June 19, 2024 • 37 mins
In this episode of 'All About HR', host Laura Hundley delves into the transformative potential of AI in human resources with expert Michelle LeFavor. With over 15 years at top consumer brands like Nike and Wayfair, Michelle now focuses on AI adoption, helping organizations enhance productivity and creativity. They discuss AI's role in applicant tracking, job requisition writing, bias reduction, and employee engagement. Michelle also shares practical tips for using AI in everyday life and emphasizes the importance of human oversight in AI applications. Tune in to learn about the future of AI in HR and how to leverage it for sustainable success.
About our Guest: Michelle LeFavor is a leading AI adoption consultant with over 15 years of experience in transforming top consumer brands. She specializes in guiding organizations through disruptive change with customizable programs that enhance productivity, creativity, and employee satisfaction. Michelle's expertise in change management, combined with her ability to unite diverse perspectives, fosters innovation and sustainable success. A sought-after speaker and thought leader, she actively promotes responsible AI adoption and champions diversity, equity, and inclusion in the tech industry.
Where to find Michelle:
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- Music: Hall & Oats, Podcast: Simon Sinek's A Bit of Optimism
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:01):
Welcome to All About HR, the podcast dedicated to unraveling the mysteries of
human resources and empowering you with the knowledge to navigate the modern
workplace successfully.
I'm your host, Laura Hundley, and I'm thrilled to embark on this HR journey with you. Let's go.
Welcome back to All About HR, where we talk to experts and thought leaders in the HR space.
(00:25):
Joining us in the conversation today is Michelle LeFevre, who brings over 15
years experience in spearheading transformation initiatives at top consumer
brands like Nike and Wayfair.
Currently, Michelle is ushering organizations into a new frontier of productivity
and possibility as an AI adoption consultant and trainer.
She empowers teams with accessible learning around AI applications that decrease
(00:50):
drudgery while unlocking greater creativity, competitive edge,
and employee satisfaction.
Her customizable programs align to company values, smoothly facilitate cutting-edge
tools through tailored change management and policy guidelines.
Michelle has an innate ability to meet people where they are while bringing
everyone together under a common vision.
(01:10):
Michelle's superpowers of empathy, insight, and inspiration promise to empower
many more workforces to adopt technology for sustainable success in the future.
She is a mom of two who lives in South Boston. when she's not learning the latest AI applications.
She's creating in other ways like inventing games for her kids,
writing and testing new recipes, or making art.
(01:31):
Welcome, Michelle, and thank you for joining us. So before we jump into your
area of expertise, I always like to ask, what are you listening to right now? What's in your ears?
Oh, yeah. First of all, thank you for having me. I'm so excited to be here.
I bounce back and forth between a couple of different things when I'm listening,
depending on where I am, what I'm doing.
(01:52):
But in terms of audio, I'm doing the book, The Undoing Project by Michael Lewis.
I love that book so much. It's great. And I love it on audio.
He has such a great storytelling voice. So I really appreciate that.
And then I will intersperse that with Simon Sinek's A Bit of Optimism podcast.
I just love the guests that he has on there and always find something interesting.
(02:16):
Oh, nice. Yeah, I read Read The Undoing Project.
I listened to it on audio, and then I also made a point of reading it a couple years ago.
And I think recently, it was Daniel Kahneman just recently passed away.
So I know there were a lot of things that came out as kind of memorials for
him and people just talking a little bit more about his research.
(02:37):
But yeah, I thought I loved that book so much.
His passing was what prompted me to get into the book. Yeah.
So that makes sense. Yeah. So your area of expertise is around AI.
And how it's being used in organizations now. So I guess maybe let's start there.
How is AI currently being utilized either in human resources or in organizations?
(03:00):
Yeah, sure. So I'm super excited about AI coming into the workplace.
I'm excited about the tech in general. I think it's the most powerful tool that
has been made available to literally anyone with an internet connection.
And I think it's really amazing.
What I'm super interested in is seeing it implemented in the workplace in a lot of different ways.
(03:22):
In HR, we all know now we're using it for applicant tracking systems to help
really large amounts of data that are coming into talent acquisition teams be filtered.
But some other ways of using it in HR is around writing better job requisitions,
getting away from that copy-paste of an old rec, and really helping to dive
(03:45):
into what are the needs of this role, AI can help do that.
I'm also exploring a lot of ways right now that we can start as HR teams using
AI to increase accessibility for employees in the workplace.
So one thing I'm super excited about is AI's ability to dictate audio.
(04:06):
For example, if you're someone who is dyslexic.
More needs to have, or does better having information come in an audio way,
you can copy and paste a long email into your AI system and have it just read to you.
That's a super simple, easy way to really change the game for someone who has a neurodiversity.
(04:28):
Oh, yeah. I love that. I meant to ask earlier, how did you get into AI in general?
Yeah, sure. I came about it in a really natural way. I was just interested.
So I started hearing the headlines about when ChatGPT was first launched,
I got on the waiting list because I was really interested in the tech.
It's funny because I'm really not a techie person. I wouldn't call myself someone
(04:50):
who dives into those things early, but something about the capability really struck me.
And as soon as I started playing with it, I realized how cool it was.
I also think one thing that really drew me to using AI, specifically the long
language models, like ChatGPT or Cloud,
(05:10):
is that ability to have a place to bounce your idea off of, right?
So it's almost like having a teammate.
And there was something about that really stuck with me and encouraged me to keep diving in.
Nice. So you mentioned earlier, there's one of the way human resources and organizations
are utilizing AI is helping filter the massive amounts of data.
(05:35):
Maybe give us some perspective of what that looks like. So in terms of like
benefits, integrating that into those processes.
Sure. For teams who are hiring, for example, if they get 100 applications.
And a team has to go through those manually, just reading through all of the
(05:57):
resumes and qualifications, it's going to take a long time.
An applicant tracking system that has an AI filter in it can filter out resumes
that don't match up with the qualifications that the team is looking for.
Now, the pluses of that is that you've taken a huge load off of the TA team
(06:17):
or the hiring team that has to go through those resumes.
The downside is that you might miss out on somebody who maybe has a unique background
or unique qualifications that a human might say, you know what,
this isn't exactly what we asked for,
but I think it has real application here.
And so that's why I'll say this many times over today, but in so many places, it's.
(06:42):
AI plus human is really the winning combination.
When you take one or the other out, that's when you start to see some of the pitfalls.
So yeah, I think it's a really, it's a powerful way to help teams find what they're looking for.
But it's also so important to add that human element in so that you don't miss the unicorns. Yeah.
(07:04):
So it does sound like it's a pretty valuable tool in terms of hiring and recruiting
and the selection process.
Do you, and I don't know if it's chicken egg kind of situation,
like people started using AI to help write their resumes first or was it that,
say on the recruiting side,
(07:25):
that it was used more as a tool to help filter out and fine tune or funnel the applicants.
Where do you feel, say if that was a chicken egg argument, which one you feel happened first?
Or do you know? I think by and large, it was the ATS systems came first.
Those are less sophisticated than perhaps what you would imagine,
(07:47):
like your long language models that you might have played with before.
Those systems are pretty simple.
And then once those came out, we then had systems to help get around them, right?
