0:08
Hello, and welcome back to our Argyle HR Leadership Forum.
0:11
My name is Brittany Sullivan with Argyle and it’s great to have everyone joining us today.
0:16
I would love to introduce our moderator, Stacy Lewis, Chief Executive Officer and Chief Disruptor at HR Interrupted.
0:46
We are excited to have Stacy and our panelists with us for a panel titled Recruitment, Retention, and Workforce Management in the Age of AI.
0:54
Welcome, Stacy, and over to you.
0:57
Thank you, Brittany.
0:58
Welcome to everyone who’s joining us today for this.
1:01
I’m going to speak in the future insightful and thoughtful conversation about recruitment, retention, and workforce management in AI. Again, I’m Stacy Lewis and I’m so honored to serve as your conversationalist with this panel.
1:16
So we’re going to do we’re going to jump right in.
1:17
First, I like to let this amazing panel introduce themselves, and then we’re going to get into a very rich and robust conversation about AI, and before we do that, as we’ve been talking about it, I’ve been in this work since it was called personnel.
1:34
I was back in the day when you wrote everything, and things are changing, and nothing is instant except destruction.
1:40
Everything else takes a plan.
1:42
And so as you are bringing as we’re talking about AII know some of you, some of you out there are like I’m so excited.
1:50
We have an amazing panelists who’s going to give you a wonderful story about the excitement of AR.
1:55
But I got to tell you, as a panel member, I’m struggling with a little bit.
2:00
So both sides will be represented today.
2:02
But more importantly, you’re going to walk away with some tools and some tips With that, let’s take it away.
2:06
We’ll start introducing our panel members.
2:08
Monica, why don’t you start for us today?
2:12
Hi, everybody.
2:12
I’m Monica Panetta, I am the chief people officer at Pareto Health based out of Philadelphia.
2:18
And I’m excited to talk with you all today.
2:21
Thank you, Elizabeth.
2:24
Hello.
2:25
I’m Elizabeth Hall.
2:26
I’m vice president of employee experience and talent for Cambia Health Solutions.
2:31
We are a Blue Cross Blue Shield organization serving members in Washington, Oregon, Idaho, and Utah.
2:37
And I’m coming to you from Bellevue, WA, just outside of Seattle.
2:42
Thank you, Elizabeth.
2:44
I call her JCK, but she’s Jenny.
2:46
Go ahead, Jenny.
2:48
Hi there.
2:48
I’m Jenny Kodi Kangas or JCK for short.
2:51
I am a Director of Talent Transformation at Eightfold AI.
2:54
Eightfold is a human capital AI company that helps organizations and individuals find the right role for them.
3:01
I’m very excited to be here today and I’m coming from the Twin Cities of Minneapolis, MN.
3:07
All right, team, let’s jump in again.
3:10
Our topic is recruitment, retention, workforce management, and work workforce management in the age of AI.
3:16
We’ve spent a lot of time behind the scenes talking about AI in the workplace, its impact on human resources, and the pros and cons.
3:25
What should we be looking at?
3:26
So Jenny, I want to start with you.
3:29
And again, for those of you that are joining in this, this is not going to be one of those she and we ask this is a conversation.
3:35
I so as you’re putting your questions in the chat, anything that you want to know, please, please utilize the chat feature.
3:42
Jenny, start with us from your experience, how have you seen emerging technologies in in the gender of AI influence establishes on HR functions, including recruitment, retention, any workforce management share some real life examples for us as we get started.
4:02
Absolutely great question.
4:04
And so from my experience I’ve worked alongside of a lot of large Fortune 100 and Fortune 50 companies helping them to get their transformation initiatives right.
4:15
And so I’m, I’m blessed to have an opportunity to be at the front line of so many different wonderful AI implementations and experiences.
4:25
And one of the great things about AI is nobody’s doing it exactly the same, which is a good thing in a lot of ways.
4:31
But I’d say some of the people who are doing it most interestingly in the AI space, they’re they’re leveraging AI to democratize the understanding of skills in the context of work, because as it relates to skills, we don’t teach people how to understand their skills.
4:48
And so if we want to get kind of skills-based transformations, right, we have to first solve for that democratization of that skill that isn’t taught, right?
4:57
So a number of organizations that are putting that at the front of their priority list and many others that are using AI to shift more skills based talent planning.
5:10
And I feel like I’m monologued up, but in lots of different ways.
5:14
No, anything else. Any specific example of an organization that somebody is specifically doing?
5:20
I mean, you talked about from a broad perspective, but is there a client you’re working with that you can give a specific example? At Cambia, we’ve been undertaking an effort to increase our veteran hiring and we’ve been using a tool called Higher View to help us sift through candidates more effectively.
5:40
Well, it’s not generative AI, it is machine learning.
5:43
And we have been able to boost our veteran hiring because we found there were some veteran candidates kind of deeper in the virtual stack of resumes that our recruiters just as humans, we’re not able to get to.
5:58
But using a higher view, we’ve been able to surface those veteran candidates and move them along in our pipeline and increase our veteran hiring.
6:07
So Elizabeth, building on that, and I think that that’s great.
6:12
What are some of the risks, though, right?
6:13
Because when you think about revolutionizing HR, we can all say that, you know, HR is known as just being rope, doing the same thing the same time, bad reputation that we have because when done right, we are the synergy of the organization.
6:25
But that’s another archive seminar.
