Suggested that Adams should be suing President Biden instead, because it's Biden's policies that
are to blame. I'm Chris Karragio, NBC News Radio on Board caseaas Inland
Express, KAA Rome Linda En fifty AM the station that needs notice here behind
the information economy as a ride. The world is teeming with innovation as new
business models reinvent every industry industry. Inside Analysis is your source of information and
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at inside analysis dot com, Inside analysis dot com. And now here's your
host, Eric Kavanaugh. Oh yes, folks, welcome to the future.
Indeed, your host here, Eric Kavanaugh, the only coast to coast radio
show that's all about the information economy. It's time for Inside Analysis and today,
folks, we are to talk about the human touch. Yes, indeed
AI for HR human Resources, so artificial intelligence for human resources. All the
folks in HR who hire fire try to help us through the whole journey of
working for a company. We're going to learn from a couple of experts.
Here. We've got Jeff Webb, head of Solutions Strategy at HR tech Company.
I solved and Jyleen Owen, head of HR at Hams Corporation, calling
it all the way from Alaska, and Jeff is is over in Houston,
and yours truly is in Texas as well. So we've got two Texans and
an Alaskan on the show to talk about AI and HR. And HR is
changing a lot these days largely due to AI and also analytics and being able
to track and understand, and of course it's used in the hiring process where
they have algorithms now that are just reading these resumes coming across the transom.
It's changing how we work day to day. All kind of stuff is happen.
So let's dive right in and talk to the experts. First. Jayleen
Owen of Hames Corporation, tell us about yourself and how you are thinking about
AI for HR. Well, I am an HR professional. My background is
in industrial organizational psychology. I have just completed my seven years here being in
Alaska. I'm not originally from Alaska, however, though I came to join
the company in twenty sixteen. For the family Haines, they are are fourth
generation family owned and operated organization here based in Sitka, Alaska. We have
a multitude of diverse operations including retail stores, a grocery store, liquor stores,
we have gas stations, residential, commercial properties, you name it.
We've got a lot of diversity. So I joined the team here in twenty
sixteen to help kind of bring them out of the dark ages. Being on
an island, there's a lot of challenges, there's a lot of resource limitations,
and so having my professionalism to be able to kind of address some of
their concerns was why I was brought on. Very cool, and we've also
got Jeff Web out there. Jeff, tell us about what you do in
the HR space for I Solved. Yeah, thanks. So I lead the
solution strategy team with I Soolved. So my job is to make sure that
we're constantly looking ahead, evaluating the needs of our customers and partners, and
making sure that we're building that into the roadmap for the technology and the services
that we deliver to companies here in the US. So I get to talk
to a lot of HR professionals, which is how I got to name Jayleen,
and I also get to spend a lot of time looking at how the
technology and the sort of human experience in the workplace intersect which has has just
been fascinating and evolving so very very quickly at the moment. Yeah, maybe
so, Jailine, I'll start with you the human touch. You know,
people want to be treated fairly, they want to be treated honestly, and
they don't I mean personally. One of my pet peeves, I can't stand
talking to your voices, Like when you call these days, it's like I'm
a computer and I can speak in complete sentences. I usually throw some abstract
philosophy at it and like I'm sorry, I didn't get that. It goes
back to me. People want to deal with people. We understand that there
are technologies behind conversations and behind businesses. That's very well understood. But how
are you navigating this change of infusion of AI and analytics in what is a
very personal job of hr AH very carefully. I believe that there are a
lot of pros, but there are also a lot of cons And when it
comes to implementing any kind of artificial intelligence, whether it be in human resources
or you're going to actively take a role, I think in your business,
you need to take a look at what both positive and negative challenges may have
As implement implications to your business. For us here, we've been looking at
things like could it automate some repetitive tasks On an island, we have a
limitation of bodies. There's literally about eight thousand residents that live here year round.
We are driven by kind of a tourist industry, but because our industry
isn't it's a year round industry, there is a lot of concerns related to
whether or not that is going to be an efficient decision for us. Is
it going to be cost effective? Is there going to be the appropriate support.
Even when we went through the process of implementing I Solved. I Solved
is so on the top of the cutting edge of technology that many of my
employees here were not too sure if they were going to like that development,
and so it's been a challenge. We don't really have a lot of remote
work here on the island, but the idea of being able to support some
of our employees. I do have one employee that is in Idaho, and
so being able to utilize AI the advancements to connect us so that we don't
have that distance between us any longer has been what I've been focused I Okay
that's pretty cool. How about you, Jeff, I mean, you're obviously
in the thick of these things at I evolved trying to help organizations leverage all
this new technology. I'm sure you've sat around and thought about the ethics of
all this. But it's like the train has left the station, right,
I mean, the I joked earlier today, the chat is out of the
bag, if you will chat gep. AI is everywhere, and we're going
to have to learn to deal with How are you navigating this interesting change in
how we do business? Yeah? Absolutely well. I mean for us,
we focus very much on small to medium business in the US, So you
know there's about one hundred and seventy thousand companies we touch every day, and
really our focus is going to be to make it consumable and easy and non
threatening for them. So the trick here is to sort of is to stay
focused on the outcomes. Like I think, it becomes very easy with AI
to become very wrapped up in the art of the possible and then the technology
and the reality is, you know, you're running a small business. You've
got a lot of things to deal with. I'm looking to retain good people.
