Good afternoon, everyone. We're going to give people
a minute or two to log on, and then we're going to get started.
Hi everyone. Thank you for joining today's webinar, AI and HR: Top five risks and opportunities. We're
recording today's session and we'll share it with you following the webinar. If you have
any questions, please use the Q&A function on your Zoom panel. Now, I'd like to hand it over to our
moderator for today's session, HR Acuity CEO, Deb Muller. Thanks, Lexi. Hi everyone. I see everybody's
sort of telling us where they're calling in from or dialing in from. I don't know what
we do. We zoom in from, I guess, is the... the language we should be using now. But it's
like everybody's from all over, and I know this is a great topic of interest. A lot of people
are joining, but we're going to get started because we have a lot of content to get through as well.
So, I'm going to do a little bit of an overview, some welcomes and introductions, and then hand
it over to our two facilitators today that we're really excited we have with us. I'll get to that
in a second. So, let's see, why don't you just run to the next slide. Okay, so maybe you
don't know about HR Acuity, but we are a technology company. We are the leading provider of employee
relations and investigations case management, and we like to say that we help organizations manage
things from intake all the way through aftercare with really deep analytics. So instead of just
tracking issues, you can help use data to inform what happens next in your organization. So if you
want to learn more about our platform—we're not going to talk about it today—but if you
want to learn more about what we're doing and the exciting things that we're doing with AI, which is
relevant to this conversation, please reach out to us, and we'd love to take you on a curiosity
tour. And we welcome all of our customers. I see some of them logging in here today as well.
So thanks for joining us. Next slide. Okay, a little bit more about HR Acuity. Yes, we are a technology company,
but we're really proud of the employee relations community that we've built over the past decade-plus
together. The first thing is our benchmark study, and I'm excited to say that we actually
just launched today our ninth annual employee relations benchmark study. Lexi is going to put
all the links in the chat, but if you have not gone to our website and downloaded it, or actually this
year you can read it all right online—go, I mean, don't go now—after the hour is over, you'll go and
look through it, download it. It is chock-full of great information, some new insights, some things
about AI I'm going to go through in just a second. So please do that. And if you're interested in
participating next year, our 10th annual, we want to make it bigger than ever. Although this year
it does cover about 9 million employees. Let us know if your organization has more than a thousand
employees. We also have our empower network. It's our employee relations online community. It is free.
There's an app, you can do it online. If you're in employee relations, if you do anything related to
employee relations, you should join. Have your team join. It's a great community. I believe there are
close to 7,500 members right now, all levels from all over the world on live feeds, talking about
what's going on, what questions they have, collaborating. There are small groups to talk about
specific topics like labor, to talk about global issues. It's really... you have to be there. So make
sure you go there. We also have our ERQ, which is our Employee Relations Quotient. It is our ER
maturity model. Go on our website, take it, see where you can go, get some insights as to how you can
move along the maturity for your organization in ER. And finally, if you are an employee relations
leader at an enterprise organization, we'd love for you to join us at our roundtable. I think it's
our ninth or 10th year doing that as well. It's an annual in-person event of global ER leaders. We get
together, we collaborate, we really start defining the future of employee relations. So if you want to
be part of that, it's a very unique opportunity that is going to be in October. Our website
gives you all the information. It's in Boston this year. We'd love to have you join us. All right, next
slide. Important dates. I talked about the benchmark. It is out now. Other things happening this month:
we have another webinar coming out about the benchmark. So I'm going to go into more detail with
our partner, our research partner, Isos. We're going to talk about the results, so mark your
calendar for that on June 24th. June 25th, we're going to have an Empower discussion. So different
from the webinars, if you're in Empower, we have ER professionals that lead these discussions.
So, you know, now we're going to talk about, we're going to hear about AI and ER, what to look out for,
the risks. This is when we talk about it. What are we doing? How are we experimenting? What's really
happening in our companies and helping each other through this new phase of how we do work? And
then again, I mentioned the roundtable. We hope that you can join us, and
that's October 28th and 29th. Again, we will make sure you have all these links. Next slide. Okay, my
favorite slide. For those of you that have attended a webinar, as a thank you to our presenters, we like
to make a donation for their time and for all of you for joining us. And we ask them to,
you know, choose if there's an organization that's meaningful for them or one that's in line with
our mission and vision. And so, we're so excited today to be making a donation to the Just in
Time for Youth, foster youth nonprofits in San Diego. I mean, I can't think of an organization that
is more important to help children that are in the system. And this looks like they do it when they
get out of the system, transitioning out. And I mean, a wonderful organization, and we're so proud to be
able to make a donation. So thank you for coming and thank you to Eli and Oliver for sharing your
expertise so that we can go ahead and make this donation. Okay, so I'm going to do a little starting,
and this is a little sneak preview of what's in that benchmark study because, of course, we
asked about AI in there. And maybe not a big shocker, it's the buzz, you know, when it comes to
employee relations, it is still early days. Nearly half of organizations that we surveyed in this
say that their use of AI is nonexistent, and only about a third are experimenting or piloting.