6:27
But what are some of the risks associated when you’re bringing in like this new technology, looking at hiring veterans, especially if the potential of the implementations are overestimated, right?
6:43
What, what are some of the risks that we’re looking at?
6:46
Well, I’ll say first of all that I am bullish on generative AI.
6:51
And personally, I’d rather be on the side of over than underestimation.
6:55
But the unfettered use of generative AI in HR is definitely a risky proposition.
7:02
And there are two things that I would keep an eye on.
7:05
Of course, we all want to keep an eye on the risks of bias and discrimination in generative AI, just as we’re all familiar with the risks of bias and discrimination among human decision-makers.
7:18
But two things that I would look at.
7:19
One is vendor lock-in.
7:21
I think at this point it’s still early days and we want to keep our options open as a buyer for us at Cambia, we have an in-house large language model that we’ve developed, we call it Cam and Cam is built on one particular LLM, but we actually have another one that runs in the background in case that first LLM just the terms of their business agreement which we had happened early in the development of Cam.
7:52
So we kind of have another horse in the stable should things change and we want to make a make a switch over.
8:00
And we’re trying to be very thoughtful about that with other vendor selection as well that we not get locked in with particular vendors too early as this field is really emerging.
8:10
The second one I think is the over reliance on Gen AI.
8:15
AI for innovation that might best be done by humans.
8:20
It’s great to use Gen
8:22
AI is an augmentation, but we want to make sure that a topic near and dear to my heart, employee experience, that we’re still using kind of humans in the loop on that decision making.
8:36
I see you, I see you smiling.
8:39
JCK, you have something to add to that.
8:41
I just think Elizabeth and Cambria’s approach of having a backup LLM or a large language model and it’s just brilliant.
8:48
So I just to, to build upon what Elizabeth said, you know, I think one of the, one of the risks that we have in the HR space is that we approach AI like a project instead of a program.
9:01
And so a lot of the times in HR, we have been almost conditioned where there’s a start and there was an end to any sort of project or any sort of change.
9:10
And when it comes to AI, y’all who are listening, that is not the case.
9:14
AI is something you choose to begin and it’s something that you have to establish governance for.
9:18
How are you going to ensure that the guardrails you’re putting in place are still hard or soft as you continue?
9:26
Because a lot of the nature by nature of a lot of this AI, there are a lot of different types of AI, right?
9:31
Sometimes, the AI can learn, and it can learn the wrong behaviors, and we need to make sure that we have the ability to go back and check that, or sometimes our strategies change, right?
9:41
If there’s anything that we can agree on with the the future of work, it’s that it’s probably not going to look like the way it does today.
9:48
So you have to be very careful of how you’re going to implement that AI, making sure that you have the ability for agility in your solutions versus that more rigid way that we’ve kind of looked at things before.
9:59
So that was just the piece that I wanted to add there.
10:03
Monica, I’m, I’m listening in awe of the advances because I actually, I would say we’re probably not as progressed as Elizabeth is in our journey, but you know, we have done some things that are pretty, I think, pretty cool.
10:25
And, and we’ve enabled a lot of efficiency using AI, say we just kind of hit the little tip of the iceberg.
10:32
But one thing that we’ve done is we do utilize some assessments, pre-employment assessments, and those assessments kind of travel with employees through their life cycle.
10:44
So we’ve been able to do some pretty awesome things like ask AI, you know, how could this manager work best with this employee?
10:52
What are some communication styles, you know, suggestions for them and things?
10:57
And we’ve made it much more effective for people to transition to different managers and leaders and different roles by using some of that without us actually spending the two hours reading through every little detail and, you know, going through that.
11:10
So I love that.
11:13
And JCK really loves what you’re what you’re talking about: HR view and AI as a program and not a project, not a start/stop, which is typically what we do in human resources.
11:24
And I think that will speak to me because I’ve shared with you that I’m working with clients now who are like, I don’t even know where to start.
11:30
I don’t even know if I want to start right.
11:33
And you were talking about being agile and really assessing it out of what needs to happen.
11:39
And a lot of, you know, my clients are like, it’s just too unreliable, the human factor, all the things you’re mentioning.
11:46
So Monica, if you could kind of navigate our conversation around, and I’m talking to those clients, how can I, how can organizations prepare for new technology advancements?
11:59
We’re talking about AI, but I would go for anything.
12:03
JCK said that the face of work did not look like this in 1985.
12:07
And in 2028, it’s not going to look like this.
12:09
So how can we prepare for those new technology advancements within HR and what steps can we take?
12:17
And I’m going to use this term to stay ahead of the curve.
12:20
JCK said the curve changes literally almost daily.
12:23
So really, how can they just get started?
12:25
Yeah, I mean, I think the first step is aligning a strategy, right and understanding what your vision is for implementing this.
12:34
One of the other things that we’ve done is is also looked at the pain points.
12:38
So, you know, where’s where are the most painful things that we could either make more efficient or, you know, add some productivity gain in our everyday work as a starting point?
12:51
And I think one of the most important things too is like, fail fast.
12:55
Like don’t be afraid to try something and then say, oh, Nope, that didn’t work.
12:59
Let’s try this instead.
13:00
But if you don’t dip your toe in and at least at least try, you’re not going to know because this is all new to, to everybody.
13:08
So it might work for some organizations and it may not work for others.
13:13
So I would say those are probably the top three things that that I keep in mind as a starting point.
13:19
I love that.
13:20
Fail fast.
