How Lumen Is Preparing Leaders for Humans + AI Agents (w/ EVP & CPO Ana White)

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Hey everyone, welcome to another episode of Future Ready Leadership.
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My guest today, Anna White, EVP and Chief People Officer at Lumen.
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We're going to be talking a lot about leadership, how it is blending with this new AI world that we're all a part of.
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And Anna's going to be sharing some of the cool stuff that she's doing and thinking about inside of her company.
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Before we jump into our conversation, of course, if you're a CHRO or a Chief People Officer,
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And you too are moving beyond traditional HR and focusing on the future of work and employee experience.
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Check out our CHRO group, futureofworkleaders.com.
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We have around 32, 33 CHROs in there.
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A lot of cool stuff is happening.
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Again, futureofworkleaders.com.
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Having said that, Anna, welcome.
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Thank you for taking time out of your day.
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Thanks for having me, Jacob.
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So for people who are not familiar with Lumen, why don't we start with just a little bit of background information about the company and what you guys do?
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Yeah, that sounds good, thanks.
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So we're a networking company.
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We really work to connect people, data, and applications, and really ideally unleash the world's digital potential.
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We have a really vast fiber network, and we're creating some very innovative services.
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Just a little bit on the from too,
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We're going from a pretty legacy telecommunications company, which is more point-to-point analog solutions, to much more of a digital networking services company that provides AI-ready solutions.
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And how many employees do you guys have?
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Just over 20,000.
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Oh, you're actually bigger than I thought.
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For some reason, I thought you guys were a little bit smaller than us.
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20,000, massive, massive.
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Yeah.
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Yeah, that's a massive organization.
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Okay, so today we're obviously talking about this theme of leadership in the world of AI, and there's obviously a lot going on there.
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Why don't we just start at very high level in terms of are you seeing leadership changing in the context of AI, and if so, what are some of the big changes or trends and things like that that you're paying attention to?
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Yeah, I'm definitely seeing leadership changing for a lot of different reasons.
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And I think a lot of the changes are actually really good.
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One of the biggest ones I'm seeing is the absolute importance of curiosity and a growth mindset.
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And so I'm seeing with AI especially, really leaders prioritizing having learning agility and curiosity.
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And part of the reason for that is with AI,
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Answers are out there right away.
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You no longer need the smartest person in the room, raising your hand right away or being the knower, the leader knower.
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It's more important, in my opinion, to be a learner, a learn it all versus a know it all.
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That's, I'd say, one of the biggest shifts.
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And then things are also changing so rapidly.
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Like the one constant in this world is change.
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And so people really need to be agile and curious and be willing to pivot and change as they learn more.
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So I think that's really important.
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And another thing I would add that's always been important in leadership, but in my opinion is even more important, is kind of empathy.
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Um, and compassion with people going through so much change.
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Uh, and when people feel that empathy, um, and that authenticity and trust from their leaders, it goes a long way.
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So those are a few of the things that I think are really important.
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Curiosity.
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trust, empathy, and along with curiosity, I really bucket this concept of growth mindset, which is one of a really important behavior.
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We have something in our culture we call illuminate culture.
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It's eight behaviors.
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And one of those behaviors is growth mindset.
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I love it.
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Let's talk about this idea of learning agility, because to me, this is a very important one.
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Even in my new book, I talk about this idea of grow or go.
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And this law that I have out of the eight laws is the idea that learning is a new form of job security.
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And so it's so important to create environments where...
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all employees are able to kind of learn and grow and develop.
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So inside your organization, when you think about learning agility, how how is that structured?
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Do you have specific programs in place?
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Is it courses?
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Is AI a part of that?
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Like what does that actually mean for you, Lumen?
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Yeah, no, I have a lot of things to say on learning agility.
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I'll give a few examples.
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Maybe I'll start with AI and then I'll go more kind of to leadership.
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So around AI, we want people to be constantly learning.
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So we've created kind of an AI literacy academy for all of our 25,000 employees that they can all go learn together at scale and
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in the time that works for them.
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They don't all need to do it at the same time.
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It's very agile.
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And then we have very specific role-based trainings on AI that go much, much, much deeper and a lot of different areas.
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And we've even partnered for some leaders with Harvard and Microsoft on this frontier firm concept to teach them how to learn more about AI.
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The other thing I'd say is learning agility.
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Can I ask you a question about that really quick?
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Yeah.
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So when you say that you're kind of rolling this, is this like a course that people take?
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Is it a certification?
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Like what is the actual training?
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Yeah.
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So we have an AI Academy and that is created within our organization using our learning platform and they
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Our HR team, our learning and development team, partners with our engineering team to kind of determine what's most relevant for AI.
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And we use our learning platform to share that for all employees.
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And so they can get on there and they can get certified and follow through on the programs for all employees, and then very much role-based programs as well.
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And then we've done other programs in partnership with Harvard and Microsoft for a subset of our leaders
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to really lean into AI even more deeply.
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And that's been a lot of fun.
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And are these mandatory trainings for the 20 to 25,000 employees or is it optional?
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And I'm assuming it's like a series of videos that employees watch to kind of learn how to prompt and do all that stuff.
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All different modalities, but it is a lot of videos.
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And we also have co-pilot through Microsoft and we have trainings for that as well.
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We don't require it, but our co-pilot utilization is at 98%.
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So it's really high, high utilization on co-pilot.
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What I better wanna understand is what is the deep utilization?
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Like how often are people really using it?
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What value is it accruing to?
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So we have more to do versus just the baseline of utilization.
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So we have many, many options for AI.
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And then I'd say the one area I'm most passionate about is the learning we're doing and the training we're offering around our culture and around leadership.
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So do you want me to talk a little bit about that?
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Because they interrelated my opinion.
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One last question on that.
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So it seems like Copilot is the main tool that you're using inside of Lumen.
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Is it only Copilot or employees allowed to use Gemini or Grok?
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We have a few other things that employees can use, but Copilot's the primary.
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It's the main one where we've given more licenses.
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Not everyone, but a large number of a large percentage of our population has them.
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And of those that have the licenses, 98% are actively using it.
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And then we have other offerings we allow as well.
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But that's the primary driver just to have simplicity.
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Okay.
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Yep.
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Okay.
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Okay.
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Yeah.
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Yeah.
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You can jump to the next thing that you were good.
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Something I'm really excited about, and I think it's made a huge difference, not only in our culture, but in our business results is the training that we've offered around culture and
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We dare to lead.
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So it started a few years ago.
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I joined just two and a half years ago from a cybersecurity company, F5, where I was the chief people officer there.
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Oh, yeah.
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Cool.
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Good.
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That was a hard decision.
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I love that company.
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But I thought Kate is embarking on a huge transformation at Lumen.
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She's transforming the culture, the technology and the business.
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Right.
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all at once.
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And so I thought this is a really big challenge.
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And she really encouraged me to join, to try and make history with her and turn this company around.
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And that's what we're trying to do.
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But we've really been partnering a lot with Brene Brown on her dare to lead.
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So, you know, the dare to lead book, we've been doing really in-depth leadership training and the way we did it,
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We were one of the first to scale it very broadly, not just for all leaders.
