PMP - HR Leaders Adapting Tools & Systems - August, 2024

David Rice: [00:00:00] This is the latest in our community event series. We're happy to see these continue growing and being a valuable way for our members to engage experts who contribute to people, managing people who, you know, who are volunteering their time to come in front of us and share their, share what they know. So, uh, thank you for showing up today and taking part.

For those of you who don't know, my name is David Rice. I'm a senior editor here at people managing people. Uh, today's session will focus on getting ahead of AI as much as that's possible. Uh, how are HR leaders, uh, you know, adapting their tools and systems for this new technology, and we'll be speaking with some of the top voices and thought leaders in this space.

I'm really excited to welcome, uh, Jason Herring. Jason is a global HR leader and a writer with people managing people. It's also a big college football fan season's about to start Jason. Uh, What's your prediction? Who's gonna [00:01:00] win it?

Jason Herring: Yeah.

David Rice: And why it Georgia

Jason Herring: David , David. That, that's a really good question.

I had this, um, my brother and I were talking about this recently, and, um, you know, he's a huge Michigan fan. I'm a South Carolina Gamecock fan. Shout out to Gamecock fans out there. Um, I do think it's gonna, the championship will stay in the Big 10, but I think it's gonna be the year of the Buckeyes, so, uh, yeah.

Yeah. Bold. Okay. Yeah. Yeah. All

David Rice: right, well, welcome, Jason. Uh, next up we have Gil Gerstel, uh, VP of data products, governance and ops at ADP. Uh, Gil, you shared on our intro call that you're expecting a six child, all of which are under the age of nine. Uh, first of all, congratulations. And then I kind of got to ask, is it that you just dislike sleeping or?

Gil Gerstl: I'm a bit confused. said a word called sleep that I'm not familiar with. Um, I remember I had a feedback [00:02:00] collection of that. Um, well, well sleep is for the week right now. It's, it's, uh, They're just a funnel of joy, I'll say it that way, but yeah, maybe it'll work.

David Rice: I kid, I kid. I, uh, you know, I always like to tell my girlfriend, especially when we travel, I'll sleep when I'm done.

So, um, and next up we have Karen Weeks. Karen's the global chief people officer, obviously, and founder of Shine at Work and also, uh, an editorial advisory board member here for people managing people. So Karen, you started your. career coaching business nearly about four years ago. I'm curious, you know, have you always felt drawn to coaching or was there like a moment that inspired you to go out and, and offer up your expertise and knowledge?

Karen Weeks: Yeah, it was a little bit of both. It was something that I'd always done in my HR career internally. And I thought, how can I help more people outside of the companies that I work for? And because of the timing around COVID, I actually thought I was making a bad decision by [00:03:00] starting it around that time.

And it actually was a time that people were really reflective on what do I want to do? How do I feel like my company's handling this as an HR person? What do I do? So it ended up being a really, uh, interesting time to start my coaching business on the side while the world was being turned upside down and it fed into the mission of how can I help more people?

David Rice: Yeah. Yeah. That was, uh, The moment that everybody got a little bit more desperate for advice, I would say, um, little quick note about the people managing people membership. If we have a few guests to view guests from outside the community today. So that's you. Welcome. Uh, this is 1 in a series of monthly events with, uh.

Various HR people, operations professionals that we hold for our members, you can learn more about it at people managing people dot com forward slash membership. So please do check that out. Okay, so let's get into it. You know, obviously, um, AI, you can't go on linkedin [00:04:00] without hearing about it, right? You can't actually go.

I don't think anywhere without hearing about it at this point. Uh, so we kind of want to start with like, what's really changed though, and kind of taking stock and just for, uh, you know, kind of real quickly, each of us, I'd like to know how has AI changed your workplaces over the last two years? Um, and we'll start with you, Karen, I'd like to hear from you.

Karen Weeks: Yeah, it's really interesting because I think that the ability to leverage it as 1 of our tools is really where I've seen it. And I know there's a lot of fear and we'll talk about that as well around, you know, is it going to take my job? Is it going to put us in a place where I don't know how to either do my work or the value of my work?

But initially, I've really seen it as. Almost like this, uh, assistant where, you know, if I have to write a job description, a job description for a product manager at the baseline is the same, no matter where you work. So instead of starting from scratch, grab something and ask AI to help you work on it.

And then, you know, you want to make sure you take it, you tweak [00:05:00] it, you customize it, you everything. To make it sound like your voice in your company, but you know, even with marketing campaigns, you know, what's a way to reach out to X consumer using this thing again, don't, you don't have to start from a blank piece of paper every time, let technology help you.

And then. You can then add what you can add from a human standpoint around customization, voice, thinking about your audience, all those things that only I will know is the human on the team versus the computer helping me. All

David Rice: right, Jason, what about you?

Jason Herring: Yeah, you know, it's a good question. It's, you know, there was a study recently done where, um, study by PwC where it said 70 percent of CEOs are concerned about about the lack of available skills in the organization.

And so this has led to a really big emergence of a lot of companies looking towards reskilling and upskilling. Um, people see this somewhat as [00:06:00] problematic, and it is from a lack of skills, but, um, I see this really is an opportunity to really engage and retain your workforce. People are hungry to actually want to learn and grow and develop their careers.

They're now in some ways being given opportunities to do that. So organizations just need to engage with their workforce and work with them and create, uh, Um, plans to help build their careers, establish career ladders, those types of things. The one thing I do want to talk about when we talk about upskilling and reskilling is a lot of times people think about that from a tech standpoint.

Um, and when you think about upskilling and reskilling, it's not solely, solely just tech skills. In fact, McKinsey did a study Um, recently where they said they identified 56 foundational skills, but only a quarter of those were technical or digital skills. The vast majority of those were salt or power skills.