13:21
You can’t fail unless you try, right?
13:23
Fail fast.
13:24
JC can’t Elizabeth add to that?
13:26
Let’s talk about I think that’s she gave some good points.
13:29
Yeah, I think the the companies that I’ve experienced who are doing this best, this being leveraging AI best often have one thing in common and that’s that they understand the problems within their organization that they’re trying to solve.
13:45
And so there are people who will sometimes be like, oh, you know, there’s an initiative, I’m going to use AI because my CEO told me I needed to use AI, right.
13:52
If you’re not, if you don’t have clarity to that, like North Star of the problem, the likelihood that I’ve seen where success ends up happening ends up being a lot less.
14:02
And so again, having crystal clarity of what is the problem we’re looking to solve and what’s the why behind the what for it too.
14:12
And kind of building up of what Monica said, being able to take your processes from high to post post higher, right and map out what are my existing practices today, Starting with that map and then being able to look at, OK, where is their pain?
14:27
And then taking those pain and working as a group to be able to calibrate that pain to like these are the things where we all agree that there is pain.
14:37
And then being able to prioritize those in terms of like what we’re going to reverse engineer into our strategies.
14:43
I’d say those are a couple of different places that people can look to begin.
14:47
And then again, like, just like Monica said, fail fast.
14:50
It’s, it’s, but always fail forward, right?
14:54
So even if you fail, making sure to ask what worked, what didn’t, what do we change?
14:58
Because so often we forget that it’s, it’s through failure that we glean the blueprint to get things right.
15:06
And we we have to embrace failure, especially when it comes to AI, because you’re on the forward edge, right?
15:12
Some of the stuff hasn’t been done before.
15:14
And so, yeah, that’s just what I would have to add.
15:17
Elizabeth, how about you?
15:20
Totally agree with you, Jenny.
15:22
I’m getting clear on the problems you want to solve, getting clear on the potential ROI for an AI investment.
15:30
I would add, you know for us at Cambia, we are hyper vigilant around our members data and how data flows into and out of the organization.
15:40
We have a responsible AI committee and I would suggest organizations be very thoughtful about looking at the responsible use of AI.
15:51
Our team includes, you know, it ethics, compliance, legal sourcing, HR, so really cross functional view on the responsible use of AI.
16:02
And we are currently talking with all of our current vendors through that responsible AI team to understand, you know, vendors who are already in our ecosystem, how they are introducing generative AI components into their tools so that we understand, you know, the changes that are going to happen with our current partners before we engage in new partnerships as well.
16:27
That’s amazing.
16:28
I have to talk to you that I love that ACK.
16:33
You said the blockbuster statement in HR Anyone who’s a good practitioner, you always start with what are we doing?
16:42
Why are we doing it and who are we doing that for?
16:45
When I have the honor to speak with new, new HR influencers coming in, I’m, you know, I have 40 years of HR experience in anything that you’re doing, whether you’re doing a recruitment, whether you’re implementing anything, you always need to, what are we doing?
16:58
Because sometimes we move into the action and the action is not appropriate for what we’re trying to achieve.
17:03
So we cause more harm.
17:05
So getting that clarity around that, right.
17:08
And that’s not treating AR as AI as a project, it’s treating as a program.
17:13
Perfect.
17:14
Moving with that was that was a great segue.
17:21
Jenny, what would you say are the most important considerations?
17:27
And I think we talked about the what the one who but build on that, what are the most important considerations when evaluating new HR technology?
17:37
New HR what, what should we consider what’s most important?
17:42
So the thing I want to remind people of questions are your pickaxe.
17:46
That’s how you understand kind of what the problem is or in the situation, what the different technology is.
17:51
And when we’re going with those questions, it’s really, really critical to instead of using what or why questions, you want to use how questions.
18:00
Show me how you are are creating, you know, checks and balances within your AI.
18:06
Show me how your governance is working.
18:08
Show me how customers like myself are using this and to remember, if your vendor does not have a clear answer to how that is a red flag we want to run away from, not towards, right?
18:21
Because if, if AI is being leveraged and it’s not being well thought out and there are some situations of us out there because things are going so fast and so furious, that could be opening yourself up to risk and you don’t want that.
18:34
And so those, again, those how questions are going to be absolutely important.
18:37
And building upon what Elizabeth said too, you have an existing HR tech stack today.
18:42
Everybody here has one, you have vendors that you’re working with.
18:45
Everybody is likely going to be implementing AI somehow.
18:50
And so first going back and asking like, which what are you doing today?
18:53
Like what?
18:53
What’s new, right.
18:54
Making sure you’ve got that feedback loop with your existing customers is important as well.
19:00
And yeah, I think that’s, that’s, that is like the the how question.
19:06
I think it’s the thing that I want to lead people with.
19:07
OK.
19:09
What else want to jump in on that?
19:10
What are most important considerations when evaluating new HR technologies?
19:17
I would say, I mean, obviously that’s the very HR answer, but I always keep it at the forefront coming from the healthcare space.
19:23
I mean, privacy, you know, making sure that we’re keeping control of our data to the degree we can, you know, that’s always a big one for me being in that in the healthcare field.
19:34
So that’s Elizabeth.
19:40
I would say return on investment.
19:42
Just to add to the conversation, Jenny, I love questions.
19:46
Are your pickaxe?
19:47
I’m going to borrow that if that’s OK.
19:49
But getting really clear on what’s the potential return.