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We offered it to all 25,000 employees and we put them in cohorts by level bands.
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And we did it over the course of time.
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So over one quarter, we have small cohorts meet.
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do training videos, test and learn, come back and share their learnings.
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Because I'm a huge believer in a training that if you do it in an agile way and you keep learning and you share your learnings and you keep tracking, it lives more than a training that's one day, great training, you kind of forget about it.
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We wanted it to be embedded into what we did.
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So that has been super helpful.
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We also pulled our VPs for an offsite where we went deep with them on these dare to lead concepts.
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And it gave us a common vernacular and a common toolkit that we could use so people could really live our culture.
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And one example of our Illuminate behaviors, I've already mentioned growth mindset, but one of our other key behaviors to win on this massive turnaround transformation we're doing
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is courage.
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And Dare to Lead is so good at building courage and building resilience.
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And so it's been really good to change how our employees interact.
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Don't talk about someone, talk to someone.
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Be direct, be respectful, know how to rumble,
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So we've given them this pretty significant toolkit in how to operate differently.
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And when people come into the company and they see, wow, this culture is real.
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In the interviews, it sounded great, but it's really makes a difference.
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It's been very powerful.
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So we required, the reason I wanted to bring this up, you asked about requiring or not requiring.
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Yeah.
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For our people leaders, we required Dare to Lead, and it was mandated, which is pretty rare.
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We don't like to require a lot of things, but we felt like for us to truly live this culture that was very different before Kate coming in to now, we wanted to invest in that and have people lean into it to truly live the culture.
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We didn't require it for our individual contributors.
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We allowed them to opt in.
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So the first year that we rolled it out, we had over half opt in and do that course.
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And then all of our people leaders have done it.
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And it's made a huge difference.
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And one other very cool thing we've done that's led to better leadership is
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is we've also, Brene started partnering with BetterUp.
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BetterUp is a coaching platform with really good, high quality coaches.
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And what we said when we partnered is, we wanted coaches that were Dare to Lead certified so that everything we've already learned, when they go to their coach, it comes to life in a concrete way.
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So what we did last year is we offered that for all of our vice presidents.
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And they really said, not only was it massively impactful from a professional perspective, but from a personal perspective, it was life changing.
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So I'm a huge believer in coaches.
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I think a lot of leaders don't believe in coaches.
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I think athletes, everyone has a coach because everyone can be better.
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I feel the same way about us.
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We can always be better.
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And so having that coach that can help you on your learning journey and be the best leader you can be has been very powerful.
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You mentioned you're going through a massive transformation.
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Can you share a little bit about what that is?
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So what's the transformation that you're going on and why?
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Yeah, so I'll give you maybe a little bit of history to start.
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So you kind of, you know, where we came from and where we're going to, does that work for you?
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Yeah, yeah, for sure.
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Okay, cool.
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So Lumen years ago, you know, about three years ago, right before, you know, Kate came in, came in, she came in just over three years ago, actually, really found itself in the same place as quite a few telecom companies.
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It was a bit of a state of decline, revenue and decline, declining stock price.
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significant debt because Lumen is almost a century-year-old company, and it had done many acquisitions over the course of time.
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So some of that debt was coming due a few years out.
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And it was a little bit of a playing not to lose mindset with some declines.
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And that's when Kate came in is it was a pivotal moment, frankly.
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So in order to really survive, we had to transform.
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And that's why I joined.
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I love a really big challenge.
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And so that was a huge challenge.
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So we really came in and the board brought Kate in with the intent.
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She transformed the company on every level.
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Like I said, business, technology, and culture.
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So when I joined, we really created a new senior leadership team.
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So we brought in leaders with new skills to drive this large scale transformation.
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We created a North Star for the company to really transform.
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We cut our dividend, which you can imagine is not well received.
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by your investors.
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But the reason the dividend was cut was to fund the growth for the transformation and the pivot that Lumen needs to do for that turnaround.
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And then I give a lot of credit to our CFO and the finance team.
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They very successfully refinanced billions of dollars in debt, really optimizing our cost structure.
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And then we did a pivot to say, yeah,
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We are going to become the trusted network for AI.
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So those are kind of the things we did on the business level for transformation.
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And when I joined, the stock price was, I think, around $1.60.
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And now- I was just going to say, yeah.
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I think I should check.
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Oddly today, I have not even checked our stock price.
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It's been such a busy day.
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But let me- Today it went up around 7%.
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And I was going to say- We're at 8.9.2.
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I did not know this.
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This is great news.
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But when I came in, it was $1.60 at one juncture.
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It went just below $1.
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Very stressful.
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But Kate, the CFO, and I had so much faith in Lumen and the turnaround we were driving.
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We all bought a significant amount at that low price as well.
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And we're not done.
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We still have a lot, lot more work to do.
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But of course, I'm biased in saying this.
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But I think the culture change that we've driven has been a real impetus to help to drive business success.
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And oftentimes, Kate, our great CEO, is asked by media, oh, has AI saved your company?
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And her answer is no, our culture has saved our company.
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And that's why I joined Lumen.
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I knew she was a huge believer in people and culture and the difference cultures can make.
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So I'm glad I made that decision.
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Yeah, I remember when I spoke with Kate, it was a few years ago.
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And I remember the stock price was to your point, it was like a dollar something.
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And I even remember getting some people commenting on my YouTube videos because we did a conversation.
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Yeah, yeah.
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People were commenting, they're saying, oh, this stock sucks.
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It's like a dollar and like, what's going on over there?
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And, and now here you go.
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It's, you know, 8x since then.
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So clearly you're moving in the right direction with whatever changes and transformations you're, you're in the process of implementing, which is very exciting.
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Thank you.
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A lot more work to do.
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Yeah.
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Well, I mean, it's a massive organization.
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It takes time for change to happen.
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Yeah.
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Getting back to the leadership piece with AI, I'm also really curious, are you seeing any challenges with the leadership and the AI piece?
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Because to me, when I think about
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leaders in AI, there are some new skill sets that leaders have to know how to practice as well.
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So for example, when you're talking to employees, if your employees, for example, are using CoPilot or Gemini or ChatGPT to get their work done, leaders have to know how to pull out the human critical thinking aspect, right?
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Yes.
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So are you training leaders at all?
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Sure.
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How do you have conversations with employees
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when you know that your employees are using AI to do work and to submit work.
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Yeah.
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And because, for example, if I'm, let's say, in HR and, you know, I have my employee who's saying, hey, you know, Anna, here's the new, like, comp plan or here's the new, you know, hiring plan or workforce development plan for the next two years.
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And I know that they use AI to put that together.
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Yeah.
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You can't just take that and say, great, let's run with it.
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But you have to know how to pull out, like, did you use AI for this?
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What do you think AI missed?
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Like, how do you, you know what I mean?
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Like, test the critical thinking to make sure it's something that's usable.
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Yeah.
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Yeah, I think we not only coach our managers and leaders how to do that, we also coach our employees because I think there's accountability on both sides for this.
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And it relates to work slop, as I know you know.
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So we're asking our employees as well as our leaders to do is...