And it sort of makes sense, and that's why we're having this conversation today. ER
deals with incredibly sensitive and complex issues, so legal, ethical, and compliance concerns are real,
and I think it's causing organizations to move more cautiously. We're going to talk about that
today because just because we have to be cautious doesn't mean we can't use AI. It doesn't mean there
aren't applications. We're excited about it at HR Acuity. There's real opportunity, but we have to be
intentional and responsible. So, you know, I think this shows you don't feel bad if you're sort of
behind the eight ball. You're going to get there. And we're going to
we're going to help you do that in the way that actually supports what we're doing.
It doesn't eliminate what we're doing, but it really supports and makes us better as
employee relations professionals and investigators. Interestingly, we also asked—if you can just move
to the next slide—we asked last year about interest in AI. So we started talking about it.
You can see here the top interests were things like uncovering trends, analyzing case data, improving
workflows. Over 75% of respondents said they were interested in these areas. So fast
forward to this year, not a lot has changed, right? Only 28% are using AI for writing assistance, with much
smaller numbers for things like policy recommendations, case transcription, and analysis.
So, I think that's part of the conversation today. People want to use it, but we need to make sure if
we unlock the benefits, we're doing so responsibly. There are guardrails. They enhance, not replace,
the human work of ER. And this is just so relevant to today's conversation. So this and
more in the benchmark study. So download that after the session. But for now, I do want to introduce our
presenters and really thank them for being here. So we have two great guest speakers that are going to
share with us their wisdom on this. We have Eli Makus. He is the managing partner of Van Dermyden Makus
Law Corporation. It's a preeminent investigations firm with attorneys across California and Arizona.
They are devoted to promoting fair workplaces and safe campuses through industry-leading neutral
fact-finding services. Drawing upon his extensive employment law background, Eli conducts
complex and sensitive investigations involving a variety of workplace complaints for public
and private employers throughout California. And if you can handle California, you can handle anything
else. It's a country in and of itself. Eli regularly trains on employment law topics, including training
internal and external investigators on how to conduct impartial workplace investigations, and he
is the immediate past president of the Association of Workplace Investigators Board of Directors and
regularly serves on the faculty of AWI's ANAB-accredited training institute. So thank you, Eli,
for joining us. And Oliver McKinstry is the vice president, associate general counsel of labor,
employment, and investigations at DaVita, a Fortune 200 kidney care company. He leads DaVita's labor
and employment law and HR compliance teams. And in his role, Oliver oversees all of DaVita's labor
and employment advice and litigation, including wage and hour and union-related matters. He also
leads a team of lawyers and HR professionals focused on workplace investigations, employee
relations, HR policies, personnel files, and license and certification requirements.
Since joining DaVita in 2016, Oliver has helped streamline the investigation process, implement
a process for addressing threats of workplace violence, standardize investigation reports, and
implement artificial intelligence to help with the intake process and draft corrective actions.
Oliver is currently the vice president of the Association of Workplace Investigators
and will become president in November 2025. Congratulations. So welcome, both of you, and
looking forward to an awesome conversation. We encourage people to ask questions
in the chat—we're going to monitor that—or if you prefer to do it anonymously, there is a Q&A
option as well. And we're going to try to fit in your comments and questions in between where it
makes sense. But I know we have a lot of content to cover. So thanks for joining us, Eli and Oliver.
Great. Thank you so much, Deb. We're so excited to be here. This is a topic that Oliver and I have both
talked a lot about and presented on before, and one we think is just particularly pertinent
and relevant to the experience of being a human resources professional right now. I wanted to
mention one more thing about our firm. So our firm, which is based in California and Arizona, we
focus on conducting workplace investigations when employers hire us to do that, but we also recognize
that most investigations should be conducted by well-trained internal HR professionals. And
so we have a separate training company called VM Mastered, which is dedicated to training, creating
customized training programs for internal HR professionals who are conducting workplace
investigations, and in those conversations, AI continues to be a big topic. And so we're
excited to address it today. With that, I'll turn it over to Oliver, and Oliver is going to kick us off.
Yeah, great. Thanks, Deb, for the intros and thanks, Eli, for passing it over. I'm very happy to be here.
Uh, I am, I call myself a recovering lawyer, although I am a practicing lawyer and helped
DaVita with our legal practice. I think my heart is in HR, and so I identify first as
an HR professional, an investigator, and an employee relations professional. So hopefully some of
what I'm going to talk through, you'll feel a little bit of kinship with me and what
we're going through at this time with AI and what to do in the HR profession. So let's
move to the next slide. Eli and I today wanted to really dive into AI from the perspective of
general HR and look at it with an eye toward where are our greatest opportunities and maybe
where our greatest risks. And so what we're going to talk about is the top five risks and top five
opportunities for AI in HR. I think Deb mentioned a little bit of research on AI in the employee
relations space. Let's take a little bit of a broader look at some research just in the HR
space in general. Um, Eli and I looked into some of this, and it turns out a large majority of people
in the workplace are already using AI. Over half of employees, and probably over half of you
in attendance, are already using AI somehow in your daily work. And I think it's really interesting
to think about the ways different generations use AI in the workplace. The research indicates that
older employees are really using AI and specifically ChatGPT more as a search engine,
something like Google, where they can type in some kind of prompt and get an answer, whereas
twenty-somethings and those kind of just older than twenty-somethings tend to use it more as a life advisor. And
I identify with that age group, and I will admit that I often use AI to help me plan my
travel, where to go, what to see, what to eat. So I think I fall into that category. And
it turns out college students are actually using it basically as an operating system. It's doing
many different functions for them. They really have adopted it very thoroughly. So interesting to
start from the place of many people are already using AI in the workplace. I think it's important
to think about why they're doing that. And the first main reason that comes from the
research, most people are saying, is efficiency, trying to do things in a more streamlined way
and save time by doing that. Certainly one of the reasons I have it make my travel plan,
so that makes sense to me. Uh, another top reason is just simply access to information, trying
to find information that we think is out there, but maybe we don't know where to get it, having
AI go and do that for us. The next one is just innovating, coming up with some kind of
new process or approach to things. That is another function that AI can do for us. And
then the last one is improving our work quality. And people are already doing that,
using AI to try and improve their work quality. So probably not a lot of surprises
here. And I think many of the opportunities we're going to discuss will certainly hit on
these use cases for AI in HR, and then we'll talk about some of the risks with that, too.