19:53
There’s so much vaporware and a lot of, you know, sort of promises over and above reality right now in the generative AI space.
20:04
So I think getting really clear.
20:07
On what is likely to be both the efficiency play and the effectiveness play with any new tool that’s AI enabled.
20:16
So let me throw this in on this question.
20:18
We’ve talked a lot about vendors, we’ve talked a lot about the process.
20:24
Is there any human consideration when you are evaluating a new HR technology from HR perspective?
20:30
Is there any behavior?
20:32
Is there any consideration for the users is talk to me about that because I what?
20:37
And I’m asking that question Elizabeth because I’m here and working with clients.
20:41
There is a feeling that introducing AI and all these new HR technologies, and this might be the old school, I’d use that word, HR, We’re losing.
20:54
We’re losing what we’re supposed to do, and that’s taking care of the organization’s most valuable asset, which is their employees.
21:00
I’m not saying that’s true, but there’s a faction that believes that with all this technology.
21:04
So when I’m asking about any other considerations, is there a human component that we should take in consideration, yes or no?
21:10
Just want to introduce that.
21:11
Yeah, I’ll say yes for sure.
21:13
And it’s wise to be skeptical because this is a really emerging field that is revolutionary for how we work in any field.
21:24
I know we’ve talked a little bit about healthcare today, but true for for any field.
21:29
I, you know, I earlier used the phrase humans in the loop.
21:32
And I think it’s really important that we that and not just take the output of generative AI, whatever it is, at face value, but continue to use our own human intelligence to assess what the AI is serving up.
21:52
Whether it’s something simple like ideas for a panel presentation you might be giving on generative AI or something more complex.
22:01
You know, here, here’s my recommended candidate for this role.
22:05
We want to make sure that we always have a human in the loop for that decision making process.
22:12
OK, Jenny, I saw her give me that side eye, a good side eye.
22:19
I hope my face has like subtitles.
22:23
So I apologize in advance.
22:24
You can always kind of think what my face always has subtitles.
22:28
Apologize, but you know, when it comes to AII think that the important thing to remember is you can find the right technology for your organization.
22:39
But when you come in to deploy that technology right within your organization, you have to remember the users or the humans who you support are going to have a design bias of how a you’ve either brought technologies or or changes to life before, or they may have a fear that AI is going to replace their jobs.
22:59
And so it’s not just about finding the right technology.
23:02
It’s also about selling that with them.
23:05
What’s in it for me?
23:07
And it’s so incredibly important that again, going back questions are your pickaxe, you should be when you’re going through these, these journeys, you should be taking cross sections of your population and asking them what works, what doesn’t, what would you change?
23:21
What’s keeping you up at night, right?
23:22
Because often I know like I’ve, I’ve had the pleasure of developing AI technologies in, in the last couple years.
23:30
And there were a couple situations where my own design bias said, so we want to have the human in the loop for the specific part of the town acquisition process.
23:40
But when I actually researched with my candidates, they didn’t want the human in the loop.
23:45
They actually preferred to work.
23:46
And they were able to build like stronger tactical empathy with the bot versus the human and felt more connection to that.
23:53
And so that was completely backwards, right?
23:56
But it’s so, so, so important to understand what design biases are at play within your population and then make sure that you’re speaking again, what’s in it for me?
24:06
Speak to that with them.
24:07
It’s not just about finding the right technology.
24:09
It’s also about selling.
24:11
What does it look like on the flip side for this?
24:13
And I have it over here.
24:15
But when you’re doing that selling, Please remember that blueprints do not sell cars you do not sell with a blueprint.
24:23
And so often in HR, we are really good at building blueprints and SLPS, but blueprints don’t sell.
24:29
So you’ve got to make sure that you’re, you’re keeping it short, keeping it sweet and hitting on that almost brochure type approach to, to managing that change.
24:38
Love that.
24:39
Thank you.
24:39
That, that is it.
24:40
How are we engaging the users, the workforce in this conversation?
24:45
Because at the end of the day, and I do think I’m wondering if you want, I do think it, that is some of that now I do, I know listening to some of the clients that I’m working with, they want to, but it’s breaking away.
24:58
It is what is in it for me as opposed to people thinking this is going to replace my job.
25:02
You know, now, you know, some of the leaders are saying now I already don’t get my staff to work.
25:07
Now they can just write this memo and I’m not going to get anything.
25:10
And so they’ve told themselves a story, right.
25:12
And so I’m glad we’re having a conversation that we’re unpacking all of it.
25:17
If you if you want to say Monica, I’m not.
25:19
I had AI have a next.
25:20
No, I was just going to say actually, some of it is at least what I find it’s that, but also they don’t know how.
25:27
So I think the other piece is training and making sure that you arm them with the appropriate way to use these tools so that they do see the ROI because not it does not come intuitive to people.
25:42
So I love that.
25:45
So building on that team, Elizabeth, you can kind of start this for us.
25:52
Sure.
25:52
We just had a robust conversation about really, you know, what should we take into consideration?
25:57
And I think that’s key.
25:58
So how should HR leaders, influencers, those employees who sit in H, you know, leadership roles emphasize the importance of a new investment of the importance of this new investment to ensure that the right tools are in place.
26:16
JCK talks a lot about, you know, making sure that you’re using what strategies should be used to gain that leadership buy in, you know, everything we hear in HR, you got to start with make sure you get support from the top.
26:28
And that is important.
26:30
But sometimes you just got to keep the door open and then hopefully, you know, you bring the top with you.
