The panel discussed how various companies use data analytics to improve employee engagement and workforce management. Brian Padilla from Lionsgate highlighted the use of surveys and metrics like attrition rates. Andrew Dufresne from UST emphasized the shift from annual surveys to real-time pulse surveys and external benchmarking. Rebecca Warren from Eightfold AI focused on using data to identify skill gaps and career pathways. Michelle Seidel from Aon stressed the importance of predictive and prescriptive analytics for workforce planning. The panel also addressed the balance between data collection and maintaining employee trust, and the potential of AI to enhance efficiency and decision-making in HR.

Panelist introductions and company roles

• Stacy Perman introduces the panelists and asks them to introduce themselves, their roles, and how their organizations use data to connect with their workforce.
• Brian Padilla from Lionsgate discusses his team’s use of data for metrics like attrition rates and turnover information, as well as survey data.
• Andrew Dufresne from UST talks about using data for both internal and external purposes, particularly in response to feedback from surveys and exit interviews.
• Rebecca Warren from Eightfold AI explains her role in talent-centered transformation and the use of data for customer experience, skills visibility, and career pathing.
• Michelle Seidel from Aon describes her role in human capital consulting and the importance of data in understanding the health of the workforce, including physical, financial, and emotional well-being.

Traditional vs. new survey methods

• Stacy Perman asks Andrew Dufresne about traditional survey methods and new ones being tried out.
• Andrew Dufresne explains the shift from annual employee engagement surveys to more frequent pulse surveys and real-time engagement platforms.
• Michelle Seidel adds that many organizations are using simple emoji surveys and sentiment scraping to get immediate feedback.
• Rebecca Warren emphasizes the importance of understanding the “why” behind survey responses and aligning them with the company’s vision and strategy.

Engagement surveys and analytics

• Stacy Perman directs a question to Brian Padilla about methods for gauging employee engagement and the nuances involved.
• Brian Padilla highlights the importance of designing surveys to assess engagement and understand the reasons behind satisfaction or dissatisfaction.
• Michelle Seidel discusses the link between engagement, productivity, customer service, employee retention, and attrition, and how data can help prioritize and address issues.
• Rebecca Warren talks about using AI and analytics to identify skill gaps before they become performance gaps and model career pathways.

Predictive and prescriptive data

• Stacy Perman asks Michelle Seidel about using data to be both prescriptive and predictive in understanding talent pools.
• Michelle Seidel explains the difference between predictive (identifying talent needs) and prescriptive (crafting reward strategies to attract talent) analytics.
• Rebecca Warren adds that understanding the reasons behind turnover and addressing them proactively is crucial.
• Rachyll Tenny emphasizes the importance of transparency and communication in the survey process to build trust and ensure actionable results.

Balancing data collection and trust

• Stacy Perman asks Andrew Dufresne about balancing data collection and maintaining trust in the workforce.
• Andrew Dufresne discusses the importance of transparency and intentionality in data collection processes, especially with the increasing regulatory requirements.
• Rachyll Tenny adds that trust and transparency are built on communication and context, ensuring employees understand why data is being collected and how it will be used.
• Rebecca Warren highlights the importance of compliance and ethical use of data, as well as the need for anonymization and tracking to ensure accountability.

AI and human-centric workplaces

• Stacy Perman asks how to keep humans in the front when using AI in the workplace.
• Rebecca Warren emphasizes the importance of mindset, skill set, and tool set, and the need for a shift from job titles to skills-based organizations.
• Brian Padilla discusses Lionsgate’s approach to introducing AI tools and the importance of constant exposure and training.
• Rachyll Tenny talks about the efficiency of AI in freeing up time for more people-centered activities and connecting with leaders and teams.

Using data to address business hypotheses

• Stacy Perman asks Brian Padilla about using data to address business hypotheses.
• Brian Padilla shares an example of using data to address a turnover issue and uncover a talent management and succession planning problem.
• He explains how the data helped frame the issue back to the executive and led to a more proactive approach to talent management.
• The discussion highlights the importance of using data to vet business hypotheses and inform strategic decisions.

Future applications of analytics

• Stacy Perman asks the panelists about their future aspirations for using analytics to make better decisions and gain insights.
• Rachyll Tenny expresses excitement about using analytics to understand leadership development and skill sets.
• Michelle Seidel hopes to use data to identify the best places to invest in the workforce for the greatest return on investment.
• Rebecca Warren wants to use data to reimagine HR and talent acquisition processes, breaking away from outdated methods.
• Andrew Dufresne and Brian Padilla discuss the potential of AI to drive efficiency and inform strategic decisions, aligning with business objectives.