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Definitely leverage AI, definitely lean into AI, but the human judgment factor and critical thinking is super important.
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And I haven't seen as much quote unquote work slop as I might've thought.
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And I think part of that is we're continually reinforcing that your judgment is.
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Your experience is so important, and we can't just completely rely on AI.
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There's context it doesn't know.
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And frankly, people also need to improve on their prompting.
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So it's not like our prompting is all perfect.
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And so we are reminding not only leaders, but employees to take ownership and have judgment factors and even build in things like especially as you look to see can agents play a role?
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How do you build in the human judgment factor, the overlay to make sure it's not pure AI?
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We're being kind of really careful on that.
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And for people not familiar with the concept of work sloth, it's basically this idea where you use AI to get work done, you kind of turn it in or submit it or do whatever with it, but then you realize that there's actually a lot of errors in it.
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And so what a lot of organizations are struggling with is they're spending more time kind of QAing the work that employees are submitting that's done with AI, and they're saying it's actually taking more time, not less time to do work.
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So that's kind of a challenge that some organizations are dealing with right now.
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So it sounds like in your case, you're really talking about this idea of critical thinking and kind of pulling it out from employees, which I think is a huge element there.
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And so that does require kind of a new set of training, talking about with employees, making sure they understand this, right?
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Right.
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Yep.
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Yep, exactly.
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And that's the other thing that I think is really important is agility, even in training your employees, because it's constant test and learn and grow because there's things we're learning along the way.
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And I just put someone new over our learning and development team and she is very,
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very, very deep in AI, Sarah.
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And she's really trying to ensure that our training's the most relevant and incorporates things like the concept of work slop, the increasing the relevance of prompting and all of those things.
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And ideally building this into ways of working in the normal way.
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So it's not some separate thing you go to, but it's embedded into your job.
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So it's natural and easy.
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And funnily enough, now that I say this,
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We did the same thing for our culture.
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I had one question for you, something interesting that you mentioned on the accountability piece.
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Yeah.
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So if an employee does work with AI and they submit it, and then there's a problem with it, are they allowed to then say, well, oh, I'm so sorry, like the AI did this?
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Um, because that, that's kind of an accountability issue, right?
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Where employees are like saying, well, it wasn't me, it was the AI.
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But it sounds like what you're saying is that if employees use AI and they're turning into work, they're accountable for whatever the AI produces because it's their job to check it and make sure that it's correct.
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The thing I'd say is they are accountable, but also we ask people to put things in place because we may not know everything ourselves.
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So an employee can't know if AI was perfect.
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So they can incorporate other steps of human judgment
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from other people as well.
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So I think that's really important that you don't just rely on yourself.
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Given the stakes of what you're doing, let's do a hypothetical.
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Let's say you're leveraging AI co-pilot for a board deck.
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You get a really great baseline in the board deck.
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you're going to then incorporate other people that are deep subject matter experts to cross-check, overlay, add their context, add their opinion.
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We should never just rely on what AI did.
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So that's more of the extreme example, I would say.
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But we want people to incorporate experts depending on what you're working on.
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Or if you're doing something for a customer, let's not just rely on AI.
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Let's add the experts to overlay and cross-check, et cetera.
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I think that's where a lot of the debates in terms of productivity and cost savings comes from.
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Because now you have that extra layer of, well, if AI is helping me do the work, I also now need to QA the work.
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And now I need the time of various subject matter experts to help kind of make sure...
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that the work is correct.
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And so that kind of like takes a chunk out of the productivity piece, right?
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So now you're kind of, you're tapping into more time of employees.
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Are you, I mean, would you say that's a fair argument?
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Does that make sense?
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I kind of, yes and no, I would say, because normally without AI, you'd be doing it, but you'd also be working with many other groups.
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So you'd already been reaching out.
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So I think you have even a better baseline with AI.
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And then you incorporate the people that you need to to supplement.
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Carver's done this great research around AI on its own, an individual on its own team, team plus AI.
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and team plus AI is so much better.
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I don't remember all the details of it, but it's the power of humans plus AI is really like none other.
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So if you can do it in a way where AI gets really good results and then you supplement in the best way possible, those are the best results and it shouldn't be that much more work because AI saved a lot of the initial time upfront.
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There's just that cross-check.
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And again, it depends so much on what topic we're talking about
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and who the audience is as to how much overlay needs to be done.
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Are you in the process of trying to understand and measure the ROI of AI?
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Because today, for example, in our monthly CHRO group meeting, we had the chief technology officer of Uber join us.
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And one of the questions that came up from CHROs for the CTO of Uber was around measuring ROI.
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And his response was that for now, they're just focused on getting as much adoption as possible.
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Yep.
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And they've done some surveys from employees where they kind of subjectively tell the company what they're seeing in terms of productivity increases.
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But he said the challenge is once you start actually trying to quantify ROI in terms of dollars and cents, you bring in now the CFO and the finance team and it becomes kind of a whole different conversation.
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And I'm hearing that from a lot of different CHROs as well, where they're really focusing on the adoption piece first, but they're not quite at that ROI measuring stage.
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How do you feel about that?
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Where are you with that?
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Yeah, in general, I totally agree.
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We're very much focused on the adoption.
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The one area I might differentiate slightly is in the planning for AI.
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So when we have kind of this team we call an AI diffusion team that takes different functions, different leaders across different functions and looks at AI holistically across the company.
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And we did some great learning with this team.
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It's been a really good team.
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Okay, I'm going to ask you about that in a minute.
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That's interesting.
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Okay, great.
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Good, but in the planning, we've done a lot to determine what are the big rocks that we want to focus on and what kind of, at a very high level, what ROI might we expect?
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So maybe in the beginning, in the planning phase is more where we use the ROI, but I completely agree.
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We're looking at adoption as the key metric, really more than ROI, but in planning, it's helpful just at a high level.
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Does that make sense?
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Yeah, it's actually, yeah, I mean, I kind of wonder if the ROI discussion is even gonna be relevant for AI, because if you look at any tool that you use inside your company, I mean, do you know the ROI of using a laptop?
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Do you know the ROI of- No, not really.
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Yeah, so I don't know how this conversation is gonna progress because even if employees are able to do things a little bit quicker,
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that's going to become like the new standard.
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And the time they save is going to be filled with other work for them to do.
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So it's just like, so it's hard to, but at the same time, right?
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You have a board and you have a CFO who's like, Oh, this is how many millions of dollars to invest in this?
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Like these AI.
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So I don't know how are you finding that your executive team and your boards are
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wanting justifications or are they just kind of like, go, we understand?
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It's a good question.
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They like the plan at a high level.
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And the thing that we're also saying, though, is we want to be agile about this.
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Things are changing and moving so rapidly.
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So you can't plan as much.
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You might have to pivot and tweak along the way with a growth mindset because everything changes.
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changes so rapidly.
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So I think they understand that.
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And I'll use an example.
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So I mentioned, you know, we have a lot of big rocks we're working on across the company.
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A lot of them are very functionally focused, big rocks.
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We're stepping back now and saying,
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That's good.
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But how do we think more panlumen what makes sense?