We have now software that you you can just plug your resume into and have it
rewritten to get around those systems.
And so that's, again, where things start to fall apart a little bit, right?
(08:10):
When you just, if you pull the human out and say, okay, I'm just filtering with
AI and I'm just writing with AI, that's where I think, you know,
a lot of the fodder for headlines comes from as well.
Those are the things that people tend to talk about. What I would say though,
is just to make sure I give of the other side of the story.
(08:30):
Imagine someone who's really struggling with, say, for example,
wording on a resume, and they take a written resume that they've already completed.
And pull it apart and ask their AI system to help refine or make something a
little bit more articulate in the way that they want to hear it or sound, AI can do that.
(08:55):
And that's a space where I think it's a really important distinction to make.
If you come into a system with something completed or a first draft and ask
it to refine or help you make something a little bit more easy for somebody
else to understand, that's where these systems really shine.
And I think those are the best ways to go about using AI for your advantage.
(09:17):
Oh, yeah, that does sound really powerful.
So you'd mentioned too before where the winning combination is you both so AI
and human, what would you say might be the ethical considerations when around that?
Sure. I love this question.
Because again, it comes back to if you pull the human out, you're probably going
(09:38):
to get something that doesn't really resonate as human with a lot of people.
Again, this is something that we hear of often where, let's take the cover letter, for example.
Oh, I got a cover letter and I could totally tell it was written by ChatGPT.
That's probably true. It is pretty easy to spot. They will all come back differently,
but have the same general sound.
(10:00):
And this is where I think, again, you pull the human out of it and you're really
not getting the great work.
So the systems can create something that's unique, its own thing.
It might pull in phrases or sentences from data that it was trained on,
but it's wholly a unique document.
(10:22):
But without the human oversight and interaction, it's going to land pretty flat
and a little strange sounding. It'll sound like a robot wrote it. Yeah.
On the other hand, when again, if a job seeker.
Creates a cover letter that really encompasses their voice, their tone,
(10:43):
and gives all of their data and background, and then asks the system to clean
it up, make it sound a little bit more professional,
make it sound a little bit more, you can play with tone in so many ways.
Maybe it needs to be refined. Maybe the writer has a hard time making it a little bit shorter.
The systems can do all of that and really encompass what you're looking for.
(11:05):
And the output that you're going to see there is a much more natural,
your tone, what feels really comfortable to you. Okay.
I do like that. I like the idea of it as a kind of polishing or being like a
final editor in some way that, yeah, here's the raw version,
and then here's more of the polished version.
(11:26):
Yeah, really tune that in. We were talking a little bit about,
say, the ethical side of it.
How, along those lines, how might it help in reducing those biases or maybe
even promoting diversity in hiring, if it's even possible?
Sure. So I would say on the promoting diversity in hiring or helping to reduce
(11:48):
bias, the systems can work actually in both ways.
So systems can perpetuate bias, and we have to be really knowledgeable about
that and understand that and vigilant to look for it. And I think that's really
an important note to take.
These systems are only as good as the data that they're trained on.
And most of them are trained on vast amounts of data from the internet,
(12:10):
which has an bias. So we need to be aware of that.
But the flip side is that you can actually ask the system to be aware of its
own bias or check your work for bias.
So it's as simple as creating a job rec.
Giving the system some context, letting it know what you're looking for,
you know, what the job entails,
(12:32):
asking then for it to look over your rec for bias or something like,
can you help me write this so that it attracts a more diverse group than what
I'm currently attracting or something of that nature?
And it will help you rework that maybe with some language that you weren't used to using in the past.
(12:55):
So it's a really great way, again, to expand what you're able to do as a tight team. Oh, wow. Okay.
It occurs to me, I feel like maybe this is something I should have asked initially.
But in my head, AI is like this one giant entity.
So I don't know why that's a visual that I got. I don't think that's true.
(13:16):
So maybe could you tell me, does each organization that might use an AI tool?
Do they have one that's tailored to them? Is it something that they got off
the shelf and then made the tweaks for themselves?
How exactly does that work? Yeah, fantastic question.
If you think of AI, and I saw a great model recently that was like concentric circles.
(13:38):
So the outer circle is like all of AI, right? All artificial intelligence.
And then as you move into the circle, you have things like generative AI,
which can create create things.
And then if you get all the way to the center of the circle,
you have your long language models that can perceive, reason,
generate, things like that.
(13:59):
What I really focus on with a lot of my clients is around the long language model space.
And I think that's,
The reason why I do that is because that's the most advanced tech where we're really leaning toward.
There's a lot out there right now, for example, integrations into our HRI that
are more into that kind of like analytical or generative space that can be plugged into a system.
(14:26):
Organizations are doing a multitude of things right now. There are some that
are creating GPTs that are just for their organization, which is super cool.
You have them in simple spaces like a chatbot.
That's artificial intelligence, right? And then you have things that are a little
bit more integrative for the employee, which is something like an internal GPT.
(14:51):
So an internal GPT would be something that is trained on the sort of large amount
of data, but then also can be trained on just your organization's data.
So if you think about an organization, maybe you're doing some sort of customer management.
And you can ask the GPT, bring up all the data on the LeFevre family that we're
(15:14):
currently working with, and it can pull that all together for you and give you
a synthesis of what's going on. So there's a couple different ways to do it.
And then there's lots of off the shelf options as well.
I'm a huge fan of Microsoft Copilot, because that kind of really feels like
a safe space, the work that you're doing in there is protected.
And it's also integrative across the whole suite.
(15:37):
So you can use AI, not just in a language way, but also with your Excel,
for example, across your email, all sorts of different ways.
Ways to use that. Oh, that is fascinating.
And maybe I missed it somewhere, but is GPT an acronym for something?
It is an acronym and it's used. So there's chat GPT, which is its own entity owned by OpenAI.
(16:05):
And then GPT is also a way to phrase a generative AI system.
I gotcha. Okay. All right.
So maybe you were touching on it, but say the potential risks or challenges
that might be associated with,
and we can even just narrow it down to something that you just mentioned,
where that Microsoft has their own version of an AI that it can be used across their suite.
(16:30):
So are there potential risks or challenges associated with that?
Yeah. In the case of Microsoft Copilot, which is a sort of a paid subscription
service, it's just an add-on to like your Microsoft suite.
So that's all encompassed in the firewall that's in there.
So in the same way that you wouldn't really worry at the same level of caution
(16:55):
that you would put around writing an email about something, that's the same
level that you can think about using your AI in that space. So using the co-pilot.
I talk to teams a lot about the open systems.
So things that are free on the internet, like ChatGPT or Claude.
I like Perplexity a lot and Pi.
(17:15):
So there's like tons of them out there. They all have little specialties and
those can all be used free.
Those ones are not protected if you're using the free version.
I wouldn't say that they're necessarily inherently unsafe, but it's important
to remember to keep any sort of protected data out of them.
(17:36):
So in the same way that you're not going to put your name, address,
and social security number on a Facebook post, but you're not going to add that
information into an AI system that's open to the web.
Yeah, I was just about to comment or ask about that where I'm sure,
and we don't even have to go down this rabbit hole,
but unless you wanted to touch on it briefly, but say even the legalities of
(17:59):
who owns that, whatever is created, I get the difference between say the data
going in and then what comes out on the other side.