And so I think there's an opportunity for folks to reskill, um, even in those areas, because skills like that are going to be, um, vastly important as we move into the future as well. So reskilling [00:07:00] and upscaling, definitely seeing, um, seeing that a lot more people giving us more thought.

David Rice: Thank you. I'm definitely interested to hear your answer.

You obviously work in a big and interesting organization that we all know well, but also see a lot of other organizations. So what are you, what would you say is, uh,

Gil Gerstl: and I'd say that when I think I think both Karen and Jason have great points here. I think it's a, we are at the point that there's. We've seen some productivity gains and some, some of those changes starting to happen in the workplace.

Um, and, and I think there's also kind of, there's the notion of, uh, of how would this impact, right? How, like, how would this impact my job and how will this impact me moving forward? And it is an opportunity for people to really do more and to take away from a lot of the more tedious, Work, right? Or the more the things that can be automated can be simplified and we see that opportunity start to show [00:08:00] up and and And a lot of different companies are bringing forward these tools to really enable and empower like their clients and their associates to, to come in and do more with their time and kind of be more attentive, spend more time with those kind of higher level cycles of thinking more deeply and then areas that can remove the more automated.

Work that they do and kind of take that away off the table And I think that to Jason's point like upscaling and allowing people to level up and do more of that automation And spend more time and freedom to to the higher value task is is a lot of what's happening And it's still early days right for a lot of it, but there's uh, there's much to see

Karen Weeks: And I'd love to add something to that is, I think that as organizations, it's actually opportunities for us when we think about retention and really helping our employees through this is how can we offer that for them?[00:09:00]

So, you know, if I think about from, like, a coaching hat, I'm talking to my clients around, well, what do you need? What, you know, what pieces don't you have when you look at a resume or when you hear feedback from your manager? But as an internal HR person, how can I be proactively building that into our culture and seeing where the future is going and saying, we're going to offer you that training.

And we're going to, you know, to Jason's point, you know, those are skills that you can't necessarily learn online. So how can we do some internal coaching and mentoring and, and things like that. So really expanding our development programs to incorporate all this, to also show. Not only should technology be seen as a helpful tool and we're going to help you grow with it.

And I think that could be a really powerful thing that maybe HR folks aren't necessarily proactively thinking about.

David Rice: One of the things I find fascinating, and this is like, there's this shift for managers going on. I remember I saw this thing from corn fairy and they were calling it the idle [00:10:00] AI workday, right?

And it was just basically people are just using AI now and then they're not doing anything. But I think there's. It's a very mixed bag, right? Because then you have people who are maybe a little bit nervous about AI, and they don't want, they don't like using it, or they don't know how. And then you've got people who are just like using it like crazy.

And so I'm curious to get you all's opinion on sort of, for managers, sort of identifying strengths and how people are using AI and creating a sort of best practices standard. And like we've been talking about this focus on reskilling and upskilling. What's that challenge that you're seeing those managers have and how are you know, how would you kind of guide them through it?

I guess I'm going to open it up. Anybody can jump in. Cause I know that was a loaded and difficult question to answer.

Jason Herring: Yeah. You know, anytime you create kind of an upscaling reskilling program, there's, [00:11:00] I've seen a few challenges, um, with creating them. One is, um, correctly assessing what your needs are. Um, so usually.

You know, you'll understand, Hey, I need to reskill my folks. There's this new technology or whatever it is we need to do. Um, but do you understand really what the problem is? And do you understand what the, what the skill gaps are? Have you assessed your current talent pool? And I think a lot of people kind of mess up that.

there by not truly understand what the talent gaps are within the organization and then truly understanding what's the real problem you're trying to solve for. So that's I think number one in terms of the way managers feel anytime you have a rescaling upscaling program, it takes employees away from their current job.

And so managers are like, well, I need this person to get this job done. And so how are we? I think there's a question mark. Um, of how are we supporting our managers when these programs are happening, and we're taking their employees and re skilling their employees to work on other [00:12:00] elements, I think managers want to be supportive, but they also know that they're held to a certain standard, and they have to get work done.

So how do we help support them during that time where maybe they're a person or two down because of these roost re skilling efforts.

Karen Weeks: I would also add in, you know, especially because the beginning part of your question was about that article or whatever, I think, assuming good intent as a starting point. So, I remember when everybody started going remote for the 1st time, and there were all these articles about, well, what if they're not doing work?

And what if people are like, pretending Netflix on the side or and all these bad scenarios of what people were going to be doing by working from home? And, you know, Sure, there's always someone who abuses some policy that was happening in the office. It happens. If someone wants to abuse a policy, they will find a policy to abuse.

But I think if we start with the intent of, okay, well, maybe this person is struggling because they're not comfortable with technology, or they're nervous that it will take their job, or they [00:13:00] don't understand how it's a supplement to the work that they do, or, um, they, you know, Don't know they're allowed to use it, you know, it's a cheating and like using cliff notes or something.

So I think going back to that, I assume that my team wants to be successful, learn, grow, do great work. So how can I help them in that? And if they're not doing something that would have expected, let me just ask some questions about it, because it's probably they want to, and they don't know how or why or what versus up.

Yeah, no, they're just copying and pasting. They're cheating on their book report again.

Gil Gerstl: I'd say kind of to build on top of it. I think one is it's important as we go about and as businesses and leaders are go into this realm of utilizing these, this new set of tools that are that are being brought forward, we do need to be very clear. And that's work that every company, every organization needs to do for themselves.

Like, what are the guardrails that need to be in place? To be able to responsibly use this technology. The [00:14:00] governance aspects here are extremely important as well. To know how am I using this, how does this impact my data, how does this impact the work that I do. And I feel that on our side we spend a lot of time because we safeguard our clients information and we think about it and then we do it.