Stacy Perman, Staff Writer, The Los Angeles Times (0:05)

Good afternoon. Thank you all for coming. I think we have a really interesting panel for the, I guess, first post-lunch panel. So first, I’m going to ask our panelists to introduce themselves, tell us your role at your company, and identify your company. Also, maybe you can give us an example of how your organization is using data to better connect with your workforce—your employee workforce. So we’ll start with Brian. Great.

Brian Padilla, SVP, HR Business Partner, Lionsgate (0:34)

Hi everybody. I’m Brian Padilla. I lead a team of HR business partners at Lionsgate Entertainment. We are an independent TV and film studio, and my team works with our television side of the business—so everything from TV sales and TV production to the productions themselves as well. I’ve been at Lionsgate for eight years. I don’t know how that happened. Time flies when you’re having fun. We use data in a lot of ways. You know, when we think about data specific to people, we leverage the systems that we have in place to look at a lot of different metrics. Maybe they’re things like attrition rates or turnover information. We also make use of survey data as well, which I know we’re going to get into, so I’ll pass it along.

Andrew Dufresne, Head, HR Operations and Employee Experience, North America, UST (1:16)

Thank you. Hi everybody. So my name is Andrew Dufresne. I’m straight out of Shawshank Prison. I have funny stories there; maybe there’s a happy hour later where I can share. I work for UST; we’re a digital transformation company consulting in the technology space, and I’m the Head of HR Operations for the Americas. As far as what we’ve done with data analytics, we’re on both sides. We’re looking at data both internally and externally to see how we can help our clients. But one area we focused on was feedback during surveys and exit interviews about our onboarding process and general employee experience. We got a new Chief People Officer who gave me an opportunity to build a team to just do that. In terms of how quickly we respond to the feedback that we get from our employees, we take a lot of pride in that. So thank you. Hello.

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (2:18)

Rebecca Warren with Eightfold AI. Funny you talk about eight years; I’m coming up on five years in a couple of weeks, which, who knows how that happens with a tech company? I’m a former TA practitioner who moved over to Eightfold to build out Customer Success. I am a “skills story” because I did not know Customer Success or tech, and then I spent about three years there. Currently, I’m in Talent-Centered Transformation. I sit under Marketing, and we focus on thought leadership, understanding how to connect the dots for prospects and customers, and supporting internal and external efforts using data, talent intelligence, and all of those pieces. I would say with the customer experience, our job is to really unlock potential. So it’s making better matches for folks applying to organizations. It is giving skills visibility, internal mobility, personalized career pathing, workforce planning—all of the pieces to make that talent lifecycle better for everyone involved. Great.

Michelle Seidel, Human Capital Client Leader, Aon (3:23)

Michelle Seidel, I’m here from Aon, a human capital and risk consulting firm. I am the Human Capital Client Leader for the Pacific Southwest. That is a mouthful. Basically, my role is to connect the dots across the workforce elements, whether we’re talking about total reward strategies, talent strategies, or performance. As we’re talking about today, employee experience and employee engagement are not programs; it’s an ecosystem, and that ecosystem requires strong data and analytics to address and create interventions. When we think about using data, we think about it from the perspective of the health of the workforce. And when I talk about health, I really mean it from a holistic perspective—from the physical and financial to the emotional well-being of our talent. From the productivity perspective: are you providing the tools and enablement for your employees to bring everything they need to the table to be productive and deliver on your results? Ultimately, the third component that we tend to look at is retention. Are you retaining the talent and the capabilities you need to achieve your business goals and KPIs?

Rachyll Tenny, Chief Talent Officer, People Strategy & Organizational Impact, Capstone Partners (4:45)

My name is Rachyll Tenny. I currently serve as the Chief Talent Officer of Capstone Partners, a financial wealth management firm based out of Newport Beach. I have led teams through hospitality and finance startups. But ultimately, I really enjoy what I do because I get to really engage people and understand how we actually use technology and data to be more efficient with our time as leaders, and then how do we actually get that production up for our company—really optimizing people, process, and performance.

Stacy Perman, Staff Writer, The Los Angeles Times (5:22)

Okay, thank you. So I’m going to kick off the first question. I’m going to direct it to Andrew. So when you gauge employee sentiment, what kinds of traditional survey methods are you using, and what kinds of new ones are you trying out?