So yeah, who owns what comes out on the other side.
Yeah. Yeah. Yeah. I think a lot of that's still being worked out.
There's not a lot of clear governance yet, I think on that, which is why I think
(18:20):
focusing in so much on the kind of small wins that you can have with these systems
makes a huge difference, I think. Oh yeah. That is fascinating.
Okay. I feel like this is a good place to take a little break.
We're going to take a break for the HR hot sauce and we will be right back.
(18:40):
All right, we're here with a HR hot sauce with Michelle. Michelle, are you ready?
I think so. Okay, here we go. What's the best job you've ever had? Oh, this one.
Okay. For sure. Nice.
What's the one phrase at work that drives you nuts?
(19:01):
Low-hanging fruit. That was a big one at my first job, and it just sounds weird.
That's fair. Hey, totally fair. Do you like working on rainy or sunny days? Sunny days. Nice.
How can someone make your day at work?
Telling me that they tried something that I suggested and it worked. Nice. Oh, I love that.
(19:24):
Best useless skill?
I am great at reading stories with the voices to my kids.
Oh, I wish we had time for that. how about mild medium hot or nuclear a medium
okay favorite interview question to ask or be asked,
(19:49):
if you didn't need to work for a paycheck what would you do oh yeah i love that
all right last one song to bring you out of a funk brown eyed girl oh i love
that song all right michelle that's That's it.
That's it for the HR hot sauce. You made it. Let's get back to the show.
(20:10):
All right, that was a fun HR hot sauce. We are back. So we're jumping back into
the conversation around AI and AI, specifically almost in the HR space in organizations.
We've talked about a little bit of Michelle's background in AI,
how she got into it, and some of what's been happening, how it's being utilized in the HR space.
(20:33):
So getting back into the conversation.
So we in HR, we do talk a lot about employee engagement. It's definitely about the people.
And I firmly believe people make the difference in an organization.
How might you say AI could affect or impact employee engagement and retention strategies?
(20:54):
Yeah, absolutely. I love this one. The way that I envision the future of work
is using AI to help take some of the sort of drudgery tasks off of employees' plates.
We all have things that we dread doing at work.
And if you can imagine even 10% of that to be lifted off of your plate that
(21:15):
you don't have to do anymore.
As an employee, what would you do with that extra time?
Imagine being able to really dive into the things that feel like they're contributing
to your development or your growth in your role.
And I really think that as organizations start to implement that for their employees,
that That is going to lead to a much higher level of job satisfaction,
(21:39):
success, internal growth.
And I see so much retention happening in that space.
Coming from a talent background and doing a lot of the data analytics around
what are making employees happy or not, that's a huge part of it is that job satisfaction.
Action and spending too much time in the space that doesn't feel developmentally
(22:01):
additive is a real predictor of loss.
Oh, yeah. And I think and.
Anything where it comes to people, it's more complex than just saying it's this
one thing across the board for everyone.
So I imagine that is that something where AI, as a tool, it's better to,
and again, I'm going to use the phrasing because I love it, is that AI and a
(22:24):
person together can help with recognizing those things. Yeah, absolutely.
And I think it's a space where you can be really custom about it as well.
Your dreaded work is going to be totally different from my dreaded work.
Yeah. And as long as that's written into our governance, the thing that we want
to take off of our plate is okay to take off, then we each get to come to that
(22:47):
better, more satisfactory place of work in our own way.
Yeah, I'm just thinking about it. I want to take better notes when I do check-ins
with my employees. I'm not really good at it. And I don't genuinely enjoy it.
I like I like more just say I like the interaction of the check in.
I just don't love say, Oh, yeah, here are the things that we needed to take note of or follow up on.
(23:10):
So yeah, that's something that could be taken off. That would be amazing.
I have a couple of you want some like real world examples.
Yeah. I was actually just speaking about this recently.
A couple of things have come into my field of vision recently on note-taking
and employee development.
So one is if you and your direct are both amenable to it, you can actually have
(23:35):
an AI system take the notes during the meeting for you, especially a lot of
folks are using them during Zoom calls.
AI is really adept at pulling the information that is being spoken back and
forth and actually reorganizing it into very clear, cohesive, concise notes.
Another way to do that, I personally like taking notes, but I like to handwrite them. Okay. And then...
(24:01):
I wind up with 25 notebooks, things all over the place.
And I can handwrite my notes in my paper notebook, which I just can't really get away from just yet.
Take a photo of those notes and load it to an AI software and it will actually
interpret my chicken scratch into actual notes in a format that is clear and
(24:24):
easy to understand that I could also easily email to you if you were part of the meeting.
And so if you think about a one-on-one where maybe you don't want to turn on
a recording device, you can take those handwritten notes and then just move
them right into an online filing system via an AI. That is amazing.
Wow. Yeah. I guess truly the applications are endless.
(24:46):
Speaking of which, what skills would you recommend to anybody,
but I guess specifically we are talking about HR in this conversation.
What skills are necessary to effectively leverage AI technology? Yeah.
So I think the number one skill is being comfortable using it.
The wonderful thing about a lot of this AI is that it's very intuitive.
(25:11):
Generally, it's working on natural language. So prompting in any situation is
just using regular words.
There is nuance to prompting. There are great ways to do it and there are crappy
ways to do it. And those are pretty easy to understand. There's a lot of cheat sheets out there.
In fact, LinkedIn was like a treasure trove a year ago of cheat sheets on how to prompt things.
(25:35):
But the biggest, I think, hurdle for folks is the comfort level of using it.
And what I like to coach people to do now is before you are beginning to implement
AI at work, start implementing it at home.
So use it for things that you're doing in your everyday life.
Start getting comfortable there. there and then that will make bringing it to work a lot easier.
(25:59):
So I can give you a few fun kind of real life examples that I like to use.
So one is around the meal planning. I'm a mom.
I plan the meals for my family and it gets boring after a while.
I love to cook, but man, like every week, the same thing.
And so you can ask your AI system for help figuring that out.
(26:20):
You can put in your family's preferences.
You can even take a photo of your refrigerator and implement or load that into your AI system.
It can see everything that's in there and then tell you some recipes that you
can make based on what's already in the fridge or the pantry.
That's been super fun. I've also used it at the gym. So I'm looking to build muscle in these places.
(26:46):
I'm following a certain macro diet. Here's my height and weight.
Can you tell me what to do?
And it'll give you a full trainer's guide to how you can get there. Wow.
And then the third one that I love, I think resonates with a lot of people.
If you have kids, nieces and nephews, grandkids, neighbors, whatever,
(27:06):
ever, ask your AI systems to write them a bedtime story.
So you can integrate like the things that they're interested in,
their names, it'll do it age appropriately.
I have an early reader in my house. And so I asked the system to create some
early reader stories that my daughter could read on her own.
And it does a fantastic job. Oh, that's really sweet.
(27:27):
It made me think of, oh, yeah. So when you say the AI systems,
again, they'll say I'm very new to it myself. self.
You're telling me that I could go to say like one of the free open AIs like
chat GPT and basically like type in,
yeah, like I like to eat grilled chicken dishes and it would say,
(27:47):
all right, here's what we would recommend. It's that easy.
