Welcome to Mercer's podcast series on the new shape of work. I'm Kate [? Bravery, ?] Mercer's advisory and insight leader. And today I'm thrilled to be speaking with Jason Averbrook, a global leader who was recently named a top HR influencer. Jason, I hope I've got that right.

Jason is the co-founder and CEO of Leap Gen, a leading digital HR firm that recently joined the Mercer family. Jason, really great to have you on the call today.

Hey, it's so great to be here. I've been waiting for this for the six months I've been part of the team. So I'm so, so excited that you finally invited me.

I have been pursuing you for six months. I think because the area of building digital first cultures has just been such a hot topic for all of our clients. And, of course, with the AI explosion, I think your feet haven't touched the ground. And I think I only managed to pin you down because you're actually in the UK. So wonderful to have you here.

And maybe we could start with, why did you join Mercer? What are you now see as some of the opportunities, as our capabilities are becoming fused?

So Kate, to talk about why I joined Mercer, it goes back to a little bit different story about why I do what I do and why I've build companies to do what I do. And that's all been centered around how do I make work better for my kids? So now I have an 18-year-old and a son who just - an 18-year-old son and a 16-year-old, son who just turned 16.

And really back in September of 2004, I actually said my goal was to figure out how to make work better for them. Not that work wasn't bad, but work was clunky. Work had a lot of processes. HR spoke a lot of stuff that only HR people would understand.

And I knew there were better ways, A, I knew that the technology out there was not the way to get to where we were trying to get to. It was how we actually designed for the right people. How we designed with how they want to work, how they work outside of work, that actually could get us to where we were trying to get to. But leveraging technology to do that.

So why joining Mercer is so important to me is because of the global scale that Mercer has, the clients that Mercer works with today. And every single client that Mercer works with today, or that doesn't work with today, for that matter, every HR organization is going through some sort of a transformation today, whether they like it or not. There's been more change in work in the last three years than there were in the previous 50.

And even if I just took generative AI as an example, which I don't want to focus too much on that, but even if I did, that means every HR organization should be reinventing itself, as we speak. So that's why it's so exciting to me. And that's all tied to making work better.

I agree with you. And I think it's perpetual reinvention, whether it's forming those gen AI plans, rethinking your digital strategy more broadly, or redesigning the EX with new technology, I think it's constant. And it's also constant if we want to keep the people in our mind sight, as technology gets better.

And if I could, just really quickly, there's reinvention and there's re-imagination. And really what I'm trying to work with organizations on today, when I work with them, is not necessarily to reinvent ourselves, but as part of the reinvention, infuse imagination into it. Because there are no rules about what we have to be. And I want to make sure that people approach this new world with an open mind and unlearn a lot of the stigmas of the past.

Well, I think we got a lot of opportunity. Some of those words that you use there, clunky, process driven, HR language, we definitely don't have that in our rear view mirror as yet. But maybe we can start with what do you see as some of the most pressing trends impacting HR that we've all need to be cognizant of?

So I think that, well, I think there's three. The first one is that of employee experience. And when I say employee experience, employee experience means 19 things to 19 people. Actually, it probably means 29 things to 19 people, and to be honest with you. What I'm talking about employee experience, I'm talking about how do people feel while they're interacting with their colleagues, while they're interacting with the HR function, and they're just trying to get stuff done so they can do the job that they were hired to do.

It's a micro view of employee experience because it doesn't include what's the benefits like, it doesn't include what's the lunchroom like, et cetera, et cetera, et cetera. But what it does include is how easy or hard is it for me to get something done? Because that's the number one thing that's driving engagement.

And it's the number one thing that drives abandonment from processes. And it's the number one thing that keeps our HR business partners not business partners, but transaction partners. OK. Because what happens is people abandon a bad employee experience and they call someone.

You know, it reflects more, I think, some of the comments you get in the engagement survey free form answers, when it's real people writing about real challenges that they have, rather than maybe what's in its survey, which is often quite HR self-serving. You said you had three trends. So [INAUDIBLE] number one.