Andrew Dufresne, Head, HR Operations and Employee Experience, North America, UST (5:35)

Yeah, no, that’s a great question. So we used to do the traditional annual employee engagement survey model. We would roll it out in October or November. It would get analyzed, we’d get the results in February or March, and talk to our leaders about it in April or May. And now it’s six months old, right? So how can you act on that for the remainder of the year? We’ve definitely moved toward more pulse surveys and real-time engagement. We have a new employee engagement platform within the company where, when people are actually going through processes as part of their employee experience, they’re getting polled in real time. So if they’re going through a promotion progression cycle, they’re asked, “How did this process go for you? What could be done better?” and we’re collecting that feedback as those processes are happening. And then the last big piece is that we have partnered with external organizations, like Great Place to Work and Top Employers Institute, and we use those both for benchmarking and to solicit feedback from our employees as well.

Stacy Perman, Staff Writer, The Los Angeles Times (6:36)

So Michelle, I see you nodding. I didn’t know if you wanted to weigh in on this as well.

Michelle Seidel, Human Capital Client Leader, Aon (6:40)

Yeah, so I think it’s so interesting—the move away from the traditional employee engagement survey. I think I overheard a conversation at lunchtime where people were talking about how they do it. It’s kind of like taxes: you know it’s coming, you dread it. But what we’re seeing is a lot more pulse surveying. I know of organizations who are using really simple emoji surveys where you just click the happy face or the sad face. You have immediate feedback; you can respond to it in hours or days, versus the 90 days that’s typical from a traditional survey. The other thing we often see is sentiment scraping—that outside-in look at data. So you are able to scrape information that people are talking about regarding your organization, whether it’s Glassdoor or Indeed. At Aon, what we can do for our clients is categorize that feedback into categories that are important to employees, so things like rewards, benefits, manager effectiveness, and culture. Then we can give you a gauge on how you measure up against peers, whether it’s trending up or down, and help you identify gaps and strengths.

Stacy Perman, Staff Writer, The Los Angeles Times (7:52)

Rebecca, did you want to add anything?

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (7:54)

I think they’ve set it up. Yeah.

Stacy Perman, Staff Writer, The Los Angeles Times (7:57)

I don’t want to cut anyone off before I move on to the next question. So Brian, I’m going to have you kick this one off. What kinds of methods can you use to find out how engaged employees are, and are there nuances in that?

Brian Padilla, SVP, HR Business Partner, Lionsgate (8:12)

Yeah, I would say definitely. I think the survey is a really important method there—probably one of the major ones, right? But I think one of the things that we tend to focus on, and really dig deep into, is to delineate that—to determine what is satisfaction versus what is engagement-based. When you’re designing these surveys, especially since so many of them are geared toward understanding, you know, which jelly flavor is more preferable, right? Versus how someone is feeling about the way in which their job connects to the bigger picture, and connects to the results and the outcomes that we’re looking for within the business. So I think that’s a huge component: really ensuring that these surveys are designed strategically from that lens so that we’re getting the information that we really want, which is how engaged somebody is versus how satisfied they are with, again, the flavor of the jelly in the donut.

Stacy Perman, Staff Writer, The Los Angeles Times (9:02)

Just to follow up on that, can analytics suggest what kinds of changes can be made? I mean, I’ll address that to you since you were answering, but you know, any—

Brian Padilla, SVP, HR Business Partner, Lionsgate (9:10)

You’re saying sort of the analytics as a result of the data that you’re getting from these surveys? I would say absolutely. Because you’re probably going to speak to this better than I can in terms of how these surveys are built—I don’t tend to build them directly; that’s the L&D team at Lionsgate—but they’re really designed to assess engagement and then also point to the reasons why someone might not be engaged. Maybe they don’t have a clear understanding of how their role fits into the bigger picture, or they don’t feel supported by their manager, as an example. Things like that we’re getting, of course, in the data.

Rachyll Tenny, Chief Talent Officer, People Strategy & Organizational Impact, Capstone Partners (9:45)

Definitely. I think when you think about an engagement survey, we’ve all seen them. We’ve all seen restaurant surveys or, now we have all these surveys for all these retailers, but it’s really understanding the “why” behind the survey. Why are we doing this survey? How frequently are we doing this survey? And then I think the nuance is understanding why do people feel that way? We have to take it one step further and understand that they put something on a survey for a reason. Why did they do that? And ultimately, is that getting to the goal or the vision and strategy of your company?