It's that easy. Okay. And the other kind of fun thing to do there is you could
say that I like to eat grilled chicken dishes. is, give me a recipe in the style of Julia Child.
Oh, wow. We did this one as an example in a meeting once. And it's just phenomenal.
And you will get a big kick out of it.
(28:08):
You can also ask, if you're maybe not trusting the system just yet,
ask it to cite its sources.
And so it'll give you the links to where it pulled from in order to get the
information to create that dish.
Oh, wow. That is really cool. Oh, man. Okay. I got like very literally the very
first thing I'm going to do once we're done with this conversation is that very thing.
(28:28):
I'm pretty excited to try this. This happens a lot. Let's get through this so
I can go try something. Yeah, yeah.
We've touched on a couple things as it relates to HR.
We also know performance management is a big piece in the HR space and also employee development.
How is AI being used in that area? Yeah, absolutely.
So one, I would go back to that note-taking example of having those one-on-ones
(28:52):
and then having an AI system help you curate those notes.
So if you think about, you have a one-on-one with your direct once a month and
you implement those or load those notes into your AI system each month,
maybe you're keeping it in a document or wherever.
(29:13):
And then at the end of the year, you now have these notes that are all taken
in the same way, the same concise sort of format,
you can then take the whole thing and pop it into an AI system and ask it to
pull out key themes, basically ask it to.
Act as a manager and you're looking for performance.
(29:34):
You can also use it in a sort of coaching development way.
So I'm seeing this happening. In fact, there's some large companies piloting
this program where you essentially ask an AI system,
and there's some that are developed specifically for this, that you ask it to
act as an employee coach or a career coach, specifically for managers.
(29:56):
Middle management has been hit so hard since the pandemic with With so much
extra work on their plates, they're really managing a lot. Right.
And it can be a tough space. And there's not always a lot of support at the top end for managers.
And so they can use these coaching systems to ask for help in dealing with employee situations.
(30:16):
And they're coming back with remarkable answers.
It's really helping a lot of folks who maybe don't have that support from a
direct superior to help them work some of these systems out.
And then on the development end, I love seeing what's happening right now in
the learning and development space, real opportunities for organizations,
(30:38):
HR teams to create learning systems using AI.
So there's so many out there. You can use one of these free systems like a ChatGPT
or a cloud, but you can also use these great, there's a great one that I've
been using recently called FreeFuse, which integrates.
(30:59):
Video voiceover. You can also just throw a whole PowerPoint in there and it
turns into this sort of create your own adventure.
Oh, wow. Oh, my gosh. It's just it's awesome. And you can do it as a one person show.
You know what I mean? Yeah, that's amazing.
So that actually makes me think a little bit, too, about what is we've talked
a lot about what it's what are the possibilities?
(31:22):
What do you what have you seen or what do you think are the limitations or even
and possible pitfalls of AI? Yeah.
Limitations, I think, I would say for organizations that aren't ready to invest
yet, for teams that are just using free systems, there are limitations around
what kind of information you can add in there.
(31:44):
You do have to be careful about, especially employee data, ensuring that all
of your stakeholders are kept safe in the process.
And then the other, I think the big limitation is that folks just don't know how to use it yet.
Anytime you're introducing a new tech, it's going to take a bit for people to get up to speed.
I think that's why I'm so passionate about folks learning to use it at a really
(32:10):
base level so that it's a lot easier when organizations do implement their internal systems.
You've got that leg up because you've been playing with it for a while.
Yeah, that sounds not to get too into the weeds of it, but that does sound like
what you do and what you're passionate about is helping organizations get it right.
Right. Like you could you can go in there and if an organization would like
(32:33):
to implement or start using AI, depending on what their strategy might be,
that's something you very much can help them with. Is that right?
That's exactly right. Right. Awesome. And then I would just call out the other
sort of limitation is really around governance.
I feel passionate about organizations having a strategy, having a plan for communication
and how they want their employees to implement AI into their work.
(33:00):
In some of the research that I've done, there's a host of different things happening.
Some organizations are putting it directly behind a firewall.
You can't get to it. We're just not having the conversation.
There are others that are not talking about it, but it's inside the system.
So folks are using it, whether or not they're being told to.
And they don't really have a clear
(33:21):
understanding of what is and isn't appropriate to be putting in there.
And so I think a limitation right now is just how,
not having guidance if you haven't set that strategy yet.
Oh, yeah. I was going to say that sounds, it does make me think of,
yeah, what level of transparency organizations should have to ensure that.
(33:41):
Because I think, I mean, that could easily be a pitfall.
And or what, you know, what accountability pieces you could put in place in
a system that, yeah, you still want both parties, you want the AI and a person
in charge of that. But what does that look like? Exactly.
And that's another reason why I think it's so important that HR teams are empowered
(34:03):
to understand and use this tech.
Because if we bury our heads in the sand, it's going to be really difficult
to put that strategy together and to really guide employees on what is and isn't
appropriate if you've never used the system yourself. Oh, yeah, absolutely.
So getting into wrapping up the conversation, what do you see the how do you
(34:28):
see the future of AI being used in organizations?
What does that look like? Yeah, absolutely. I foresee it in every part of our work stream.
I think it'll become as ubiquitous as opening your laptop.
Your computer is part of nearly every part of your work stream during the day.
And I think AI will be have that same sort of place in in the workspace. space.
(34:53):
And maybe touching on it a little bit, and this is a across all types of organizations,
small, medium, large, fortune 500. What do we see this everywhere?
I do. Obviously, it's going to come first for knowledge workers,
but I see it on Frontline. I see it everywhere.
It'll be a part of every part of the work that you touch, I think.
(35:14):
All of us will be dealing with it, at least in one way or another.
Other. Think about our homes.
We talk to our robots here and ask it to play our favorite songs or pull up the calendar.
I think that was really hard to picture 10 years ago.
And now my kids use it. But it's a really simple kind of integration.
And I think that because of the power of AI and the way that it's being created
(35:39):
to be intuitive, I think it's going to flow right in whether or or not we realize it.
Yeah, I was actually just thinking about it because prior to talking to you,
I wouldn't have thought AI was much of my life at all.
But even now you just mentioned it. I have Spotify.
And one of my favorite features is the list that it creates for me or the there's
(35:59):
here's your own personal DJ based on the things that you like.
And it's some things that you have already listened to, but then brand new music
and artists that you've never heard of, which is amazing. I I love that.
Absolutely. Or if you've ever sat and watched Netflix and found a new show because
it recommended something to you, that's AI at work.
Yeah, yeah. Again, I'm probably going to see it more now after this conversation than before.
(36:23):
Michelle, it has been an absolute pleasure talking with you.
I've learned so much more. I'm really excited to do a bunch of things that we've talked about.
I know you have some things coming up and I know with a webinar with People
Element, would you like to put in a plug for that right now?
I would, yes. Yes. So we're going to be talking about all things AI at work.
Really, it's a deeper dive on this conversation, how we implement these systems
(36:47):
into our daily life, especially if you're a tight HR team, you will get so much out of this.
Love it. Love it. And what was the date for that one? That is June 18th.
June 18th. That's right. Okay. And we'll have detail.
We'll provide more details as well in our show notes.
Again, thank you so much. It's been a pleasure. I learned so so much today.