So a couple of the maybe challenging components of using AI in the workplace, of course, data
sensitivity is one of them. And the research that we reviewed shows that a lot of people are using
AI in a way that they actually are uploading sensitive company or customer information into
public generative AI tools. And that is a big challenge with these tools, is once you upload your
information, you may lose control over it, and you don't know how it's being used. And unfortunately,
it looks like a lot of the workforce is already out there doing that. I hope that's
not anyone on the call today, but that's going on. So that's something we should be aware of. Also,
um, a large majority of people have relied on AI output without actually critically evaluating the
results. And I think Deb mentioned this previously. It is essential to maintain the human judgment
component even when we're using AI. But as you can see in this data, that is already a challenge that
people are not using their critical thinking when using these tools. And then last here on this slide,
um, people are making mistakes when they rely on AI tools. And I think in the legal profession,
we've seen this in some filings in court where lawyers are using AI to come up with some case
law citations, and the AI is hallucinating cases that actually don't exist. Uh, those have ended
up in briefs that have been filed with the court, and it turns out judges actually read the briefs
that lawyers file, and they have figured out that those citations don't exist. And lawyers
have been sanctioned. And I think it's just a good example of a profession where we think
people are very careful with the things that they file, but when relying on AI, you really have to
double-check the results that it's giving you before using them. Thanks, Oliver. And before
we really jump deep into the opportunities and risks that we see around artificial intelligence,
we think it's worth just level setting on what it is that makes this such a unique and
tantalizing and also challenging technology to adopt, to understand, and why the nature of
the technology itself makes it particularly concerning in an HR context, given the type
of work that we do. So I want you to think about this distinction between deterministic versus
probabilistic technology. And I'll break it down with this example. If you think about using a
calculator, if you enter 6 * 8 into any calculator anywhere at any time, you're always going to get
48 as the answer. You're never going to get an answer that says, "Well, 51% of the time it's 48,
but 32% of the time it may be 23, and you know, one or 2% of the time it could be five." It's always
going to give you the same output. And that's what we're used to when we're talking about existing
technology. When we use a computer, we expect that if we type "deterministic" into Word, we are always
going to get deterministic output from our keyboard. So the technology that we're used to
is based on predetermined conclusions. We know we're always going to get the same answer and
the same output based on the context or the information we provide to it. But artificial
intelligence is actually very different. It's probabilistic technology. The interesting thing
about this is it's actually... this is actually how people think. People are probabilistic technology.
If you give us a bunch of information and you ask us for an answer, we're going to weigh that
information, and based on our experience and our knowledge and everything we've learned over the
course of our lifetime, we're going to give you our best estimate or best guess on what the right
answer is to your question based on our experience and knowledge. But it's just a probabilistic
outcome. It's our best guess based on what we know. And that's how AI tech, artificial, generative
artificial intelligence works as well. The decisions or the outputs that you receive from
an AI tool are based on the tool's best estimate of the answer as it goes out and looks at all of
the data that it's been trained on. So this means that output can be pretty inconsistent under the
same conditions. And my guess is many of you have experienced this where you put a question into an
AI tool at 3:00 and you get one answer, and then at 3:05, you put the same question in and you get
a slightly different answer. And maybe they're both good answers, but they're different. And
I think this is one of the things that's most confounding and both creates opportunities, but
also obviously flags significant risks around using artificial intelligence. So I want you to
think about that distinction between deterministic and probabilistic technology as we talk through
some of these opportunities and risks. I also want to highlight another challenge with AI is
just the variations in how it's used. Oliver already touched on how generational, different
generational differences impact different ways of using or thinking about artificial
intelligence, but there are other factors, too. There are socioeconomic differences. This, you
know, much of the writing on AI talks about the concern around whether different socioeconomic
uh, communities will have the same access to to AI and whether they'll have the same education and
training and understanding of the risks of it. So that's a significant factor we have to consider as
well. Then there's just organizational differences. My guess is that there are some folks on this
uh, webinar who work in an organization where the organization has said, "Absolutely not. Under
no circumstances may you use AI in the execution of your work duties." I know that
there are large law firms who have taken that approach, even though AI has a lot of potential
in the legal field. So there's just going to be different experiences for organizations
and how to navigate AI. And then there's just individual comfort. Some people are going to
be more comfortable using the technology, others less so. And bringing a whole organization up to
a common level of experience and utilizing AI in a function like human resources is going to
be pretty challenging. The reality is, too, there's just not a... there's a lack of clear direction on
best practices for using AI in human resources. And I think that's what's really amazing about
the Empower community and that discussion that's coming up later, I think in in June,
I think it was, is just the opportunity to sit and discuss with your colleagues, "How are you actually
using this in a safe place where you can talk about the challenges and the pitfalls in doing
so?" So continue that dialogue because that's ultimately going to be how we come up with
what the best practices are. So with that, why don't we jump into our top five opportunities?