I want to recruit new people, I want to develop them. That
you must stay focused on what is the outcomes we're trying to drive here,
and how do the technologies like AI, how do they help us get their
easier and quicker and with less work and less just less friction in the whole
process. And so for us, it's a case of thinking about where does
AI get deployed appropriately, what are the right tools that you can infuse AI
with in other words, to make smarter and more self you know, independent,
And then how do we communicate that in a way that is honestly not
threatening to the folks that we're talking to who already have too much to deal
with in technology coming out in every direction and compliance changes and you know,
expectation changes from changing workforce. So I think half of it's a technology question
and connecting the technology to outcomes, and the other half is just communicating that
in a way that's understandable. Yeah, that's a that's a great line.
And you know, jainly not throw it back over to you because you mentioned
I think the key facet of this whole equation here, which is automation and
automating tedious tasks. That's something that hr technology has a fair amount of and
automation is not new if you think about it. Just to be blunt,
every piece of software ever written automates something. That's what you're doing. Whether
it's a word processing application or a photo editing application, whatever it is,
you're automating something. So automation isn't new. But this machine learning and AI
is relatively new, at least in terms of practice. Right we've had AI
for probably forty five fifty years, but it just wasn't very It wasn't readily
available because it was very expensive. We didn't have the compute power. Now
we have all that compute power, and so we can automate things and we
can see things in a different way. That's what kind of gets me excited
about this stuff, is that machine learning algorithms can crawl around in the background
and just look for patterns of things. And what I've noticed anytime I've used
the tool that has that kind of functionality is they will almost always come up
with something you did not expect. And I think that's one of the most
positive aspects, is that you're going to learn things about yourself and your team
and your organization that you didn't know before. Because this all seeing eye is
just kind of watching and never blinking and serving up little observations. But what
do you think, Oh, I definitely think you're right. When it comes
to the idea of how we could utilize the AI, I think you're absolutely
right. We're kind of behind the ball at this point. The cat's already
out of the bag. And in the case of where the cost of effectiveness
can meet the actual resource requirements, it's going to be the most vital piece
I think any business can focus on. You also are going to have the
issues of that privacy if the information's collecting and analyzing your data. Not only
are you concerned about what data does it have access to, but the privacy
of that, especially from like a perspective of a small business when it comes
to proprietary stuff. We're our competition here isn't another grocery store. It's a
hospital, it's a bank. When it comes to establishing the clear policies on
how that data is collected, how it's stored, how it's used, ensuring
that our employees aren't gonna be worried about their privacy, let alone the company
being worried about ours. I also think that it's important to look at the
resistance and how that trust can impact things like job displacement when we start looking
at automating repetitive tasks, the smaller tasks. Yeah, it's great, especially
when we have a labor issue right now and I don't have the skilled individuals
to take over some of these jobs. AI can step in. But I
definitely do agree that there's a lot of ethical consideration that we also have to
take into consideration. Yeah, right, well, I'll bring them Jeff back
in and we talk about things like payroll for example. That's a wonderful use
case for automation. You want to be able to set up a system and
then monitor it. And again getting back to the power of these machines and
the power of algorithms, they will catch errors that human beings can make.
Humans are error prone, I mean we just are. We get distracted,
but take it about something else, so we don't do something correctly. And
the nice thing about these machines, they will catch those mistakes and nothing happened
here. Take a look at that. So payroll is a great use case
for automating, right absolutely. You know, there's a whole bunch of places
where you can use that capacity that you referenced of AI to sort of to
learn and look for changes to great effect, and payroll obviously is one of
them where you can you can have an AI platform essentially learn lots of normal
payroll run for you as a business, but also learn actually at an individual
level, what's normal for people to be paid. And then if you make
a mistake, and it happens, even with the best, you know,
best policies and procedures and even with good automation technology, mistakes can be made.
People accidentally get paid twice for example, I've seen that happen, and
having something like AI just go, you know, this is odd, this
is unusual. You don't normally do that. Is that a normal payroll run
for you? Is there a reason you're suddenly paying a lot more or a
lot less. Having that backstop there starts to again reduce the workload on the
person doing the task and reduce the because they don't have to check as closely
that they know that they've got something running that can do it for them,
and also just improve the sort of the general experience less you know, pay
wrong mistakes are a reason that people leave their current company. It's nothing more
annoying than not getting paid the right amount of money. So yeah, it's
a great place to put it. There are a bunch of other places,
you know. We think of those really as being the sort of three areas
we think if AI we're really working in the role of HR, and that
would be one of them. It's sort of in the flow of work where
you've got AI watching things happen and helping to sort of improve and smooth out
the automation process. There are obviously other areas too, but just at the
most basic level, getting stuff done and reducing the workload and reducing errors is
a huge win for every over world overstressed HR payroll and benefits administrator out there,
that's right. Well, you know the other area, and I'm sure
is very rich with possibilities here is benefits and just understanding benefits and different packages
and different programs. And I don't know about you, but I just I
shudder when I look at some manual that I'm supposed to read about. I'm
like, oh my god, are you serious, Like I'm particularly this whole
thing. Well, that's something that these large language models are very good at,
and AI is getting very good at. It's being able to take large
amounts of text, feed them into this machine and say give me a summary
to explain what that means to me. I'm thinking that's a huge area because
you know, many people probably don't want to admit that they don't really understand
what these benefits packages do because that's kind of embarrassing. But it's complicated,
right, So Jeff first to you and then over to Jen about that.