(37:08):
Thank you for having me. This was fantastic. Awesome.
Thank you so much for tuning in to today's episode of All About HR.
I'd like to take a moment to thank our producers, Kristen Romero and Sam Cortez,
as well as Kevin McDonald, who wrote our theme music.
Stay tuned for more engaging conversations and expert insights.
(37:28):
Until next time, this is Laura Hundley signing off. Take care,
stay inspired, and keep innovating in the world of HR.
Understand engage inspire and retain your people like never before people elements
employee experience and engagement
solution delivers powerful intelligence giving you the confidence.
Welcome to All About HR, the podcast dedicated to unraveling the mysteries of
human resources and empowering you with the knowledge to navigate the modern
workplace successfully.
I'm your host, Laura Hundley, and I'm thrilled to embark on this HR journey with you. Let's go.
Welcome back to All About HR, where we talk to experts and thought leaders in the HR space.
(00:25):
Joining us in the conversation today is Michelle LeFevre, who brings over 15
years experience in spearheading transformation initiatives at top consumer
brands like Nike and Wayfair.
Currently, Michelle is ushering organizations into a new frontier of productivity
and possibility as an AI adoption consultant and trainer.
She empowers teams with accessible learning around AI applications that decrease
(00:50):
drudgery while unlocking greater creativity, competitive edge,
and employee satisfaction.
Her customizable programs align to company values, smoothly facilitate cutting-edge
tools through tailored change management and policy guidelines.
Michelle has an innate ability to meet people where they are while bringing
everyone together under a common vision.
(01:10):
Michelle's superpowers of empathy, insight, and inspiration promise to empower
many more workforces to adopt technology for sustainable success in the future.
She is a mom of two who lives in South Boston. when she's not learning the latest AI applications.
She's creating in other ways like inventing games for her kids,
writing and testing new recipes, or making art.
(01:31):
Welcome, Michelle, and thank you for joining us. So before we jump into your
area of expertise, I always like to ask, what are you listening to right now? What's in your ears?
Oh, yeah. First of all, thank you for having me. I'm so excited to be here.
I bounce back and forth between a couple of different things when I'm listening,
depending on where I am, what I'm doing.
(01:52):
But in terms of audio, I'm doing the book, The Undoing Project by Michael Lewis.
I love that book so much. It's great. And I love it on audio.
He has such a great storytelling voice. So I really appreciate that.
And then I will intersperse that with Simon Sinek's A Bit of Optimism podcast.
I just love the guests that he has on there and always find something interesting.
(02:16):
Oh, nice. Yeah, I read Read The Undoing Project.
I listened to it on audio, and then I also made a point of reading it a couple years ago.
And I think recently, it was Daniel Kahneman just recently passed away.
So I know there were a lot of things that came out as kind of memorials for
him and people just talking a little bit more about his research.
(02:37):
But yeah, I thought I loved that book so much.
His passing was what prompted me to get into the book. Yeah.
So that makes sense. Yeah. So your area of expertise is around AI.
And how it's being used in organizations now. So I guess maybe let's start there.
How is AI currently being utilized either in human resources or in organizations?
(03:00):
Yeah, sure. So I'm super excited about AI coming into the workplace.
I'm excited about the tech in general. I think it's the most powerful tool that
has been made available to literally anyone with an internet connection.
And I think it's really amazing.
What I'm super interested in is seeing it implemented in the workplace in a lot of different ways.
(03:22):
In HR, we all know now we're using it for applicant tracking systems to help
really large amounts of data that are coming into talent acquisition teams be filtered.
But some other ways of using it in HR is around writing better job requisitions,
getting away from that copy-paste of an old rec, and really helping to dive
(03:45):
into what are the needs of this role, AI can help do that.
I'm also exploring a lot of ways right now that we can start as HR teams using
AI to increase accessibility for employees in the workplace.
So one thing I'm super excited about is AI's ability to dictate audio.
(04:06):
For example, if you're someone who is dyslexic.
More needs to have, or does better having information come in an audio way,
you can copy and paste a long email into your AI system and have it just read to you.
That's a super simple, easy way to really change the game for someone who has a neurodiversity.
(04:28):
Oh, yeah. I love that. I meant to ask earlier, how did you get into AI in general?
Yeah, sure. I came about it in a really natural way. I was just interested.
So I started hearing the headlines about when ChatGPT was first launched,
I got on the waiting list because I was really interested in the tech.
It's funny because I'm really not a techie person. I wouldn't call myself someone
(04:50):
who dives into those things early, but something about the capability really struck me.
And as soon as I started playing with it, I realized how cool it was.
I also think one thing that really drew me to using AI, specifically the long
language models, like ChatGPT or Cloud,
(05:10):
is that ability to have a place to bounce your idea off of, right?
So it's almost like having a teammate.
And there was something about that really stuck with me and encouraged me to keep diving in.
Nice. So you mentioned earlier, there's one of the way human resources and organizations
are utilizing AI is helping filter the massive amounts of data.
(05:35):
Maybe give us some perspective of what that looks like. So in terms of like
benefits, integrating that into those processes.
Sure. For teams who are hiring, for example, if they get 100 applications.
And a team has to go through those manually, just reading through all of the
(05:57):
resumes and qualifications, it's going to take a long time.
An applicant tracking system that has an AI filter in it can filter out resumes
that don't match up with the qualifications that the team is looking for.
Now, the pluses of that is that you've taken a huge load off of the TA team
(06:17):
or the hiring team that has to go through those resumes.
The downside is that you might miss out on somebody who maybe has a unique background
or unique qualifications that a human might say, you know what,
this isn't exactly what we asked for,
but I think it has real application here.
And so that's why I'll say this many times over today, but in so many places, it's.
(06:42):
AI plus human is really the winning combination.
When you take one or the other out, that's when you start to see some of the pitfalls.
So yeah, I think it's a really, it's a powerful way to help teams find what they're looking for.
But it's also so important to add that human element in so that you don't miss the unicorns. Yeah.
(07:04):
So it does sound like it's a pretty valuable tool in terms of hiring and recruiting
and the selection process.
Do you, and I don't know if it's chicken egg kind of situation,
like people started using AI to help write their resumes first or was it that,
say on the recruiting side,
(07:25):
that it was used more as a tool to help filter out and fine tune or funnel the applicants.
Where do you feel, say if that was a chicken egg argument, which one you feel happened first?
Or do you know? I think by and large, it was the ATS systems came first.
Those are less sophisticated than perhaps what you would imagine,
(07:47):
like your long language models that you might have played with before.
Those systems are pretty simple.
And then once those came out, we then had systems to help get around them, right?
We have now software that you you can just plug your resume into and have it
rewritten to get around those systems.
And so that's, again, where things start to fall apart a little bit, right?
(08:10):
When you just, if you pull the human out and say, okay, I'm just filtering with
AI and I'm just writing with AI, that's where I think, you know,
a lot of the fodder for headlines comes from as well.
Those are the things that people tend to talk about. What I would say though,
is just to make sure I give of the other side of the story.
(08:30):
Imagine someone who's really struggling with, say, for example,
wording on a resume, and they take a written resume that they've already completed.
And pull it apart and ask their AI system to help refine or make something a
little bit more articulate in the way that they want to hear it or sound, AI can do that.