Yeah, great. Hopefully that is uh thought-provoking already. Uh, starting out with one of the most
significant opportunities for AI in my mind is really using AI as kind of an HR generalist type
function. If you're anything like me, you probably spend a lot of your day or your
time really pointing people in the right direction. That may be just telling someone where to find a
specific policy, maybe it's a travel policy, maybe it's connecting someone to your internal leave
of absence team or the external provider that you have for that. Um, maybe it's just contact
information that you're providing. But I think a lot of HR generalists, a lot of HR people spend
time really just connecting employees with the right resource. But all of that information
usually exists in policy documents or practice one-pagers that AI tools can really easily digest
and learn. And then a valuable way to use AI is to have your employees go to this type of
AI agent and ask those questions to the AI agent, whether that's a bot or some other kind of tool,
and it can just simply guide them to those resources based on the policy documents that
it has access to or the practices that you've trained it on. And in doing so, think of how much
time that might take off of your plate or your HR generalist's plate. So I think of this as a really
great opportunity to create those efficiencies that we talked about that people are looking for
with AI, by really helping that HR generalist function. Often it's the more junior folks in HR
who are doing those things, but can really create efficiencies for them and do some
of those simpler functions that they spend time on. Eli, anything you'd add there? Well, I think, I mean,
as we talk through these opportunities, there's always another side to that, too, which is there
probably are some challenges or risks around delegating this responsibility to an AI assist...
an HR AI assistant as well, right? So I can imagine concerns around accuracy might be something that
one would be concerned about. Is that fair? Oh, for sure. You know, if you're anything like
companies I've worked for, sometimes you have multiple policy documents or you even have
um, some conflicting practices depending on an organization, or maybe it's just you've
had several iterations of the same policy. So it's possible that the AI might not get
the accurate answer. Maybe it goes to the wrong document or can't compile all of the documents
to come up with something. So I think that's a realistic challenge with this approach. You have
to be careful about the inputs, so you get the right output. And I want to address Paula's
question in the chat here, and I want to just sort of acknowledge one of the more
challenging aspects of a webinar like this, and I'm afraid that we're going to do the same thing that
you've probably heard in other webinars. We're going to talk a lot about what AI can do, what
it, what some of the risks are, but we're not going to give you any guidance on what tools you should
actually explore or adopt or identify even what specific tools are worth evaluating.
And that's largely because there are so many different tools and they're at such an early stage
that it's virtually impossible to make that kind of a recommendation. Well, I think what our goal
is that you take away from this discussion what the potential is so that as you are going out in
a more systematic process in your organization and beginning to evaluate tools, you have some
framework for questions and how to evaluate what might be an appropriate tool for your organization.
And so Paula, to your question, you know, how does this work from a—and I'm no technologist—but from
a technical perspective, basically current AI tools are already trained, their large language models
are already trained on large volumes of data. And so a lot of what the new AI tools are doing is
they have an LLM as a foundation for the tool, and then they are developing specific agents which
have a specific purpose. An agent is a technical term that describes a basically a plug-in to the
AI tool that goes out and, for example, in this scenario, it would go out and it would look at what
your policies are, which would be uploaded into the tool, and it would evaluate based on its estim... you
know, based on its best guess review considering all the data it's been trained on, it would
evaluate looking at your policies what the best answer is to the prompt that was given. So, this
is a really good question and one that as you're evaluating products that are promising that
they can do all sorts of amazing things in the HR space, you really want to make sure that the vendor
understands the answer to questions like this and can explain to you how it's going to work and how
you're going to avoid errors, for example, or inaccuracies or hallucinations. So with that, why
don't we move on to another opportunity? And with all of these, there's a double-edged
sword, right? But I think one area that AI certainly has potential for is summarizing and triaging
conversations that occur in the HR space. In our experience, in the tools that we've experimented
with—and I should just acknowledge one thing—in our law firm, we've decided to develop our own
tool. So we're working on a customized tool that is unique to our firm, to workplace investigations, the
way we do them. And so a lot of our experimentation has come through use of that tool. And we found, for
example, that AI-supported transcription is really quite accurate. And so one tool or one function
that AI does pretty well is transcribing or creating a transcript of a conversation. It could
be transcripts of a hotline complaint. It could be transcripts of an intake call that you have
with somebody who's raising an employee relations concern. And once it does that first step of
summariz... of transcribing the call or the interaction, an AI tool can begin summarizing
or triaging the key elements or proposing next steps based on what the issues were that
were raised in that conversation. So this can reduce the note-taking burden.
I know that there are some HR departments where they will have two people in every meeting, which
may still be a good practice, but one, the purpose of one of those individuals is to take notes
or to try to take verbatim notes while the other person is carrying on the conversation.