Yeah, yeah, absolutely, it's funny. Actually, that would be probably
the other area where we see AI. One of the other areas where we
see AI starting to come really effective in sort of improving and elevating the experience
overall of employees and also as a result of the business, and that's personalization
and to your point, exactly understanding things and helping shape and personalized recommendations.
This is a good benefits package for someone like you, or here's how to
find out more about this particular set of benefits is one of the areas that
we think it's going to have a lot of value. There are plenty of
other areas you think about adding in the ability of AI to recommend not just
the benefits package that you might want, but training you might need in your
job. You know, people in your job should take these sets of courses
to improve improving just any sort of set of you know, things that the
company delivers and personalizing them for that set of employees is you know, you
can scale that with AI, which is really hard to do when you're doing
it manually with people. That's a good point and that gets us to really
the crux of what we're talking about is we want to use these technologies to
tackle the tedium and to tackle the complexity tea where possible. And you know,
we had to show a couple of weeks ago where we were talking about
scheduling and if you've got let's say a hospital with two hundred nurses, for
example, and one of them wants to take time off, well, right
now, it's a very manual job for a lot of places, and some
person that has to sit there and look at the schedule and say, all
right, well, if I put Bob over here and Susie goes there,
that's a pain in the rear to do. But some of these technologies can
bang that stuff out in their sleep. Right. It's just like you just
entered so and so wants this stop being used, you know, send it
to Fred, send it to John, send it to Jim or whatever.
The machines can do that stuff extremely well. And then also it's a machine
doing it, and it's not Jim, the person who oversees who's gonna get
you know, a bunch of people mad at him because he said, Okay,
you do this, you do that. I'll throw that over to Jay
Len. That's a classic example of how these machines can do a very tricky
job with no bias because they're just getting the job done. What do you
think, Oh, that's a that's a loaded question in my opinion, because
I think that it's important to also to go back a little bit to what
Jeff was saying, especially like when you look at things like benefit management,
employee classification. Larger companies that you're gonna have a lot of different types of
plans. How you manage that ensuring that they are getting the kind of assistance
that they need that an HR professional like myself would sit down and help them
through maybe an open enrollment process. You're looking at data validation and the accuracy,
and I think AI's algorithms can really help us analyze that payroll data allowing
for those inconsistencies and those errors to be flagged, whether it be tax compliance
or just the automation of data in general. When it comes to AI,
though, in my opinion, I think it's really really important that when we
look at the type of AI you're using, because as you indicated, the
natural language processing and the way that the chatbots are engaging and learning to assist
our employees, our HR professionals will aware of and still actually engaged in that
process to be able to utilize that and not allow for possible issues to become
an issue. It could be ambiguity and maybe the system doesn't provide the information
you want it as an HR professional, or it could just be a bias
built into the language model. That's an interesting point, and you know we
are in the early phases of this. I know with the large language models
there is this tendency to hallucinate. We've got to watch out for that.
But AI is it comes in all sorts of shapes and sizes, and the
key to your point, Jane, and I see Jeff nodding his head as
well, is that when you implement these technologies, you always want to be
able to track and trace and understand what they're doing, and you have to
watch out for black boxes, as their call, that just make decisions about
people's lives. You've got to be very careful about that. So you want
the transparency. You want to be able to audit the system and to understand
what it's doing and to map out and see and you know, there are
all sorts of ways that that's done these days with log files and things of
that nature. But you definitely want to be very careful before you put any
kind of automation into an organization, especially for something as serious and significant as
human resources, because that's dealing with the people who are your company. And
every company is a bunch of processes and assets and people. But folks don't
touch a doubt. They're right back. You're listening to Inside Analysis. Welcome
back to Inside Analysis. Here's your host, Eric Tavanaugh. All right,
folks back here on Inside Analysis talking to a couple of experts in h R
Human resources. Of course, we've got Jeff Webb and Jayleen Owen and Jeff,
I'm going to give you the hard question that I'll let Jayleen think about
it for a second. How do you help people who have great, deep
skepticism about AI in their organization how do you help them understand that really there
are positive sides of this that the negative sides can be dealt with. How
do you talk to people who are very skeptical about this stuff. Yeah,
that's really the heart of a lot of where we're at. I think as
a lot of organizations. You know, probably I spent this year traveling around
the US. I did fifty odd different cities. I probably met a couple
of thousand HR people, and a lot of the conversations we talked about AI
came up a lot, and some of the times there's a lot of interest
in you know, well, what are you doing? What should we do?