26:34
But another thing, So what, what should we take into consideration with strategy?
26:40
Great question and I’m going to take it in a couple different directions.
26:44
So first, if my finance friends were with me on the call, they would tell me I absolutely should emphasize the return on investment.
26:52
And I’ll see we’ve, we’ve seen that play out in a couple different ways at Cambia 1 is an efficiency play where we’re just able to move faster.
27:01
For example, in crafting marketing materials, we’re using Gen.
27:06
AI to do some of the early drafting to help us move more quickly, but there’s also an efficiency play.
27:13
So our talent acquisition team is using Gen.
27:17
AI, again, our in house large language model to make sure that all of our job postings are custom crafted to the position and we’re not just using our more stale HR language in our job descriptions and using that as a marketing tool.
27:36
That’s something we just didn’t have bandwidth to do in the past that that we’re now able to do with Gen.
27:42
AI.
27:43
So ROI first and foremost.
27:46
The second thing I would say is start before you have an investment on the table and start future proofing your HR organization so that they understand more about Gen.
27:58
AI.
27:59
At Campbell.
28:00
We’ve I’ve seen a couple different things happen for my own team.
28:04
We had a book club, we all read Co Intelligence by Ethan Malik.
28:10
I believe he also has a podcast and lots of things available.
28:15
And we had a great discussion about the future of Gen.
28:18
AI kind of writ large before we got hyper focused on how we would be using those tools in our HR function.
28:26
The other thing I’ve seen part of our organization do is what they call Genius Bar missions where they had different kinds of fun tasks for people to complete either using our Cam or in house LLM or using Microsoft Copilot through the Office 365 suite to just experiment with some different ways of using Gen.
28:49
Gen.
28:49
AI in their work.
28:51
And then have a discussion about that.
28:53
And they gameify that you could earn points and prizes by completing different missions within Gen.
28:58
AI.
28:58
So I would say don’t, don’t be afraid to start now with future proofing your organization to make sure that everybody really understands what Gen.
29:07
AI is and how it can work for them.
29:10
So Elizabeth, this just got just came to me.
29:12
I love the ROI with AI being so new and a lot of organizations kind of just approaching that.
29:19
What factors are you going to consider for the ROI?
29:22
Because you don’t you you haven’t you see what I’m, you kind of know where I’m going with the question because I’ve heard that yes, Stacey, we can do ROI.
29:29
But what are we what are we challenging that against or what are we basing that against because we haven’t done this before.
29:35
Yeah, that’s great.
29:36
So I would say if it’s an efficiency play, it should be time on task and you know, measuring the time savings.
29:48
If it’s an effectiveness play, then it’s you’re, you’re doing something brand new with Gen.
29:54
AI.
29:54
And then so you have to think about what is the impact of that particular activity and how would you measure the impact of that activity on your business results?
30:02
Perfect.
30:03
Thank you.
30:04
Yeah, you’re welcome.
30:06
Anybody else want to jump in on that one?
30:09
Yeah, I think so.
30:11
So the thing I just wanted to build up with, with what Elizabeth said, we can have a a tendency in HR to fall back to the design bias of the metrics that we’ve historically used in the past.
30:24
And when you were talking about AI, you need to remember that there are metrics that you may not have ever considered.
30:29
You could potentially be able to, to, you know, to call on.
30:34
And I have a lot of clients who will come to me and they’ll be like, how do I make a business case?
30:39
How do I get started?
30:41
How do I story tell the ROI right?
30:43
And for a lot of them, it actually comes down to step 0 for them is if you can’t collect the dots, you can’t connect the dots.
30:51
So their business case is actually, I don’t have those dots collected in my data today.
30:56
I need AI to come help.
30:59
And, you know, democratize that data from multiple different sources in order to to link it so that I can tell stories about what’s going on throughout my, you know, Canada population or whatever it might be.
31:08
So sometimes it’s not necessarily about finding that metric, but it’s also about if you don’t have any of the data, you can sometimes make the business case for the fact that you don’t have that data to help to future proof your your HR strategy.
31:23
I like that.
31:24
Thank you, Monica.
31:26
I like that.
31:26
JCK.
31:27
If you don’t have, you have to collect to connect, you should write a book.
31:31
You have a lot of wonder isms, the JCK ISM.
31:36
Your questions are your pickaxe.
31:38
I love that.
31:39
I digress, Monica, I was going to say I I agree and I I like the collect the connect phrase there.
31:47
So I think that’s great.
31:51
So I think we’re we’re coming kind of towards the end a little bit.
31:57
I know we want to leave time for some questions from our our other our audiences with us.
32:02
Can the three of you, and I’ll start with you, Monica, just share any strategies for determining which processes to automate first and how to refresh your existing HR operations, Right.
32:18
And it’s just kind of technical, just kind of what are you doing?
32:21
What is your refresh point?
32:23
Anything that you’re doing that can be helpful in this space?
32:25
I know the question is kind of technical, but you know what it is.
32:27
Yeah.
32:28
But just what could you share with us?
32:30
Yeah.
32:31
I mean, like I said before, I think when you talk about like the what’s in it for me trying to help move along some of the pain points.
32:40
So understanding what those pain points are, whether it’s surveys or you know, having discussion groups about that, understanding frustrations in the current workflow, bottlenecks in the current workflow starting there to try to show, you know, the quick wins and hey, this is this is going to make your life better and easier.