Michelle Seidel, Human Capital Client Leader, Aon (10:20)

Let me just layer on to that. So we were talking about engagement surveys, and let’s remember: the reason we’re asking about engagement isn’t because it looks good on a scorecard, right? It’s because engagement is linked to productivity. It’s linked to customer service. It’s linked to employee attraction and retention. So there’s a reason we measure this. When we look at the survey results in the data, it tells us what’s going on, why it might be happening, and what we can do to fix it. Sometimes it even tells us how to prioritize those issues and when we need to fix them by.

Brian Padilla, SVP, HR Business Partner, Lionsgate (11:02)

To add to that, I was going to say you can use a survey, actually, to sort of vet even your hypothesis in some regards. So maybe you know that you’re seeing turnover consistently at a certain level within that one-to-two-year mark. Maybe you care, maybe you don’t, but if you care that that’s consistent at these certain levels, you want to dig into that and understand why. So you’re going to design that survey to figure out how to get those responses out of those folks who are leaving at these levels consistently within that one-to-two-year mark so that we can design some kind of intervention, if that’s what we want to do.

Stacy Perman, Staff Writer, The Los Angeles Times (11:37)

Rebecca, when you’re working with your partner companies, how are you using AI and other analytics to find where skills need to be upgraded, and how can you do that most effectively?

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (11:49)

Yeah, I think so. With our customers that we work with, the first thing is that they’re not guessing about what needs to happen. They are able to use the data that we have. We’ve looked at over a billion and a half career trajectories. We understand the adjacencies. We understand what they tie to and what they work with. So the first step is that there is a baseline of “we know we’ve got something to think about here.” Going into that, using data to understand that, they can use it to look at skill gaps before they become performance gaps—because that’s really when it hits you hardest: when you’re like, “We don’t have it, and we can’t train for it, and we don’t know how to get it.” So it’s preventing those performance gaps, making sure that folks can see where that break is. I took notes because I tend to talk about all kinds of things, so I’m sticking to my… so the other thing it does is it helps to model career pathways. I’d mentioned I came from TA and then moved into Customer Success and then moved into Marketing and Talent Transformation. Had somebody not been able to look at my skill set… when I looked at Eightfold and somebody reached out to me and said, “You should apply for this job,” I said, “You’re crazy.” But they were able to look at the skill sets and say, “Okay, from a TA practitioner role, what does that look like? Leading teams, working with TA, working with a lot of different industries—how does that transfer over to Customer Success?” It wasn’t a real hard transition, right? Because I was able to look at people and understand those pieces moving into my role. Here, it’s understanding those skills and giving multiple different pathways. It doesn’t mean I look at my resume as a list of things I never want to do again, right? Like, I’ve already done them; I want to do something new. So how do we help people look at those multiple career paths? I was kind of at the end of TA going, “I want to do something different,” but I didn’t know what it was. So when you look at those skills, you’re able to unlock that potential. Make sure that you’re not looking at gaps in your workforce; make sure you understand what that career path looks like. And then the last piece that I’ll say there is really tying the performance and the development opportunities to business objectives, right? This isn’t an HR problem. This isn’t an IT problem. This isn’t a department problem. When you are looking at skill gaps or looking at how do you fill those spots, it really is an organizational challenge that you’re working to solve. So tying hiring, development, and skill gaps to what the business is trying to achieve makes all the difference instead of trying to plug gaps in a leaky bucket or in whatever we’re trying to plug.

Stacy Perman, Staff Writer, The Los Angeles Times (14:37)

Andrew, did you want to add to that?

Andrew Dufresne, Head, HR Operations and Employee Experience, North America, UST (14:39)

Well, yeah. I mean, when we had our kind of gathering and had the opportunity to listen to Rebecca, I was like… but one of the things that we’re doing—and I think a lot of you might be in this process too—is transitioning from a role-based career architecture to skill-based. Especially in the consulting and technology space, you need to be able to look at those things. And if you don’t have the data to back that up and know both from your developmental side where the business is coming from and where the needs are, you’re just walking completely clueless. So yeah.

Stacy Perman, Staff Writer, The Los Angeles Times (15:25)

Before we move on, I just see a lot of nodding heads. So I didn’t know, Brian, if you wanted to…

Brian Padilla, SVP, HR Business Partner, Lionsgate (15:31)

Covered it. It’s great. Yeah.

Stacy Perman, Staff Writer, The Los Angeles Times (15:33)

So Michelle, I wanted to address you with this first: how can data be used to be both prescriptive and predictive? For example, in understanding your talent pools.