And this tool would help lift that burden. Any thoughts, Oliver, from your perspective on
this opportunity? Well, I love the sound of it. Um, I have struggled to take notes accurately in
many an interview and, you know, had to go back and interpret them. And so I really do see this as
an opportunity, but one of my big concerns is just the accuracy component of this tool. And I
think that's really important to analyze because I think for those of us who have done investigations
or in employee relations, sometimes it's just not so cut and dry. Someone may say something to you
um, that is coded or may not even be clear in the words that they're using, but you pick up on the
fact that there is a bigger issue and you need to dive into it. And I think some of that doesn't
always come across in a transcript that you get. And so it is important that you are reviewing
transcripts even when you use a tool like this to make sure that the context is included and that
what is coming across in the transcript makes sense and is accurate. And unfortunately, I think
that means it's maybe not always as efficient as we want because if you have to go back and review
it all, you know, that that takes time, too. So I think some concerns with using a tool like
this, but I am hopeful that there is improvement and this just continues to get better and better
because I do think it could be an efficiency save. Yeah, Oliver, I'm just going to add to that as well.
Um, it can work, but I mean, you need to review it like right after you have the conversation, too,
right? You know, I know in interviews, they're sort of in my head, I can hear that person talking and
emphasizing things, but sometimes transcriptions, you'll go back a week later and you'll be
like, "What were they talking about with the the owl and the pink purple suitcase?" You know what
I mean? Like, or someone asked here about sort of accents and whether they picked up something. And
if you were to go back right after and look at it and review it and sort of make changes and
say, "No, no, that's not what they said, or here's the context," it could be helpful. But, you know, we
know ER people are really busy. They're moving from one thing to another and they're humans. And so
will they do that? And that is a concern. Yeah, and Deb, there's a concept called verification drift
that some researchers around AI have talked about, which is a recognition that AI can be effective so
long as you're doing what you just described, which is reviewing it really carefully. But there's this
recognition, sort of a psychological recognition that people tend to begin to rely more and more
on the output of the tool because a lot of it is seems accurate, seems good. Referencing
back to the attorneys—I am an attorney—and and referencing back to the attorneys Oliver
mentioned who have submitted case law that was not actually real, they're not bad lawyers. They relied
too heavily on the tool before submitting their briefs. And that's something as HR professionals
we have to be really careful about. I want to just address quickly June's question here about
discovery of transcription. And that's an issue that I can certainly see coming up, which is if you
use a transcript in your investigation and your subsequently, that matter is subsequently
litigated, and you have to produce that transcript and explain how it was created, we have not seen
that yet at all at such an early stage in the use of this technology, but it is something that we're
talking with our investigations team about is how do we make sure that we have an explanation of how
we use AI technology in our investigations and what are our answers going to be when a
plaintiff's attorney challenges us or disputes what we said or what conclusions we found.
And I think that's something we'll have to address with the transcription piece of it, too. And to
Katrina's comment about AI in other languages, I do believe that AI transcription is pretty effective
in other languages so long as the tool is fine-tuned for that. But background noise or
folks who have an accent that can impact the quality. So it goes back to what Oliver said, which
is you have to be vigilant about double-checking this. Back to you, Oliver. Yeah, thanks. I I'll stay
kind of, um, off the top here on this one. Please, I recommend working with your employment counsel,
your lawyer, your in-house counsel on some of these topics, you know, especially if you're talking
about discovery. They'll likely have opinions about that, so make sure to check with them, too. And
I think benefits is another topic that comes up as an opportunity, but you wouldn't want to explore
without consulting your your internal counsel, too. I have benefits on this list because
with benefits comes a lot of data. This is a very data-rich area. For those of us who provide benefits
to our employees, which I think is probably most people on the call, it comes along with a lot of
data that's both data of who's using the benefits, how often they're using the benefits, you know,
once they register, do they continue using them? But also the vendors that we use collect
a lot of information from our employees in providing those benefits. Think about health
benefits, you know, the conditions that employees have and how often they're seeing
providers because of those. It's a very data-rich environment around benefits. And that means it's
an opportunity where AI can help us with some of the things we need to do. And that could be
uh, maybe personalizing some of the offerings that we have to our employees, um, you know, helping them
navigate what can be very tricky benefits by making suggestions based on demographics. People
who have similar demographics used these benefits in the past, so maybe they would be
interesting or useful to this type of employee. Another way that this could be helpful is
just tracking and determining what benefits are most used. You know, we often do that just
based on registration, but we don't always know after registration how the benefits are being
used, how thoroughly. So analyzing that data, having some AI help can help understand how useful
our benefits are and whether you want to continue them. Then the last would just be costing, you know,
predicting the costs based on the data that you have and helping you better understand your
costs. So some benefits there potentially from AI in the benefits space. Our next opportunity is
certainly close to my experience, which is around employee relations and investigations. And
I think this is a... this is a challenging opportunity because it's so wide-ranging,
like there are so many different things or ways that an AI tool could be incorporated
or relied upon in the employee relations space, and specifically in the context of investigations.
What I hear a lot of people talking about when they think about using AI in investigations, and
this is something that we've talked with Deb about, too, is everybody says, "Oh, well, I want to use it
to summarize my interview notes," or, "I want to use it to summarize evidence." And as we've already
alluded to, there are some real risks around that. And so I want to focus our attention rather on
some other skills that AI can do pretty effectively in the context of investigations.