Sometimes is simply a case of please don't use AI in polite conversation.
I think we've all reached a point where there's so much AI conversation in the
news it's sometimes difficult to cut through to the realistic, actual meat of the
topic. So part of the challenge is just being down to earth and realistic
about what we're going to achieve now and what we should be achieving in the
future, and then put it in context, you know. I also we
look at survey results, We do studies ourselves of organizations across the us and
look at their attitudes and employee attitudes, and we try to be sensible and
clear about what AI is going to be good at and what people are going
to be good at. And I think we're going to come I think we're
even in this conversation, we're sort of circling around on the areas of,
you know, what what A really good at and what do people do so
much better than AI can do for the foreseeable future. That is reassuring in
many ways. But yeah, we've asked I think we ran a survey earlier
on the Shire couple of thousand employees in the US We asked what they thought
about AI, and actually it was close to seventy percent. I think we're
relatively positive that it could improve their work experience. So I think there's optimism.
I just think there is skepticism, which is good, and then a
little concern about what, you know, how's this actually going to roll out
and what it'll look like when it starts to become much more real in the
workplace. Yeah, right, and what about EUGENI Leen, you obviously have
to counsel people who come to you and say I don't like this SAI stuff
At all, what's going on here? Tak my job? How do you
deal with that? Well? For me, especially living on an island,
we have a unique social, cultural, and even economic dynamics here. There's
a lot of specific factors that come into play when we're looking at how you
approach people in a small community like this. The sensitivity of taking what would
be a personal possible conversation or interaction and expecting the individual to possibly embrace that
the same way that they would if it was a person. That's a big
concern of mine. Ensuring that there's a community engagement through the AI is going
to be important because those interpersonal relationships matter, and they matter especially to people
in this local community when it comes to decision making and being transparent. The
use of AI really does concern a lot of people, especially when it comes
to the building of the trust of that information and when we get into,
as I stated before, privacy or even fairness concerns. But to kind of
speak to how we would be able to address that skepticism, in my opinion,
I think it's really just going to come down to education and awareness,
providing and offering clear, accessible information and really bridging the gap between things that
might be demystifying of that AI. It'll allow those individuals to really see what
could be augmented and allowing maybe some showcases where right now, because we have
a limitation of bodies, my business, or at least my experience here on
an island is almost like a fish bowl or a case study because we are
limited on the resources here, and so we have to kind of think outside
of the box and look at things that would possibly allow us to maybe create
a user friendly process to this new technology. That's a good point, and
Jeff, I'll bring you back in. I'm a big lover of data and
being able to track things, and I'm just guessing here that one of the
cool things you can do, and I saw this when someone uses your technology
to help with their HR. Over time you can see, for example,
you can score resumes coming in to help you better understand who to hire.
But then over time you can also see how well that works out. And
the real value to AI is when you get enough data into a system to
be able to ascertain what works and what doesn't work, and then you can
do you know, what they call regression analysis for example, and go back
and look, okay, well, what did happen there and why did this
not work? And you can really learn a lot about the people who have
come into your company, who works, who doesn't work, because something like
a corporate culture is very difficult to define. But if you have enough parameters
and you have enough data, over let's say a year or two years,
for example, all the sudden you should be able to get better and better
at who you hire, when you hire, what kind of jobs and roles
you actually need in the company. All that stuff theoretically gets better and better
over time if you're using the technology to track the data and be able to
understand it. What do you think about that, Jack, Yeah, absolutely,
that's you know, one of the things that we talk about really early
on as organizations to think about adopting technology is a single platform that kind of
captures all of the employee interactions. Becomes essential to ultimately unlocking that power of
that data later. If you know everything that's happened to that employee, if
you capture every interaction, you know, every performance report, every time they
log in, every time they check out. Not again to your point,
not in some sort of creepy, big brotherish kind of way, but simply
tracking the flow of work and things that are happening, and you know who
your good employees are and you can evaluate that. You can very quickly start
to identify where are the good employees coming from, where are we spending the
most money, where it could we save money on, you know, not
hiring people that quit three months eg. Later. And you can also start
to do really interesting analysis things like, you know, we had one one
company we work with, they wanted to move one of their officers. And
what they're able to say was, if we move this office from this location
to this location, which employees are likely to be at risk of leaving because
their commute has become unpleasant And we already know that's a factor for those kinds
of employees for example. Or you know what kind of money should we be
spending on benefits for this group of employees? Or you know, what's the
salary changes we should make for those So you get you get good operational uh
you know, data that you can use to sort of improve performance of the
business. You also get bigger sort of macro perspectives on your organization. What
does the diversity look like at my organization? Is it changing? Is it
is it different in different departments, are certain managers more more likely to lose
employees over a period of time than others. So there's a lot of power
that you get looking at your own company from the data, and that I
