden now
that AI is here, it's as if we've forgotten all the good principles that
we've tried to incorporate in all our practices over the years.
So I think the first role is how does HR start owning the narrative in their
(03:20):
broader business? And we see all leading companies have got cross-functional teams
that's adopting AI. So it's a HR slash people conversation
with IT, with risk, with operations sitting around the same table
there. Your point, and that kind of leads me towards the second role,
is I think, how do we level up the function as HR? And I think ever since
the 1990s, we've been talking about strategic business partnership and,
(03:41):
you know, this promise that technology is going to take away all transactional administrative
work. If we're honest for a moment, that's never happened. And I
think for the first time we're in a position to really start rethinking
and redesigning how the HR operating model works and
incorporating AI in a meaningful manner, which for me still means that
we augment the human experience that we want to create that works. And
(04:03):
that for me is the real opportunity. It's interesting, the data tells
us at the moment, if people know nothing about AI, they fear it. If
they know a little bit about AI, they kind of get a little bit more comfortable. And
then when they start utilizing it a lot, they get extremely excited, but also aware
of what it can actually accomplish. So you have to give people exposure
(04:23):
How do you think HR teams should think about utilizing
data analytics in this new environment? And what I mean by that is,
you know, I think, you know, if I look through my own career, 20 some
years of HR, it's like, you know, you had spreadsheets, right? And
those people who like could do pivot tables were like, Oh my gosh,
you know what I'm saying? You know what an HR lookup is, right?
(04:45):
Like they were, they had these skills, right? And
then we went through that phase where, remember we talked about big data, like
all the time, big data, it's gonna have this impact. And like, it
didn't really because we didn't know how to utilize it. So now
we're at this interesting, I don't know, pivoting point, I think, where now
we have the data, we have the ability
(05:08):
to use AI to interpret and help us use
that data. How does that change how we think
about data analytics going forward? Because there is
a fear of, hey, I don't want to put all my proprietary
or employee data into these tools, because while you might
say, OK, hey, the data is a U.S.-based company, for
(05:28):
example, But you don't actually know that the data is actually being
sent overseas to get evaluated and then comes back, right?
And that might go against everything that your company stands
Yeah, and I think it speaks to a couple of things, right? I think the first one is, as
you correctly say, in the HR domain, data literacy and
digital skills have always been the ones that's lagged. We've been talking about becoming a
(05:50):
lot more evidence-based, data-driven over the last
10, 15, 20 years already within the HR domain. I think with the adoption of
AI and the opportunities that it does present in the analytics space, we
are finally getting there that it's no longer the skill set that you require to
work with the data. It's a lot more around, do I know which questions to
ask? Can I do it in a particular context? And can I then start infusing
(06:12):
data into the way I make decisions about where we want to go as
an organization? And I think that's a wonderful opportunity because
not everybody needs to be a data scientist. That's not what data-driven HR
means at all. It means that the context within which you apply it
is going to become a lot more important. And that's a very different skill
that I think as HR professionals, we already have. We ask really
(06:33):
good questions. We are able to phrase things as what
is the hypothesis if this happens? And I think if we're able to then leave
each AI for what it's good at, looking at patterns within large data
sets, that opens up a whole new possibility of the way that we partner with
the business. business. Your second point then is you have to know
which models you're using. You have to know how secure they are because
there are safe ways of utilizing this, but it is about being
(06:57):
aware of what can I put in, what should I not be putting into these AI models,
into these large language models. Usually that's where
you do need to partner quite stringently with your IT department to
understand the models that we utilize, how secure are they? What
is the latest legislation telling us about what we can and cannot share?
Because that's also an ever evolving environment that you have to keep an eye on
(07:17):
at the moment. But the myth I want to almost bust a little bit there is
that you can use these tools in a safe way, regardless of
where you're based in the world. But it is to be very aware of what
you're using, what you're putting it in and what you're using it for. And then I think
What ethical considerations have you seen HR start to talk about
(07:38):
in relation to that? And I
think about this from the perspective of, I heard somebody else talking the
other day about using AI to
evaluate like a video that was submitted. like an
interview and, you know, how it can pick
up things like eye contact and speech patterns
(07:58):
and that kind of stuff. And while I thought it was very exciting, the other
side of me goes, yeah, but what about neurodivergent individuals who
don't show up the same way as somebody who's neurotypical? And
that's an ethical concern as you think about having a
And for me, I think the one that everybody's talking
(08:22):
about is obviously how we use AI, right? So in which processes. So
for example, there has been a lot of pushback against video hiring
based utilization of AI for exact reasons that you've mentioned now.