(08:55):
And that's a space where I think it's a really important distinction to make.
If you come into a system with something completed or a first draft and ask
it to refine or help you make something a little bit more easy for somebody
else to understand, that's where these systems really shine.
And I think those are the best ways to go about using AI for your advantage.
(09:17):
Oh, yeah, that does sound really powerful.
So you'd mentioned too before where the winning combination is you both so AI
and human, what would you say might be the ethical considerations when around that?
Sure. I love this question.
Because again, it comes back to if you pull the human out, you're probably going
(09:38):
to get something that doesn't really resonate as human with a lot of people.
Again, this is something that we hear of often where, let's take the cover letter, for example.
Oh, I got a cover letter and I could totally tell it was written by ChatGPT.
That's probably true. It is pretty easy to spot. They will all come back differently,
but have the same general sound.
(10:00):
And this is where I think, again, you pull the human out of it and you're really
not getting the great work.
So the systems can create something that's unique, its own thing.
It might pull in phrases or sentences from data that it was trained on,
but it's wholly a unique document.
(10:22):
But without the human oversight and interaction, it's going to land pretty flat
and a little strange sounding. It'll sound like a robot wrote it. Yeah.
On the other hand, when again, if a job seeker.
Creates a cover letter that really encompasses their voice, their tone,
(10:43):
and gives all of their data and background, and then asks the system to clean
it up, make it sound a little bit more professional,
make it sound a little bit more, you can play with tone in so many ways.
Maybe it needs to be refined. Maybe the writer has a hard time making it a little bit shorter.
The systems can do all of that and really encompass what you're looking for.
(11:05):
And the output that you're going to see there is a much more natural,
your tone, what feels really comfortable to you. Okay.
I do like that. I like the idea of it as a kind of polishing or being like a
final editor in some way that, yeah, here's the raw version,
and then here's more of the polished version.
(11:26):
Yeah, really tune that in. We were talking a little bit about,
say, the ethical side of it.
How, along those lines, how might it help in reducing those biases or maybe
even promoting diversity in hiring, if it's even possible?
Sure. So I would say on the promoting diversity in hiring or helping to reduce
(11:48):
bias, the systems can work actually in both ways.
So systems can perpetuate bias, and we have to be really knowledgeable about
that and understand that and vigilant to look for it. And I think that's really
an important note to take.
These systems are only as good as the data that they're trained on.
And most of them are trained on vast amounts of data from the internet,
(12:10):
which has an bias. So we need to be aware of that.
But the flip side is that you can actually ask the system to be aware of its
own bias or check your work for bias.
So it's as simple as creating a job rec.
Giving the system some context, letting it know what you're looking for,
you know, what the job entails,
(12:32):
asking then for it to look over your rec for bias or something like,
can you help me write this so that it attracts a more diverse group than what
I'm currently attracting or something of that nature?
And it will help you rework that maybe with some language that you weren't used to using in the past.
(12:55):
So it's a really great way, again, to expand what you're able to do as a tight team. Oh, wow. Okay.
It occurs to me, I feel like maybe this is something I should have asked initially.
But in my head, AI is like this one giant entity.
So I don't know why that's a visual that I got. I don't think that's true.
(13:16):
So maybe could you tell me, does each organization that might use an AI tool?
Do they have one that's tailored to them? Is it something that they got off
the shelf and then made the tweaks for themselves?
How exactly does that work? Yeah, fantastic question.
If you think of AI, and I saw a great model recently that was like concentric circles.
(13:38):
So the outer circle is like all of AI, right? All artificial intelligence.
And then as you move into the circle, you have things like generative AI,
which can create create things.
And then if you get all the way to the center of the circle,
you have your long language models that can perceive, reason,
generate, things like that.
(13:59):
What I really focus on with a lot of my clients is around the long language model space.
And I think that's,
The reason why I do that is because that's the most advanced tech where we're really leaning toward.
There's a lot out there right now, for example, integrations into our HRI that
are more into that kind of like analytical or generative space that can be plugged into a system.
(14:26):
Organizations are doing a multitude of things right now. There are some that
are creating GPTs that are just for their organization, which is super cool.
You have them in simple spaces like a chatbot.
That's artificial intelligence, right? And then you have things that are a little
bit more integrative for the employee, which is something like an internal GPT.
(14:51):
So an internal GPT would be something that is trained on the sort of large amount
of data, but then also can be trained on just your organization's data.
So if you think about an organization, maybe you're doing some sort of customer management.
And you can ask the GPT, bring up all the data on the LeFevre family that we're
(15:14):
currently working with, and it can pull that all together for you and give you
a synthesis of what's going on. So there's a couple different ways to do it.
And then there's lots of off the shelf options as well.
I'm a huge fan of Microsoft Copilot, because that kind of really feels like
a safe space, the work that you're doing in there is protected.
And it's also integrative across the whole suite.
(15:37):
So you can use AI, not just in a language way, but also with your Excel,
for example, across your email, all sorts of different ways.
Ways to use that. Oh, that is fascinating.
And maybe I missed it somewhere, but is GPT an acronym for something?
It is an acronym and it's used. So there's chat GPT, which is its own entity owned by OpenAI.
(16:05):
And then GPT is also a way to phrase a generative AI system.
I gotcha. Okay. All right.
So maybe you were touching on it, but say the potential risks or challenges
that might be associated with,
and we can even just narrow it down to something that you just mentioned,
where that Microsoft has their own version of an AI that it can be used across their suite.
(16:30):
So are there potential risks or challenges associated with that?
Yeah. In the case of Microsoft Copilot, which is a sort of a paid subscription
service, it's just an add-on to like your Microsoft suite.
So that's all encompassed in the firewall that's in there.
So in the same way that you wouldn't really worry at the same level of caution
(16:55):
that you would put around writing an email about something, that's the same
level that you can think about using your AI in that space. So using the co-pilot.
I talk to teams a lot about the open systems.
So things that are free on the internet, like ChatGPT or Claude.
I like Perplexity a lot and Pi.
(17:15):
So there's like tons of them out there. They all have little specialties and
those can all be used free.
Those ones are not protected if you're using the free version.
I wouldn't say that they're necessarily inherently unsafe, but it's important
to remember to keep any sort of protected data out of them.
(17:36):
So in the same way that you're not going to put your name, address,
and social security number on a Facebook post, but you're not going to add that
information into an AI system that's open to the web.
Yeah, I was just about to comment or ask about that where I'm sure,
and we don't even have to go down this rabbit hole,
but unless you wanted to touch on it briefly, but say even the legalities of
(17:59):
who owns that, whatever is created, I get the difference between say the data
going in and then what comes out on the other side.
So yeah, who owns what comes out on the other side.
Yeah. Yeah. Yeah. I think a lot of that's still being worked out.
There's not a lot of clear governance yet, I think on that, which is why I think
(18:20):
focusing in so much on the kind of small wins that you can have with these systems
makes a huge difference, I think. Oh yeah. That is fascinating.
Okay. I feel like this is a good place to take a little break.
We're going to take a break for the HR hot sauce and we will be right back.
(18:40):
All right, we're here with a HR hot sauce with Michelle. Michelle, are you ready?