33:01
I also think that there’s a lot of value in understanding the high impact areas, even if they may not be the pain points, but understanding where you can see the most ROI and you know, saving time and understanding, you know, onboarding and offboarding for example, you know, that’s a great place where we kind of started.
33:22
That’s a very human touch oriented piece that we were able to kind of streamline into a more efficient way and save time for both us and the employee and have a better experience coming in.
33:35
And then I think also just looking at standardization and complexity is another one, areas that you can standardize complex areas that you could make a little bit more efficient.
33:48
I’m a big quick wins person.
33:50
I’m sure that JCK has an, you know, acronym for that.
33:54
But the quick, the quick wins are the way to go for me.
33:58
And then showing the quick wins equal, you know, a positive outcome for for everyone else, right?
34:05
OK, anyway, I, I love that.
34:07
I love that quick wins, right?
34:09
You had one too Fell, you know, fell fast, right?
34:12
And and we’re not making a lot of it, but these things really matter, right?
34:15
We we’re talking about AI.
34:17
We don’t need a lot of words.
34:18
Fell fast, fell forward quick wins, right In this space, Elizabeth, any strategies on maybe automating first?
34:26
Anything refreshing your operations?
34:29
Yeah, I mean, I I get, I’ll get super practical with it.
34:32
So, you know, here’s three places I would look in your HR organization for opportunities to do see that return on investment with generative AI.
34:43
You know, first would be around recruitment and talent acquisition, looking for ways to automate tasks, improve candidate matching.
34:53
Second, I would say employee onboarding, look for ways that you can customize employee onboarding and really personalize it to the individual’s experience as well as reducing some administrative tasks.
35:06
And then lastly, workforce planning and analytics.
35:10
Jenny, I can help you analyze, you know, large data sets.
35:14
Once you have collected those dots and you can connect them by using a Gen.
35:18
AI tool as well as identifying trends and doing some of the predictive analytics that can be challenging for us to do as humans put Gen.
35:28
AI to work for you on that.
35:29
And I think, you know, right now, those are the areas when I look across HR that I think are most ripe for seeing a solid ROI from Gen.
35:38
AI investment.
35:40
Love that, Elizabeth, thank you for the practical approach because I think at the end of the day, right, that that that’s what we want.
35:46
So thank you, JCK, Jappa’s one of your isms.
35:53
That’s what I’m trying to think of is strategies on automating.
35:57
First, I think might not just be a word of caution, which could be an ISM, I guess you could say just because you can doesn’t mean you should.
36:06
And so making sure that like, as we’re going about this, there was actually some very, very large companies that are out there who we all interact with on a daily basis that took their their recruiting process and completely and totally took the human out of it.
36:22
And they found that it crashed and burned.
36:24
They actually had the opposite.
36:26
That was they they intended to happen happen with their candidates.
36:30
And so they swung the pendulum this way and then they ended up swinging it back.
36:34
And so I think so, so, so important as you decide to begin, as you choose to choose to start with, what are we automating?
36:41
What are we not making sure too that you have those checks and balances to ask what’s working, what doesn’t, what do we change?
36:47
And to understand that there may be things that you automate that you end up pulling back on your automation for because they are not resonating with the humans who you support.
36:55
And at the end of the day, our job is to to support those humans.
36:59
And so making sure again, that you have that feedback loop to ask them what, what would they want automated?
37:05
What would they want not want automated, right?
37:07
Like there are some pieces and there are some opinions that people might have.
37:10
And it’s important to have their voice in that conversation.
37:13
I love that human piece of it.
37:15
I was sharing with you all.
37:17
I was sharing with you that, you know, I have a client that they an employee.
37:20
This is very practical requested at a an accommodation and AI was one of the the instruments that they’re using.
37:28
And what happened was individuals now in the organization that may not have, they disclosed that they had a disability also wanted to use AI in the same manner.
37:38
And so it just raised all of these, you know, and they were saying no.
37:41
And the employees were like, well, this is a tool that’s available for us just because we haven’t brought any documentation per SE, very practical to say we can’t use it.
37:51
You know, what’s the problem?
37:52
And so we’re sharing with you all that, you know, they brought to legal and legal like, you know, we don’t want to touch this.
37:56
We want to see what the case law is going to say.
37:58
There is no case law out there.
37:59
So as Jenny said, you know, be the first fail 1st and fail forward.
38:03
Somebody’s going to have to establish it.
38:05
So thinking about just bring that human piece of it.
38:08
So as we’re going to close out, we have some questions that are coming.
38:12
Any final thoughts, please?
38:14
Just give us some final thoughts.
38:16
You guys have been amazing.
38:18
I love this conversation.
38:20
Give us some final thoughts and we’re going to jump to the questions because we have a couple of them.
38:23
So I’ll start with you, Elizabeth.
38:25
Any just final thoughts you want to share with the individuals that came with us?
38:28
Yeah, I’ll say if you are not of a generation that is a digital native, do not be afraid and don’t stick your head in the stand.
38:38
Be courageous.
38:39
Educate yourself.
38:41
You learned how to use the Internet.
38:42
You can learn how to use this too and have some fun with it.
38:47
Love that.
38:48
Monica.
38:49
Any final thoughts?
38:50
Yeah.
38:51
I mean, I would say, you know, embrace this is this is the way the future.
38:57
And don’t be afraid.
38:59
And you can reach out.
39:01
I’m sure I’ll speak for my my fellow panelists here, but you can reach out to any of us with ideas or questions.
