WEBVTT

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Suggested that Adams should be suing President
Biden instead, because it's Biden's policies that

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are to blame. I'm Chris Karragio, NBC News Radio on Board caseaas Inland

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Express, KAA Rome Linda En fifty
AM the station that needs notice here behind

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the information economy as a ride.
The world is teeming with innovation as new

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business models reinvent every industry industry.
Inside Analysis is your source of information and

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insight about how to make the most
of this exciting new era. Learn more

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at inside analysis dot com, Inside
analysis dot com. And now here's your

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host, Eric Kavanaugh. Oh yes, folks, welcome to the future.

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Indeed, your host here, Eric
Kavanaugh, the only coast to coast radio

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show that's all about the information economy. It's time for Inside Analysis and today,

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folks, we are to talk about
the human touch. Yes, indeed

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AI for HR human Resources, so
artificial intelligence for human resources. All the

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folks in HR who hire fire try
to help us through the whole journey of

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working for a company. We're going
to learn from a couple of experts.

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Here. We've got Jeff Webb,
head of Solutions Strategy at HR tech Company.

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I solved and Jyleen Owen, head
of HR at Hams Corporation, calling

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it all the way from Alaska,
and Jeff is is over in Houston,

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and yours truly is in Texas as
well. So we've got two Texans and

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an Alaskan on the show to talk
about AI and HR. And HR is

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changing a lot these days largely due
to AI and also analytics and being able

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to track and understand, and of
course it's used in the hiring process where

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they have algorithms now that are just
reading these resumes coming across the transom.

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It's changing how we work day to
day. All kind of stuff is happen.

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So let's dive right in and talk
to the experts. First. Jayleen

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Owen of Hames Corporation, tell us
about yourself and how you are thinking about

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AI for HR. Well, I
am an HR professional. My background is

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in industrial organizational psychology. I have
just completed my seven years here being in

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Alaska. I'm not originally from Alaska, however, though I came to join

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the company in twenty sixteen. For
the family Haines, they are are fourth

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generation family owned and operated organization here
based in Sitka, Alaska. We have

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a multitude of diverse operations including retail
stores, a grocery store, liquor stores,

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we have gas stations, residential,
commercial properties, you name it.

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We've got a lot of diversity.
So I joined the team here in twenty

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sixteen to help kind of bring them
out of the dark ages. Being on

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an island, there's a lot of
challenges, there's a lot of resource limitations,

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and so having my professionalism to be
able to kind of address some of

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their concerns was why I was brought
on. Very cool, and we've also

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got Jeff Web out there. Jeff, tell us about what you do in

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the HR space for I Solved.
Yeah, thanks. So I lead the

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solution strategy team with I Soolved.
So my job is to make sure that

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we're constantly looking ahead, evaluating the
needs of our customers and partners, and

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making sure that we're building that into
the roadmap for the technology and the services

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that we deliver to companies here in
the US. So I get to talk

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to a lot of HR professionals,
which is how I got to name Jayleen,

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and I also get to spend a
lot of time looking at how the

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technology and the sort of human experience
in the workplace intersect which has has just

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been fascinating and evolving so very very
quickly at the moment. Yeah, maybe

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so, Jailine, I'll start with
you the human touch. You know,

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people want to be treated fairly,
they want to be treated honestly, and

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they don't I mean personally. One
of my pet peeves, I can't stand

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talking to your voices, Like when
you call these days, it's like I'm

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a computer and I can speak in
complete sentences. I usually throw some abstract

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philosophy at it and like I'm sorry, I didn't get that. It goes

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back to me. People want to
deal with people. We understand that there

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are technologies behind conversations and behind businesses. That's very well understood. But how

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are you navigating this change of infusion
of AI and analytics in what is a

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very personal job of hr AH very
carefully. I believe that there are a

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lot of pros, but there are
also a lot of cons And when it

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comes to implementing any kind of artificial
intelligence, whether it be in human resources

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or you're going to actively take a
role, I think in your business,

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you need to take a look at
what both positive and negative challenges may have

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As implement implications to your business.
For us here, we've been looking at

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things like could it automate some repetitive
tasks On an island, we have a

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limitation of bodies. There's literally about
eight thousand residents that live here year round.

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We are driven by kind of a
tourist industry, but because our industry

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isn't it's a year round industry,
there is a lot of concerns related to

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whether or not that is going to
be an efficient decision for us. Is

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it going to be cost effective?
Is there going to be the appropriate support.

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Even when we went through the process
of implementing I Solved. I Solved

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is so on the top of the
cutting edge of technology that many of my

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employees here were not too sure if
they were going to like that development,

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and so it's been a challenge.
We don't really have a lot of remote

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work here on the island, but
the idea of being able to support some

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of our employees. I do have
one employee that is in Idaho, and

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so being able to utilize AI the
advancements to connect us so that we don't

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have that distance between us any longer
has been what I've been focused I Okay

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that's pretty cool. How about you, Jeff, I mean, you're obviously

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in the thick of these things at
I evolved trying to help organizations leverage all

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this new technology. I'm sure you've
sat around and thought about the ethics of

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all this. But it's like the
train has left the station, right,

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I mean, the I joked earlier
today, the chat is out of the

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bag, if you will chat gep. AI is everywhere, and we're going

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to have to learn to deal with
How are you navigating this interesting change in

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how we do business? Yeah?
Absolutely well. I mean for us,

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we focus very much on small to
medium business in the US, So you

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know there's about one hundred and seventy
thousand companies we touch every day, and

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really our focus is going to be
to make it consumable and easy and non

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threatening for them. So the trick
here is to sort of is to stay

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focused on the outcomes. Like I
think, it becomes very easy with AI

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to become very wrapped up in the
art of the possible and then the technology

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and the reality is, you know, you're running a small business. You've

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got a lot of things to deal
with. I'm looking to retain good people.

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I want to recruit new people,
I want to develop them. That

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you must stay focused on what is
the outcomes we're trying to drive here,

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and how do the technologies like AI, how do they help us get their

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easier and quicker and with less work
and less just less friction in the whole

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process. And so for us,
it's a case of thinking about where does

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AI get deployed appropriately, what are
the right tools that you can infuse AI

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with in other words, to make
smarter and more self you know, independent,

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And then how do we communicate that
in a way that is honestly not

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threatening to the folks that we're talking
to who already have too much to deal

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with in technology coming out in every
direction and compliance changes and you know,

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expectation changes from changing workforce. So
I think half of it's a technology question

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and connecting the technology to outcomes,
and the other half is just communicating that

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in a way that's understandable. Yeah, that's a that's a great line.

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And you know, jainly not throw
it back over to you because you mentioned

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I think the key facet of this
whole equation here, which is automation and

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automating tedious tasks. That's something that
hr technology has a fair amount of and

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automation is not new if you think
about it. Just to be blunt,

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every piece of software ever written automates
something. That's what you're doing. Whether

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it's a word processing application or a
photo editing application, whatever it is,

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you're automating something. So automation isn't
new. But this machine learning and AI

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is relatively new, at least in
terms of practice. Right we've had AI

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for probably forty five fifty years,
but it just wasn't very It wasn't readily

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available because it was very expensive.
We didn't have the compute power. Now

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we have all that compute power,
and so we can automate things and we

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can see things in a different way. That's what kind of gets me excited

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about this stuff, is that machine
learning algorithms can crawl around in the background

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and just look for patterns of things. And what I've noticed anytime I've used

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the tool that has that kind of
functionality is they will almost always come up

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with something you did not expect.
And I think that's one of the most

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positive aspects, is that you're going
to learn things about yourself and your team

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and your organization that you didn't know
before. Because this all seeing eye is

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just kind of watching and never blinking
and serving up little observations. But what

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do you think, Oh, I
definitely think you're right. When it comes