I think so. Okay, here we go. What's the best job you've ever had? Oh, this one.
Okay. For sure. Nice.
What's the one phrase at work that drives you nuts?
(19:01):
Low-hanging fruit. That was a big one at my first job, and it just sounds weird.
That's fair. Hey, totally fair. Do you like working on rainy or sunny days? Sunny days. Nice.
How can someone make your day at work?
Telling me that they tried something that I suggested and it worked. Nice. Oh, I love that.
(19:24):
Best useless skill?
I am great at reading stories with the voices to my kids.
Oh, I wish we had time for that. how about mild medium hot or nuclear a medium
okay favorite interview question to ask or be asked,
(19:49):
if you didn't need to work for a paycheck what would you do oh yeah i love that
all right last one song to bring you out of a funk brown eyed girl oh i love
that song all right michelle that's That's it.
That's it for the HR hot sauce. You made it. Let's get back to the show.
(20:10):
All right, that was a fun HR hot sauce. We are back. So we're jumping back into
the conversation around AI and AI, specifically almost in the HR space in organizations.
We've talked about a little bit of Michelle's background in AI,
how she got into it, and some of what's been happening, how it's being utilized in the HR space.
(20:33):
So getting back into the conversation.
So we in HR, we do talk a lot about employee engagement. It's definitely about the people.
And I firmly believe people make the difference in an organization.
How might you say AI could affect or impact employee engagement and retention strategies?
(20:54):
Yeah, absolutely. I love this one. The way that I envision the future of work
is using AI to help take some of the sort of drudgery tasks off of employees' plates.
We all have things that we dread doing at work.
And if you can imagine even 10% of that to be lifted off of your plate that
(21:15):
you don't have to do anymore.
As an employee, what would you do with that extra time?
Imagine being able to really dive into the things that feel like they're contributing
to your development or your growth in your role.
And I really think that as organizations start to implement that for their employees,
that That is going to lead to a much higher level of job satisfaction,
(21:39):
success, internal growth.
And I see so much retention happening in that space.
Coming from a talent background and doing a lot of the data analytics around
what are making employees happy or not, that's a huge part of it is that job satisfaction.
Action and spending too much time in the space that doesn't feel developmentally
(22:01):
additive is a real predictor of loss.
Oh, yeah. And I think and.
Anything where it comes to people, it's more complex than just saying it's this
one thing across the board for everyone.
So I imagine that is that something where AI, as a tool, it's better to,
and again, I'm going to use the phrasing because I love it, is that AI and a
(22:24):
person together can help with recognizing those things. Yeah, absolutely.
And I think it's a space where you can be really custom about it as well.
Your dreaded work is going to be totally different from my dreaded work.
Yeah. And as long as that's written into our governance, the thing that we want
to take off of our plate is okay to take off, then we each get to come to that
(22:47):
better, more satisfactory place of work in our own way.
Yeah, I'm just thinking about it. I want to take better notes when I do check-ins
with my employees. I'm not really good at it. And I don't genuinely enjoy it.
I like I like more just say I like the interaction of the check in.
I just don't love say, Oh, yeah, here are the things that we needed to take note of or follow up on.
(23:10):
So yeah, that's something that could be taken off. That would be amazing.
I have a couple of you want some like real world examples.
Yeah. I was actually just speaking about this recently.
A couple of things have come into my field of vision recently on note-taking
and employee development.
So one is if you and your direct are both amenable to it, you can actually have
(23:35):
an AI system take the notes during the meeting for you, especially a lot of
folks are using them during Zoom calls.
AI is really adept at pulling the information that is being spoken back and
forth and actually reorganizing it into very clear, cohesive, concise notes.
Another way to do that, I personally like taking notes, but I like to handwrite them. Okay. And then...
(24:01):
I wind up with 25 notebooks, things all over the place.
And I can handwrite my notes in my paper notebook, which I just can't really get away from just yet.
Take a photo of those notes and load it to an AI software and it will actually
interpret my chicken scratch into actual notes in a format that is clear and
(24:24):
easy to understand that I could also easily email to you if you were part of the meeting.
And so if you think about a one-on-one where maybe you don't want to turn on
a recording device, you can take those handwritten notes and then just move
them right into an online filing system via an AI. That is amazing.
Wow. Yeah. I guess truly the applications are endless.
(24:46):
Speaking of which, what skills would you recommend to anybody,
but I guess specifically we are talking about HR in this conversation.
What skills are necessary to effectively leverage AI technology? Yeah.
So I think the number one skill is being comfortable using it.
The wonderful thing about a lot of this AI is that it's very intuitive.
(25:11):
Generally, it's working on natural language. So prompting in any situation is
just using regular words.
There is nuance to prompting. There are great ways to do it and there are crappy
ways to do it. And those are pretty easy to understand. There's a lot of cheat sheets out there.
In fact, LinkedIn was like a treasure trove a year ago of cheat sheets on how to prompt things.
(25:35):
But the biggest, I think, hurdle for folks is the comfort level of using it.
And what I like to coach people to do now is before you are beginning to implement
AI at work, start implementing it at home.
So use it for things that you're doing in your everyday life.
Start getting comfortable there. there and then that will make bringing it to work a lot easier.
(25:59):
So I can give you a few fun kind of real life examples that I like to use.
So one is around the meal planning. I'm a mom.
I plan the meals for my family and it gets boring after a while.
I love to cook, but man, like every week, the same thing.
And so you can ask your AI system for help figuring that out.
(26:20):
You can put in your family's preferences.
You can even take a photo of your refrigerator and implement or load that into your AI system.
It can see everything that's in there and then tell you some recipes that you
can make based on what's already in the fridge or the pantry.
That's been super fun. I've also used it at the gym. So I'm looking to build muscle in these places.
(26:46):
I'm following a certain macro diet. Here's my height and weight.
Can you tell me what to do?
And it'll give you a full trainer's guide to how you can get there. Wow.
And then the third one that I love, I think resonates with a lot of people.
If you have kids, nieces and nephews, grandkids, neighbors, whatever,
(27:06):
ever, ask your AI systems to write them a bedtime story.
So you can integrate like the things that they're interested in,
their names, it'll do it age appropriately.
I have an early reader in my house. And so I asked the system to create some
early reader stories that my daughter could read on her own.
And it does a fantastic job. Oh, that's really sweet.
(27:27):
It made me think of, oh, yeah. So when you say the AI systems,
again, they'll say I'm very new to it myself. self.
You're telling me that I could go to say like one of the free open AIs like
chat GPT and basically like type in,
yeah, like I like to eat grilled chicken dishes and it would say,
(27:47):
all right, here's what we would recommend. It's that easy.
It's that easy. Okay. And the other kind of fun thing to do there is you could
say that I like to eat grilled chicken dishes. is, give me a recipe in the style of Julia Child.
Oh, wow. We did this one as an example in a meeting once. And it's just phenomenal.
And you will get a big kick out of it.
(28:08):
You can also ask, if you're maybe not trusting the system just yet,
ask it to cite its sources.
And so it'll give you the links to where it pulled from in order to get the
information to create that dish.
Oh, wow. That is really cool. Oh, man. Okay. I got like very literally the very
first thing I'm going to do once we're done with this conversation is that very thing.