Well, that a very high regard to make sure that we continue to protect it, and there's no change to that. So that's how do we build the process to enable and accelerate while retaining the confidence that we're doing this responsibly and not in everyone's anyone's information. But but once that those guardrails are in place, we do need to empower and ensure that teams and individuals know how to utilize it.

And where are the strength of these tools to be able to utilize them? And people, everyone wants to get into AI, like it's kind of the hot buzz. I haven't met many people. Like, when was the last time that your, that my mother talked to me about a new technology stack? [00:15:00] Right? I think that's it. But now this has become so democratized that everyone wants in.

So we need to, to help our teams understand, like, where is this useful? Where can this be used? But also, Where should it not, right? Because there are areas and there are edges that this, these tools can have other implications or are not useful for them. And we really need to kind of spend time with our teams to make sure that, like, yes, I agree completely, like good intent.

So how are we using this to the benefit without, while avoiding the pitfalls?

David Rice: Yeah, that's a great point because you may have a good intent and the result is not what you were hoping. Um, since chat GPT surfaced, there was what, uh, Yeah. A little over two years, it was about two years ago now, I'm guessing, I think, something like that.

So much has changed, uh, in that period of time, and I'm curious to hear what you all, now that there's all these different, we have [00:16:00] generative AI, we've talked about, I've heard about interactive AI and all these other, like, futures for state things, but I'm curious, you know, what do you think will be completely different in the next, Somewhere between six months in the next two years.

Right? Because things are changing fast. And I know that's pretty hard to answer, but I'm curious to get your thoughts on this. And Gil, we can start with you.

Gil Gerstl: Sure. So, so I think 1, I think making this organization, I'm still trying to understand the true impact of this technology, right? That's, uh, and lean into what actually works.

And there, there's so much potential in this technology, but. But where could it be leveraged and can be used? Um, I think I think it's still key, and everyone's still trying to figure it out, whether it be it on the day to day productivity or as they implemented their technology stacks and their products and services.

What I do think is more productivity is coming, right? That that is happening. That is around the corner, [00:17:00] and it really is going to be focused on how do we make tedious tasks simpler? How do we take? Interactions with applications or or taking the first step in writing a document or or those aspects. How do we take that away and allow for faster, easier access to information to processes?

And that is that is coming and that we've already seen the 1st. Like the first, uh, products coming to market and doing it, I think at ADP, at least we're focusing on the long game. We're focusing on how can we make a real impact to our clients and associates and their practitioners and their associates in making their lives easier and, and solving for their most difficult tasks.

How can we just simplify and move that forward? And we know that we're bringing productivity there, but we also see productivity gains on our side even internally in changing the way we [00:18:00] interact with some of our processes. So, so really streamlining tedious tasks, easier access to information, easier way to, to initiate complicated processes.

All of that's coming. It's here somewhat, but much more of that is coming in the next 6 12 months. But honestly, like, you asked about 6 12 months. I think we need to ask Sam Altman, like, what's the next version of, uh, ChatGPD gonna do? Because the technology is just changing so rapidly. It's just, uh, I don't think we've lived in an age that new versions came out as rapidly and it really changed the capabilities and in such meaningful ways.

Uh, so we will need to continue to adapt. Um, and that is a never ending story. Yeah,

David Rice: I mean, it's not like, well, it's not like an Apple iPhone upgrade, right? Where you're like, you're like, I don't know what they fixed, but they did something. You notice the difference with this. [00:19:00] Uh, Jason, I would like to get your thoughts on this as well.

Jason Herring: Yeah. Um, man, it's hard to follow the old that one. That was the response. You know, there's a couple things that I'm seeing. Um, one, you know, there have been some company reordering structures that are happening, and I don't think they're happening because necessarily of A. I. There's a number of reasons they happen.

But what is interesting is that when companies are putting out their statements, they are saying that we're taking these dollars to invest into a innovations. And so I think we're starting to see that quite a bit. And I think we'll start to see a little bit more of that. The second thing is there's been a significant spike in cyber attacks.

And what's interesting about these cyber attacks is because of the use of AI, they can become a bit more sophisticated. So we're seeing a lot more ransomware. We're seeing a lot more with phishing attempts. And prior and previous years, right, we could look at a phishing attempt and we can see that, oh, there's [00:20:00] misspellings or something isn't right.

And now they just. They look a lot better than what they have in the past, and so we just have to be more diligent in terms of looking at these emails that we're receiving or whatever, and ensuring that it's from the right sender, it's from the right place, it's the right link. With that being said, cyber teams are exhausted because of this spike, because there's a spike in attacks, but there's not necessarily a spike in more people, companies aren't hiring more cyber folks.

And so, you know, if you're working with cyber folks. Hug and thank your cyber folks. Um, they're working hard. There's a lot going on with them, but I think that will continue to be a theme that goes on in terms of the increase in cyber attacks. And then the last thing I would say with any emerging technology, there's usually a frame, there's a framework that can be used and it's called FATE, F A T E, which stands for fairness, accountability, transparency, and ethics.

And so when you think about fairness, Of AI. It's talking about eliminating bias, ensuring equality, um, [00:21:00] getting rid of impartiality, um, at a very high level. I'm not technical, but at a very high level, the way I understand AI works, it's a bunch of information from text, audio, video goes into a system based on a prompt.

It kicks out something right based on your prompt. The question that I think that needs to be asked more often is, Then it's probably being asked is what's the source of that data and what's in that data to create that outcome. And if it's discriminatory information that goes in there, if it's biased information that goes in there, and nobody is, you know, cleaning up that data, mitigating it, then the outcome is going to be discriminatory data, impartial data.

And so I think that's something that needs to be really focused on as we look to move forward. And I think companies will start. Taking a closer look at that, the A stands for accountability. And so when you think about accountability, it's who's responsible for the data, right? Is there human oversight?