One of the ways that we found AI, our AI tool to be particularly effective is just vetting or
or querying the evidence we've gathered. So we might want to say, "How many witnesses
uh, talked about this meeting on June 3rd?" And you can, you know, upload, in our case, we upload
transcripts from interviews that we've conducted and emails that we've gathered, and the tool is
pretty effective at going out and searching which employees referenced or talked about that meeting
on June 2nd. Another way that we've experimented with it is saying, "Okay, well, how many witnesses
corroborated that, you know, John made a comment about age in that meeting on June 2nd?"
And the tool is pretty effective at going out and identifying where in the evidence and
which witnesses made these particular comments or can corroborate that they heard John make
that comment, for example. So querying evidence and querying information you've gathered during
an investigation is a skill that an AI tool does pretty effectively, and it doesn't have some of the
risks that delegating the full responsibility for summarizing a witness interview has. The
reason that we're concerned generally about the idea of summarizing a whole witness interview
with AI is that if you go and tell the tool, "Go summarize, you know, summarize Sally's complaint,"
if that's all the information you've given it, the tool is going to come back with radically
different responses on three different occasions that you give it that that direction.
So the point is, if you think back to what I said earlier about probabilistic technology, it's making
its best estimate based on the information you've given it as the user or the prompter, and based on
the volumes of data that it's been trained on. So if you want a better summarization or you
want a better output on a summary, for example, of Sarah's complaint, you have to as the user provide
a really detailed, narrow context or prompt about what specific information you want the tool to
summarize. So that's one of the challenges that you have to think about. Another skill
that we found it to be pretty effective about in the context of investigations is drafting,
identifying witnesses who you might want to interview, or identifying documents that you
might want to gather, identifying the potential scope of the investigation. You can upload the
complaint, for example, and it does a pretty good job of identifying the scope if you include your
policy, it can identify what policies it might be... the conduct might violate. Uh, it can also
propose questions that you should be considering during the investigation. So one way to think about
an AI tool in this context is sort of like an eager associate investigator, super smart, really
eager to help and support you, but actually has no understanding of the investigation process
without you providing it lots of information and training. So those are a couple thoughts
where I think there's an opportunity there, but as with everything, there's some risk around it.
Deb, what are your thoughts on that? And you're on mute. Thanks. Yeah, I have lots of thoughts on this,
but I agree with everything you said. I think the problem with a summary—a summary by definition is
sort of taking something and condensing it. And as you said, it's deciding what's important and what's
not. You can assume that AI bot has never worked in a plant, has maybe never done a sales job. So those
little things that actually, you know, being in your organization or just, you know, being an outside
investigator knowing enough to ask those questions may get glossed over or may get eliminated. I'll
never forget, I had, this was not AI, it was years ago, but I had someone take notes for me and they
weren't a very good notetaker, and there was one thing that the person said that afterwards I was
like, "Oh my god, could you believe..." And I went to their notes, they didn't even mark
it down because to them that was not relevant. To me, it was very short of a smoking gun. And so
um, because I was thinking about the connection, everything like that. So I think
I love, and that's some of the things, you know, not to give away things that we're doing in
HR Acuity, because we know there are ways to assist the investigator, make sure you scope it well, you do
a comprehensive investigation, which we know is one of the things that as an investigator
you need to do to make sure it's compliant. Um, and that's exciting. Oliver, I want to ask you a
question in this in the context of employee relations and investigations with AI because
I think one of the one of the sort of the devil's... you know, the devil's in the details
issues here is the risk of delegating authority for an investigation or for an employee relations
decision. Oliver, how would you evaluate that risk? Oh, very... I would say it's high. I would not want
um, to delegate authority. And for some of the reasons that you all mentioned, I think it's really
important to have that human decision-making point all through the investigation. So I think AI can
help the human make decisions. And I think, Eli, you raised a great point about how a human can use AI
to be more prepared to make a better decision by querying the evidence that exists in the tool. But
I really think that it should be the investigator who uses their critical thinking to make judgments
throughout the investigation and certainly to make the ultimate finding. You know, there are so
many things that go into that. I don't think that AI is sophisticated and advanced enough yet to
be delegating that to. And, you know, that's a good thing for our profession. We're still
necessary as humans, HR professionals, investigators to do that. I think one of my other concerns is
some of the things you're saying AI can do, that's how I train people on investigations and
employee relations. You know, Deb mentions having someone take notes and then kind of issue spot.
Well, that's how I learned, and I assume that's how a lot of people learn how to really understand
what's important when you're talking to people and how it plays into the bigger picture in an
investigation. So if you're having AI do that, you know, do we miss a generation of investigators?