think is part of the job really is to unlock that and make it very
consumable for HR folks and business leaders. I actually think there's another area too
that we forget about. You know, we just talking about our own you
know, I sold as a company, we touched the lives of what about
seven million US employees every day, and you think about you think about what
that means when a company, you know, you're running a bank and des
Moin for example, and you're not sure why your bank tellers are leaving,
Well, you have the ability, using these kind of technologies to say,
well about are we losing bank tellers faster or slower than other bank tellers in
des Moines or in Iowa or in the US, And are we paying more
or less? Are we spending more or less on benefits? So that capacity
to not only unlock the sort of insight of what's happening operationally in your own
business and therefore impact the experience of your employees, but to then go benchmark
yourself against other organizations and say, well, how are we doing? Are
we better, are we worse? You know, what are the areas we
could improve it? That becomes incredibly powerful too. So, yeah, you're
right that that sort of capacity to hold and analyze, to consume data and
sort of spits out results and insights really starts to redefine what's possible for HR
people, business leaders, payroll folks and so on to just you know,
drive better business outcomes. Yeah, that's a great that's a great point that
you just made. And it kind of gets us back to being data driven
and doing these comparisons, because again you might think, oh, we're doing
a terrible job, are you. If you have the capacity to see what
your peers are doing and what those rates are for those other companies, now
you understand, oh no, this is actually normal. And Jamie and I'll
throw this one over to you. The other really interesting space I have to
believe is in training and research and learning and being able for compliance purposes but
also just for improving the workplace, having people take classes on various aspects of
the business. Being able to track Okay, we rolled out this program to
help people be more sensitive around diversity. For example, did the complaints go
down a month later or did they stay the sane did they go up?
You can kind of better understand what's working and what's not working from an educational
perspective, right, Oh, definitely, as long as the information that you're
receiving is accurate. Where I get concerned about is the unintended consequences that the
AI system may produce, especially if that information or the data isn't being set
up properly, or the interpretation of the data is being determined by maybe another
AI because you get that bias involved. But yeah, definitely, I think
that when it comes to the dynamics, especially within a small workforce like myself,
education and training are vital. It's what determines our success, and especially
me being on an island where I have a devoid of skill level and education
or even knowledge, and I'm bringing people in because the labor sure, I
need to be able to benefit from whatever technology can give me and speed those
processes that to give me a balance between the innovation with the traditional. Yeah,
that's a good point. And you know, speaking of bias, I
remember learning about this story is back in twenty eighteen, Amazon which of course,
is a very big company, very analytics and AI friendly, very focused
on that stuff. They rolled out this AI recruiting tool that basically said don't
hire women, and so they're like, what, oh, no, hold
on, turn the thing off. You do have to watch out. That's
the sort of common sense moment. I'll throw it over to Jeff. AI
get stuff wrong. I mean it is artificial intelligence, and real intelligence can
get stuff wrong. People get stuff wrong all the time. So AI doesn't
mean it's perfect. It's just making some recommendation based upon a model, based
upon data that's fed. And you got to watch for these things and watch
out for it. And that was just a class as an example that yes,
bias does exist, then you have to watch out for it. So
you always have to, you know, keep your human hat on as you're
using things like AI. Right, Jeff, Yeah, absolutely, And you
know, I think Jalen kind of nailed it a couple of times there,
you know, her commentary about transparency earlier, on her commentary about you know
about bias right now, I one hundred percent agree, And I think that's
the challenge. We know that you can build it. You can build a
model that inherently is fine, but you train it on bad data, it'll
produce bad results. And so part of the you know, the challenge that
the organizations like our as every organization that's using AI, but especially when AI
is becoming infused in HR processes, the challenge is to make sure that you
don't allow that to happen, that you're you're using it in a way that
is ethical and transparent and there's unbiased as physically possible, you know, for
things like recommending a course that someone should take, or checking a payroll,
or you know, simply being a chat but we you know, do a
lot of work with chatbots to answer basic HR questions like is next Thursday at
a company holiday or not? Those kind of things you're relatively unlikely to see
as a significant bias, but you have to be very careful. And even
for us, when we start to do things like stack rank resumes to compare
them against job openings, you've got to be really careful to not exclude people
from that process. You must, you know, provide to your point.
You can provide insight, you can provide recommendations, but ultimately the AI should
be working alongside the professional like Janalene who understands the broader context, and that
that really starts to define I think how we'll see AI becoming realized in hr
processes. It's a lot of the time it's going to be working as a
counterpart to a professional who can then provide context. And the other thing that
humans do that AI does not do, which is empathy. Context and empathy
is what we are. You know, we're magnificent context and empathy machines.
AI is a great analytic. You should combine those things when you're thinking about
affecting the lives of people in the workplace. You shouldn't have one override the
other. Yeah, that's a really, really good point. Empathy is so
important in business, and I think we're moving through generations right. We were
talking in the break about how we have these multi generational companies now where you've
got baby boomers and Gen xers and Gen Y and millennials and Gen Z like.