Is there human involvement for the outcome output of [00:22:00] that data? So some of us are aware there's something called hallucinations, right? That come from AI. And what a hallucination is, if you're not familiar with it, is AI, Gen AI will provide a response and the response looks very incredible. But it's also very wrong.

Um, and I have my own test that I've done with every A. I. And I provide a lyric from a song and, um, it hasn't gotten it right yet. I say, Hey, here's this lyric. Who did this lyric? And it hasn't gotten it right yet. But it gives you a very credible answer. And then I'll tell it it's wrong. And it gives me another wrong answer.

And, you know, obviously that stuff will be cleaned up in terms of hallucinations of the percentages of number of hallucinations I've seen very widely in terms of what people have recorded. But it's still there, which means the data needs to be, there needs to be a trust, but verify. And so ensuring who's the responsible party of that output is very important.

Um, the T represents transparency. And so we talk about, you know, Um, you know, from a clarity and openness [00:23:00] standpoint, the data that's collected, are we sharing that information or letting our employees know the data that's collected, the limitations, the implications, how we're using this data and not just with our employees, but even with our customers.

I think all of that is really, really important. Um, some companies have already, um, they've started using AI to monitor employee performance and behavior. Well, in those cases, are the employees aware of that? Um, because I think they should be aware of what's happening and how, how AI is being used. And then the E stands for ethics.

And that's more about, you know, the moral standards and things like that. And the big thing I love to talk about as we talk about ethics is anytime there's technological advances, there's an expectation that people move faster, but there's a direct correlation between speed and ethical dilemmas. And so how do you ensure there isn't this dichotomous behavior between, um, speed and ethics?

How do we ensure that, yes, we want to move [00:24:00] faster, but we maintain our ethical standards? Um, and so those are the things that I think over the next few months that we're going to see, see a bit more of. I can't believe you said it with, I was a

Gil Gerstl: hard act to follow with that answer. I actually, I just wanted to lean in on that just a little bit because I think it was, I think you had some amazing points there.

And I think, like, I can, I can attest to what we do in some cases. Right. So at ADP, even before like the, this onset of generative AI that popped into our lives just shy of two years ago. We've had an AI data and AI Ethics Council that's been going for for five to six years now. I'm not sure about the exact date or holding to it.

But, um, and, and that has been focused about how do we responsibly use our, our data and make sure that we ethically use it and AI and everything that we do. And if you think about ADP provide services, such as You know, like compensation benchmark, uh, as an example, and that's an area where, like, [00:25:00] the world has bias, right?

Like the, even before going into it, like the data itself, the clean data as it is, has baked in bias in it, sadly. So, so correcting and measuring for bias in everything that we do with AI is so extremely important. And it obviously becomes just even more important as the use of AI is increasing. is increasing.

So, so I think you had some amazing points there, but with the focus on how do we responsibly use it? How do we ethically use it? How do we ensure that we keep looking for and correcting for issues that are not necessarily a product of the model or the AI algorithm, but rather of society, we need to also try and correct for those to not exacerbate the problem.

That exists in, in the world. Um, so, so that is something that, that we've been actively focused on and we've been very careful in what we release and what we do just because of that. We'd rather do this responsibly rather than accelerate too much. [00:26:00] And for, for, for what it's worth. And, and, and to your your other point, like we've established this year, like this past two years, I guess at this point, we've established a process that everyone can suggest a use case.

We have our strategic use cases in areas, but everyone across the company can come in and suggest a use case for generic AI use for any right, like either for the benefit of our clients or for internal operations, service, sales, anything. But all of that has to go through a robust governance and compliance process that looks at is this a viable use case.

Is this within our ethical and legal comfort zone? Is this, uh, does this retain and continue to protect our, our client's privacy and everyone's private information? And then is it, does it stand the test of security? Because to your point, There's, uh, there are more and more risks with it in this world as well.

Um, and only once those all have been cleared, teams are allowed to go in and [00:27:00] experiment and potentially develop a product. And then we have additional checks for that thereafter, like for experimentation is one thing, but before anything goes into in front of a client or into production with real data, he goes through a similar process.

So, so that ethical security, legal, like privacy. Lens has to be in everything that we do in this space and we'll have to continue to be

David Rice: something

Karen Weeks: that we need to consider to as users of these pieces. So, you know, in my HR role, I'm always getting sales calls or whatever about this new technology that's out there.

And we use this and we use that. And there was 1 that was proposed to me where conceptually, I loved it. It basically looked across the board. Messages and slack and activity and productivity and all these things to identify engagement risks. Um, and to Jason's point, I was a little nervous about, you know, well, do they know we're watching them kind of big brother, but the concept was great, especially [00:28:00] because so much of the HR trends are around data, data, data.

Like where's the data to prove this. Don't tell me you feel this way. Like, where's the thing to prove it. And in theory, this was going to help me prove it. But I was really worried that that was the science behind it, but I was going to miss the art behind it around. Okay. Well, maybe that person isn't on Slack as much right now because they're going through this family thing or they're burnt out or they're really busy and their heads down on this huge project.

So yeah, they're not on Slack because I've told them not to be on Slack so they can focus on this project that they've got to get out the door. So I think, especially as We're talking about it within our companies and for our employees, but also if we're approached with new ways of using it, sort of using our own human gut check around.

I love that. It can do it for me. What? But at what cost? And am I losing something by leveraging it too much? And I think the other thing I wanted to add, um, was. To Jason's point around, you know, cybersecurity and some of those places, maybe not having the investment in. [00:29:00] So if you're someone who's listening to this, or, you know, someone who is concerned about, oh, well, my job is more task oriented, and I'm worried that it might go away.