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to the idea of how we could
utilize the AI, I think you're absolutely

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right. We're kind of behind the
ball at this point. The cat's already

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out of the bag. And in
the case of where the cost of effectiveness

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can meet the actual resource requirements,
it's going to be the most vital piece

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I think any business can focus on. You also are going to have the

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issues of that privacy if the information's
collecting and analyzing your data. Not only

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are you concerned about what data does
it have access to, but the privacy

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of that, especially from like a
perspective of a small business when it comes

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to proprietary stuff. We're our competition
here isn't another grocery store. It's a

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hospital, it's a bank. When
it comes to establishing the clear policies on

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how that data is collected, how
it's stored, how it's used, ensuring

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that our employees aren't gonna be worried
about their privacy, let alone the company

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being worried about ours. I also
think that it's important to look at the

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resistance and how that trust can impact
things like job displacement when we start looking

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at automating repetitive tasks, the smaller
tasks. Yeah, it's great, especially

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when we have a labor issue right
now and I don't have the skilled individuals

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to take over some of these jobs. AI can step in. But I

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definitely do agree that there's a lot
of ethical consideration that we also have to

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take into consideration. Yeah, right, well, I'll bring them Jeff back

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in and we talk about things like
payroll for example. That's a wonderful use

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case for automation. You want to
be able to set up a system and

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then monitor it. And again getting
back to the power of these machines and

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the power of algorithms, they will
catch errors that human beings can make.

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Humans are error prone, I mean
we just are. We get distracted,

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but take it about something else,
so we don't do something correctly. And

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the nice thing about these machines,
they will catch those mistakes and nothing happened

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here. Take a look at that. So payroll is a great use case

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for automating, right absolutely. You
know, there's a whole bunch of places

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where you can use that capacity that
you referenced of AI to sort of to

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learn and look for changes to great
effect, and payroll obviously is one of

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them where you can you can have
an AI platform essentially learn lots of normal

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payroll run for you as a business, but also learn actually at an individual

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level, what's normal for people to
be paid. And then if you make

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a mistake, and it happens,
even with the best, you know,

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best policies and procedures and even with
good automation technology, mistakes can be made.

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People accidentally get paid twice for example, I've seen that happen, and

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having something like AI just go,
you know, this is odd, this

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is unusual. You don't normally do
that. Is that a normal payroll run

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for you? Is there a reason
you're suddenly paying a lot more or a

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lot less. Having that backstop there
starts to again reduce the workload on the

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person doing the task and reduce the
because they don't have to check as closely

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that they know that they've got something
running that can do it for them,

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and also just improve the sort of
the general experience less you know, pay

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wrong mistakes are a reason that people
leave their current company. It's nothing more

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annoying than not getting paid the right
amount of money. So yeah, it's

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a great place to put it.
There are a bunch of other places,

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you know. We think of those
really as being the sort of three areas

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we think if AI we're really working
in the role of HR, and that

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would be one of them. It's
sort of in the flow of work where

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you've got AI watching things happen and
helping to sort of improve and smooth out

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the automation process. There are obviously
other areas too, but just at the

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most basic level, getting stuff done
and reducing the workload and reducing errors is

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a huge win for every over world
overstressed HR payroll and benefits administrator out there,

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that's right. Well, you know
the other area, and I'm sure

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is very rich with possibilities here is
benefits and just understanding benefits and different packages

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and different programs. And I don't
know about you, but I just I

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shudder when I look at some manual
that I'm supposed to read about. I'm

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like, oh my god, are
you serious, Like I'm particularly this whole

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thing. Well, that's something that
these large language models are very good at,

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and AI is getting very good at. It's being able to take large

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amounts of text, feed them into
this machine and say give me a summary

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to explain what that means to me. I'm thinking that's a huge area because

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you know, many people probably don't
want to admit that they don't really understand

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what these benefits packages do because that's
kind of embarrassing. But it's complicated,

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right, So Jeff first to you
and then over to Jen about that.

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Yeah, yeah, absolutely, it's
funny. Actually, that would be probably

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the other area where we see AI. One of the other areas where we

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see AI starting to come really effective
in sort of improving and elevating the experience

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overall of employees and also as a
result of the business, and that's personalization

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and to your point, exactly understanding
things and helping shape and personalized recommendations.

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This is a good benefits package for
someone like you, or here's how to

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find out more about this particular set
of benefits is one of the areas that

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we think it's going to have a
lot of value. There are plenty of

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other areas you think about adding in
the ability of AI to recommend not just

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the benefits package that you might want, but training you might need in your

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job. You know, people in
your job should take these sets of courses

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to improve improving just any sort of
set of you know, things that the

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company delivers and personalizing them for that
set of employees is you know, you

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can scale that with AI, which
is really hard to do when you're doing

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it manually with people. That's a
good point and that gets us to really

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the crux of what we're talking about
is we want to use these technologies to

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tackle the tedium and to tackle the
complexity tea where possible. And you know,

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we had to show a couple of
weeks ago where we were talking about

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scheduling and if you've got let's say
a hospital with two hundred nurses, for

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example, and one of them wants
to take time off, well, right

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now, it's a very manual job
for a lot of places, and some

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person that has to sit there and
look at the schedule and say, all

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right, well, if I put
Bob over here and Susie goes there,

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that's a pain in the rear to
do. But some of these technologies can

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bang that stuff out in their sleep. Right. It's just like you just

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entered so and so wants this stop
being used, you know, send it

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to Fred, send it to John, send it to Jim or whatever.

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The machines can do that stuff extremely
well. And then also it's a machine

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doing it, and it's not Jim, the person who oversees who's gonna get

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you know, a bunch of people
mad at him because he said, Okay,

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you do this, you do that. I'll throw that over to Jay

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Len. That's a classic example of
how these machines can do a very tricky

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job with no bias because they're just
getting the job done. What do you

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think, Oh, that's a that's
a loaded question in my opinion, because

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I think that it's important to also
to go back a little bit to what

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Jeff was saying, especially like when
you look at things like benefit management,

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employee classification. Larger companies that you're
gonna have a lot of different types of

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plans. How you manage that ensuring
that they are getting the kind of assistance

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that they need that an HR professional
like myself would sit down and help them

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through maybe an open enrollment process.
You're looking at data validation and the accuracy,

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and I think AI's algorithms can really
help us analyze that payroll data allowing

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for those inconsistencies and those errors to
be flagged, whether it be tax compliance

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or just the automation of data in
general. When it comes to AI,

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though, in my opinion, I
think it's really really important that when we

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look at the type of AI you're
using, because as you indicated, the

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natural language processing and the way that
the chatbots are engaging and learning to assist

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our employees, our HR professionals will
aware of and still actually engaged in that

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process to be able to utilize that
and not allow for possible issues to become

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an issue. It could be ambiguity
and maybe the system doesn't provide the information

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you want it as an HR professional, or it could just be a bias

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built into the language model. That's
an interesting point, and you know we

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are in the early phases of this. I know with the large language models

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there is this tendency to hallucinate.
We've got to watch out for that.