(28:28):
I'm pretty excited to try this. This happens a lot. Let's get through this so
I can go try something. Yeah, yeah.
We've touched on a couple things as it relates to HR.
We also know performance management is a big piece in the HR space and also employee development.
How is AI being used in that area? Yeah, absolutely.
So one, I would go back to that note-taking example of having those one-on-ones
(28:52):
and then having an AI system help you curate those notes.
So if you think about, you have a one-on-one with your direct once a month and
you implement those or load those notes into your AI system each month,
maybe you're keeping it in a document or wherever.
(29:13):
And then at the end of the year, you now have these notes that are all taken
in the same way, the same concise sort of format,
you can then take the whole thing and pop it into an AI system and ask it to
pull out key themes, basically ask it to.
Act as a manager and you're looking for performance.
(29:34):
You can also use it in a sort of coaching development way.
So I'm seeing this happening. In fact, there's some large companies piloting
this program where you essentially ask an AI system,
and there's some that are developed specifically for this, that you ask it to
act as an employee coach or a career coach, specifically for managers.
(29:56):
Middle management has been hit so hard since the pandemic with With so much
extra work on their plates, they're really managing a lot. Right.
And it can be a tough space. And there's not always a lot of support at the top end for managers.
And so they can use these coaching systems to ask for help in dealing with employee situations.
(30:16):
And they're coming back with remarkable answers.
It's really helping a lot of folks who maybe don't have that support from a
direct superior to help them work some of these systems out.
And then on the development end, I love seeing what's happening right now in
the learning and development space, real opportunities for organizations,
(30:38):
HR teams to create learning systems using AI.
So there's so many out there. You can use one of these free systems like a ChatGPT
or a cloud, but you can also use these great, there's a great one that I've
been using recently called FreeFuse, which integrates.
(30:59):
Video voiceover. You can also just throw a whole PowerPoint in there and it
turns into this sort of create your own adventure.
Oh, wow. Oh, my gosh. It's just it's awesome. And you can do it as a one person show.
You know what I mean? Yeah, that's amazing.
So that actually makes me think a little bit, too, about what is we've talked
a lot about what it's what are the possibilities?
(31:22):
What do you what have you seen or what do you think are the limitations or even
and possible pitfalls of AI? Yeah.
Limitations, I think, I would say for organizations that aren't ready to invest
yet, for teams that are just using free systems, there are limitations around
what kind of information you can add in there.
(31:44):
You do have to be careful about, especially employee data, ensuring that all
of your stakeholders are kept safe in the process.
And then the other, I think the big limitation is that folks just don't know how to use it yet.
Anytime you're introducing a new tech, it's going to take a bit for people to get up to speed.
I think that's why I'm so passionate about folks learning to use it at a really
(32:10):
base level so that it's a lot easier when organizations do implement their internal systems.
You've got that leg up because you've been playing with it for a while.
Yeah, that sounds not to get too into the weeds of it, but that does sound like
what you do and what you're passionate about is helping organizations get it right.
Right. Like you could you can go in there and if an organization would like
(32:33):
to implement or start using AI, depending on what their strategy might be,
that's something you very much can help them with. Is that right?
That's exactly right. Right. Awesome. And then I would just call out the other
sort of limitation is really around governance.
I feel passionate about organizations having a strategy, having a plan for communication
and how they want their employees to implement AI into their work.
(33:00):
In some of the research that I've done, there's a host of different things happening.
Some organizations are putting it directly behind a firewall.
You can't get to it. We're just not having the conversation.
There are others that are not talking about it, but it's inside the system.
So folks are using it, whether or not they're being told to.
And they don't really have a clear
(33:21):
understanding of what is and isn't appropriate to be putting in there.
And so I think a limitation right now is just how,
not having guidance if you haven't set that strategy yet.
Oh, yeah. I was going to say that sounds, it does make me think of,
yeah, what level of transparency organizations should have to ensure that.
(33:41):
Because I think, I mean, that could easily be a pitfall.
And or what, you know, what accountability pieces you could put in place in
a system that, yeah, you still want both parties, you want the AI and a person
in charge of that. But what does that look like? Exactly.
And that's another reason why I think it's so important that HR teams are empowered
(34:03):
to understand and use this tech.
Because if we bury our heads in the sand, it's going to be really difficult
to put that strategy together and to really guide employees on what is and isn't
appropriate if you've never used the system yourself. Oh, yeah, absolutely.
So getting into wrapping up the conversation, what do you see the how do you
(34:28):
see the future of AI being used in organizations?
What does that look like? Yeah, absolutely. I foresee it in every part of our work stream.
I think it'll become as ubiquitous as opening your laptop.
Your computer is part of nearly every part of your work stream during the day.
And I think AI will be have that same sort of place in in the workspace. space.
(34:53):
And maybe touching on it a little bit, and this is a across all types of organizations,
small, medium, large, fortune 500. What do we see this everywhere?
I do. Obviously, it's going to come first for knowledge workers,
but I see it on Frontline. I see it everywhere.
It'll be a part of every part of the work that you touch, I think.
(35:14):
All of us will be dealing with it, at least in one way or another.
Other. Think about our homes.
We talk to our robots here and ask it to play our favorite songs or pull up the calendar.
I think that was really hard to picture 10 years ago.
And now my kids use it. But it's a really simple kind of integration.
And I think that because of the power of AI and the way that it's being created
(35:39):
to be intuitive, I think it's going to flow right in whether or or not we realize it.
Yeah, I was actually just thinking about it because prior to talking to you,
I wouldn't have thought AI was much of my life at all.
But even now you just mentioned it. I have Spotify.
And one of my favorite features is the list that it creates for me or the there's
(35:59):
here's your own personal DJ based on the things that you like.
And it's some things that you have already listened to, but then brand new music
and artists that you've never heard of, which is amazing. I I love that.
Absolutely. Or if you've ever sat and watched Netflix and found a new show because
it recommended something to you, that's AI at work.
Yeah, yeah. Again, I'm probably going to see it more now after this conversation than before.
(36:23):
Michelle, it has been an absolute pleasure talking with you.
I've learned so much more. I'm really excited to do a bunch of things that we've talked about.
I know you have some things coming up and I know with a webinar with People
Element, would you like to put in a plug for that right now?
I would, yes. Yes. So we're going to be talking about all things AI at work.
Really, it's a deeper dive on this conversation, how we implement these systems
(36:47):
into our daily life, especially if you're a tight HR team, you will get so much out of this.
Love it. Love it. And what was the date for that one? That is June 18th.
June 18th. That's right. Okay. And we'll have detail.
We'll provide more details as well in our show notes.
Again, thank you so much. It's been a pleasure. I learned so so much today.
(37:08):
Thank you for having me. This was fantastic. Awesome.
Thank you so much for tuning in to today's episode of All About HR.
I'd like to take a moment to thank our producers, Kristen Romero and Sam Cortez,
as well as Kevin McDonald, who wrote our theme music.
Stay tuned for more engaging conversations and expert insights.
(37:28):
Until next time, this is Laura Hundley signing off. Take care,
stay inspired, and keep innovating in the world of HR.
Understand engage inspire and retain your people like never before people elements
employee experience and engagement
solution delivers powerful intelligence giving you the confidence.
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