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So if we come up with a big rock that's crossed all functions, we're probably then going to reprioritize and slow down or stop some other work so that we can do more of a panlumen type of thing.
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That's one example.
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And that's something I think a lot of companies struggle with is prioritization.
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There's just so much going on and so many things you want to change.
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So we're really trying to crack that code on how do we better prioritize in an agile way.
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Okay.
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Tell me a little bit about this AI Diffusion team and what it is and any details you can share.
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Oh, I'm happy to.
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So we created kind of this, we call it AI Diffusion.
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We established both AI governance and AI Diffusion teams to come together to really create a connected AI roadmap where we can look across the company and prioritize high impact initiatives
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kind of starting small and intentionally in pilots, but focusing on where AI could remove friction without compromising trust.
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And so that's been a lot of fun.
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And we've also, I would say, really used a growth mindset on this one.
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And so we've had this kind of learning methodology because no one knows it all today with AI.
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So what we did is we did this thing we called AI Discovery Days.
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And we did it with partners like...
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Microsoft, Amazon, BCG, PwC.
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And we went to kind of their headquarters, just a subset of us that were driving AI diffusion.
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And we just listened and learned to so many things that they have on their current plan and their future roadmap so that we feel like we could be as informed as possible as to what's out there before we finalized our plans.
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And then we've gone to different sort of events.
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We've done a lot of research on AI to truly try and learn as much as possible.
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Another thing we did more focused in one of the functions.
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So like I said before, a lot of our bigger rocks are functional focused.
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So our sales organization is partnering with Harvard and Microsoft.
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Lumen is one of, I believe, 14 companies in Harvard's inaugural Frontier Firm program.
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And we're really focused on embedding AI into business functions.
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to really look at where can we get performance gains and really explore kind of human and AI collaboration by looking at agent assistant tasks and where we can autonomous workflows, where we have some agent work, but also we have humans governing.
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So we're just starting that work and it's really cool, the frontier firm work.
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Do you also have like a ethics group?
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Like, is that a conversation that comes up?
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Because as we're recording this, I'm noticing, you know, there's a class action lawsuit against eightfold AI.
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I know Workday was in a lawsuit.
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And so a lot of...
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And I expect this is going to continue because AI for a lot of people is this black box.
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And so if AI is figuring out who should be hired, who should be promoted, who should be moved to a team, then a lot of employees are going to want clarity around it.
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How does all this work?
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How is it deciding?
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Is that an issue that's coming up at all?
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Are you working with your legal team?
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We haven't done, to be honest, we're not the most leading edge on leveraging AI for those types of decisions.
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But we are looking at that kind of thing, but we haven't gone so far there.
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So we do have governance.
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We have AI governance and ethics in our legal department.
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And I feel really good about the work that team's doing and the dot connection because they are a part of the AI diffusion team as well.
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And so they oversee AI crossed lumen.
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So I feel very good about that.
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But again, learning growth mindset will always be learning.
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Things change quickly.
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So we definitely don't know it all.
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We're not the know-it-alls.
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The more of the, I'd say, learn-it-alls there.
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Yeah.
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Are you finding that you're changing the way you think about high potential?
433
00:30:37.520 --> 00:30:46.125
Because it seems like in a world where skills are expiring faster than ever, you know, by the time you graduate from a four-year university, most of what you learned is outdated.
434
00:30:47.145 --> 00:30:51.768
Is it forcing you to think differently about what it means to be a high potential employee?
435
00:30:51.808 --> 00:30:55.850
Because it seems like learning velocity is now an important criteria.
436
00:30:56.050 --> 00:30:56.291
Yeah.
437
00:30:56.351 --> 00:30:56.731
Is that...
438
00:30:57.390 --> 00:30:59.472
Yeah, I completely agree with you.
439
00:30:59.492 --> 00:31:07.740
I personally and in our HR team thinks learning velocity and learning agility is one of the most important things.
440
00:31:08.561 --> 00:31:19.531
And we're actually incorporating that into a lot of our HR work, not only around high potential, but also even around performance and promotions and things like that.
441
00:31:19.851 --> 00:31:22.054
How do we better incorporate learning?
442
00:31:22.694 --> 00:31:24.336
I like how you said it, learning velocity.
443
00:31:24.416 --> 00:31:25.558
I tend to say learning agility.
444
00:31:25.578 --> 00:31:26.279
I like your term.
445
00:31:26.539 --> 00:31:27.080
It's a good one.
446
00:31:27.380 --> 00:31:33.467
We are incorporating that because we think it's so important today, more important than it was before, for sure.
447
00:31:34.028 --> 00:31:34.228
Yeah.
448
00:31:35.089 --> 00:31:35.429
Yeah, it's...
449
00:31:36.534 --> 00:31:42.418
I mean, it's interesting, but a tough time, I think, to be a leader because you almost have this difficult debate.
450
00:31:42.458 --> 00:31:43.979
You know, what's harder?
451
00:31:44.039 --> 00:31:46.841
Is it letting go of something old or is it learning something new?
452
00:31:47.402 --> 00:31:47.982
Yeah.
453
00:31:48.022 --> 00:31:52.025
Because you probably have a lot of seasoned leaders who, I don't know, maybe they have MBAs.
454
00:31:52.065 --> 00:31:54.026
They kind of grew up in the traditional corporate world.
455
00:31:55.087 --> 00:31:59.449
And they're having to let go of a lot of these these ways of working that are no longer relevant.
456
00:31:59.829 --> 00:32:00.610
Yep.
457
00:32:01.410 --> 00:32:11.115
And one of the other interesting debates that a lot of people are having is how are you having to change workplace practices around AI, if at all?
458
00:32:12.299 --> 00:32:23.848
Because, for example, if you still have the same bottlenecks for decision making, if you still have all the same processes, then really you're not going to get much value out of ROI because it's still bogged down by your organizational structure.
459
00:32:24.288 --> 00:32:24.489
Yeah.
460
00:32:24.589 --> 00:32:31.534
Are you seeing any changes to the core workplace practices or hierarchy or structure internally?
461
00:32:32.035 --> 00:32:37.801
Yeah, we are doing a lot of pilots in this space, and we do look at processes.
462
00:32:38.021 --> 00:32:45.389
And I think that's an important thing because if you start with a super bad process or bad data, adding AI is not going to help much.
463
00:32:45.890 --> 00:32:47.652
And so we are looking at that.
464
00:32:48.253 --> 00:32:51.116
Maybe I talk through a pilot we're doing if that works to kind of bring it.
465
00:32:51.136 --> 00:32:51.937
Yeah, that would be great.
466
00:32:52.197 --> 00:32:53.098
with an example.
467
00:32:53.638 --> 00:32:56.981
Okay, so we're doing some kind of smaller scale pilots.
468
00:32:57.081 --> 00:33:10.111
I'm a huge personal fan of pilots, but what we're doing is human agent talent mapping, kind of looking at roles, skills, where agents might be able to enable work,
469
00:33:10.611 --> 00:33:12.351
And we're just looking at it in a few areas.
470
00:33:12.571 --> 00:33:13.992
I thought, let's start with ourselves.