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But AI is it comes in all
sorts of shapes and sizes, and the

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key to your point, Jane,
and I see Jeff nodding his head as

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well, is that when you implement
these technologies, you always want to be

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able to track and trace and understand
what they're doing, and you have to

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watch out for black boxes, as
their call, that just make decisions about

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people's lives. You've got to be
very careful about that. So you want

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the transparency. You want to be
able to audit the system and to understand

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what it's doing and to map out
and see and you know, there are

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all sorts of ways that that's done
these days with log files and things of

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that nature. But you definitely want
to be very careful before you put any

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kind of automation into an organization,
especially for something as serious and significant as

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human resources, because that's dealing with
the people who are your company. And

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every company is a bunch of processes
and assets and people. But folks don't

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touch a doubt. They're right back. You're listening to Inside Analysis. Welcome

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back to Inside Analysis. Here's your
host, Eric Tavanaugh. All right,

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folks back here on Inside Analysis talking
to a couple of experts in h R

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Human resources. Of course, we've
got Jeff Webb and Jayleen Owen and Jeff,

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I'm going to give you the hard
question that I'll let Jayleen think about

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it for a second. How do
you help people who have great, deep

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skepticism about AI in their organization how
do you help them understand that really there

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are positive sides of this that the
negative sides can be dealt with. How

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do you talk to people who are
very skeptical about this stuff. Yeah,

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that's really the heart of a lot
of where we're at. I think as

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a lot of organizations. You know, probably I spent this year traveling around

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the US. I did fifty odd
different cities. I probably met a couple

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of thousand HR people, and a
lot of the conversations we talked about AI

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came up a lot, and some
of the times there's a lot of interest

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in you know, well, what
are you doing? What should we do?

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Sometimes is simply a case of please
don't use AI in polite conversation.

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I think we've all reached a point
where there's so much AI conversation in the

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news it's sometimes difficult to cut through
to the realistic, actual meat of the

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topic. So part of the challenge
is just being down to earth and realistic

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about what we're going to achieve now
and what we should be achieving in the

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future, and then put it in
context, you know. I also we

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look at survey results, We do
studies ourselves of organizations across the us and

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look at their attitudes and employee attitudes, and we try to be sensible and

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clear about what AI is going to
be good at and what people are going

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to be good at. And I
think we're going to come I think we're

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even in this conversation, we're sort
of circling around on the areas of,

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you know, what what A really
good at and what do people do so

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much better than AI can do for
the foreseeable future. That is reassuring in

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many ways. But yeah, we've
asked I think we ran a survey earlier

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on the Shire couple of thousand employees
in the US We asked what they thought

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about AI, and actually it was
close to seventy percent. I think we're

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relatively positive that it could improve their
work experience. So I think there's optimism.

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I just think there is skepticism,
which is good, and then a

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little concern about what, you know, how's this actually going to roll out

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and what it'll look like when it
starts to become much more real in the

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workplace. Yeah, right, and
what about EUGENI Leen, you obviously have

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to counsel people who come to you
and say I don't like this SAI stuff

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At all, what's going on here? Tak my job? How do you

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deal with that? Well? For
me, especially living on an island,

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we have a unique social, cultural, and even economic dynamics here. There's

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a lot of specific factors that come
into play when we're looking at how you

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approach people in a small community like
this. The sensitivity of taking what would

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be a personal possible conversation or interaction
and expecting the individual to possibly embrace that

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the same way that they would if
it was a person. That's a big

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concern of mine. Ensuring that there's
a community engagement through the AI is going

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to be important because those interpersonal relationships
matter, and they matter especially to people

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in this local community when it comes
to decision making and being transparent. The

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use of AI really does concern a
lot of people, especially when it comes

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to the building of the trust of
that information and when we get into,

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as I stated before, privacy or
even fairness concerns. But to kind of

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speak to how we would be able
to address that skepticism, in my opinion,

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I think it's really just going to
come down to education and awareness,

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providing and offering clear, accessible information
and really bridging the gap between things that

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might be demystifying of that AI.
It'll allow those individuals to really see what

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could be augmented and allowing maybe some
showcases where right now, because we have

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a limitation of bodies, my business, or at least my experience here on

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an island is almost like a fish
bowl or a case study because we are

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limited on the resources here, and
so we have to kind of think outside

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of the box and look at things
that would possibly allow us to maybe create

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a user friendly process to this new
technology. That's a good point, and

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Jeff, I'll bring you back in. I'm a big lover of data and

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being able to track things, and
I'm just guessing here that one of the

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cool things you can do, and
I saw this when someone uses your technology

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to help with their HR. Over
time you can see, for example,

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you can score resumes coming in to
help you better understand who to hire.

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But then over time you can also
see how well that works out. And

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the real value to AI is when
you get enough data into a system to

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be able to ascertain what works and
what doesn't work, and then you can

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do you know, what they call
regression analysis for example, and go back

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and look, okay, well,
what did happen there and why did this

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not work? And you can really
learn a lot about the people who have

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come into your company, who works, who doesn't work, because something like

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a corporate culture is very difficult to
define. But if you have enough parameters

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and you have enough data, over
let's say a year or two years,

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for example, all the sudden you
should be able to get better and better

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at who you hire, when you
hire, what kind of jobs and roles

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you actually need in the company.
All that stuff theoretically gets better and better

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over time if you're using the technology
to track the data and be able to

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understand it. What do you think
about that, Jack, Yeah, absolutely,

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that's you know, one of the
things that we talk about really early

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on as organizations to think about adopting
technology is a single platform that kind of

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captures all of the employee interactions.
Becomes essential to ultimately unlocking that power of

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that data later. If you know
everything that's happened to that employee, if

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you capture every interaction, you know, every performance report, every time they

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log in, every time they check
out. Not again to your point,

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not in some sort of creepy,
big brotherish kind of way, but simply

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tracking the flow of work and things
that are happening, and you know who

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your good employees are and you can
evaluate that. You can very quickly start

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to identify where are the good employees
coming from, where are we spending the

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most money, where it could we
save money on, you know, not

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hiring people that quit three months eg. Later. And you can also start

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to do really interesting analysis things like, you know, we had one one

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company we work with, they wanted
to move one of their officers. And

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what they're able to say was,
if we move this office from this location

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to this location, which employees are
likely to be at risk of leaving because

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their commute has become unpleasant And we
already know that's a factor for those kinds

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of employees for example. Or you
know what kind of money should we be

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spending on benefits for this group of
employees? Or you know, what's the

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salary changes we should make for those
So you get you get good operational uh

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you know, data that you can
use to sort of improve performance of the

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business. You also get bigger sort
of macro perspectives on your organization. What

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does the diversity look like at my
organization? Is it changing? Is it

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is it different in different departments,
are certain managers more more likely to lose

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employees over a period of time than
others. So there's a lot of power

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that you get looking at your own
company from the data, and that I

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think is part of the job really
is to unlock that and make it very

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consumable for HR folks and business leaders. I actually think there's another area too

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that we forget about. You know, we just talking about our own you

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know, I sold as a company, we touched the lives of what about

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seven million US employees every day,
and you think about you think about what

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that means when a company, you
know, you're running a bank and des

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Moin for example, and you're not
sure why your bank tellers are leaving,

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Well, you have the ability,
using these kind of technologies to say,

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well about are we losing bank tellers
faster or slower than other bank tellers in

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des Moines or in Iowa or in
the US, And are we paying more

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or less? Are we spending more
or less on benefits? So that capacity

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to not only unlock the sort of
insight of what's happening operationally in your own

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business and therefore impact the experience of
your employees, but to then go benchmark

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yourself against other organizations and say,
well, how are we doing? Are

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we better, are we worse?
You know, what are the areas we

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could improve it? That becomes incredibly
powerful too. So, yeah, you're

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right that that sort of capacity to
hold and analyze, to consume data and

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sort of spits out results and insights
really starts to redefine what's possible for HR

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people, business leaders, payroll folks
and so on to just you know,

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drive better business outcomes. Yeah,
that's a great that's a great point that

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you just made. And it kind
of gets us back to being data driven

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and doing these comparisons, because again
you might think, oh, we're doing

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a terrible job, are you.
If you have the capacity to see what

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your peers are doing and what those
rates are for those other companies, now

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you understand, oh no, this
is actually normal. And Jamie and I'll

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throw this one over to you.
The other really interesting space I have to

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believe is in training and research and
learning and being able for compliance purposes but

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also just for improving the workplace,
having people take classes on various aspects of

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the business. Being able to track
Okay, we rolled out this program to

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help people be more sensitive around diversity. For example, did the complaints go

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down a month later or did they
stay the sane did they go up?