39:09
We’re happy to help be a resource.
39:10
And yeah, we’re just excited for what’s to come.
39:16
Great, JCK.
39:18
Any final thoughts?
39:20
Yeah, just feel big a pod with with what Elizabeth mentioned about not being a a generation that is, is a digital native.
39:29
One thing I haven’t hit on here, but I actually have a disability and AI for me has been a huge accommodation tool in that path.
39:38
But my disability has pretty significant memory loss.
39:42
So I don’t remember anything from before 2020.
39:45
And as a result, I don’t remember a world where AI didn’t exist, which might be kind of crazy to think about, but I’m effectively a four year old.
39:54
And so when I came back into the HR space, not knowing how things had historically been done before, I wasn’t asking should we use AI?
40:02
It was how could we not use AI?
40:06
Because we are having to make decisions about, you know, that base of Maslow’s hierarchy of needs.
40:12
And it’s really, really important that we have.
40:15
The utmost objectivity, objectivity in terms of who’s going to fit or who’s going to not.
40:19
And if I have the ability to make a decision when I’m really hungry about who’s going to fit from a, from a hiring standpoint versus who’s not, we got a problem.
40:27
So I think, you know, like I, you can begin with AI, right?
40:34
Like I’m a four year old and I’ve been able to learn this.
40:37
It’s a skill that you put in your toolbox.
40:39
And if it’s not there today, put it there and then continuously make sure it’s sharp because you want to make sure that it’s able to do work for you.
40:47
So that’s just what I wanted to share.
40:49
I love that you guys.
40:50
Thank you so much.
40:51
And Elizabeth closing and we’re going to give you the questions.
40:53
Thank you for saying that.
40:54
Like thank you for saying if you are not part of digital, we haven’t forgotten about you.
40:58
So all of those individuals on here who are HR practitioners, don’t forget about those individuals in your organization who might struggle with this.
41:05
We got to bring them along, but acknowledge it, right?
41:08
I think that’s the human piece of it as well.
41:10
Bring them along and say we see you, we got you.
41:13
So thank you for mentioning that.
41:14
All right, Brittany, Elizabeth, JCK, Monica, great.
41:20
We got some questions.
41:21
Let’s jump in.
41:22
Take it away, Brittany.
41:23
Thank you guys so much for such an excellent session.
41:26
We did have some questions come through.
41:28
And as a reminder, audience members can still enter any questions that you may have.
41:33
So this first question that came through, how can AI be leveraged to help create better efficiencies?
41:40
So not sure Stacy or anyone in the group wants to take that one.
41:44
Yeah, you guys just jump in.
41:45
We have about four questions.
41:46
So maybe a high level overview on that so we can get to them Anyone how can they leverage, create better efficiencies?
41:53
I think you need to start understanding what’s not efficient.
41:56
If you want to be able to drive efficiencies, you need to 1st be able to be again have crystal clarity on what isn’t efficient because that’s going to give you that baseline in terms of your data kind of story.
42:07
And then as you put in that efficiency, you’re going to be able to go back and story, tell the leadership.
42:12
Hey, this is how our, you know, practice has been able to impact that efficiency, if that makes sense.
42:22
OK.
42:22
And as we go through them, if something comes back, we have time.
42:25
You can.
42:25
I think that was great, Brittany.
42:28
Yeah.
42:28
The next question.
42:29
So what role will human intuition and empathy play in an AR driven HR landscape?
42:36
I love this question.
42:40
Human intuition.
42:41
We’ve been talking about that.
42:42
Elizabeth, go ahead.
42:42
I see you.
42:43
Yeah.
42:44
I mean, don’t we all want it to play a big one?
42:47
I mean, I think this is what humans bring to the table and why we want to keep a human in a loop on key decisions where an AI tool is making a recommendation.
43:00
I will also say there, there are AI tools and, and even, you know, many of the large language models right now that have remarkably human like empathy.
43:13
And we we also shouldn’t discount that.
43:15
So I, I think there’s a role for both.
43:18
And, you know, just eyes wide open to Jenny’s point on where you are applying AI in hopes of seeing greater efficiency to make sure you keep that human in the loop, right.
43:31
And I think sometimes there’s a thought that AI is going to replace us.
43:36
I mean, I know, I know I’m, I’m saying that right, But in not in the HR space.
43:40
So that human component, that empathy, that intuition, it drives all of this.
43:46
Do you say JCK said, you know, hit a component and they, the candidates said that they preferred the bot as she thought it was the human component.
43:56
I’m sure conversely, if it was the bot that they had and they preferred the human, she would have made that that change.
44:02
So I think that’s really telling the story that AI is not to replace.
44:07
It is a tool just like any other tool that we have implemented in the HR space that took us from personnel not to generative AI.
44:18
The only, the only thing that I would add on to that is, you know, human intuition is, is based on our, our historical experiences, right?
44:25
Like it is what we’ve gone through before that’s giving us almost that spicy sense of like, oh, what about this?
44:32
And I think there are aspects of AI being somebody who doesn’t have any of my memory from before 2020, where I’ve often gone to a Gen.
44:39
AI and asked, what might I be missing here?
44:42
Right?
44:42
Like this is a situation, this is what’s going on.
44:46
What perspectives may not may have I may I not have actually taken into consideration.
44:53
Torin Ellis is an incredible diversity thought leader in the HR space and he always challenged me as a mentor earlier on in my reintroduction to HR.