It does not take into account the diversity of approaches that
people have, cultural nuance, et cetera. And I think it's something over time that
we will get better at, but the models are not smart enough yet to
(08:42):
be able to do that in a, can I say in an unbiased manner. On
the other side, I think there's also this question around, but where do we
use AI? Because just because we can does not mean we should. And I
think that's such an important piece to really have that conversation first.
And there's an interesting thing that's starting to happen. And there's a study that came out
in the last week or so that talks about the fact that People almost disconnect
(09:04):
to the work that they're doing if they let AI do the work for them. But
if they use it as a thinking partner, they still hold ownership and engagement of
the pride that I almost have in the work that I've delivered. Now, it's a
silly example, but if you take that into an HR practice and into an
HR process, it means we have to be a lot more intentional about where
we are going to apply AI, because we can apply it across the
(09:24):
whole board, but should we? I don't think so. There's a great example, an
organization we worked with in Singapore, actually said, you know, what's
the most biased process you have in HR? I
said, performance. Right, what we're going to do is we're going to replace the manager
and let AI give feedback to the employees. Completely backfired
because it turns out, as you can imagine, I
(09:44):
might not be doing well, but I still want a manager who might be subjective
to give me that feedback because it's just a much more human experience that
I'm creating. and they rolled it back but then they kind of went
back to the drawing board and then implemented AI in the preparation phase
for both the manager and the employee, leading to a much better conversation.
So I think ethics is not just about is it biased, is the data diverse
(10:07):
enough that we're utilizing, that for me is kind of table stakes. On
the other side, I think we have to go a step further and really give some thought to
where are we inserting AI into the processes and does it make sense to
Yeah, I had a conversation with somebody on a webinar recently,
and she talked about a similar
concept, but her kind of bet was when
(10:28):
you look at your organization, you might not
say every role 100% should have AI involved in
it. You might say, hey, you know what, this group right here,
let's implement AI at 80% there, but this group over
here, maybe only 20%, and really getting, like you
said, granular and understanding, like, what actually is
(10:49):
going to help us. And I love that idea of, instead of letting
the bot, right, give the feedback, right, give it the resources to
the humans so that they can be more effective. And
you're totally right. The times that I've been like, hey, write me an email from scratch
compared to, Here's my draft. Help me
think through, this is what I'm trying to accomplish. This is how I'm trying
(11:10):
to accomplish it, how I want to come across. Not only do I get a better product
from the AI tool, but I do feel ownership
because it's like, well, it didn't write it for me. I wrote it. It just didn't
Somebody made a comment to me once, which I thought was very appropriate. They said, remember, AI
is like having 50 interns. They're all willing, but you need to give them guidance.
You're still accountable for whatever they deliver, and you have to engage with
(11:33):
them in a direct, assertive manner around what it is that you want.
And I think we fall into the trap of throwing around the word adoption broadly.
But adoption happens at different levels. So individual adoption, you know, it's
what you and I do when we write an email, summarize a data set, look
at sentiment data to gather some themes. I think that
is where most roles will have an element of that because it just improves individual
(11:55):
productivity. I think one level up, and that's where organizations are
struggling, is at that team process practice level. Because
to collaborate through AI is much more difficult. And there I agree with you. There
I think you have to be a lot more intentional about what's the use cases
that's going to work in your context for your organization. Otherwise,
AI becomes a novel thing, but not a very valuable thing. And
(12:16):
then the last level is, you know, at the enterprise level, and I don't even think we're
there yet. I think that's something that over the next couple of years, we'll start
understanding how do we really utilize AI almost, and that's a
little bit more in the agentic space around really driving certain
One of the things that we've done recently in that kind
of enterprise space for the team that I sit on is
(12:39):
we've used an AI tool and we've loaded
in our brand voice into it so that it can help
us with our communication, which seems like very simple, but
from a team perspective, if you have all these people, different people
in in this space writing content, whether it's a blog or
a webinar presentation or whatever, like having
(13:01):
that tool, be able to say, hey, like this actually doesn't quite match
your brand voice. Let's try tweaking it. It's
been really, really helpful for us. As you as you think
about the larger HR group, and I
might have shared this on the podcast, but I'm working on my doctoral degree,
and part of it is around HR competencies. And so it's top
(13:22):
of mind all the time for me. So as I think about this group, how
do we start thinking about our own skill set? And
I don't know, beyond just like learn AI, like that's great,
yeah, we should all be learning AI, but like what really do I need
So something we're talking a lot about at the moment is how do
(13:42):
you learn yourself AI fluency, right? And AI fluency means
almost the adoption of a new language to incorporate so that you can
utilize these tools to its fullest extent. But underneath that, there's
a whole bunch of different skill sets that you're going to have to master over time.