471
00:33:14.412 --> 00:33:16.592
Let's not just push everyone else to do everything.
472
00:33:16.892 --> 00:33:18.793
Let's look within HR at what we're doing.
473
00:33:18.853 --> 00:33:39.918
So we're looking in HR, we're looking in finance, we're looking in sales, doing some pilots, and really defining what AI-enabled work looks like across functions and trying to augment or automate tasks, look at where we can understand skill shifts that would be required for employees if we do do that.
474
00:33:40.818 --> 00:33:48.045
And looking at our processes, like you said, and like I've said, we know we have a lot of processes that need to be better.
475
00:33:48.926 --> 00:33:50.888
So let's look at that holistically.
476
00:33:50.908 --> 00:33:54.932
And that's why I like we're doing pilots, not trying to boil the ocean with this.
477
00:33:55.913 --> 00:33:59.296
And so that would be one example, but let me bring it to life.
478
00:33:59.376 --> 00:34:02.780
So within HR, let me make it more real.
479
00:34:03.280 --> 00:34:28.451
So within HR, we're kind of looking at shared services and compensation and looking at how can we redefine roles in those areas and look at what are the future skills required for those and how do we leverage AI for the very tactical baseline skills and then upskill our talent to do the other things.
480
00:34:28.911 --> 00:34:30.372
And so we're also looking at...
481
00:34:30.832 --> 00:34:41.360
How do we map the workflows that we have today to really splice and say, what should be human and what should be an agent task and allocating that?
482
00:34:42.141 --> 00:34:50.808
And then infusing in these pilots that are just kicking off, infusing just in time learning solutions kind of to upskill and reskill.
483
00:34:51.488 --> 00:34:53.350
And it's really interesting.
484
00:34:53.470 --> 00:34:57.813
It's very early days, but like I said, we're piloting and mostly in HR.
485
00:34:57.893 --> 00:35:00.075
We're looking in finance at some billing work.
486
00:35:00.455 --> 00:35:02.076
that can be done more by agents.
487
00:35:02.316 --> 00:35:06.818
And then looking at processes, there's a lot of processes that we can improve.
488
00:35:07.519 --> 00:35:13.842
And maybe you don't have to start from scratch because AI can take this on and kind of get rid of that process, but you have to be really careful.
489
00:35:14.062 --> 00:35:24.287
And that's why we're starting kind of small and we're working with an outside-in vendor to help us to do this because we know this is not something that we're just naturally good at.
490
00:35:24.768 --> 00:35:24.948
Yeah.
491
00:35:25.168 --> 00:35:26.689
Does that make sense to you?
492
00:35:26.709 --> 00:35:26.809
Yeah.
493
00:35:27.660 --> 00:35:33.483
Yeah, I was going to ask if you can share, because a lot of people are, I think, would be very, very interested in that and kind of knowing how to proceed with that.
494
00:35:33.503 --> 00:35:40.446
So was the first step for the pilot, let's say you picked some roles in HR and maybe it's an HR business partner with shared services.
495
00:35:41.286 --> 00:35:49.670
So was the first step to kind of take the role and to break down the role into the tasks that make up that role and then kind of say, okay, these tasks can be done by AI?
496
00:35:49.830 --> 00:35:49.990
Yep.
497
00:35:50.330 --> 00:35:50.490
Yeah.
498
00:35:50.530 --> 00:35:59.578
And we're going to create an agent in that space that can answer, you know, the baseline things and then upscale folks that are answering some tier one, tier two type things.
499
00:36:00.039 --> 00:36:00.980
So, yeah.
500
00:36:02.621 --> 00:36:02.901
Okay.
501
00:36:02.941 --> 00:36:11.068
So what would be an example of like something that an agent can do for, I don't know, for one of your roles or functions?
502
00:36:11.109 --> 00:36:11.329
Yeah.
503
00:36:11.349 --> 00:36:16.974
So let me give you an example because HR shared services, one thing benefits like within the U.S.,
504
00:36:18.307 --> 00:36:24.069
We have very complicated benefits and we have different types of employee populations, union, non-union, all sorts of things.
505
00:36:24.730 --> 00:36:34.954
And so we're going to use an agent that can know all of the different benefit offerings, see the profile of the employee asking whether that's retirement, health care.
506
00:36:35.394 --> 00:36:39.816
And then we'll start with them first partnering with one of our front lines.
507
00:36:40.096 --> 00:36:41.577
Then we move to autonomous.
508
00:36:42.257 --> 00:36:46.060
agents once we've really, really beta and tested it.
509
00:36:46.341 --> 00:36:52.226
So you have phases that you go through once you get certain milestones met and your confidence level increases.
510
00:36:52.526 --> 00:36:59.772
So the benefits would be another one, another area outside of shared services that we're looking into that's new.
511
00:36:59.812 --> 00:37:02.915
So I can't speak to it in detail is compensation.
512
00:37:02.935 --> 00:37:03.195
Yeah.
513
00:37:03.455 --> 00:37:06.076
Compensation is rapidly changing.
514
00:37:06.137 --> 00:37:07.857
It's super interesting right now.
515
00:37:08.278 --> 00:37:13.180
And I have a comp leader, Chris, on my team that is, she used to be my chief of staff.
516
00:37:13.300 --> 00:37:17.523
So she is so heavily, she's one of the biggest AI users I know.
517
00:37:17.923 --> 00:37:24.926
And when she moved to lead comp from my chief of staff role, she's been leading the charge on AI in a really impressive way.
518
00:37:25.006 --> 00:37:27.588
So we expect some great things to come out of comp too.
519
00:37:28.108 --> 00:37:30.569
which I haven't heard as many companies talking about comp.
520
00:37:30.969 --> 00:37:40.451
You usually hear HR shared services, some benefits queries, things like that, a little bit recruiting, but we're going to try and go kind of big on comp, um, in different ways.
521
00:37:41.731 --> 00:37:43.812
So what's the like dream scenario?
522
00:37:43.852 --> 00:37:50.593
So let's say you get out of pilot, um, and it's, I don't know, one year from now and you have autonomous agents.
523
00:37:50.613 --> 00:37:51.774
Like what's the idea?
524
00:37:51.834 --> 00:37:57.455
What would you like to have the autonomous agent do completely on its own without human intervention?
525
00:37:57.595 --> 00:38:16.842
Yeah, I think definitely the very transactional work where you're scouring systems for data, you're pulling insights, you're responding to employee questions, and then you have humans managing those agents to ensure that it's going well and partnering with other humans.
526
00:38:16.982 --> 00:38:26.205
And at the same time, throughout that year, that hypothetical year that we're talking about right here, we've done a lot of upskilling for our talent that's currently...
527
00:38:26.885 --> 00:38:41.496
scouring and pulling insights and information and data and responding to employee queries or doing this deep, deep analysis and analytics to do more strategic things, more leading edge things, more future forward.
528
00:38:41.896 --> 00:38:43.758
That's what I envision is that up.
529
00:38:43.938 --> 00:38:51.310
Upskilling, and I think that's really important, and it's something Sarah and my team talk a lot about, is upskilling as we do these pilots.