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You can kind of better understand what's
working and what's not working from an educational

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perspective, right, Oh, definitely, as long as the information that you're

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receiving is accurate. Where I get
concerned about is the unintended consequences that the

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AI system may produce, especially if
that information or the data isn't being set

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up properly, or the interpretation of
the data is being determined by maybe another

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AI because you get that bias involved. But yeah, definitely, I think

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that when it comes to the dynamics, especially within a small workforce like myself,

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education and training are vital. It's
what determines our success, and especially

401
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me being on an island where I
have a devoid of skill level and education

402
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or even knowledge, and I'm bringing
people in because the labor sure, I

403
00:29:00.039 --> 00:29:07.480
need to be able to benefit from
whatever technology can give me and speed those

404
00:29:07.559 --> 00:29:12.720
processes that to give me a balance
between the innovation with the traditional. Yeah,

405
00:29:12.799 --> 00:29:15.480
that's a good point. And you
know, speaking of bias, I

406
00:29:15.599 --> 00:29:19.720
remember learning about this story is back
in twenty eighteen, Amazon which of course,

407
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is a very big company, very
analytics and AI friendly, very focused

408
00:29:25.240 --> 00:29:30.079
on that stuff. They rolled out
this AI recruiting tool that basically said don't

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hire women, and so they're like, what, oh, no, hold

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on, turn the thing off.
You do have to watch out. That's

411
00:29:37.839 --> 00:29:41.720
the sort of common sense moment.
I'll throw it over to Jeff. AI

412
00:29:41.920 --> 00:29:45.559
get stuff wrong. I mean it
is artificial intelligence, and real intelligence can

413
00:29:45.599 --> 00:29:49.039
get stuff wrong. People get stuff
wrong all the time. So AI doesn't

414
00:29:49.119 --> 00:29:53.200
mean it's perfect. It's just making
some recommendation based upon a model, based

415
00:29:53.279 --> 00:29:56.839
upon data that's fed. And you
got to watch for these things and watch

416
00:29:56.920 --> 00:30:00.799
out for it. And that was
just a class as an example that yes,

417
00:30:02.039 --> 00:30:03.960
bias does exist, then you have
to watch out for it. So

418
00:30:04.359 --> 00:30:07.400
you always have to, you know, keep your human hat on as you're

419
00:30:07.480 --> 00:30:11.079
using things like AI. Right,
Jeff, Yeah, absolutely, And you

420
00:30:11.160 --> 00:30:15.440
know, I think Jalen kind of
nailed it a couple of times there,

421
00:30:15.519 --> 00:30:18.759
you know, her commentary about transparency
earlier, on her commentary about you know

422
00:30:19.720 --> 00:30:23.319
about bias right now, I one
hundred percent agree, And I think that's

423
00:30:23.400 --> 00:30:26.799
the challenge. We know that you
can build it. You can build a

424
00:30:26.880 --> 00:30:30.720
model that inherently is fine, but
you train it on bad data, it'll

425
00:30:30.839 --> 00:30:34.160
produce bad results. And so part
of the you know, the challenge that

426
00:30:34.640 --> 00:30:40.440
the organizations like our as every organization
that's using AI, but especially when AI

427
00:30:40.640 --> 00:30:45.039
is becoming infused in HR processes,
the challenge is to make sure that you

428
00:30:45.119 --> 00:30:47.920
don't allow that to happen, that
you're you're using it in a way that

429
00:30:48.079 --> 00:30:52.680
is ethical and transparent and there's unbiased
as physically possible, you know, for

430
00:30:52.799 --> 00:30:56.119
things like recommending a course that someone
should take, or checking a payroll,

431
00:30:56.359 --> 00:31:00.039
or you know, simply being a
chat but we you know, do a

432
00:31:00.079 --> 00:31:03.640
lot of work with chatbots to answer
basic HR questions like is next Thursday at

433
00:31:03.680 --> 00:31:08.079
a company holiday or not? Those
kind of things you're relatively unlikely to see

434
00:31:08.079 --> 00:31:11.480
as a significant bias, but you
have to be very careful. And even

435
00:31:11.559 --> 00:31:18.200
for us, when we start to
do things like stack rank resumes to compare

436
00:31:18.240 --> 00:31:22.279
them against job openings, you've got
to be really careful to not exclude people

437
00:31:22.640 --> 00:31:25.440
from that process. You must,
you know, provide to your point.

438
00:31:26.039 --> 00:31:30.440
You can provide insight, you can
provide recommendations, but ultimately the AI should

439
00:31:30.440 --> 00:31:34.640
be working alongside the professional like Janalene
who understands the broader context, and that

440
00:31:36.559 --> 00:31:41.839
that really starts to define I think
how we'll see AI becoming realized in hr

441
00:31:41.920 --> 00:31:45.640
processes. It's a lot of the
time it's going to be working as a

442
00:31:45.759 --> 00:31:49.599
counterpart to a professional who can then
provide context. And the other thing that

443
00:31:49.680 --> 00:31:53.039
humans do that AI does not do, which is empathy. Context and empathy

444
00:31:53.480 --> 00:31:57.000
is what we are. You know, we're magnificent context and empathy machines.

445
00:31:57.359 --> 00:32:01.559
AI is a great analytic. You
should combine those things when you're thinking about

446
00:32:04.079 --> 00:32:08.240
affecting the lives of people in the
workplace. You shouldn't have one override the

447
00:32:08.319 --> 00:32:13.599
other. Yeah, that's a really, really good point. Empathy is so

448
00:32:13.759 --> 00:32:17.839
important in business, and I think
we're moving through generations right. We were

449
00:32:17.880 --> 00:32:22.799
talking in the break about how we
have these multi generational companies now where you've

450
00:32:22.839 --> 00:32:27.559
got baby boomers and Gen xers and
Gen Y and millennials and Gen Z like.

451
00:32:27.720 --> 00:32:30.440
These are very different groups of people
in how they react, how they

452
00:32:30.480 --> 00:32:35.720
respond, and probably how they respond
to data. I mean, kids just

453
00:32:35.960 --> 00:32:37.880
grew up with iPhones these days.
They know how to use this stuff.

454
00:32:38.200 --> 00:32:43.039
Baby boomers did not, and so
you always have to keep that in mind

455
00:32:43.079 --> 00:32:45.480
and that's a very personal thing.
I'll throw it over to Janean real quick

456
00:32:45.480 --> 00:32:50.519
about ninety seconds. It's very important
to have that empathy and to always be

457
00:32:50.720 --> 00:32:53.400
human to the human people in your
organization. Right, well, yes,

458
00:32:53.599 --> 00:32:57.599
definitely. There's one word I would
have to say sums this all up.

459
00:32:57.680 --> 00:33:02.599
It's the authenticity of it. AI
comes from what we tell it and what

460
00:33:02.759 --> 00:33:08.240
we program into it. But as
Jeff indicated, the empathy doesn't come natural.

461
00:33:08.480 --> 00:33:15.319
It's something that I'm concerned as we
move forward with this is eventually everything

462
00:33:15.400 --> 00:33:17.680
that we can give it as a
personality is going to be built in there.