Where should I learn new skills? These are the areas where you should potentially learn more skills, because if a company is reinvesting more in AI or cybersecurity, et cetera, you want to make sure that you've got the skills to be considered for those jobs. My partner comes from a town that was a very factory based town, and in the 70s and the 80s, it was like, very, very popular.

You know, it gave a lot of jobs. And then as the 90s came in, and of course, more technology came in, the hands on factory workers weren't needed because they had computers and machines to do things. And those workers didn't know where to go because no one in the city was offering, well, no, maybe that job isn't available anymore, but we'll teach you to code because this tech company might be coming in or, you know, whatever the situation could be.

So, I think if you're listening to this and you're trying to think about, well, where could my career go? If AI [00:30:00] legitimately might change my role and scope, think about what is using AI and how can you get upskilled in those areas? It's not just the skill itself.

David Rice: Absolutely. I imagine you're, uh, you probably get questions a lot, Karen, about this from clients.

Absolutely. Yeah. Your answer is probably changing all the time, right? Um, I don't want to talk tactics for a few minutes before we get to the audience questions. How have you applied AI in your own processes? You know, uh, are there any specific tools that you found have vastly improved with AI or that have, you know, uh, You know, you can even say some of your own if you want, but, uh, you know, or that you've been able to really kind of change the way your workflow unfolds.

Um, Karen, we can start back with you. [00:31:00]

Karen Weeks: Yeah, I think for me, I use it, especially because I tend to be on smaller HR teams or, you know, within my own business. There's not a ton of us. So I really look for anything that can help me scale. So whether that is. You know, and this is not new over the last couple of years, but, you know, ATS systems that help me source based on some keywords or help me think about if I have to write a new policy, you know, everybody has some policy that I'm trying to write.

So, where can I grab it from the world? I sort of think about it as, you know, if you used to Google the thing now, I can help you do it instead. Um, or like I said, help me do some of that legwork since I don't have a sorcerer and SDR, like some of those things. So where can I leverage the technology to help me be more productive?

Since I tend to be a small team, a team of one, those sort of places. So that's where I'm using it the most is I'm actually taking the advice we're giving and saying, like, how can I use this to make me more productive so that I can focus on the things I need to [00:32:00] focus on?

David Rice: Uh, Jason, how about you?

Jason Herring: Um, you know, I found AI is incredibly helpful in terms of, you know, so because I've been writing a lot lately, there's a lot with writer's block. And so usually if there's a sentence, I just can't get to go right. It's a fantastic tool to use to say, Hey, help me find a way to transition this sentence to this sentence.

But I also find it's a really good thought partner. Um, so as you're thinking through different scenarios, or you need to use it to kind of test your knowledge a little bit, I found it to be incredibly helpful in that case. Again, the, the, the thing to be mindful of are those hallucinations and be mindful that, um, trust, but verify any information that you receive from AI.

David Rice: And Gail? Yeah, I think, I believe, [00:33:00]

Gil Gerstl: at ADP we're thinking about AI in a few different, um, I would say pillars. We, we have our Like, how do we improve our service experience for our associates and for our clients? We have kind of a sales productivity angle, and we have the product innovation. How do we change the experience and interaction that our clients and their, and practitioners have with our products?

I'd say that, like, we will not be, we won't be like a single solution shop in terms of what we do, but I'd say that we've been implementing a lot of tools and capabilities, for example, for our service associates to be able to be more, like, to take away again some of that Like manual work. Uh, essentially, you know, call summarization is being like mentioned everywhere, right?

That's a good strength of this technology. How can we remove that like more technical work and enable them to be more present and [00:34:00] more attentive, uh, to our to our clients, right? And give them the best service experience that they that they can and reduce some of that more manual, uh, Things that are not as value add.

So, so issues such as things like customization are better access to knowledge articles or internal knowledge and being able to share it. I think that we're focusing on a lot, um, in enabling our internal processes. And again, with the goal of. Really? How do we improve client experience? We thankfully we have great service, but how can we even level it up and make it, uh, and make that experience for clients even, even better?

Uh, I would say, uh, beyond that is like, we're in our, in our products. I know you didn't want us to touch on our products, but, um, but in our products, really, how do we change that interface? And make it a much more simple, smart and human interface and how we allow our clients to engage, how do we meet them where [00:35:00] they need to be and enable them to do processes that could have been complicated before and make it much, much more simple and on a regular basis.

So, so we've launched or continue to launch more capabilities to really enable and empower them to do this and everything in a much more simple way. Um, and and allow them to increase their productivity and even allow them to do more internally within their organization. How can we feed them up to do more value at work in their space?

So that's a lot of our focus as well. And those are the things that we we keep developing for their benefit.

David Rice: Yes, we talked earlier about seeing productivity gains and we mentioned that people have to do other things and there was a question earlier is about kind of like, are you able to share how you're measuring productivity?

And are you able to isolate AI's effect on that?

Gil Gerstl: Yes, I would say when, when we, like over a year [00:36:00] ago, when we were starting to do some work, or we were already well into some of our work around, uh, Around tools for our for our service associates. We're trying to think like, how do we measure this? Right?

There's like, we know we need to do this, but how can we measure measure this over time? And there's And there are a few layers to this. And I know, uh, in the question that was written and that was in chat, um, the causation here is, right? There is a lot of things happening at the same time. And associates are being impacted by a lot of different things, right?

They are, they get new tools, but they also have, like, a race cycle and bonuses. And, and what impacts their experience more. So the measure of level of engagement, of level of satisfaction of associates is important, but it's very hard to see how has like a new tool, uh, improved on that and kind of really weed that out from, from all the other things.