And for me, the first one is, and for lack of a better word, call it
AI awareness. You need to know what it is and where I can utilize it
(14:03):
and how I actually leverage it for value. Otherwise it's forever going to
be a tool that you utilize to calculate the
fastest way to get to whichever destination you want to go to. I
think the second one is a little bit more in the tool space, which
is around understanding what different tools do, how you can utilize them,
their pros and their cons and their limitations. Because I think as these different
(14:24):
models learn, they will be utilized in vastly different ways. So your example
of some of them are great for brand voice and for writing. Others
are fantastic for coding. Others are fantastic, again, for data analysis.
Know what these things are for and how you can leverage and utilize them. We've spoken
quite a lot about the ethical use, how I apply them ethically, how do
you ingrain that into your own thinking whenever you adopt it. And
(14:46):
I think there's also a skill set there around what we just call human-AI collaboration.
How do do I know where my earlier point, do I actually want
to go and insert this into the way that I, you know, engage
with these tools and my processes and my practices and where should I
avoid it? Where is it okay to go slower? Because it's more meaningful to
do that in that particular way. And then I think maybe the big one then
(15:07):
is also the experimentation and learning piece is around how
do you continuously in a, in a space that's ever evolving, continue
learning and upskilling yourself pertaining to these different elements. And
for me around the skills that I've mentioned is the human skills. Do not forget
the human skill sets that's going to make you win in this market. And that's for me, critical
thinking, problem solving, and the ability to deal with the
(15:30):
What about transparency to the organization? It's
funny now because I use AI every day. I spend a
lot of time with it. We have lots of conversations about lots of different things.
And what's funny now is I notice now on
social media or sometimes I'll get an email back and I was like, oh,
(15:50):
you clearly did not write that. I
mean, M-dashes give it away every time. Nobody needs
that many M-dashes in a communication. But that being said, I
don't think it's bad that people are using these tools. But
there's this transparency component. How do
we as organizations communicate out to our employees, hey,
here's how we're using AI with your information. And,
(16:14):
I guess and, should we give
them an opt-out option, and how do we even tackle that
if we're going to start using, you know, lots of data into
Yeah, and I mean, it boils down to trust on the one side, right? In
terms of what is the culture that you are creating in your organization around the
(16:34):
adoption of these different elements. And I want to almost equate it back to
when everybody started working remotely and there was this massive surveillance productivity,
are you working, is your little green light on movement? And I think
we're slightly in a similar situation there. I think organizations, and
I'm sounding like a broken record player, but own your narratives around AI
internally and be transparent about where you're experimenting with
(16:56):
it, what data are you utilizing as part of that. firm
believer, and I know not everybody shares this view, that employees should
be able to opt out if they don't want to be able for their data to be
utilized in certain things. However, I think before that happens, we
need to educate them what's the benefit for them in an individual sense
of being part of these AI experiments. So, for example, if
I give my data as part of career and internal mobility practices. There's
(17:20):
massive impact for me as an individual, for me to be able to
start understanding what do different career paths look like for me, individualized
skills development, et cetera. But if I don't know what it's being used for,
my first initial reaction is going to be, no, don't
talk to me about that at all. So I think it does start with a bit of education,
continuous communication, transparency, and be able to answer the questions
(17:41):
around where you're using it and where you're not using it. Last point I
wanna make on this, which I ponder about the future, is I wonder
if there's not going to be this big moment towards authenticity, where
people want to understand what was actually created by
human beings without the use of AI or augmented by AI. And
think about the learning space, which we play in quite a bit. I think there
(18:02):
will be a drive there where people actually say, but I want to be taught by a
real person. I don't mind that they use AI to help them write their script,
but I want to be taught by a real person with real experience. Where's
the stamp of authenticity on that? And I wonder if that's not going to happen
It's really interesting. I think about that. I saw a presentation, this
was a couple of months ago now. And the person was like, yeah, I did
(18:24):
the whole thing through AI. And part of it was like, yeah, I love that. But
the reality was this, this person was still delivering the content and
like, they still were bringing in their own stories. If
they had just gone up and read the script. I
think I would agree with that. I would feel like, why am
I listening to you when I could adjust, you know, ask AI
(18:46):
What's unique about this? What is different about this? What's the context
pertaining to it? And I agree with you. I think the augmentation piece,
I think people will still value, but the completely replaced, it wasn't
a real individual that I can kind of feel and attach to. I think
Okay, I want to switch gears kind of as we wrap
up our conversation. You know, I've had the fortunate opportunity
(19:09):
to work with data scientists over the years. And usually
they're not embedded in HR, they're embedded somewhere in the business, right?