530
00:38:51.370 --> 00:38:54.716
Some people think about it like an after-the-fact or it's a side thing.
531
00:38:54.756 --> 00:38:58.241
We're embedding it into the pilots, so it's happening at the same time.
532
00:38:59.530 --> 00:39:06.338
Because I think when a lot of people think of the autonomous piece, like the chatbot, I think that concept has been around for a while, right?
533
00:39:06.358 --> 00:39:16.511
There have been a lot of vendors where you kind of like embed your employee handbook and all that sort of stuff in your benefits and the system reads it and then employees can ask questions.
534
00:39:17.512 --> 00:39:18.293
And get answers.
535
00:39:18.773 --> 00:39:26.520
Are you hoping to get to a point where employees can actually take action with the agents, whether it's request time off, whether it's make changes to their benefit?
536
00:39:26.680 --> 00:39:27.081
Totally.
537
00:39:27.261 --> 00:39:28.242
So we can take action.
538
00:39:28.582 --> 00:39:31.104
I can tell you another cool one that's not exact.
539
00:39:31.144 --> 00:39:36.469
It's not an agent, but it's a cool thing that I think has created for us a lot of business value.
540
00:39:36.830 --> 00:39:39.292
And it's something we created in-house, if that's okay with you.
541
00:39:39.880 --> 00:39:40.360
Yeah, please.
542
00:39:40.380 --> 00:39:40.780
Okay, cool.
543
00:39:41.321 --> 00:39:42.401
We call it GoalPro.
544
00:39:43.021 --> 00:39:47.523
And the HR team partnered with the engineering product team and created GoalPro.
545
00:39:47.624 --> 00:39:55.247
And GoalPro is an AI tool in-house that helps employees, we call our employees luminaries, to create smart goals.
546
00:39:55.387 --> 00:39:58.208
Specific, measurable, achievable, results-oriented, time-bound.
547
00:39:58.568 --> 00:40:05.130
So there's iteration with the tool to get the smartest kind of goals you can possibly get for the year.
548
00:40:05.210 --> 00:40:12.852
And when you're undergoing a massive transformation and there's so much to do and you want to ensure the goals accrue to the big thing, it's been very helpful to have that.
549
00:40:13.232 --> 00:40:25.075
Last year, we up-leveled our GoalPro tool such that the GoalPro tool now tells us the level of alignment from top to bottom of the SMART goals.
550
00:40:25.415 --> 00:40:26.775
So let's say I'm a manager.
551
00:40:26.995 --> 00:40:28.056
This is all true, actually.
552
00:40:28.196 --> 00:40:31.716
I am a manager and I'm looking at my direct reports goals.
553
00:40:32.157 --> 00:40:35.077
And this is so real time perfect for 2026.
554
00:40:35.577 --> 00:40:39.958
And looking at what is the percentage of my direct reports that have smart goals?
555
00:40:39.998 --> 00:40:40.858
What percent is that?
556
00:40:41.179 --> 00:40:49.740
I can see by person the level of smartness and I go to them and say, I'd like you to increase the specificity, whatever it is of their goals.
557
00:40:50.160 --> 00:40:51.901
And then I can say for myself,
558
00:40:52.581 --> 00:40:58.750
What percentage alignments do my goals for 2026 have to my direct reports goals?
559
00:40:59.150 --> 00:41:01.553
And then I can find out where there's a disconnect.
560
00:41:02.274 --> 00:41:07.421
And that's, I think, been very helpful for us to get alignment down because I
561
00:41:07.982 --> 00:41:09.343
The smart goals is fine.
562
00:41:09.463 --> 00:41:11.645
That's very baseline kind of thing, I would say.
563
00:41:11.965 --> 00:41:15.969
But the alignment is super important because we have company-wide goals.
564
00:41:16.349 --> 00:41:18.491
We've said to the street, we would meet these things.
565
00:41:19.051 --> 00:41:23.435
And then as you click down, sometimes in some companies, there ends up being a disconnect.
566
00:41:23.915 --> 00:41:28.860
And when you have alignment from top to bottom, I think that massively helps you drive business results.
567
00:41:29.180 --> 00:41:32.643
So that AI tool, I always give credit to our engineers.
568
00:41:33.283 --> 00:41:35.145
So appreciate them helping to create that.
569
00:41:36.083 --> 00:41:38.405
That can actually be a very cool place to use an agent, right?
570
00:41:38.425 --> 00:41:41.868
Where the agent automatically knows like what the smart threshold needs to be.
571
00:41:42.228 --> 00:41:44.169
So when you create your goals, it'll just reach out.
572
00:41:44.189 --> 00:41:46.491
Hey, you got to fix this and fix that.
573
00:41:46.511 --> 00:41:47.152
Hey, thank you.
574
00:41:47.172 --> 00:41:48.353
That's our next roadmap.
575
00:41:48.433 --> 00:41:49.394
I'm adding that right now.
576
00:41:49.434 --> 00:41:50.795
Literally, I'm writing that down.
577
00:41:52.296 --> 00:41:52.876
Thank you.
578
00:41:53.677 --> 00:41:55.659
Yeah, I'll be applying for a job tomorrow.
579
00:41:55.779 --> 00:41:56.459
Sounds good.
580
00:41:56.479 --> 00:41:56.960
Let me know.
581
00:41:57.240 --> 00:41:58.481
We'll talk to you.
582
00:41:58.501 --> 00:41:58.901
Just kidding.
583
00:41:59.061 --> 00:41:59.462
Yeah.
584
00:42:01.090 --> 00:42:07.432
I mean, as soon as you said that, I was like, that would be like a perfect place for AI as an agent to kind of create that.
585
00:42:08.292 --> 00:42:11.793
And same thing for even like employee engagement surveys or anything like that.
586
00:42:11.953 --> 00:42:21.596
How cool would it be for you as the CHRO to be able to go to one of your tools and say, hey, I want to get kind of a pulse check for an engagement survey and an action plan.
587
00:42:21.676 --> 00:42:23.097
And it just kind of goes and does it.
588
00:42:23.237 --> 00:42:24.137
And it automatically...
589
00:42:24.837 --> 00:42:31.219
surveys and reminds employees to participate in the survey, develops the plan, sends the summaries to the leaders, creates the checklist.
590
00:42:31.239 --> 00:42:32.999
Like, I mean, that would be amazing.
591
00:42:33.599 --> 00:42:34.320
I totally agree.
592
00:42:34.680 --> 00:42:40.841
And I really appreciate all the strides that have been made in the last few years around surveys on that note.
593
00:42:41.201 --> 00:42:46.943
I was reflecting back to probably four years ago when I was at F5, how I used to read
594
00:42:47.623 --> 00:42:49.625
every single comment in detail.
595
00:42:49.705 --> 00:42:51.187
And it took me so long.
596
00:42:51.567 --> 00:43:04.079
I love how AI can rapidly cut the data every which way can do comment sentiment analysis by country, by level, by anything you want, but it's so much better now, so much easier and great analytics.
597
00:43:04.119 --> 00:43:04.760
You can get back.
598
00:43:05.385 --> 00:43:05.645
Yeah.