I think we can see it, especially if we look at the service space, we can see client satisfaction, right? Which is more [00:37:00] directly impacted by this, uh, like, Do they feel our associates are more, are more attentive? We can see the volume that they're able to, to handle, like how much of their time that we freed up, uh, to be able to, to take on more of our, of our clients, uh, requirements, our clients needs, and those are areas where you can specifically, we can measure much more clearly, um, and within say the sales realm realm, uh, productivity is while we do a variety of different things at the same time, I probably gave productivity gains.

Uh, kid are more measurable. I think there are areas where it's like it's softer and harder to measure. That's for sure. Um, but we always have to keep thinking about because what businesses learn more and more is that besides this being an expectation now, it is also a very expensive thing to go down a process to go down or or path.

Um, so balancing the The benefit is extremely important as [00:38:00] well. And what is the work measure for you is important to define early on in the process. As we do this,

David Rice: all right, we are, uh, getting a little closer to time. Well, we got about 5 minutes left before we go straight into questions. But before we do that, I just want to, uh, basically kind of, I know like some people will end up hopping off early, right? Because I got to get to the next meeting. And if that's you, I just want to say before you go, thank you again for joining us today.

If you're loving this and we'd like to see you at more of our events, we've got a lean coffee sessions coming up every month. We've got our ask the expert sessions. We've got more panels. So do check us out. Uh, people managing people. com forward slash membership. Uh, one thing I wanted to ask about before we get right into the questions, we talk a lot about identifying higher value tasks for people, but I've seen in several organizations, [00:39:00] there's sort of a blurring, the blurriness to what higher value tasks means to people.

It sort of differs from one team to the next. I'm curious, how do you go about identifying those for people? Because I think some people have, I don't know one person I was talking to said. Isn't that actually my manager's job, what they're talking about me doing. You know what I mean? And so, uh, the question was, okay, then what is my job now?

Um, and so how do you kind of avoid that and identify those tasks and make sure that there's a sort of a clear path forward and expectations for what somebody should be using it for and what they should be doing, what, what it's supposed to free them up to do. And I'll just open it up.

Karen Weeks: I can jump in there.

I think it is a mix of. Where does technology help and where can you not use technology? So maybe technology helps with the more simpler versions of the task and you need the humans to do the more complex version, right? If [00:40:00] you need to do whatever, this is a lame example, but like 4 plus 4 equals 8. Okay.

Maybe technology can do that. But if it's. 4 plus 4 plus 5 plus 7 plus 7, you know, and like, whatever, that's a harder answer to get to. And so you need someone to be able to analyze it, tell the story of it, figure out, um, you know, do I have the right inputs going in? So, so sort of thinking about. What is the next level of complexity if it's not sort of that strategic piece and then I think we have to help define strategy for folks.

Um, in a feedback training. I always do. I always laugh and about, you know, the worst piece of feedback I ever got was, well, Karen, you need to be more strategic. Well, what the hell does that mean? Right? That can mean so many different things. And I think sometimes our team members, to your point, like, well, strategy, I mean, that's the manager's job.

No, you can actually be more strategic and in any role. It's just getting ahead of something. It can be proactive. It can be recommending something. It can be helping someone else with something. So [00:41:00] where can you level? Where can you sort of aim your employees in something that is sort of above and beyond what the technology can potentially do?

And then how do you define that? How do you change roles and responsibilities? How do you reset performance expectations? Are you going to get feedback on it? And so you're sort of redefining your org in the role and sort of rolling all that out for everything that will help you as the manager, the employee, the HR team, everything to then Manage against it.

David Rice: That's great advice.

Jason Herring: Yeah, Karen 100 percent agree. I think we're in a place right now where just roles are going to roles are going to be a bit more fluid and because of a I and there needs to be more dialogue, more conversations with employees about Hey, yeah, your role is going to it could alter over time.

And that's a great thing because As we're able to introduce and identify more, um, more [00:42:00] opportunities through A. I. That's going to free you up to learn and do different things. And that makes you more marketable, provide maybe more opportunities for you in the future. And I think that has to be the conversation.

But understand that these job descriptions, that's great. But this is going to evolve because we see how quickly this technology is evolving. Your job description is going to evolve along with it. Um, so your job today might not look the same as it does tomorrow. Um, but that's a great thing. And here's why it's a great thing.

And so I think that has to be part of the conversation.

Gil Gerstl: It is. Obviously, if we take away the, the more, the simpler tasks, the automated tasks, that frees up time and that there's an opportunity to level up. And I think, like, is this my My, my boss's job is like, let's think together, like, how can we bring more, like, do things that are higher value or, or are, I don't say higher value, but at a higher level that can drive it, drive the direction rather than the tactics.

Um, [00:43:00] and. But, but there's also, as was mentioned, we are still very much at the point that we need to do a lot of validation, right? We need to make sure and we need to, to, there's a path and we'll take a while until we're at the point that we can just let go and trust, right? We do need to look at, like, what do we put in and what is coming out and that's work by itself.

And no one could do it better than our most selfish matter experts, right? Those are the people that can say, like, is this going the right path? And help build out those capabilities so they can free themselves up to do more. And to start thinking about what is the next capability that I need or what more can I drive for this organization again, assuming, as Karen mentioned, like best intent, people care about where they work, they care about doing more and making more of an impact.

And that is the starting point. And now that they are free, what more, where else can they contribute in areas that [00:44:00] are still not in the realm of AI that can be impacted in a meaningful way just yet.

Jason Herring: Yeah, you know, the way I've always looked at it is no one's going to care more about your career than you do.

And so you've got to paddle your own canoe and you've got to look for those opportunities where you can grow at times. If you have, you know, your leader will sit down and talk to you and that's fantastic when your leader does that. But there's other times you need to raise your hand and be seeking yourself.

What are those opportunities? How can I get more involved? How can I learn and continue to add more value to the organization and ultimately make myself more valuable to the organization?