You know, I think that's incumbent upon us as leaders to think through if we use AI in this
way, how are we going to train the next generation? I don't have an answer to that, but it's definitely
on my mind. Yeah, that's a great observation. Go ahead, Deb. The other opportunity or the issue is
just bias, right? We know AI is inherently biased. And what have we been trying to do for years
and decades is take bias out of the investigation process. So now you're layering something on that's
known to have that problem. So also a big concern, obviously. For sure, great point. Well, why don't we
talk about our last opportunity, which I, Oliver, I hope there are no risks with this one. I hope
we can just start using it immediately to do this. Yeah, I'm sorry to be the bearer of bad
news. This one is complicated, but, you know, a lot of employers have data when it comes to
performance. And especially in retail, healthcare space, things that are important to us include time
and attendance and productivity. And those are both very data-rich environments. And AI can help
us there, compile, you know, how people are living up to their time and attendance requirements
and how productive they're being, and then put that into a nice little package for a leader on
their each individual employee. And I, you know, I'm sure you've all heard this. It's hard
to write PDRs. It's it's time-consuming. And so this is an area where AI may be able to help. However,
it is a very risky area, and performance is something where context matters, and the human
decision-making discretion is very important. So I've listed this here with the caveat that
you got to be very careful. Yeah, it sounds like a tremendous opportunity, and I know that there are
going to be tools that are that are fine-tuned to to help with this, but it also sounds like, as with
many of the opportunities we're talking about, the importance of human judgment and making
sure that the human is still the decision maker, is still the one driving the analysis,
and is only using the tool to the extent that it can be helpful in gathering or consolidating,
you know, summarizing or collecting information, but ultimately the user has to be the one who's
driving this, right? Yep, totally agree. Well, that probably felt like five opportunities with a
lot of risks. Now we're going to talk about five risks that also have some opportunities. I think
that's the challenge with this topic, but it's also the thing that makes it kind of exciting.
Uh, so why don't we move on to the first risk, which is using AI to make employment decisions?
Now, this obviously is a significant area of risk because as we've talked about, and I see some
of the comments in the chat even expressing some concern around delegating too much responsibility
to AI, and certainly relying on the tool to make an employment decision, which that might look
like saying, "Okay, here's the investigation report. What should the action be?" Well, that would be an
inappropriate delegation of authority. The user, the HR professional, simply has to be the one
who's evaluating past practice, has to be the one who's evaluating the impact and relevance of
the evidence that was gathered, the findings that were made in the investigation or the analysis. So
I certainly think there's tremendous risk in using AI to make employment decisions. On the other hand,
Oliver, there probably are some opportunities here around using AI to inform or help an HR
professional in this way. Is that true? Yeah, I think so. I do think there are organizations
that don't have other systems that that can do some of what you're saying, you know, track
what they've done in the past. And so there may be opportunity for AI to give you data on that
rather than make a decision, really give you that data. Also could help give context around
what past practice has been or what other company documents, policies, practices would
indicate. So I do think there's some opportunity to leverage AI here. Actually making employment
decisions, I think that has to really rest with the professionals and rely on the human judgment.
Uh, related to that is the hiring decision. And I include this here because a lot of the tools
that I'm coming across right now are focused on helping in the talent acquisition space,
making that process more efficient. And you know, some of that's about reviewing résumés,
um, using AI to to review résumés and then spit out the best ones based on the job description. But
I've also seen AI used to conduct interviews. You know, it can display questions that the person
answers on video, and then it can ask follow-up questions to whatever the person responds. And
so that's another function that I'm seeing. And I think all of this is very risky because that
hiring decision and, you know, even whether you advance someone's résumé to the next stage of
the hiring process, there's risk in all of that. That's all part of an employment decision, and
so it needs to be very, very careful. And bias is a huge factor in this process. And, you know, for
those who have looked into kind of résumés, for example, um, it's very common knowledge, I think,
that names on résumés can have a lot of bias. You know, we have seen just changing the name
at the top of the résumé can change the way it's interpreted, and AI will learn based on some
of those practices. So it will not be perfect, and I do have a lot of concern about using these tools
um, in the hiring process. Any opportunities you see there, though, Eli? Well, I think the bias issue
is fascinating, and we're we're going to dive into that a little bit more as risk number three. But
in this context, you know, we also have to remember that there is bias in human nature and that humans,
especially if we have folks who are maybe not well trained or don't have the experience in
making hiring decisions, that they actually could insert more bias into the process and actually
a technology tool may who that's well-formulated and well-designed and is specifically
has agents to help address bias, it actually could result in better consistency and accuracy
and could take out some of that human bias that we so much want to avoid. So I think it's
an interesting dichotomy, is that while I think there's a lot of risk here in helping employers
make responsible decisions around recruiting and hiring, I suspect that there will be AI tools
that are fine-tuned to help eliminate some of that risk that humans actually insert into the system.
Yeah, I think it's an area where we should be looking for studies and data where the
vendors who are offering to do these things have really looked into what happens when
they do things like change names on a résumé or change the appearance of someone that the
AI is interviewing, and they've proven in their their data that there there isn't bias or that
they've really improved that that component. Well, and so with that, why don't we talk a little bit
more about bias? Because it's sort of the, you know, the the 800-pound gorilla in the room is the fact
that every AI system seems to have a significant amount of bias integrated into it or built
into it. And so let's understand why that is first. And the reason that is, the reason that
we are so concerned about bias in AI tools is that as a large language model is trained on all
of the volumes and volumes of data, it is trained on data that is human-created, that has intrinsic
biases and that already has intrinsic bias or unconscious bias built into it. And so
one of the challenges is there's... and if you've, you know, read up on how these tools have been
trained, there's not a lot of focus on how to make sure that these tools are unbiased when they are
being trained on all of these volumes and volumes and volumes and volumes and volumes of data. And so
a lot of the concern is that, and if you think about it like any human being who has their own
lived experience and their own background and their own perspectives, we all come to our jobs
and to this day with our own experiences and our own biases. And that is magnified in an artificial
intelligence tool. And that's why that's so concerning about how the bias may
manifest in the outputs that these tools create. For example, this is several months ago now and,
um, but there was an experiment done where an AI tool was asked to describe individuals,
including gender, race, age of individuals in a boardroom who were board members of a large
corporation. And pretty consistently, the tools came back and described the board members as
middle-aged white men. And that is obviously reflects significant bias. And so that's
the type of bias that we're concerned about in large language models. And so if we're delegating
responsibility on decision-making or hiring or things like that and we haven't ensured that the tool is
uh, designed to eliminate or mitigate that bias, we could be inserting exactly the bias that we've
been working for so many years to try to get out of our decisions as attorneys and HR professionals.