Michelle Seidel, Human Capital Client Leader, Aon (15:45)

So first, at the risk of sounding a little didactic, I mean, we work with what we typically call descriptive data pretty regularly. That’s our dashboards; it’s the data that looks backward. But predictive and prescriptive data is a little bit different. Predictive tells us a little bit about what might happen—what’s likely to happen—and prescriptive tells us what to do about it. And so I really think of this in terms of work we’ve actually done, like workforce planning and workforce strategy. Predictive analytics: you’re taking a look at your business strategy, the work that needs to be done, the types of skills that are required to deliver on that, and then you’re evaluating your workforce. Do we have the right talent or not? “Oh, we need 50 more software engineers to get this product launched,” and so forth. That’s predictive analytics. And then prescriptive analytics is: “Now, what do we do about getting these people in the door?” We have, let’s say, this organization I was working with. They knew, based on their workforce strategy, that they were going to need a large number of new software engineers and cybersecurity experts. So what they did was they actually took a look at the types of rewards programs, compensation programs, bonus programs, and so forth that worked best for their high performers, and they crafted a reward strategy for their talent acquisition team to go out and bring in. So they were offering the right kinds of rewards package to attract the talent that they needed most, kind of making them an employer of choice. So: predictive in identifying the type of talent they would need, prescriptive in making sure that they’re offering the right types of rewards and programs to attract that talent.

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (17:42)

I’ll tack on to that. I was talking to someone yesterday who is in TA, and he was talking about how he made sure to keep his workforce at par in the restaurant business. He was saying, “Well, if I know and I look at my analytics, I tend to lose one cook a month. So I need to make sure that I’m not just hiring one cook a month; I need to make sure I’m hiring more than one cook a month.” And what I said to him is, “But are you looking at the data on why you’re losing one cook a month?” You can replace them, but if you’re not looking at why it’s happening and looking around the corner—so not just even saying, “Well, they’re leaving because they’re missing a benefit,” but is there something else? Maybe you’re hiring the wrong profile. Maybe you think that you need to hire somebody who’s got 10 years of restaurant experience in the back of the house. Maybe you need to hire somebody who’s never cooked before so you can train them and give them what they need, right? So asking different questions is that idea of, “Here’s what the data tells me, but then you have to say: What am I going to do about it?” And is the answer just replacement, or is it asking different questions?

Rachyll Tenny, Chief Talent Officer, People Strategy & Organizational Impact, Capstone Partners (18:49)

I was going to say, also, sometimes I find the best data is when we do an exit interview and we actually think about, “Okay, well, why did this person leave?” Some reasons are obviously valid, and some are like, “Well, interesting, okay, we didn’t have the right coffee.” You know, like the little things. I’m like, “Okay, we had Peet’s Coffee versus Starbucks.” You know what I mean—all the things. But it is actually looking at where people find that gap after the fact and then internalizing it. I’m a mom of four boys, and so I always think to myself, “Okay, how do I actually get them to be motivated to do those things?” But being prescriptive means being proactive and avoiding those things. So I always like to say I like to limit surprises and manage expectations.

Stacy Perman, Staff Writer, The Los Angeles Times (19:43)

I know when we talk about AI and data and analytics, there’s still a lot of wariness around the topic. So, Andrew, when you gather data about employees, where should a company draw the line between engagement and investigation? How do you maintain trust in the workforce?

Andrew Dufresne, Head, HR Operations and Employee Experience, North America, UST (20:04)

Yeah, that’s a really good question. And it kind of crosses over and blends a little bit with my role because, not only am I running HR Operations—which is a lot of the system side and a lot of the employee engagement side—but I’m also doing employee relations. And so, as an organization, being transparent about our processes: “Where are we using the data?” informing people as transparently as possible. Unfortunately, I know it creates some layers of administration, but a lot of our states are starting to basically dictate this. California is putting rules in place, and other states are putting rules in place. For me, I think it’s really about being intentional. What are the goals of the organization? Because as we know, sitting in on these panels and being part of HR, we do work for the company. Understanding why we’re doing this… obviously, we don’t want to be invasive with anybody. We’re not sitting there looking up people’s home addresses just because. But on the other side is: how do you get somebody to want to give you information? If you’re asking for things like self-identification surveys—things where we’re required to report on it—but people don’t necessarily trust that that information is going to be used in a way that’s ethical. Just having those conversations with people, explaining to them, showing them how the process works and what the end product looks like, and what actually goes out into the world. We do a lot of that in terms of communicating on that side.