Karen Weeks: And it's funny, normally I have myself on mute for that exact reason. So when my own AI device dings, it listens into our conversation.

Um, but I think it's really interesting because I think going to that piece, there's also this potential. I think sometimes we're so concerned about sort of what's happening in our world, is that there's potential for this to sort of trickle up. So [00:45:00] your manager's role is going to involve, evolve your director's role, your VP's role.

So what could I be taking off my manager's plate so they can then go And upskill and think about different things. Like, that's another great way to both drive your own career and be more proactive in the company is to say, well, if I don't have to do this anymore, that gives me 5 hours back. Is that something my manager really needs to be doing?

Maybe I can take that. Or if you're a manager. What can you be delegating to your team so that you can also uplevel? And then while I want to keep this super positive and I want everybody to adapt to change and all the things, as managers it's also okay to have a conversation where I see you struggling with this.

If this isn't right for you, let's have that conversation too. And how can I help you find something that does feel better, move into something else, whatever. I always want it to work out, but I'm also a big proponent of like, everybody has a choice, and so if it doesn't feel right for you for any reason, or if you're a manager and you're seeing [00:46:00] someone struggle with it, have that conversation too, after you've given them a chance to adapt.

All

David Rice: right. So we'll jump into some questions that have come in through the chat. I want to start with one that we can, this is kind of a quick hit, but, uh, I like it. It says, how does AI change the perception of work smarter, not harder?

Uh, Karen, go for it.

Karen Weeks: I think that's the. The efficiency piece of productivity so, you know, if work harder means more hours or work harder means I'm like, cycling through something over and over again in order to work smarter. How can I leverage something? So it takes me less time. Um, I it makes it automated.

It makes it repetitive. So I don't have to recreate the wheel every time. Those are all part of working smarter. And so I think technology can help us work smarter. And it doesn't mean you're not working hard. It just [00:47:00] means you're not on the hamster wheel of like, or banging your head against the wall.

That's the difference between work hard and work smart. You're still working hard. You're still putting a lot of passion into your work, all the things, but you're funneling it in the right direction versus on this cycle of stuff that doesn't actually help anybody.

David Rice: All right. Nobody has any more to add to that. Um, this one came as well. So what ethical considerations should be highlighted to ensure fairness and transparency, especially in relation to recruitment and performance evaluations?

Jason Herring: Yeah, this is, this is interesting. Um, you know, there is, there's a current There's a current lawsuit going on right now.

Um, and be very clear. I'm not an employment attorney. Um, there's a current lawsuit going on against, uh, Workday and there's a gentleman suing Workday. And ultimately, this gentleman, he did not, doesn't work for [00:48:00] Workday, has not applied for a job at Workday, but the jobs he has applied for are on Workday's platform.

And the German has said, Hey, I've met the qualifications of these jobs, and I don't know, um, I've met all the qualifications, but somehow I'm getting just kicked out of the system. And he mentioned how he's gotten he's received some responses that have come late at night like, 12 a. m. And he's like, I know it's not a person doing it that late.

It's got to be system generated. Um, and his, and so the reason he's suing, he is saying that, Hey, the algorithm in workday is kicking me out. It's forcing me. And it's because my resume says I went to an HBCU historically black college university. Um, I have, um, depression and anxiety. And so when he takes certain, um, tests.

from Workday or whatever. Um, he doesn't score very well. And so because of those reasons, he has sued [00:49:00] Workday. And Workday's argument is like, we're not making the hiring decision. And his response is, yeah, but it's your algorithm that's creating this. And you're the reason why I'm not, um, I'm not getting considered for these jobs.

And a judge has said that these, um, this lawsuit can move forward. It doesn't say that, you know, Workday is denying any wrongdoing, and I don't know how this thing will play out. But at the end of the day, what this, what this comes back to is something I said earlier is, you know, what's the source of the data that we're using, and are we really looking at that data?

So a lot of folks use Workday, but has anyone checked, what is that algorithm within Workday? And so it's going to be the same with any AI system, right? So if we're using something from a vendor, what's in, what, What information in from that vendor that we're using? What's that data that's in there? What's the source of that data?

And Is it impacting any? Is does it have any adverse impacts that we should be aware of? And so from a recruiting perspective, you know, I think that's that's definitely one that I've seen. Um, [00:50:00] because of that work, because of that lawsuit, that that's that's when it's been on my mind quite a bit.

David Rice: Anybody else want to jump in on this one?

All right. We're going to hop to the next one. Um, this one says, do you think, are there any companies that have an overall vision or plan for how they can move employees to higher value tasks? What is the big picture we are trying to achieve here? And it says now it feels more like everything starts bottom up, meaning people or data team experimenting with some pieces or a process or a task.

Which is maybe valuable, but not enough alone. So, uh, anybody want to jump in on this 1?

Karen Weeks: I can't I don't I personally don't know of a company that I could say, like, oh, they're doing this really well. I think that. It's probably going to be tied to companies that have strong cultures of [00:51:00] growth and learning and development because they will see it as part of their value system and how they work.

So, I think if you're looking for a company to mirror, like, think about companies that have really strong commitments to learning and development and upscaling and those sort of things. And then I think from the other side of that, you know, thinking about how to, again, always get in front of it. And it could be that your culture is more bottoms up, you know, at a company I worked at before, we loved that our engineers were looking at different, um, systems and going to meetups and like doing all the things.

Cause we wanted them to hear about what was out there and then bring it in and think about, okay, well, Maybe we can try this here and we'll try that there. And then we'll turn it into a company wide project or whatever, if it makes sense. So that was the culture of our engineering team is that we wanted the team to bring it in versus the CTO saying, we're now going to do this and have it trickle down.