463
00:33:17.720 --> 00:33:22.319
But are we going to lose that
personal touch that's the authenticity of the

464
00:33:22.440 --> 00:33:29.440
matter. Yeah. Well, I've
joked for I did a show a few

465
00:33:29.519 --> 00:33:31.480
years ago and someone made a joke
about something and I was like, they

466
00:33:31.559 --> 00:33:35.359
told me to be authentic. I
mean, come on, going to be

467
00:33:35.440 --> 00:33:39.240
myself here? You careful what you
wish for out there? Well, these

468
00:33:39.279 --> 00:33:42.839
are all excellent points. Well,
let's see, we're coming up on a

469
00:33:42.880 --> 00:33:46.839
break here. We're talking to Jeff
Web and Jalene Owen HR professionals about the

470
00:33:47.000 --> 00:33:52.240
impact of AI, and I think
in the next segment we'll dive into the

471
00:33:52.440 --> 00:33:54.599
hiring process and what these things look
for. You know, you think about

472
00:33:55.079 --> 00:34:00.279
Google and search engine optimization and what
happened there. Basically Google, who has

473
00:34:00.319 --> 00:34:04.319
algorithms for how they track an index
the web, and then people are always

474
00:34:04.359 --> 00:34:07.400
trying to gain the system, try
to get the Google room to point at

475
00:34:07.480 --> 00:34:09.559
them, and so you know that's
got to be happening in the talent acquisition

476
00:34:09.679 --> 00:34:14.519
space where we're trying to use algorithms
to sort through the data and someone's trying

477
00:34:14.519 --> 00:34:16.519
to trick that algorithm to use the
proper words to get them to look at

478
00:34:16.559 --> 00:34:20.320
your resume. We'll pick that up
after the break. Folks, don't touch

479
00:34:20.320 --> 00:34:30.360
that doll. You're listening to Inside
Analysis. Welcome back to Inside Analysis.

480
00:34:30.920 --> 00:34:37.280
Here's your host, Eric Tabanat all
right, folks back here on Inside Analysis

481
00:34:37.360 --> 00:34:42.559
talking all about HR and AI as
they come together and what that means for

482
00:34:42.719 --> 00:34:46.039
businesses. We've got Jeff Web of
i SL and Jalien Owned of Haymes,

483
00:34:46.559 --> 00:34:51.599
and Jeff, I'll throw it over
to you. The talent acquisition side is

484
00:34:51.679 --> 00:34:57.119
a very interesting space to tackle because
now you can have algorithms to capture the

485
00:34:57.199 --> 00:35:01.719
text of the resumes and sort them
based upon certain priorities and get some that's

486
00:35:01.880 --> 00:35:05.039
rise to the top. So you
don't have to go through all one hundred

487
00:35:05.079 --> 00:35:07.000
of them. You can just use
the machine to say, right, look

488
00:35:07.000 --> 00:35:08.960
at the top ten. But you
do want to understand how it's coming to

489
00:35:09.039 --> 00:35:13.519
those conclusions, right, tell us
how you folks are able to use AI

490
00:35:13.800 --> 00:35:16.639
for the talent acquisition side. Yeah, absolutely, you know it's funny.

491
00:35:16.679 --> 00:35:21.280
Actually, I mean even take a
step back from the sort of the premise

492
00:35:21.360 --> 00:35:24.119
of your question, because certainly what
we've built is, you know, we've

493
00:35:24.159 --> 00:35:29.079
built this into technology ourselves, is
the ability to sort of start to match

494
00:35:29.639 --> 00:35:31.800
the things that the resume is talking
about with the things that you're looking for,

495
00:35:32.119 --> 00:35:36.000
just at least start to say,
well, this looks like it's a

496
00:35:36.079 --> 00:35:38.920
closer match than this set of resumes
here and sort of stack rank can provide

497
00:35:39.000 --> 00:35:44.599
some guidance and path through looking at
the resumes. But you know that that

498
00:35:44.760 --> 00:35:49.880
actually isn't the problem. The problem
most organizations face isn't I've got resumes to

499
00:35:49.920 --> 00:35:52.559
deal with. It's finding good people
in the first place. It's attracting them

500
00:35:52.559 --> 00:35:57.199
in the first place. And you
know, one of the things that most

501
00:35:57.280 --> 00:36:01.320
organizations do a terrible job at writing
job ads. Like most of the time,

502
00:36:01.400 --> 00:36:05.679
if you look at job descriptions and
job ads, they sound like a

503
00:36:05.800 --> 00:36:07.480
hundred reasons not to apply for this
company. You know, you have to

504
00:36:07.559 --> 00:36:09.480
be you know, you have to
have a master's degree in this, and

505
00:36:09.599 --> 00:36:12.719
you have to have served overseas,
and you have to be able to tap

506
00:36:12.760 --> 00:36:15.199
down and play the banjo, and
you know, this is sort of a

507
00:36:15.199 --> 00:36:16.599
long list of things that you must
be able to do before we want to

508
00:36:16.639 --> 00:36:20.400
talk to you. And that's I
think how a lot of a lot of

509
00:36:20.440 --> 00:36:22.679
people have been taught how to write
job description when they put the ad out

510
00:36:22.719 --> 00:36:27.119
there. And actually, what we're
seeing is a couple of things going on.

511
00:36:27.239 --> 00:36:30.039
One is that terrifyingly enough, marketing
folks are being asked more and more

512
00:36:30.079 --> 00:36:35.960
to help market the company through the
job ads and descriptions. But AI is

513
00:36:36.000 --> 00:36:39.400
also now starting to be used in
there to help people that don't professionally write

514
00:36:39.679 --> 00:36:45.519
job descriptions and job ads to write
things that are actually inviting and interesting and

515
00:36:45.599 --> 00:36:49.840
reflect the culture of the company rather
than simply a list of prerequisites like I'm

516
00:36:49.880 --> 00:36:53.880
sort of assembling a piece of IA
furniture and so so it's interesting what we're

517
00:36:53.880 --> 00:36:59.320
seeing is AI being used to start
the process earlier. Let's write really good

518
00:36:59.400 --> 00:37:01.880
job ads to assist somebody in writing
a job AD to get people to come

519
00:37:01.960 --> 00:37:05.920
in, and then we can start
to use also then you know, a

520
00:37:06.000 --> 00:37:09.960
separate set of AI process to start
to rank and filter through and analyze the

521
00:37:10.119 --> 00:37:13.679
contents of the people that are coming
to us. But I you know,

522
00:37:13.840 --> 00:37:17.559
really you've got to tackle both ends
of that, or else you're just filtering

523
00:37:17.760 --> 00:37:22.760
bad sets of resumes coming in without
actually getting the good people in the first

524
00:37:22.800 --> 00:37:27.159
place. And that's really the challenge
most organizations face. Let's get the good

525
00:37:27.199 --> 00:37:30.119
people in here. I know this
is probably something Jayleen is you know,

526
00:37:30.239 --> 00:37:34.199
good wax lyrical about for quite some
time to given her world. Yeah,

527
00:37:34.239 --> 00:37:37.480
there you go, Jaylene, over
to you. Well, I definitely can

528
00:37:37.559 --> 00:37:43.880
see where utilizing it to do all
of the things that I can't do myself

529
00:37:43.960 --> 00:37:49.880
when I'm trying to recruit. But
I would be concerned about AI putting a

530
00:37:49.960 --> 00:37:53.239
priority on specific skills or even on
the initial screening process, and how people

531
00:37:53.280 --> 00:38:00.719
could possibly crack that, and being
that there may be this black box,

532
00:38:00.800 --> 00:38:05.480
as you had indicated before, with
kind of the reasoning behind the decisions.

533
00:38:06.960 --> 00:38:10.079
As long as we don't have any
type of an over reliance on that automation

534
00:38:10.800 --> 00:38:15.599
that we're actually looking at the results
making sure that we're taking into consideration those

535
00:38:15.679 --> 00:38:22.199
unique talents or potential contributions, not
just the status of maybe the black and

536
00:38:22.239 --> 00:38:25.079
white data. Yeah, they can
do the job, but are they going

537
00:38:25.119 --> 00:38:28.960
to be a cultural fit to my
people and are they going to be the

538
00:38:29.119 --> 00:38:32.599
kind of skill level that I need? Yeah, that's probably the hard part,

539
00:38:32.719 --> 00:38:37.039
right, And I'll throw over to
Jeff to one for a second.