How do HR practitioners start
to see that person in their org as like their BFF?
Because there's so much data scientists can teach us about understanding our
(19:30):
And there's two parts to it, right? I think the first one is going to sound a little
strange, but I think first we need to help manage the
reputation a little bit of HR because I also know a lot of data scientists
that said, there's no interesting things in HR to look at. So how
do you manage that expectation around the vast amount of longitudinal
data sets that we actually sit on and how interesting that can be when you
(19:50):
start connecting it to market data and labor trends and
those types of things, et cetera. And then I think there's a common language
that we need to learn between the two. So I think as an HR professional, it's
not expected that you need to talk deep data science language to be
able to utilize data to its fullest extent. But you do need to
know, what is it that I need to ask? What is possible? What is
(20:11):
it that we can explore? And go and build that trust and partner with
somebody and say, hey, I need your skills. I can bring you context. I
can tell you where this fits into the business. And I can tell you what difference it's going
to make if we get the right answer in terms of where this is going to
take us. very different conversation there. And I do think
that that is starting to change where a lot of HR teams have pulled people
(20:31):
analytics teams much closer to them. And I think kind
of the AI movement, the analytics movement of a couple of years ago, that's
helping a lot for these individuals to see their part of being part of
the HR organization. And I think it's that shift that we just need to
I love that. All right, as we kind of wrap up our conversation and
you look forward, I even hate saying this,
(20:52):
five years, because in the AI world, that seems like a trillion years
in advance. But what are some of the trends in
AI and analytics that you're watching that you think are really going to
the, and maybe a big one that we are watching at the moment is
workforce transformation. So what is it going to look like when
we realize that not all work is going to be done by human beings and it's a
(21:13):
blended workforce of humans, AI agents, who
manages that? Is that an HR conversation? Is that an HR IT
conversation? Is it a joint conversation? So I think how we
think about the workforce is a big one that's going to change enough with
AI and beyond. I think the second one is
what we've spoken about around AI fluency will become a ticket
(21:34):
to the game for people working in various environments. It's
the same where 30 years ago, you had to learn how to use MS
Office. And if you couldn't, you were kind of a little bit dead in the water. I
think that's also going to start shifting quite a bit more. And we'll see
that I think just the natural part of how people learn to work will become
the norm in terms of AI fluency. And the one I'm keeping an eye
(21:55):
on personally is, I think there's a massive opportunity in the HR
operating model space. When we start shifting towards AI
driven, AI led HR operating models, what does that look like? You
know, we've had similar type of models and I'm not saying they no longer work. I'm saying we
should just ask the question, the last 30 years or so, is
it not time to actually start reshaping our mandate, but
(22:15):
then having the tech and the models that's able to support that in the way that we
Yeah, so exciting what AI and data is doing in
our organizations, as well as transforming HR and
the competencies that we're going to have to have as we come to the table. So
Dieter, I'm so glad that you were able to sit down and chat with me. Wonderful
(22:38):
I hope you enjoyed today's episode. You can find show notes
and links at thehrmixtape.com. Come back
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