Stacy Perman, Staff Writer, The Los Angeles Times (21:49)

Anybody want to tack on to that? Rachyll, go ahead, and then I’ve got something.

Rachyll Tenny, Chief Talent Officer, People Strategy & Organizational Impact, Capstone Partners (21:55)

I would say yes, it’s built on trust—built on communication and transparency. So we have to be transparent in the process as to why we want this survey, the context of it, and then “What are we going to do with the survey results?” I think that is the part where we’ve taken surveys and they haven’t actually been fruitful, or we’re not actually listening to our employees. So understanding that point of view is trust, transparency, and then the context of it.

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (22:26)

She took the beginning words out of my mouth. So yes, trust and transparency 100%. And I think it’s confidence that the organization is doing the right thing. At Eightfold, we have complied with a lot of different compliance certifications. We make sure that we are compliant with the generally accepted compliance certifications that are out there. And it also then is making sure that we are using the data for the right thing—again, that trust and transparency. We anonymize data outside of an initial customer’s instance; we anonymize that data and we use it to help tell the story, but we pull out all the PII (personally identifiable information). We make sure that we are not transferring any lines. What we talk about inside of Eightfold is that we are “Responsible and Explainable AI.” Everything that we do is tracked, and we can go back and say, “This is what happened.” So if there is something that wasn’t handled correctly, we can go back and look at it much more quickly than if we had a manual process or if we weren’t tracking all of those things. Compliance with overall certifications, then also making sure we’re doing the right thing inside of our organization. We also have an Ethics Council that makes sure that we’re looking at all of that information and we’re handling it appropriately.

Brian Padilla, SVP, HR Business Partner, Lionsgate (23:44)

Yeah, to add to that, it’s really all about how it’s framed. Until recently—well, not so recent, maybe almost two years ago—we launched what we called the “Self-ID Campaign.” The idea was that Lionsgate was really looking to capture and better understand the population: sensitive information like whether somebody was a caregiver to a parent, whether they were a parent themselves, their sexual orientation, their gender identity, and so on. The way all of that was framed to the employees was really centered around wanting to better connect with the needs of those employees. And the buy-in was huge. I think we had like 90% of employees complete that self-ID; at the time, it was some 1,600 employees, because they felt that the company was taking an interest in their interests. And then, to your point, that information was handled very carefully. Not even the HR business partners have access to look at any individual information; it’s all housed on an aggregate level.

Stacy Perman, Staff Writer, The Los Angeles Times (24:42)

Kind of to follow along this theme, I think about when I think about what you’re saying and these topics… when I was covering the writers’ strike a couple of years ago—which doesn’t seem applicable—there were these great signs, and the best sign, in my opinion, was “AI didn’t have childhood trauma.” And I think you can steal it; that’s good. But I think about it when we’re thinking about the workplace and AI. This is this big cultural shift. So my question is, how do you keep humans in front of this? How are they in the lead? I mean, when we’re thinking about a world that’s going to resemble maybe Terminator, but… Rebecca?

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (25:24)

Well, it’s interesting because we talked for a long time in the HR space about how we wanted tools to do the things we needed to do. So we felt like HR was ready, and tech hadn’t caught up. Now AI has run away with the show, right? And it’s changing so fast; every day there’s a different iteration of what things look like. So where we’re seeing now is that it is a shift—the tech is ready, but people aren’t necessarily on board. We talk about what engagement looks like, what giving information and being connected looks like—that trust and transparency. I think the first thing is really building that mindset. We have to switch how we’re thinking about work. We have to switch how we’re thinking about going from a job title to a skills-based organization. Work doesn’t get done in job descriptions; it gets done in communication, partnerships, and collaboration. I talk a lot about it being about mindset. We talk about “mindset, skill set, tool set,” but if you don’t have the right mindset, it doesn’t matter what tools you bring in, and it doesn’t matter what skills you have because they’re not going to be utilized. I think the number one thing is looking at that mindset and getting people used to trust and transparency. How do we tell the story? How do we make sure that our leaders understand what’s important to employees, and employees feel like they can trust leaders? It’s those little baby steps. We spend a lot of time with prospects where they’re like, “Oh my gosh, I don’t even want to talk about AI.” They ask us, “Can we turn the AI off in the platform?” Then we’re probably not the right platform; we are an AI-native platform, and that’s what we’re built on. But the idea is, then, “Let’s give you the understanding of what that looks like. How do we help you feel comfortable? How do we share where your information is going, how it’s being used, and then let’s use that for good.” So how do we help people start to adopt the changes? We talk about “AI isn’t going to take your job; someone who knows AI is.” That’s kind of an old saying. But how are we thinking about the work that needs to get done, and how are we changing that conversation? I’ve got more to say, but I’ll stop. But mindset for me is the big thing.