599
00:43:06.245 --> 00:43:09.247
Is there anything about AI that worries you or freaks you out?
600
00:43:09.807 --> 00:43:13.109
Whether it's, I don't know, outsourcing critical thinking.
601
00:43:14.429 --> 00:43:16.830
Like there are a few things that kind of freaked me out a little bit.
602
00:43:16.910 --> 00:43:18.931
One is the idea of outsourcing critical thinking.
603
00:43:18.991 --> 00:43:23.613
So one of the things that I talk about in my new book, and I have these five different futures that I outline.
604
00:43:24.274 --> 00:43:26.575
And one of them is the future called the algorithmic enterprise.
605
00:43:27.275 --> 00:43:36.321
And that is where we become so reliant on AI that without noticing it, we are trying to kind of, and there's a Black Mirror episode about this in like the consumer world.
606
00:43:37.081 --> 00:43:40.023
But we try to please the algorithm, so to speak.
607
00:43:40.723 --> 00:43:50.910
And so if AI is trying to look, for example, who are the most productive employees and it knows how many meetings I attend, how many comments I'm responding to, like, you know what I mean?
608
00:43:50.930 --> 00:43:52.531
Like you start to game the system.
609
00:43:52.551 --> 00:43:52.791
Yeah.
610
00:43:52.811 --> 00:43:52.951
Yeah.
611
00:43:53.432 --> 00:43:59.607
or your emails start to sound a little bit too perfect, your comments are a little too perfect, your presentations are too perfect.
612
00:44:00.505 --> 00:44:07.648
And then without knowing it, all of a sudden you have 25,000 employees who are just the physical manifestations of whatever the AI is telling them to do.
613
00:44:07.708 --> 00:44:07.908
Right.
614
00:44:08.648 --> 00:44:12.210
And now the whole, it's like the Borg that takes over and employees didn't even know it.
615
00:44:12.290 --> 00:44:15.051
You know, Star Trek reference for people who don't know.
616
00:44:16.551 --> 00:44:16.891
Like that.
617
00:44:17.652 --> 00:44:18.712
That's going to bother me.
618
00:44:18.892 --> 00:44:19.112
Yeah.
619
00:44:19.332 --> 00:44:19.693
Yeah.
620
00:44:19.833 --> 00:44:25.355
Or the idea that small hallucinations over time inside the company can eventually take the whole company off course.
621
00:44:25.535 --> 00:44:25.755
Yeah.
622
00:44:26.344 --> 00:44:26.584
Right.
623
00:44:26.684 --> 00:44:35.489
It's like if you're piloting a plane or a ship, if you change course by just one or two degrees, you can end up in another continent or another country.
624
00:44:35.609 --> 00:44:35.829
Yeah.
625
00:44:36.749 --> 00:44:48.956
So if we're making decisions based on AI and it's 25,000 people have access to it, and for all 25,000 of those employees, there's small inaccuracies here and there, and they start to compound over time.
626
00:44:49.956 --> 00:44:51.257
One day you look back and you're like, wait a minute.
627
00:44:52.207 --> 00:44:53.008
How do we end up here?
628
00:44:53.228 --> 00:44:54.289
Right, exactly.
629
00:44:54.309 --> 00:44:57.051
So does anything bother you about AI?
630
00:44:57.191 --> 00:44:58.732
What you said is all fair.
631
00:44:58.772 --> 00:45:12.621
And I think, again, that's why, like you and I have talked about throughout, that importance of judgment and critical thinking is more important now than ever, is that overlay to ensure you don't have too many things off course that, again, accrue to a lot of work slop.
632
00:45:12.921 --> 00:45:14.262
That's why we're trying to avoid that.
633
00:45:14.603 --> 00:45:20.567
But the thing I think about that really worries me on AI is the bad actors out there.
634
00:45:21.107 --> 00:45:24.269
Like I come from a previously a cybersecurity company.
635
00:45:24.309 --> 00:45:30.654
So like when they can use AI, I really don't like that at all because you don't know what to expect.
636
00:45:32.676 --> 00:45:39.420
That's probably my biggest concern is when bad actors, when AI is used for bad, that's my biggest thing.
637
00:45:40.401 --> 00:45:43.563
I don't know if you saw, there was a story in the HR space.
638
00:45:43.584 --> 00:45:49.628
I think this happened in India, but an employee used AI to fake an injury and they're
639
00:45:50.150 --> 00:45:57.394
and submitted it to HR and he was granted benefits, you know, benefits, funds and to take time off work.
640
00:45:57.434 --> 00:46:03.898
And so basically what he did, he took a picture of his arm and he used AI to make it look like he got on a bike accident.
641
00:46:04.098 --> 00:46:07.520
And the AI kind of took his arm and it made it look like he had this big gash in his arm.
642
00:46:07.540 --> 00:46:08.480
And he's like, I can't come in.
643
00:46:08.500 --> 00:46:09.961
I got in this big bike accident.
644
00:46:10.801 --> 00:46:15.544
And HR saw that picture and they're like, Oh yeah, take, you know, take your time off and this and that.
645
00:46:16.424 --> 00:46:16.725
So,
646
00:46:17.845 --> 00:46:18.325
I don't know.
647
00:46:18.365 --> 00:46:23.468
There certainly are some creative and interesting ways that I think HR leaders are going to have to.
648
00:46:24.769 --> 00:46:26.750
Because you can't tell your employee, I don't believe you.
649
00:46:26.810 --> 00:46:27.491
Like, right.
650
00:46:27.811 --> 00:46:29.232
I want to come into the office.
651
00:46:29.252 --> 00:46:30.112
I want to see your arm.
652
00:46:31.233 --> 00:46:32.233
Let me talk to your doctor.
653
00:46:32.273 --> 00:46:33.074
You can't really do that.
654
00:46:33.094 --> 00:46:33.534
You're right.
655
00:46:33.594 --> 00:46:35.996
Like that kind of stuff super frustrates me.
656
00:46:36.416 --> 00:46:39.918
That's just low integrity, which is terrible.
657
00:46:41.257 --> 00:46:41.437
Yeah.
658
00:46:41.497 --> 00:46:45.240
And I think that'll be a very kind of interesting thing to pay attention to.
659
00:46:45.360 --> 00:46:45.720
Totally.
660
00:46:45.820 --> 00:46:48.422
Maybe one more question on the leadership piece before we wrap up.
661
00:46:49.262 --> 00:46:57.748
You know, there's always this conversation and it happens in our CHRO group all the time too, this idea of managing agents where you're no longer managing employees, you're managing agents.
662
00:46:58.528 --> 00:47:04.812
How are you thinking about that in terms of org design and hierarchy and your team members working with agents?
663
00:47:05.092 --> 00:47:07.033
Yeah, we've talked a lot about that.
664
00:47:07.073 --> 00:47:10.616
And Sarah on my team and I've talked and Kathy on my team about that is...
665
00:47:11.336 --> 00:47:15.697
We in HR, we don't have a lot of agents, but what we're doing is planning for that.
666
00:47:15.837 --> 00:47:24.818
And we're planning for a new hybrid workforce where we have humans and agents working side by side and you are managing both.