Fully agree. And I hope that everyone has been working to remove those biases. And I think the
only, you know, counterpoint to this is, uh, for those who haven't worked on that, the
the these vendors are working on that. So, you know, um, I think it goes back to what you
said earlier, Eli, uh, it is possible that the AI tools are better than humans.
You know, they don't have conscious bias. They have these built-in biases, but,
um, you know, I think that's something we need to think deeply about. And hopefully everyone
is working on those biases. Um, but I think this is a very important thing to be aware of with AI.
One of my um, fears in reliance on AI is that we really lose our our listening skills and ability
to hear what's really going on, maybe not the words people are using, but um, what's really going on
under the surface. And I think that is one of my biggest assets as an HR professional, is um, those
listening skills. But if we are teaching people to rely on artificial intelligence to listen to
conversations, summarize them, even identify the most important issues, I worry that over time,
um, we'll start tuning out. People will start tuning out and won't really hone those
listening skills as much. Uh, and I think that's one of the most valuable parts of our profession.
And I hope that that doesn't happen. But uh, I think there is some risk there. I think that really plays
into our last risk here, too. Uh, and I think with these two risks, I don't know that there's
a lot of opportunity. I think they're really just cautionary tales that we have to be thinking about
as we're evaluating how to integrate AI into the HR profession. I think one of the areas, and we've
alluded to this a little bit, is just the concern about reducing the reliance in an organization
on the true value that HR professionals bring to the table. Uh, HR professionals are well
trained, they are experienced, and probably the experience that many of you had is more valuable
than any training you can give in, you know, a week or, you know, five days. Um, it's really that in the
trenches experience dealing with individuals in unique, uncertain situations and using
your judgment and critical thinking skills and emotional intelligence to come up with a solid
solution to a given employee relations issue. And I think that's one of the concerns is both
reducing or suggesting to an organization, to leaders in an organization, that the true value of
an HR professional can be replaced in somehow by an AI tool. Another concern is something that
Oliver mentioned earlier, which is how do we ensure that the next generation of HR professionals is
well trained if we're delegating some of these ministerial tasks to an AI tool and not using
those as an entryway to the HR profession? Um, thoughts on those uh concerns, Oliver? Yeah, I have
all of those concerns. And I think that that makes it important for us to really start grappling with
AI and understanding it and leveraging it in ways that make sense and are lower on the risk profile,
um, but that make us more efficient, because I think leaders and organizations will demand
that we understand AI and that we're able to leverage it. And I hope that that really helps
us focus kind of at the top of our license where those HR skills, the listening skills, the judgment
skills, where that's really useful and that people still rely on us for that. I think they will if we
use AI to be more efficient on the other tasks. So wrapping it up, uh, let's talk about what
does this really mean for HR professionals? Yeah. Well, first of all, don't be left behind. There's
a lot going on in this space right now. You're you're here today, so that's a great start. But
um, you know, keep learning as much as you can about this and play with tools on your personal time. You
know, it's fun. And there are ways you can build those skill sets. So don't be left behind and get
in the game here. Make sure you're staying on top of this area that's rapidly developing.
Make sure that you understand the limitations and that when people ask
you to implement a tool, you can talk to them about why that might be a challenging idea or
even a bad idea that can include bias or the reliability of the information or even just
the adequacy of the output. Um, but make sure you're able to talk through those issues, too.
Make sure you're using it as a support tool, but not to replace your independent judgment. I think
we've talked about that a few times today. And then make sure you're analyzing the various
risks. I think I said earlier, work with your legal teams, work with your privacy teams, your IT teams.
A lot of them are focused on this right now. So make sure you're bringing in partners to analyze
all that risk. And then last but certainly not least, hopefully you heard in much of
what we said today, your independent judgment remains very important even if you're using
these tools. So keep that at the forefront, even when implementing AI. Great. Thank
you, Oliver, and thank you for the discussion, all of you. It's great to have comments and
uh, questions from you. And pleased to be here. It sounds like you have some opportunities coming
up to discuss this more with the HR Acuity and Empower team. So I'll turn it back over to Deb.
Yeah, thank you so much. This was great. Obviously timely. I think everybody was sort of leaned into
everything you were saying. Of course, for me, every time you mentioned Oliver, I thought Oliver, our ER
companion who's in our platform, would show up. But Oliver, you're on the same wavelength, so
you know, you're... all Olivers think alike, I guess. So it's great. But thank you.
You know, Eli and Oliver's contact information is here if you want to reach out to them for more
information. You should. But we love having them as part of our community. And thank you
again to everybody for joining us. And stay tuned for more conversations. This is going to... we're going to
keep talking about AI for a long time. Thanks, everyone. Have a great afternoon. Thank you so much.