So I think it really, not to sort of punt it to the culture answer, [00:52:00] but I think it is going to be either already built in who you are, and this is just going to be the next evolution of it. Or you're going to see yourself getting behind and you're realizing you need to evolve in order to be a part of it.

And this is, you know, I'm a big believer that while the DNA may not change how you reflect your values and how you reflect your culture is going to evolve either as your company grows, um, as it evolves as a company itself. And this might be one of those things that's a part of that.

Gil Gerstl: For me, I think it would be. It can be bottoms up and top down. We tried to go both top down and bottoms up the way we operate here. Because one, like, what are we trying to achieve strategically for the company? Like, where do we believe this technology can create more productivity, create more value, smooth, like, associate and client experiences and focus on the like, really focus hard and like solving those problems and bringing those capabilities from like a central [00:53:00] view.

But at the same time, empowering teams to, and individuals, not just teams, like to bring up ideas. And to experiment, um, while maintaining kind of controls and guardrails around it to ensure that we're not going down a path. But at ADP we've gotten to the point that we have, we've had hundreds of use cases that are being evaluated or tested.

Um, many of them are at our core, like verticals, the core use cases that we believe are strategic for the company. Many still are kind of bottoms up from the front lines. It's people that think like, I can use this. Can I try and can we try and use this for the benefit of our clients, our productivity or like to do this thing better and we can find a balance and allowing for for that to happen as well.

Um, over time, we're, we're starting to kind of focus this more towards what is the perceived [00:54:00] return on every one of these because we just can't spread too thin. Uh, either so so we're working on that balance over time. Um, but really enabling that innovation from the front line is extremely important to to to allow people to feel included, feel like they can make an impact and, uh, and and find what are the best ways to to elevate in that way as well.

David Rice: I'm going to, uh, ask a couple more. Um, one of the ones I thought was interesting is, is do any of you envision compensations being impacted as these roles sort of evolve? Will a more skilled slash experienced candidate be needed as roles and technology evolve? I just find this one interesting because this came up on our podcast recently and I'm interested to hear what you all have to say.

Karen Weeks: So I'm actually getting that question from people as well as, you know, well, as my role evolves, you're going to pay me [00:55:00] more, or as I help people think about their careers, you know, well, if I'm going to take this step or if I'm learning this new skills, doesn't that mean I should be asking for more money?

And so I think what's going to be really interesting. Um, and I love to hear Gil's point of view just because of the world he's in is how quickly are we going to see this in the market? So I think there's always. You know, the, the reliable answer is, well, what is the comp benchmarks coming from the market?

Am I seeing this role more highly valued because it's evolving? Or, you know, am I seeing this leveled differently because of that? So that's sort of the, the science behind how I think about compensation is. Well, what's the comp benchmarking, but I think it goes back to what we were talking about earlier is that as these things, things evolve, how do we talk to our teams about it so that we get ahead of those questions?

And it might be, Hey, this was actually always part of your job, but you were so bogged down with this other stuff that you never got to do this other [00:56:00] stuff. So I don't need to adjust your compensation or it's, yeah, we actually did move that to the side and now we're expecting these new things. I do need to think about this.

I also need to think about how many people I now need in that role because the, um, ratios or whatever might be different. So I need to be thinking about that. And then how does that affect budget? And so I think this is why we need to be as much ahead of these things, or at least intentional about some of these things, because it isn't just what my team is doing on a day to day basis.

It's Budget it's hiring. It's compensation. It's promotion paths. Everything gets affected when roles evolve and change. So I think the answer is, it depends. And then, you know, it'll be interesting to see how quickly we start to see it be impacted in the market to those are. Those

Gil Gerstl: are all great points. I think, uh.

If anything that it's all evolving. One of the challenges the compensation benchmarking across the [00:57:00] industry is for the most part is very much trailing in the right. It's something that takes time and for most compensation benchmarking. Um, surveys out there, their surveys and they're about a year to 18 months old.

We. Thankfully, at ADP, we have kind of a more current one because of changes in the market. Uh, but to be very frank, I've not checked them in the last couple of months, so I don't know to tell you if, uh, if we see significant changes, uh, in those realms. Um, I would say that there's, is, there will be, as Karen said, a change in mix of, Kind of the ratio of individuals and kind of roles that needs to be held.

Um, I can say that if if there was an expectation that new roles will get created and pop up, at least at ADP, we have not seen that yet on our on ourselves, like the we don't have prompt engineers like as a new title, but we do hire more data scientists or ML engineers or product managers with expertise in AI to be able to build.

Um, And to [00:58:00] enable, uh, and to enable more products and more innovation, I think. Um, but, but, but to Karen's point as well, I do think that there's an opportunity for individuals to actually think it will improve their world of work and they'll improve their work. Uh, some people enjoy, uh, the more repetitive tasks, but.

But we can actually allow them to take a step back from it and think more and do more. And while how will that impact their compensation? Their compensation, I think that's, uh, that's TBD. But, but I would expect some improved and improved engagement or experience, I would hope, because I can now spend time on other things and think more, or engage more with people instead of, of spending that time on, uh, on repetitive repetition.

David Rice: Well, we have reached time. That is all we have time for today. I want to thank our panelists for joining us [00:59:00] and thank the audience for being here. Please take a second to fill out the survey that we're posting in the chat. You can let us know what you thought of today's session and submit a topic that you'd like to see us cover in the future.

Uh, like I said, a big, big thank you to the panelists today. This has been a really interesting conference conversation. So much fun. Thank you for sharing your expertise with us. Uh, folks do keep an eye out for more events, the podcast, uh, keep up with what's going on the website. If you haven't signed up for the newsletter, please do that.

Uh, because as Jason said, you power your own canoe and actually we're here to help you do that. So have a great rest of your day. Keep learning.