These are very different groups of people in how they react, how they
respond, and probably how they respond to data. I mean, kids just
grew up with iPhones these days. They know how to use this stuff.
Baby boomers did not, and so you always have to keep that in mind
and that's a very personal thing. I'll throw it over to Janean real quick
about ninety seconds. It's very important to have that empathy and to always be
human to the human people in your organization. Right, well, yes,
definitely. There's one word I would have to say sums this all up.
It's the authenticity of it. AI comes from what we tell it and what
we program into it. But as Jeff indicated, the empathy doesn't come natural.
It's something that I'm concerned as we move forward with this is eventually everything
that we can give it as a personality is going to be built in there.
But are we going to lose that personal touch that's the authenticity of the
matter. Yeah. Well, I've joked for I did a show a few
years ago and someone made a joke about something and I was like, they
told me to be authentic. I mean, come on, going to be
myself here? You careful what you wish for out there? Well, these
are all excellent points. Well, let's see, we're coming up on a
break here. We're talking to Jeff Web and Jalene Owen HR professionals about the
impact of AI, and I think in the next segment we'll dive into the
hiring process and what these things look for. You know, you think about
Google and search engine optimization and what happened there. Basically Google, who has
algorithms for how they track an index the web, and then people are always
trying to gain the system, try to get the Google room to point at
them, and so you know that's got to be happening in the talent acquisition
space where we're trying to use algorithms to sort through the data and someone's trying
to trick that algorithm to use the proper words to get them to look at
your resume. We'll pick that up after the break. Folks, don't touch
that doll. You're listening to Inside Analysis. Welcome back to Inside Analysis.
Here's your host, Eric Tabanat all right, folks back here on Inside Analysis
talking all about HR and AI as they come together and what that means for
businesses. We've got Jeff Web of i SL and Jalien Owned of Haymes,
and Jeff, I'll throw it over to you. The talent acquisition side is
a very interesting space to tackle because now you can have algorithms to capture the
text of the resumes and sort them based upon certain priorities and get some that's
rise to the top. So you don't have to go through all one hundred
of them. You can just use the machine to say, right, look
at the top ten. But you do want to understand how it's coming to
those conclusions, right, tell us how you folks are able to use AI
for the talent acquisition side. Yeah, absolutely, you know it's funny.
Actually, I mean even take a step back from the sort of the premise
of your question, because certainly what we've built is, you know, we've
built this into technology ourselves, is the ability to sort of start to match
the things that the resume is talking about with the things that you're looking for,
just at least start to say, well, this looks like it's a
closer match than this set of resumes here and sort of stack rank can provide
some guidance and path through looking at the resumes. But you know that that
actually isn't the problem. The problem most organizations face isn't I've got resumes to
deal with. It's finding good people in the first place. It's attracting them
in the first place. And you know, one of the things that most
organizations do a terrible job at writing job ads. Like most of the time,
if you look at job descriptions and job ads, they sound like a
hundred reasons not to apply for this company. You know, you have to
be you know, you have to have a master's degree in this, and
you have to have served overseas, and you have to be able to tap
down and play the banjo, and you know, this is sort of a
long list of things that you must be able to do before we want to
talk to you. And that's I think how a lot of a lot of
people have been taught how to write job description when they put the ad out
there. And actually, what we're seeing is a couple of things going on.
One is that terrifyingly enough, marketing folks are being asked more and more
to help market the company through the job ads and descriptions. But AI is
also now starting to be used in there to help people that don't professionally write
job descriptions and job ads to write things that are actually inviting and interesting and
reflect the culture of the company rather than simply a list of prerequisites like I'm
sort of assembling a piece of IA furniture and so so it's interesting what we're
seeing is AI being used to start the process earlier. Let's write really good
job ads to assist somebody in writing a job AD to get people to come
in, and then we can start to use also then you know, a
separate set of AI process to start to rank and filter through and analyze the
contents of the people that are coming to us. But I you know,
really you've got to tackle both ends of that, or else you're just filtering
bad sets of resumes coming in without actually getting the good people in the first
place. And that's really the challenge most organizations face. Let's get the good
people in here. I know this is probably something Jayleen is you know,
good wax lyrical about for quite some time to given her world. Yeah,
there you go, Jaylene, over to you. Well, I definitely can
see where utilizing it to do all of the things that I can't do myself
when I'm trying to recruit. But I would be concerned about AI putting a
priority on specific skills or even on the initial screening process, and how people
could possibly crack that, and being that there may be this black box,
as you had indicated before, with kind of the reasoning behind the decisions.
As long as we don't have any type of an over reliance on that automation
that we're actually looking at the results making sure that we're taking into consideration those
unique talents or potential contributions, not just the status of maybe the black and
white data. Yeah, they can do the job, but are they going
to be a cultural fit to my people and are they going to be the
kind of skill level that I need? Yeah, that's probably the hard part,
right, And I'll throw over to Jeff to one for a second.
But back in culture, it's a very difficult thing to define, but it
is very palpable. So you know, are you able now to some extent
to be able to sort of absorb that, ascertain that and then apply that
kind of that that lends, if you will, to the hiring process.