540
00:38:37.119 --> 00:38:42.480
But back in culture, it's a
very difficult thing to define, but it

541
00:38:42.599 --> 00:38:46.119
is very palpable. So you know, are you able now to some extent

542
00:38:46.440 --> 00:38:50.719
to be able to sort of absorb
that, ascertain that and then apply that

543
00:38:50.880 --> 00:38:53.639
kind of that that lends, if
you will, to the hiring process.

544
00:38:54.440 --> 00:38:58.920
Yeah, I think this is where
the key and again it gets to really

545
00:38:58.960 --> 00:39:01.840
the heart of this whole conversation is
around there are things that we should expect

546
00:39:01.880 --> 00:39:06.000
AI to do for us, and
there are things that we should continue to

547
00:39:06.079 --> 00:39:12.360
have HR professionals and you know,
recruitment professionals bring to the table that their

548
00:39:12.400 --> 00:39:15.360
skills and again back to context and
empathy, those are the things that we

549
00:39:15.480 --> 00:39:19.320
do better than anybody else. So
I think the role here for AI is

550
00:39:19.760 --> 00:39:23.159
let's reduce the workload, let's do
initial analysis, let's provide some sort of

551
00:39:23.440 --> 00:39:30.360
guided path through the process, and
also to your point to start to look

552
00:39:30.400 --> 00:39:34.719
for things like bias creeping into the
hiring process. So don't forget we should

553
00:39:34.719 --> 00:39:38.719
also be using AI, you know, one does to evaluate the diversity of

554
00:39:38.760 --> 00:39:43.440
your organization. A certain part of
your organization suddenly becoming a whole lot less

555
00:39:43.559 --> 00:39:47.000
diverse. Are they're starting to recruit
just certain types of folks. So's there's

556
00:39:47.000 --> 00:39:51.239
an opportunity to have some checks and
balances with the AI too, to make

557
00:39:51.280 --> 00:39:55.079
sure that you're not starting to become
some you know, monolithic organization where everyone

558
00:39:55.119 --> 00:39:59.239
looks the same. But yeah,
no, you should. You should be

559
00:39:59.320 --> 00:40:02.920
recruiting for more fit with the organization. I think there's a lot of a

560
00:40:02.960 --> 00:40:08.079
lot of studies are showing that actually
recruiting for the sort of their attitudes and

561
00:40:08.199 --> 00:40:15.639
the enthusiasms and the personality are actually
far more for organizations than just recruiting on

562
00:40:15.760 --> 00:40:19.519
skills and experience. And so you
want again, AI could do some of

563
00:40:19.599 --> 00:40:22.159
this, but you've got to have
it paired with the people whose job it

564
00:40:22.280 --> 00:40:27.039
is the professionals to actually evaluate more
effectively. But that's the whole point.

565
00:40:27.360 --> 00:40:30.280
That's the point of AI. The
point of AI is to free those people

566
00:40:30.400 --> 00:40:35.159
up to do those things, to
take the workload off their plate. That

567
00:40:35.679 --> 00:40:38.639
you know, we did a study
and we found about forty percent of HR

568
00:40:38.880 --> 00:40:44.559
leaders spent most of their day answering
the same set of questions, which is

569
00:40:44.920 --> 00:40:49.039
a horrific waste of talented people's time, not to mention horribly probably demotivating.

570
00:40:49.519 --> 00:40:52.440
So you wanted to use tools like
AI and automation to take that stuff away

571
00:40:53.000 --> 00:40:57.719
so that they then have the time
to bring their skills and training and background

572
00:40:57.760 --> 00:41:01.119
and empathy and context into the things
that those things really make a difference.

573
00:41:01.119 --> 00:41:06.719
I'm sure absolutely Recruiting should be the
first thing that you're looking at there.

574
00:41:06.760 --> 00:41:10.079
How do we recruit the right people? Yeah, you know, I had

575
00:41:10.400 --> 00:41:14.760
Steve Lucas, who is now the
CEO of Boomy, but before you was

576
00:41:14.800 --> 00:41:19.079
the CEO of a company called IIMs
icim asking. They've heard about the recruiting

577
00:41:19.599 --> 00:41:22.159
company, and he showed me something
that I think they did for Target which

578
00:41:22.280 --> 00:41:27.760
was just brilliant, which is video
interviews. So instead of just entering your

579
00:41:27.920 --> 00:41:31.760
CV which is so cold and black
and white and hard to describe and hard

580
00:41:31.840 --> 00:41:36.880
to even really understand or enrich if
you're on the writing side. No,

581
00:41:37.079 --> 00:41:38.679
he just had people video and Hi, my name is Bob. I really

582
00:41:38.760 --> 00:41:42.079
like doing X, Y and Z
blah blah blah blah. So I was

583
00:41:42.159 --> 00:41:45.000
like, that is freaking brilliant because
now you can see the person instead of

584
00:41:45.079 --> 00:41:49.480
having to rely on text. I
mean, this is an example of how

585
00:41:49.559 --> 00:41:53.119
we can leverage all these new technologies
just to kind of jump start the process

586
00:41:53.440 --> 00:41:57.559
get and get through some of the
more painful stuff, because when you can

587
00:41:57.719 --> 00:42:00.760
look and talk to someone, that's
a whole lot different than looking at a

588
00:42:00.800 --> 00:42:04.800
resume, right, Jamie, Oh, definitely, especially if you can see

589
00:42:04.840 --> 00:42:09.320
the person. I've heard about the
automation of like interview scheduling and how that's

590
00:42:09.360 --> 00:42:15.119
been utilized. My biggest concern,
especially being where I'm at on an Island,

591
00:42:15.440 --> 00:42:21.960
is that the lack of explana ability
being able to understand what they're going

592
00:42:22.000 --> 00:42:25.239
to be selling me as an applicant, I need to know and be able

593
00:42:25.280 --> 00:42:29.360
to see them. And having that
opportunity, Oh, that would definitely change

594
00:42:29.400 --> 00:42:34.280
the game. Yeah, that's good
stuff. And then maybe one last little

595
00:42:34.400 --> 00:42:37.400
point on this. The other nice
thing getting back to tracking things is that

596
00:42:37.519 --> 00:42:43.360
jeffle throws to you, if you
have your AI suggesting people to hire,

597
00:42:44.000 --> 00:42:46.320
well, and you track every time
that we went with the recommendation, and

598
00:42:46.440 --> 00:42:50.840
how often did that work? Yep? Right, Like you always want to

599
00:42:50.840 --> 00:42:52.199
be tracking this stuff to see it's
like, well, for some reason,

600
00:42:52.239 --> 00:42:55.000
it keeps recommending people who don't work
out. So we got to go back

601
00:42:55.000 --> 00:42:59.360
to the sees the algorithm do something
to sort of rejigger that whole thing.

602
00:42:59.440 --> 00:43:00.800
But what do you think, Oh, yeah, absolutely well, I mean

603
00:43:00.840 --> 00:43:04.280
that's really the definition of AI anyway, is you know, you sort of

604
00:43:04.320 --> 00:43:07.360
you build a model using machine learning
of the model of the world, the

605
00:43:07.400 --> 00:43:10.280
model of the process. But really
when it starts, what defines it as

606
00:43:10.320 --> 00:43:15.880
really artificial intelligence is the capacity for
the system to alter that model as it

607
00:43:15.000 --> 00:43:16.840
learns, right, to be able
to say, you know what, I

608
00:43:16.960 --> 00:43:21.800
keep recommending this and it's not working
out. I should start to edge around

609
00:43:21.840 --> 00:43:24.840
the parameters, move around and change
the kind of recommendations of making. And

610
00:43:24.880 --> 00:43:29.519
again, if you have the i
mean just the plug for the single platform

611
00:43:29.559 --> 00:43:32.280
approach here is because you're able to
see everything from sort of pre higher all

612
00:43:32.320 --> 00:43:36.199
the way through, you actually see
what happens to people. And so as

613
00:43:36.199 --> 00:43:37.159
a result, you can see,
you know, when we hired this kind

614
00:43:37.199 --> 00:43:40.119
of person, they were wildly successful. This kind of person not so much.