Brian Padilla, SVP, HR Business Partner, Lionsgate (27:36)

To add to that, obviously we’re not—I don’t think anybody is—but we’re definitely not rolling out AI tools in a perfect way. It can feel chaotic sometimes. But I think one of the things Lionsgate has done really well is offer that exposure, and it’s constant. There are training sessions and there are live working sessions. We’ve got a huge working space for employees to gather on one of the floors, and it’s been really interesting to watch. Because the concerns like you raised were very, very apparent earlier on. But since our company, and every company, is so efficiency-focused, that’s really been an amazing way to sell it. So you’ve got, let’s say, a salesperson, and you’ve got 600 movie titles sitting on a shelf. They’re old titles; you haven’t been able to sell them recently. It’s getting them excited that they can use an AI tool to actually analyze that list of 600 titles—which would take forever as one person—and then give them ideas in terms of which titles they should go out with and why, and what the components from those titles are to really highlight. Maybe it isn’t the actor; maybe it’s actually something else. That’s been really well received. And on the HR front, too, I think there was a lot of fear that much of the HR function could be handled by AI. I don’t know that the technology—at least not the technology we have access to—is fully ready to take over anybody’s job. But again, it’s been all efficiency-focused. So if we’re talking about data, just the other day I ran a report looking at turnover over a five-year period. Copilot—not the fanciest tool, I would imagine—was able to give us a lot of information about that data: average rates of tenure, average reasons why people would leave, the number of resignations that were due to some form of dissatisfaction with a supervisor, things like that. It’s all really useful information that can be pulled together really, really quickly.

Rachyll Tenny, Chief Talent Officer, People Strategy & Organizational Impact, Capstone Partners (29:33)

It’s really efficient, right? We’re here; we want it to be efficient at the end of the day. And how does it give us back time to be more people-centered? How does it give us more time to actually connect with our leaders and with our team members and even other organizations? That’s really what I use it for.

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (29:50)

I think it’s keeping people… we talk about “keeping people in the loop.” We’re changing that vernacular and starting to talk about “keeping people in the lead.” How do we lead with that people-centered focus to make sure that everything underneath is driving the right results for people in the business?

Stacy Perman, Staff Writer, The Los Angeles Times (30:10)

So Brian, I guess this is somewhat related to what you just said, but you’ve mentioned that analytics can be used when you have a hypothesis coming out of a business—for example, if there’s an idea coming out of the C-suite. So I’m wondering if you could sort of expand on that idea.

Brian Padilla, SVP, HR Business Partner, Lionsgate (30:30)

A hypothesis could be coming from us, depending on what we’re looking at, or out of the business. I can think of an example. There was a year where a very senior sales executive, who had a large sales fleet, had a situation where over the course of five weeks, there were six resignations of individuals who were identified as key talent. The next thing you know, this executive is in the C-suite framing a hypothesis very strongly: that he’s missed opportunities to address emerging markets and to tackle new business opportunities because of high turnover. That gets everybody riled up—”Oh my gosh, what is HR going to do about this turnover issue?” So then my team was able to take a beat and say, “Okay, well, let’s look into this a bit.” We used the data that we’re able to mine out of our HCM system—which isn’t the fanciest way, but it works and gets you a lot of information. When we looked back over a period of seven years, we saw that these six people had… we used a predictive sort of approach. They left as predicted; there were no surprises. The data showed this person left at this mark at this level, which was really consistent with the data over that seven-year period. So we really didn’t have the sense that turnover was an issue—maybe it was an issue, but it wasn’t any different than it was seven years ago. The difference was that there was a new line of business that was in focus, and several employees were identified as key talent who were really critical to tackling and chasing this new opportunity. So then we pivoted from there. Once we knew… “Is turnover really the issue?” We used that data to frame that back to the executive. Because really, for the business partner team, it’s all about understanding the strategies that are coming out of the business and helping them see those through to the other side. Ultimately—I won’t take up this whole time; I could probably talk about this for an hour—after compiling a lot of qualitative data and meeting with all of the stakeholders involved, what we really came to understand was that our approach to talent management and succession planning, in particular, was too slow. We were only really delving deep into workforce planning once a year, and that was driven by Finance because Finance didn’t want to deal with changes to a budget more than once a year. So there was only one opportunity a year for executives to really address the needs of talent. When we met with the stakeholders in Finance, it was, “Well, it’s going to be too expensive; we’re spending too much money.” So again, we went back to the data. We looked at all of the off-cycle ways in which we addressed the needs of talent monetarily over a seven-year period. It turned out the money was being spent anyway; it just wasn’t part of that process. So it wasn’t as visible. Then we used that data to go back to Finance and get their buy-in and say, “Look, we’re already spending this money. In fact, if you look at several of the key executives that were addressed over the seven years, we probably paid a premium because we were reactive to their needs.” So in the end, what that executive thought was a turnover issue really wasn’t a turnover issue as much as it was a talent management and workforce planning issue that we were able to solve for. So yeah, that was a fun example.