Yeah, I think this is where the key and again it gets to really
the heart of this whole conversation is around there are things that we should expect
AI to do for us, and there are things that we should continue to
have HR professionals and you know, recruitment professionals bring to the table that their
skills and again back to context and empathy, those are the things that we
do better than anybody else. So I think the role here for AI is
let's reduce the workload, let's do initial analysis, let's provide some sort of
guided path through the process, and also to your point to start to look
for things like bias creeping into the hiring process. So don't forget we should
also be using AI, you know, one does to evaluate the diversity of
your organization. A certain part of your organization suddenly becoming a whole lot less
diverse. Are they're starting to recruit just certain types of folks. So's there's
an opportunity to have some checks and balances with the AI too, to make
sure that you're not starting to become some you know, monolithic organization where everyone
looks the same. But yeah, no, you should. You should be
recruiting for more fit with the organization. I think there's a lot of a
lot of studies are showing that actually recruiting for the sort of their attitudes and
the enthusiasms and the personality are actually far more for organizations than just recruiting on
skills and experience. And so you want again, AI could do some of
this, but you've got to have it paired with the people whose job it
is the professionals to actually evaluate more effectively. But that's the whole point.
That's the point of AI. The point of AI is to free those people
up to do those things, to take the workload off their plate. That
you know, we did a study and we found about forty percent of HR
leaders spent most of their day answering the same set of questions, which is
a horrific waste of talented people's time, not to mention horribly probably demotivating.
So you wanted to use tools like AI and automation to take that stuff away
so that they then have the time to bring their skills and training and background
and empathy and context into the things that those things really make a difference.
I'm sure absolutely Recruiting should be the first thing that you're looking at there.
How do we recruit the right people? Yeah, you know, I had
Steve Lucas, who is now the CEO of Boomy, but before you was
the CEO of a company called IIMs icim asking. They've heard about the recruiting
company, and he showed me something that I think they did for Target which
was just brilliant, which is video interviews. So instead of just entering your
CV which is so cold and black and white and hard to describe and hard
to even really understand or enrich if you're on the writing side. No,
he just had people video and Hi, my name is Bob. I really
like doing X, Y and Z blah blah blah blah. So I was
like, that is freaking brilliant because now you can see the person instead of
having to rely on text. I mean, this is an example of how
we can leverage all these new technologies just to kind of jump start the process
get and get through some of the more painful stuff, because when you can
look and talk to someone, that's a whole lot different than looking at a
resume, right, Jamie, Oh, definitely, especially if you can see
the person. I've heard about the automation of like interview scheduling and how that's
been utilized. My biggest concern, especially being where I'm at on an Island,
is that the lack of explana ability being able to understand what they're going
to be selling me as an applicant, I need to know and be able
to see them. And having that opportunity, Oh, that would definitely change
the game. Yeah, that's good stuff. And then maybe one last little
point on this. The other nice thing getting back to tracking things is that
jeffle throws to you, if you have your AI suggesting people to hire,
well, and you track every time that we went with the recommendation, and
how often did that work? Yep? Right, Like you always want to
be tracking this stuff to see it's like, well, for some reason,
it keeps recommending people who don't work out. So we got to go back
to the sees the algorithm do something to sort of rejigger that whole thing.
But what do you think, Oh, yeah, absolutely well, I mean
that's really the definition of AI anyway, is you know, you sort of
you build a model using machine learning of the model of the world, the
model of the process. But really when it starts, what defines it as
really artificial intelligence is the capacity for the system to alter that model as it
learns, right, to be able to say, you know what, I
keep recommending this and it's not working out. I should start to edge around
the parameters, move around and change the kind of recommendations of making. And
again, if you have the i mean just the plug for the single platform
approach here is because you're able to see everything from sort of pre higher all
the way through, you actually see what happens to people. And so as
a result, you can see, you know, when we hired this kind
of person, they were wildly successful. This kind of person not so much.
From this place we hired them, and they stayed. From this place
we hired them, they did not. That becomes really valuable. Yeah,
and you can understand the contact too, you know, it gets into the
concept of a good manager. And I personally, I think that management is
one of the most underappreciated jobs in the business world. I'm old enough to
remember in the nineteen eighties when we had that big flattening out. I don't
know if anyone remembers that, but like they got rid of all this middle
management because it was just an expense. It was like a cost they didn't
want to deal with. And that's a problem because when you have a good
manager. It took me till I was I think thirty four or something,
and I had a good manager. I've moved up to Seattle for him,
and I'm in my first meeting with him one on one. We get to
the last like you know, five minutes of the meeting, he goes,
no, is there anything I could do for you, and I remember like
looking over my shoulder, like did some VP walk in? And that goes
to show you what my thought process was around management. I was like,
oh my god, I apparently have never had a good manager before, because
exactly what a good manager should say to you. Good managers don't just tell
you what to do. Good managers are there to help you do your job.