615
00:43:40.280 --> 00:43:43.639
From this place we hired them,
and they stayed. From this place

616
00:43:43.880 --> 00:43:46.760
we hired them, they did not. That becomes really valuable. Yeah,

617
00:43:46.800 --> 00:43:51.239
and you can understand the contact too, you know, it gets into the

618
00:43:51.639 --> 00:43:55.239
concept of a good manager. And
I personally, I think that management is

619
00:43:55.320 --> 00:44:00.119
one of the most underappreciated jobs in
the business world. I'm old enough to

620
00:44:00.159 --> 00:44:02.960
remember in the nineteen eighties when we
had that big flattening out. I don't

621
00:44:02.960 --> 00:44:06.639
know if anyone remembers that, but
like they got rid of all this middle

622
00:44:06.679 --> 00:44:08.519
management because it was just an expense. It was like a cost they didn't

623
00:44:08.519 --> 00:44:12.400
want to deal with. And that's
a problem because when you have a good

624
00:44:12.480 --> 00:44:15.360
manager. It took me till I
was I think thirty four or something,

625
00:44:15.360 --> 00:44:19.360
and I had a good manager.
I've moved up to Seattle for him,

626
00:44:19.679 --> 00:44:22.159
and I'm in my first meeting with
him one on one. We get to

627
00:44:22.239 --> 00:44:23.559
the last like you know, five
minutes of the meeting, he goes,

628
00:44:23.840 --> 00:44:27.199
no, is there anything I could
do for you, and I remember like

629
00:44:27.280 --> 00:44:31.559
looking over my shoulder, like did
some VP walk in? And that goes

630
00:44:31.599 --> 00:44:36.199
to show you what my thought process
was around management. I was like,

631
00:44:36.320 --> 00:44:38.599
oh my god, I apparently have
never had a good manager before, because

632
00:44:39.079 --> 00:44:43.320
exactly what a good manager should say
to you. Good managers don't just tell

633
00:44:43.360 --> 00:44:46.000
you what to do. Good managers
are there to help you do your job.

634
00:44:46.119 --> 00:44:50.960
Well. It's supposed to be an
encouragement role. Yes, there's a

635
00:44:51.000 --> 00:44:53.280
disciplinary side to it, but that
should be a very rare component to the

636
00:44:53.360 --> 00:44:58.719
process, not the not the norm. Basically, so all this technology,

637
00:44:58.840 --> 00:45:01.920
what we're saying here, should be
used to encourage positive management, to encourage

638
00:45:02.000 --> 00:45:07.079
encourage good experiences at companies. We
want happy employees. We want the employee

639
00:45:07.079 --> 00:45:09.519
experience to be good because then the
customer experience will be good and all kind

640
00:45:09.519 --> 00:45:16.679
of works outs. But you folks
are listening to Inside Analysis, All right,

641
00:45:16.719 --> 00:45:21.360
folks, time for the podcast bonus
segment here on Inside Analysis talking to

642
00:45:21.480 --> 00:45:25.760
Jeff Web of I Saul and janeene
Owen of AIMES all about AI and HR

643
00:45:25.800 --> 00:45:30.760
are coming together, and we were
just chatting about bias and you know,

644
00:45:30.840 --> 00:45:34.639
what is bias and how do you
detect bias? And then once you've detected

645
00:45:34.719 --> 00:45:37.679
bias, what do you do about
that detection of bias? Right? I

646
00:45:37.719 --> 00:45:40.599
mean, the these are the hard
questions to answer. And I'm not sure

647
00:45:40.639 --> 00:45:44.880
if we're in an employee friendly or
a hiring friendly, you know, like

648
00:45:44.880 --> 00:45:46.360
a buyers market sellers market. I'm
not sure where we are right now.

649
00:45:46.400 --> 00:45:50.400
I think it's a very strange world. But in any case, we want

650
00:45:50.440 --> 00:45:53.519
to know where there's bias and and
help people understand the biases that they have.

651
00:45:53.719 --> 00:45:59.519
Because what if Mark Twain said,
biases you're is the set of experiences

652
00:45:59.599 --> 00:46:01.760
you've a cumulated by the age of
eighteen or something like that. I think

653
00:46:01.800 --> 00:46:05.559
that's that's your bias, because that's
what you grew up with. But do

654
00:46:05.599 --> 00:46:07.280
you a throw it over to you
first? How do you identify bias?

655
00:46:07.320 --> 00:46:09.719
And then what do you do about
it when you find it? Yeah?

656
00:46:10.039 --> 00:46:14.280
Yeah, this is the thing that
I think is most critical as we think

657
00:46:14.320 --> 00:46:17.519
about AI in HR. You know, I say looking for bias and transparency.

658
00:46:17.599 --> 00:46:23.000
Transparency is huge, Like it's really
important to be clear why you're using

659
00:46:23.039 --> 00:46:27.199
AI and what you're using it for. And we were talking about you know,

660
00:46:27.280 --> 00:46:30.199
this sort of the model that I
think is the way we're evolving anyway,

661
00:46:30.239 --> 00:46:34.239
which is that there will be AI
systems that are focused on tasks.

662
00:46:34.239 --> 00:46:37.159
They're very task centric, and they
will go do something, they'll go,

663
00:46:37.679 --> 00:46:38.760
you know, look for an error, they'll go look for change, they'll

664
00:46:38.800 --> 00:46:44.159
make recommendations, and ultimately I think
there will feed into other AI systems that

665
00:46:44.320 --> 00:46:46.880
can more broadly analyze what's actually happening
in your organization. And I think at

666
00:46:46.920 --> 00:46:52.280
that layer, you know, as
you start to have you've got task centric

667
00:46:52.320 --> 00:46:55.079
AIS, you've got more general evaluation, you can start to look for biases.

668
00:46:55.159 --> 00:46:59.880
You can have AIS that are trained
to look for biases, especially if

669
00:46:59.880 --> 00:47:05.000
you think about the capacity to compare
yourself to other similar organizations across the world

670
00:47:05.079 --> 00:47:07.360
to say, well, wait a
minute, are we suddenly skewing in some

671
00:47:07.519 --> 00:47:10.280
weird direction that is not normal for
our business? And then I think the

672
00:47:10.400 --> 00:47:15.360
third thing is the human aspects.
Right again, we come back to this

673
00:47:15.559 --> 00:47:21.840
that the vision here should be for
AI to be unshackling the HR professional,

674
00:47:22.039 --> 00:47:25.800
the people who are trained to deal
with people, unshackling their time and energies

675
00:47:27.000 --> 00:47:30.280
by dealing with the day to day
and helping them have more time, energy,

676
00:47:30.320 --> 00:47:34.760
and focus to bring to the table
their empathy and their context to their

677
00:47:34.840 --> 00:47:39.519
understanding and their training of what's normal
and what's appropriate for that organization. So

678
00:47:40.199 --> 00:47:45.320
you've got to you know, got
to got to see this as AI working

679
00:47:45.639 --> 00:47:50.400
as a part of the team with
an HR professional, not replacing the functions

680
00:47:50.599 --> 00:47:54.599
of HR professionals because they're too critical. That's a really, really, really

681
00:47:54.639 --> 00:47:58.280
good point. I'll throw it over
to Jail into comment on that, But

682
00:47:58.360 --> 00:48:02.280
I think it's a great closing argument
here, because you're right, human resources

683
00:48:02.480 --> 00:48:05.880
must be run by humans. At
the end of the day, it's a

684
00:48:05.960 --> 00:48:10.239
very human process hiring and firing and
managing people. It's very human. Empathy

685
00:48:10.360 --> 00:48:14.920
is crucial or no one's going to
want to work at your company. I

686
00:48:14.960 --> 00:48:16.599
can tell you that for sure,
and that reputation will get out. Jaalen,

687
00:48:16.679 --> 00:48:20.119
what do you think, Well,
I definitely think it needs to be

688
00:48:20.239 --> 00:48:23.920
representative of the population that you're serving. Not all businesses cut the same.