Stacy Perman, Staff Writer, The Los Angeles Times (34:06)

Okay, I think we have time for one more, and I’m going to address this to all of you. I’ll start with Rachyll and we’ll just move down. From your experience, what’s one way you’d like to use analytics in the future to make better decisions or to gain insight? Give us an example.

Rachyll Tenny, Chief Talent Officer, People Strategy & Organizational Impact, Capstone Partners (34:27)

Yeah, I think for me, I am excited to see how it can actually work with leaders in our development. Going back to your statement: there has to be this attrition and retention analysis, but why are people leaving? I’m really interested in understanding how AI and data go into leadership and your skill set. What actually energizes you? Where are your skill sets? And then, how do we combine teams to be more productive? Great.

Michelle Seidel, Human Capital Client Leader, Aon (34:58)

So, if I could use data and analytics to achieve one key thing, it would be to answer the question more effectively and with more precision: “Where is the best place for us or our organization to invest the next dollar in our workforce for the greatest return on investment or the greatest value?” But—and I would probably look around the room here—I was having a conversation with a gentleman downstairs named Rob, and he talked about how the bigger the organization, the crappier their data is. And so our biggest issue is our data is all fragmented, so it’s really hard to get a consolidated view of the data to be able to do the type of analytics you need in order to answer that question. My hope would be to be able, especially with AI empowering us to better pull data together, that we can create predictive analytics that can answer that question. It then gives HR not just a seat at the table—we’re going to be at the table; we’re going to be building that table. We’re moving from dashboards to data science with that.

Rebecca Warren, Talent-Centered Transformation Leader, Eightfold AI (36:11)

If I think about using data… since AI is doing a lot of work for us already, I want to use it to “blow things up,” and I mean that in the best possible way. I think the way that we do HR and TA is kind of busted. We spend a lot of time trying to put AI on top of a process, and all that does is amplify what’s already wrong with the process. So I want to use those predictive data analytics to say, if we were to do a clean slate—if we were to say, “If we didn’t have any prior understanding, what would we do?”—we probably would build things differently today than we did 20 years ago. So how do we use data to build a better process? Blow up the old things—it’s okay—and recreate them to what makes sense for today as opposed to what we were doing in the past.

Andrew Dufresne, Head, HR Operations and Employee Experience, North America, UST (37:10)

We got the roundup symbol, so I’m going to go to… yeah. I mean, they’ve said really awesome stuff. Michelle, especially talking about what HR is asked to do. Everything is a fight, or it feels that way sometimes. “Hey, we want to add in this new benefit.” The big one that we did, and we want to continue doing, is paid parental leave. That’s such a huge piece. We brought the numbers to Finance, and we actually got our CFO involved. We were going into the room being like, “Alright, if we get six weeks, it’s a win.” Our CFO looked at all the data and he went, “Well, what about 12 weeks for women?” And I was like, “Yeah!” But I think that’s… and I’ll let Brian jump in here, but I couldn’t have said it better than Michelle did.

Brian Padilla, SVP, HR Business Partner, Lionsgate (38:07)

No, that’s great. Yeah, and very much on theme. I think so much of my job is listening to ideas around strategies that are going to lead to exceeding whatever the targets are at hand. We have so much data coming out of the business; we would just love to leverage AI to be better at helping us analyze data that we already have in order to vet out potential new business opportunities and then match those opportunities with the right people that we already have potentially within the company.

Stacy Perman, Staff Writer, The Los Angeles Times (38:37)

Okay, well, thank you. I mean, obviously we could go on and on and on, but I was given the wrap-up sign. So thank you to our panelists, and thank you for joining us. Thank you.