Well. It's supposed to be an encouragement role. Yes, there's a
disciplinary side to it, but that should be a very rare component to the
process, not the not the norm. Basically, so all this technology,
what we're saying here, should be used to encourage positive management, to encourage
encourage good experiences at companies. We want happy employees. We want the employee
experience to be good because then the customer experience will be good and all kind
of works outs. But you folks are listening to Inside Analysis, All right,
folks, time for the podcast bonus segment here on Inside Analysis talking to
Jeff Web of I Saul and janeene Owen of AIMES all about AI and HR
are coming together, and we were just chatting about bias and you know,
what is bias and how do you detect bias? And then once you've detected
bias, what do you do about that detection of bias? Right? I
mean, the these are the hard questions to answer. And I'm not sure
if we're in an employee friendly or a hiring friendly, you know, like
a buyers market sellers market. I'm not sure where we are right now.
I think it's a very strange world. But in any case, we want
to know where there's bias and and help people understand the biases that they have.
Because what if Mark Twain said, biases you're is the set of experiences
you've a cumulated by the age of eighteen or something like that. I think
that's that's your bias, because that's what you grew up with. But do
you a throw it over to you first? How do you identify bias?
And then what do you do about it when you find it? Yeah?
Yeah, this is the thing that I think is most critical as we think
about AI in HR. You know, I say looking for bias and transparency.
Transparency is huge, Like it's really important to be clear why you're using
AI and what you're using it for. And we were talking about you know,
this sort of the model that I think is the way we're evolving anyway,
which is that there will be AI systems that are focused on tasks.
They're very task centric, and they will go do something, they'll go,
you know, look for an error, they'll go look for change, they'll
make recommendations, and ultimately I think there will feed into other AI systems that
can more broadly analyze what's actually happening in your organization. And I think at
that layer, you know, as you start to have you've got task centric
AIS, you've got more general evaluation, you can start to look for biases.
You can have AIS that are trained to look for biases, especially if
you think about the capacity to compare yourself to other similar organizations across the world
to say, well, wait a minute, are we suddenly skewing in some
weird direction that is not normal for our business? And then I think the
third thing is the human aspects. Right again, we come back to this
that the vision here should be for AI to be unshackling the HR professional,
the people who are trained to deal with people, unshackling their time and energies
by dealing with the day to day and helping them have more time, energy,
and focus to bring to the table their empathy and their context to their
understanding and their training of what's normal and what's appropriate for that organization. So
you've got to you know, got to got to see this as AI working
as a part of the team with an HR professional, not replacing the functions
of HR professionals because they're too critical. That's a really, really, really
good point. I'll throw it over to Jail into comment on that, But
I think it's a great closing argument here, because you're right, human resources
must be run by humans. At the end of the day, it's a
very human process hiring and firing and managing people. It's very human. Empathy
is crucial or no one's going to want to work at your company. I
can tell you that for sure, and that reputation will get out. Jaalen,
what do you think, Well, I definitely think it needs to be
representative of the population that you're serving. Not all businesses cut the same.
We all as HR professionals, know that we have different demographics. Sometimes bias
is built into that demographics. Maybe as for an example, you're in law
enforcement and you don't have the ability to be a little lax on certain types
of requirements. But I think it's going to be very important for the human
resource professional who implements the AI into their businesses to ensure that they're representing their
population. They're focused on reducing any biases, looking at how they can ensure
that that data is transparent and that when you do find those issues and you're
reviewing that data, you're cleaning it up, you're looking at what it is
and how it is impacting your job. Whether it be that you're reevaluating the
model, is this something that we want to continue that rationale, or maybe
there's some advancements that algorithmic adjustment has occurred and the technology is now catching up
with the needs that as an HR professional we may not have had in the
very beginning. That's a really really good point too, and this is we're
on a learning curve. We're all on a learning curve here, and we're
going to be on that curve for a while. That's one of the most
interesting comments I've heard from someone in this business who said about machine learning,
it's about learning, and he meant human learning. So we need to learn
how these things work, what they do, what they don't do, and
watch out for the watch out for the holes, the gaps, the biases
and learn about ourselves in the process too. And that's what life's all about,
right, is learning and having fun and working and doing cool things.
But folks, look these two up online. Jeff Webb, that's Jeff or
the g CEO f F Web with two b's of I saw Jayleen Owen.
That's j A y l e n l e n E Owen from Heytes.
Thanks so much for your time and thanks for putting the human touch on AI.
Well talking next time, folks, you've been listening to Inside Analysis.
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I'm Chris Caragio. Congressional leaders are announcing a budget agreement that'll keep the
government funded through twenty twenty four. Senate Majority Leader Chuck Schumer and Speaker of
the House Mike Johnson made a joint announcement of the nearly one point seven trillion
dollar deal on Sunday. The spending agreement will reportedly keep a number of domestic
and social programs funded, despite GOP calls for extreme budget cuts. News of
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break this week. A former House Republican is slamming those who are echoing former
President Trump's description of those convicted in the US Capitol riot as hostages. These
are people who were involved in violence against police officers in the assault on the
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Brino Valley. Years ago, a Texas lawmaker got caught using his official position
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