689
00:48:24.119 --> 00:48:30.320
We all as HR professionals, know
that we have different demographics. Sometimes bias

690
00:48:30.440 --> 00:48:34.480
is built into that demographics. Maybe
as for an example, you're in law

691
00:48:34.559 --> 00:48:39.599
enforcement and you don't have the ability
to be a little lax on certain types

692
00:48:39.760 --> 00:48:44.679
of requirements. But I think it's
going to be very important for the human

693
00:48:44.760 --> 00:48:51.360
resource professional who implements the AI into
their businesses to ensure that they're representing their

694
00:48:51.400 --> 00:48:55.519
population. They're focused on reducing any
biases, looking at how they can ensure

695
00:48:55.599 --> 00:49:00.719
that that data is transparent and that
when you do find those issues and you're

696
00:49:00.760 --> 00:49:05.920
reviewing that data, you're cleaning it
up, you're looking at what it is

697
00:49:06.000 --> 00:49:08.519
and how it is impacting your job. Whether it be that you're reevaluating the

698
00:49:08.599 --> 00:49:14.280
model, is this something that we
want to continue that rationale, or maybe

699
00:49:14.320 --> 00:49:19.599
there's some advancements that algorithmic adjustment has
occurred and the technology is now catching up

700
00:49:19.679 --> 00:49:22.280
with the needs that as an HR
professional we may not have had in the

701
00:49:22.400 --> 00:49:27.400
very beginning. That's a really really
good point too, and this is we're

702
00:49:27.440 --> 00:49:29.800
on a learning curve. We're all
on a learning curve here, and we're

703
00:49:29.800 --> 00:49:31.760
going to be on that curve for
a while. That's one of the most

704
00:49:31.800 --> 00:49:36.840
interesting comments I've heard from someone in
this business who said about machine learning,

705
00:49:36.880 --> 00:49:40.280
it's about learning, and he meant
human learning. So we need to learn

706
00:49:40.480 --> 00:49:44.719
how these things work, what they
do, what they don't do, and

707
00:49:45.000 --> 00:49:47.320
watch out for the watch out for
the holes, the gaps, the biases

708
00:49:47.440 --> 00:49:52.519
and learn about ourselves in the process
too. And that's what life's all about,

709
00:49:52.559 --> 00:49:54.480
right, is learning and having fun
and working and doing cool things.

710
00:49:54.559 --> 00:49:59.599
But folks, look these two up
online. Jeff Webb, that's Jeff or

711
00:49:59.599 --> 00:50:04.199
the g CEO f F Web with
two b's of I saw Jayleen Owen.

712
00:50:04.280 --> 00:50:07.079
That's j A y l e n
l e n E Owen from Heytes.

713
00:50:07.159 --> 00:50:12.079
Thanks so much for your time and
thanks for putting the human touch on AI.

714
00:50:12.360 --> 00:50:15.280
Well talking next time, folks,
you've been listening to Inside Analysis.

715
00:50:15.760 --> 00:50:21.320
Now you can listen to k c
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In nineteen sixty nine, actor Max
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in Studio City, California. I
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762
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want to go to your college.
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nineteen seventy nine new on ABCTV Delta
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Oh here good news for once.
My neighbors is jealous of me. You

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want to know why, because my
grass is growing and looking green and I

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can see on my sofa out in
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to overwater it anymore. You know
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806
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damn water boys on the waters on
every Thursday night on KCIA. Well,

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I got me a smart controller and
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808
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tooting. No more sneaking around and
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in the middle of the night,
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810
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And you can do it too.
Listening is easier than ever. KCIA is

811
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now screaming online. It's streaming what
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right here at KCIA, the station
that leaves no listener behind NBC News Radio.

815
00:58:00.199 --> 00:58:05.880
I'm Chris Caragio. Congressional leaders are
announcing a budget agreement that'll keep the

816
00:58:05.920 --> 00:58:08.880
government funded through twenty twenty four.
Senate Majority Leader Chuck Schumer and Speaker of

817
00:58:08.920 --> 00:58:13.880
the House Mike Johnson made a joint
announcement of the nearly one point seven trillion

818
00:58:13.920 --> 00:58:17.280
dollar deal on Sunday. The spending
agreement will reportedly keep a number of domestic

819
00:58:17.360 --> 00:58:22.159
and social programs funded, despite GOP
calls for extreme budget cuts. News of

820
00:58:22.239 --> 00:58:27.159
the breakthrough comes as both the House
and Senate are set to return from holiday

821
00:58:27.239 --> 00:58:30.320
break this week. A former House
Republican is slamming those who are echoing former

822
00:58:30.360 --> 00:58:37.159
President Trump's description of those convicted in
the US Capitol riot as hostages. These

823
00:58:37.199 --> 00:58:42.280
are people who were involved in violence
against police officers in the assault on the

824
00:58:42.400 --> 00:58:45.760
Capitol. Speaking on CBS's Face the
Nation one day after the third anniversary of

825
00:58:45.800 --> 00:58:50.960
the riot, Liz Cheney said it's
outrageous, disgusting, and a disgrace to

826
00:58:51.079 --> 00:58:55.599
use words such as January sixth hostages
for prisoners who either pled guilty or were

827
00:58:55.639 --> 00:59:00.199
convicted for their role in the riot. Cheney, in particular condemn law makes

828
00:59:00.360 --> 00:59:04.679
who are repeating Trump's claims to further
their own political careers. Chaney lost her

829
00:59:04.719 --> 00:59:08.119
Wyoming congressional seat to a Trump back
opponent in the twenty twenty two Republican primary.

830
00:59:08.519 --> 00:59:13.000
Texas Governor Greg Abbott says he is
still waiting on a response to multiple

831
00:59:13.079 --> 00:59:16.280
letters he sent to the White House
with suggestions that he has regarding the southern

832
00:59:16.320 --> 00:59:22.239
border. I've heard absolutely nothing from
the Biden administration. Speaking on Fox New

833
00:59:22.280 --> 00:59:27.199
Sunday, Abbott said that he even
personally handed letters to President Biden and Secretary

834
00:59:27.239 --> 00:59:31.360
of Homeland Security Alejandro Majorcis when they
visited al Passo. Recently, Abbot has

835
00:59:31.400 --> 00:59:37.239
faced criticism from mayors of sanctuary cities
over the bus loads of migrants the Texas

836
00:59:37.320 --> 00:59:40.719
Republican has sent there. New York
City Mayor Eric Adams filed a lawsuit last

837
00:59:40.760 --> 00:59:45.800
week against charter bus companies that have
been transporting migrants to the Big Apple.

838
00:59:45.239 --> 00:59:51.760
Abbott suggested that Adams should be suing
President Biden instead, because it's Biden's policies

839
00:59:51.800 --> 00:59:55.880
that are to blame. I'm Chris
Caragio, NBC News Radio Killstina CACAA,

840
00:59:57.079 --> 01:00:00.760
Loma Linda at one O six point
five FM, K two three CF,

841
01:00:00.840 --> 01:00:06.679
Brino Valley. Years ago, a
Texas lawmaker got caught using his official position

842
01:00:06.880 --> 01:00:08.480
to enrich himself by taking money from
special