And heat, rain, and other harsh weather. Arion Loso of the architecture
firm HGA says that to determine what's expected in a location, the industry typically
relies on historical weather data. They're generally looking at the past, but global
warming is changing weather conditions, so Loxso says if architects and engineers do not
consider future climate change, their projects may not perform well over time. For
example, buildings may not have adequately sized HVAC systems or enough insulation to keep
people cool during increasingly extreme heat waves, or a property may lack the capacity
to divert large amounts of stormwater during intense downpours. Loso co authored a recent
report that suggests ways to avoid these kinds of problems. We really feel like
there's a need for architects and engineers to be at least looking at the data
that's provided by the National Climate Assessment or by the Intergovernmental Panel and Climate Change.
To make that happen, She says, building codes and standards should be
updated, and clients should ask architects to design with climate change in mind.
Climate Connections is produced by the Yelle Center for Environmental Communication to learn more about
climate change. Visit climatec Connections dot org. Thank you Inland Empire for listening
to KCAA Radio. The information economy has a rid. The world is teeming
with innovation as new business models reinvent every industry industry. Inside Analysis is your
source of information and insights about how to make the most of this exciting new
era. Learn more at inside analysis dot Comsideanalysis dot com. And now here's
your host, Eric Kavanaugh. Yes, 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're 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 to learn from a couple of experts.
Here. We've got Jeff Webb, head of solutions Strategy at HR tech company
I Solved, and Jalen Owen, head of HR at Hames 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 kinds of stuff is happening.
So let's dive right in. We talked 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 know 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 been has just
been fascinating and evolving so very very quickly at the moment. Yeah maybe so,
Jamiene, 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 computer voices, Like when you call these 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? Being 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 focusing on. 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 geep. 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 arts of the possible and 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 do 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 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
you know, 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 a tool that has that kind of functionality is they will almost
always come up with some thing 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 is 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, where we are 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, we're taking about something else, so we don't
do something correctly. And the nice thing about these machines they will catch those
mistakes. Nothing happened here, take a look at that. So payroll is
a great use case for protonomic 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 overworked, overstressed HR. Our payroll and
benefits administrats are 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. Now, 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 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 Jayleen 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 become really a 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 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 you want to use these technologies to tackle the tedium and to tackle
the complexity where possible. And you know, we had a 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 the so and so wants this off
being used, you know, send it to Fred, senator 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 going to 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 Jayleen. 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 going to
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 are HR professionals aware
of and still act 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 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 adalgy. Right back, you're listening
to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric
Tabanaugh. 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 they're positive s to 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've 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? What the people do so much better than any I 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, what it'll
look like when it starts to become much more real in the workplace. Yeah?
Right, And what about Eugenie, Leen, you obviously have to counsel
people who come to you and say I don't like this, say nice 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 build the building of the trust of that information and when we get to,
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,
JEFFA, 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
of a 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
can we save money on, you know, not hiring people that quit three
months ef later. 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 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. Can 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 in? That becomes incredibly powerful too,
So Yeah, you're right. That sort of capacity to hold and analyze,
to consume data and sort of spit 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, 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 Hey, 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 same?
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 is sure. I need to be able to to benefit
from whatever technology can give me and speed those processes up 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 that thing off.
You do have to watch out. That's the sort of common sense moment.
I'll throw it over to Jeff. AI gets 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 classic 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 will 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 chatbot.
We do a lot of work with chatbots and 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 a professional like Jalene 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.
It's what we are, you know, we're magnificent context and empathy machines.
AI is a great analytics You should combine those things 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 you 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, they I've joked before. 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. I think you care about 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 has algorithms for how they track and 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 up 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 Iesol and Jamie Owen 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 skid
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, all
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, let me even take a step back from the sort
of the premisse 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 doing terrible job at is writing job ads. Like,
most of the time I 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 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. So let's 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 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 jayleens, you know, good wax lyrical about for quite some
time to given her world. Yeah, there you go, Deleen 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 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 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 that it gets to really the heart of this whole conversation
is around the rest 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 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 moreive for organizations than just recruiting on skills and experience. And so you
want again a I 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 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 with the CEO of a company called IIMs ICIMS.
You may have 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 blahlah 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 to
kind of jumpstart 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, j 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 explanated 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 Jeffa 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 susy algorithm do something to sort of rejigger that whole thing.
But what do you think, jee, Oh yeah, absolutely, And 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, 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. And thennineteen
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, 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 wasn't 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 posit management to a courage 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 with the podcast bonus
segment here on Inside Analysis talking to Jeff Web of I Saul and Jaine Owen
of Hames 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, 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 buias 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 accumulated 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 jeff 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 area, they'll go look
for change they'll make recommendations, and ultimately I think they 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, if 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. Done 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.
Jalene, 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
Web that's Jeff with the g G E O 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 Haytes. Thanks so much for your time and thanks for
putting the human touch on AI. Well talking next time, folks, you've
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