667
00:47:25.419 --> 00:47:30.719
And so it's a new skill set and it's going to be challenging.
668
00:47:30.759 --> 00:47:35.180
But I think having the highest degree of curiosity is super important.
669
00:47:35.600 --> 00:47:39.581
Being able to overlay judgment like we talked about and being able to do that.
670
00:47:39.701 --> 00:47:41.042
really good prompting skills.
671
00:47:41.102 --> 00:47:47.228
Otherwise you're wasting a lot of time and you're not cross-checking and giving the right information and context to the agents.
672
00:47:48.149 --> 00:47:51.112
And then teaching employees how to work across.
673
00:47:51.492 --> 00:47:52.793
So I think this is a newer thing.
674
00:47:52.813 --> 00:47:55.636
I've been actually talking to a lot of CHROs about this too.
675
00:47:56.076 --> 00:47:58.238
Leadership is going to be so different in the future.
676
00:47:58.258 --> 00:47:59.920
And we touched on this in the very beginning.
677
00:48:00.440 --> 00:48:05.082
Like that curiosity, that growth mindset is so important, but so is empathy.
678
00:48:05.582 --> 00:48:12.566
Because empathy, you have to have empathy for the people that are working with agents that are fearing, what about my job?
679
00:48:12.606 --> 00:48:13.826
What's going to happen in the future?
680
00:48:14.206 --> 00:48:17.848
So you have to have empathy and transparency and clarity.
681
00:48:18.528 --> 00:48:22.050
And some of those are part of our Illuminate behaviors.
682
00:48:22.190 --> 00:48:27.872
And so what was important to us for AI is that I think AI is like 70%
683
00:48:29.293 --> 00:48:32.215
people, culture, and 30% tools.
684
00:48:32.735 --> 00:48:52.228
So what we're doing is we're doubling down really hard on our culture itself to ensure people continue to live our culture as we learn more about AI, as we do more pilots, so that our culture is lived and we think that will be, even if it is humans and agents working together, I'll just tell you quickly our Illuminate behaviors,
685
00:48:52.888 --> 00:49:01.030
Teamwork, trust, transparency, clarity, customer obsession, growth mindset, courage, and respect.
686
00:49:01.210 --> 00:49:18.233
And I feel like if all of our luminaries truly lean into those Luminate behaviors like they are, because they're telling us it's going well so far, but it's a journey, we have a lot more to do, that makes it more possible for success in the new world with humans and agents.
687
00:49:18.633 --> 00:49:24.722
But managers have a lot of things to learn and do a little bit differently as you think about that hybrid workforce.
688
00:49:26.302 --> 00:49:31.928
I think it was McKinsey that came out fairly recently and they said we have 60,000 employees and 20,000 of them are agents.
689
00:49:33.009 --> 00:49:35.731
And so, yeah, a lot of people saw that and they're like, wait, what?
690
00:49:36.212 --> 00:49:36.772
It's big.
691
00:49:37.813 --> 00:49:44.220
Yeah, the idea of kind of a leader managing agents, I mean, there are all sorts of challenges, right?
692
00:49:44.240 --> 00:49:49.144
Because agents don't sleep, they don't need water, they don't need breaks, they can just work nonstop.
693
00:49:49.985 --> 00:49:54.648
And so there are a lot of employees who are like, wait a minute, am I going to be competing against these agents now?
694
00:49:54.688 --> 00:49:55.789
Because it's not possible.
695
00:49:57.310 --> 00:49:59.271
So, but it sounds like long-term, you're an optimist.
696
00:49:59.291 --> 00:50:00.612
You see a lot of potential.
697
00:50:01.392 --> 00:50:09.137
There's obviously a lot of roadblocks and challenges, but you're optimistic that we'll overcome them and see the positive results.
698
00:50:09.157 --> 00:50:16.902
Yeah, I think as long as we, again, not to be a broken record, but as long as we have curiosity and growth mindset and we don't boil the ocean and go big on something that's,
699
00:50:17.262 --> 00:50:22.345
really questionable doing pilots, testing and learning, evolving, improving.
700
00:50:22.966 --> 00:50:24.547
I think we can get to a good place.
701
00:50:24.567 --> 00:50:27.268
So that's why we're doing kind of this pilot methodology.
702
00:50:27.809 --> 00:50:28.049
Yeah.
703
00:50:28.329 --> 00:50:28.569
Yeah.
704
00:50:28.709 --> 00:50:29.750
It's probably a smart approach.
705
00:50:31.071 --> 00:50:33.592
Anna, thank you so much for taking time out of your day.
706
00:50:33.653 --> 00:50:39.476
Where can people go to learn more about you, Lumen, anything that you want to mention for people to check out?
707
00:50:39.496 --> 00:50:40.817
I think you're on LinkedIn as well, right?
708
00:50:40.957 --> 00:50:42.358
Yeah, I'm definitely on LinkedIn.
709
00:50:42.418 --> 00:50:42.778
It's funny.
710
00:50:42.818 --> 00:50:46.501
I'm not big on social media, but I have to say I do love LinkedIn.
711
00:50:46.521 --> 00:50:46.621
Yeah.
712
00:50:47.682 --> 00:50:49.583
It's my favorite social media thing.
713
00:50:49.623 --> 00:50:51.324
So I actually post a decent amount.
714
00:50:51.684 --> 00:50:56.286
And so my name is Anna A-N-A, and the last name is White, the color white.
715
00:50:56.327 --> 00:50:57.487
So I'm out there on LinkedIn.
716
00:50:57.507 --> 00:50:58.428
I'm pretty active.
717
00:50:58.468 --> 00:51:00.309
I just, I love that channel.
718
00:51:00.789 --> 00:51:04.010
And then, of course, for Lumen, we're lumen.com.
719
00:51:04.511 --> 00:51:08.313
And you can kind of see what we're driving in the space if you go out to our website.
720
00:51:08.593 --> 00:51:13.075
And we've really been transforming in a very, very significant way.
721
00:51:13.355 --> 00:51:16.257
And I really appreciate you inviting me to join you, Jacob.
722
00:51:16.755 --> 00:51:17.036
Yeah.
723
00:51:17.217 --> 00:51:18.481
Congrats on all the success.
724
00:51:18.702 --> 00:51:21.330
I'm sure next few years are going to be very exciting for you.
725
00:51:21.350 --> 00:51:21.812
So thank you.
726
00:51:21.872 --> 00:51:22.374
I think so.
727
00:51:22.454 --> 00:51:23.176
Thank you so much.
728
00:51:25.003 --> 00:51:28.665
Thanks for tuning into today's episode of Future Ready Leadership.
729
00:51:29.106 --> 00:51:35.990
If you want to get access to more content on how you can become future ready, make sure to subscribe to the newsletter.
730
00:51:36.390 --> 00:51:41.713
It's thefutureorganization.com forward slash newsletter.
731
00:51:42.114 --> 00:51:46.436
And if you enjoyed this episode, please send it to somebody who you would like to see become a better leader.
732
00:51:46.876 --> 00:51:49.218
Thanks for listening or watching, and I will see you next time.