45:02
And he always challenged me to ask who isn’t present here?
45:05
And so who isn’t present here?
45:07
Could be you’re building recruitment strategies.
45:10
Could a blind candidate go through your recruitment strategies, right?
45:13
Could somebody who’s deaf go through your recruitment strategies?
45:15
Have you thought about somebody who’s neurodiverse or may have autism, right?
45:19
We can leverage that AI to help us check our intuition because again, our intuition is just based on our past experiences.
45:26
And because the nature of AI is trained on so many different other experiences, sometimes it can help check our lens and help us see things that we may not have considered otherwise outstanding.
45:38
The questions are coming in team.
45:41
Yeah.
45:42
Yeah.
45:42
We’ve had quite a few questions come through.
45:44
I think we have time for at least like two more.
45:46
Can you make any predictions?
45:48
Oh, sorry.
45:49
Let me go to that.
45:49
One’s a good one to close on, on workforce management.
45:53
How can AI tools be used to monitor and improve employee well-being without crossing privacy boundaries, if anyone wants to weigh in on that one?
46:06
I mean, I actually think this is a great question, especially since I brought up the privacy piece earlier.
46:12
But I do think that there’s some ways to recognize burnout in work habits.
46:19
And I know that I look at a lot of data, whether it’s about, you know, people’s time in meetings, how long meetings are going, you know, how many hours online, whether it be overall, but having our having AI analyze that.
46:37
I mean, there’s so much more you can do with the patterns and with looking at overall themes versus, you know, individuals not always getting into like the privacy factor, but as a, as a team and as a group, you know, what habits can we help our company create to kind of avoid that burnout and, and catch it before it happens?
47:00
That’s a big one, at least from, from my perspective working at a, a smaller firm.
47:07
Love that we got quite a few, a lot of great questions.
47:11
I know there’s one about prediction, but I think we’re going to close with you want to take the last one, Britt, and maybe is there a way that we can answer these questions and give them to the audience?
47:19
Is there a way we could do that?
47:20
Yeah, we could absolutely after this follow up with any questions we did not get to absolutely.
47:25
But yeah, I think this is a great question just to close out with.
47:27
So how do you convince your leadership to, one, allocate money to implement these AI changes and two, if they have IT or information security concerns?
47:40
Jenny, I’m not sure if you wanted to kick that off first.
47:43
Yeah, yeah.
47:44
So storytelling is going to be so important with this.
47:49
So when you’re working with leadership, being able to be the neck that turns the head with the problems that you’re trying to address and then being able to do, once leadership sees what those problems are, reverse engineer your strategy and how you’re looking to solve those problems.
48:03
Leveraging AI and really speaking to not forgetting what’s the cost of inaction with this?
48:10
Because almost always there is a cost to inaction, not just action.
48:15
And often it is that being able to articulate and put numbers around the cost of inaction that I found that can often get leaders to choose to begin.
48:25
And as it relates to if they have IT or information security concerns, when you bring that situation to your leadership team, you should have done a super, super, super pre mortem and solved for those IT and information security concerns.
48:41
If you are not an expert on this, that’s OK.
48:43
If you’re working with a vendor like lean on them, help them to uplevel you and upskill you.
48:49
But your leadership is going to have these questions.
48:52
And so make sure that you’ve asked like it’s a pre mortem, right?
48:54
We’re gonna ask how could this go wrong?
48:57
Make sure to ask that proactively and answer that proactive.
48:59
So when I bring a, a strategy forward to leadership, I’ve actually compiled a brief where I take all these different questions that I assume somebody’s going to ask and I answer them proactively.
49:11
And I give that in advance of the conversation I’m having with leadership.
49:14
And so that’s just another strategy that you can consider.
49:18
Excellent.
49:19
Yeah.
49:19
Monica, Elizabeth, Stacy, any, anything to add to that question?
49:25
She said it be prepared.
49:26
Yeah, be and not just be thoroughly prepared.
49:30
Do your homework, do your research.
49:31
Sometime HR is accused of only being able to speak their language.
49:34
You got to speak the business language.
49:36
You got to speak the language when you’re going to take this forward, right, and make sure that you’re a department of credibility.
49:41
So you’re already not coming in at a disadvantage.
49:46
Awesome.
49:47
Yeah.
49:47
Monica and Elizabeth, is there anything to add before we close it out?
49:50
I would just say to that one, I have a great tech team that we partner with really closely.
49:55
And when I don’t know something, I ask them, and they’re such a great resource.
50:00
So coming in as a joint force is something that has really been, you know, helpful to me.
50:07
So collaborating with that tech team has been huge.
50:12
And I would add, don’t be afraid to be a catalyst and help your leadership start educating themselves about generative AI before you have a key business decision on the table.
50:24
That’s a good one.
50:25
It’s pretty good.
50:26
Excellent.
50:27
Well, guys, thank you so much.
50:28
That is all the time we have for the questions, but thank you so much for an amazing discussion.
50:32
I also want to thank everyone who joined us for this wonderful panel.
50:37
This session, along with all of today’s content will be made available on demand following the event.
50:41
Our next session will begin shortly at 12:30 Eastern, which will be a thought leadership titled Myth versus Fact.
50:48
Play along and get the workforce data needed to solve complex TA challenges.
50:53
Please click on the join button that will appear on your screen to be redirected. We look forward to seeing you there.
50:58
Thank you all so much again.