Just really quickly on that point that you just brought up. Like when am I best to judge my experience? In the survey? I'm actually best to judge my experience the minute I finish. Like why, when you go to a restroom in the airport, do they have those things that you're supposed to touch, which freak me out a little bit because you don't know if people have washed their hands. But the smiley face, the medium face, the sad face. I'm actually measuring the experience at the time of the experience, instead of a once a year survey.

And I also think that as an organization psychologist, there's more sophisticated ways to get a handle on that experience as well. I think the passive data, the sentiment, how people figure out how to work around systems that don't work for them. I mean, gosh, isn't that the biggest feedback you can get?

And it's so interesting because it's the survey at the end where you're given some kind of fixed choice answers that is then put in front of the executive team to say, hey, we rolled out this digital process. Not the percentage of people that aren't adopting it or working around it that just haven't been inspired to use it.

The second trend, Kate, that you know so important to me, is this world of artificial intelligence and the concept of how can we stand on the shoulders of robots or stand on the shoulders of artificial intelligence to see over the peaks, to see over the mountaintops, instead of standing on a heap of mess. I'm not going to say garbage, but a Frankenstein mess of technology. The more technology we buy doesn't mean a higher mountain. In fact, the more technology we buy, oftentimes, means a lower mountain.

So what we're trying to do with artificial intelligence is say, how do we use that to elevate people, to amplify people to help them see over the mountaintops? And that's where I really believe that this concept of AI and all of us standing on the shoulders of robots, is going to make all of our jobs better, if we actually do it right. OK.

So that's the second trend I see, is education, experimentation, evolution, three E's. And really, really focus on what are some of the things that we should be experimenting in that are going to make sure that we can drive a competitive edge, from a people function.

You know, you and I have recently just come back from Asia on different trips, and it's fascinating to see the different attitudes. Because if you're going to stand on the shoulder of robots, you've got to lean in. You've got to have a degree of trust and be willing to recognize that it probably is going to change the way you do your job. But that's actually quite exciting, if you can build the skills.

And we did some polls on the last trip that I had, just yesterday, and a lot of people aren't as excited as I thought they would be about the amplified intelligence. They are more fearful about the way it's going to disrupt the way they do things today. So I do think there's a bit of a journey to go on there.

Well, Kate, I think what's really important is that because there's been so much change in the last three years, the profession is tired. The profession is tired of change. And the profession sees this, unfortunately, at the moment, as more noise then help. So our job, as an industry, is to actually look at these tools not as how they're going to change and, oh man, more change, but how are they going to finally, finally make our jobs better?

How are they going to finally get me out of doing the jobs, the part that I don't like doing, so that I can do more of the stuff that I do like doing. And by the way, Kate, I do like the fact that said you and I were in Asia on different trips, because I don't know if that was because you didn't want to associate with me or what. But it was, I like how you called it out separately and specifically as part of the podcast.

You know, I would have loved to have been on the same trip with you. I think, I feel now that it's out there in the public, I'm going to make sure that it happens on the next one.

You said that there was three trends. What's your third one?

So the third one is all about sewing this together. OK. We can do all these things in silos. We can work on employee experience, cool. We can do generative, workshops, and education, cool. We can start to think about things like how do we transform the HR function. Cool.

All of these tools, all of these things we're talking about are tools in a toolbox to actually help us transform the function of HR into a strategic function. So all the little cliches we've been using forever about how does HR get a seat at the table, Oh, it's actually got a seat at the table, but it doesn't know what to bring. OK, cool.

Let's take all of this, all of these tools, and bring them together. Hey, wouldn't it be nice if I shifted from counting people to making people count? Cool. Let's take all of these tools, together not in silos, but together. And Kate, that's where we talk about this concept, that every people function today has to think and be digital. Not do technology projects, but think and be digital, which means focus on being agile, focus on competitive edge, and focus on what it's going to take to drive and align directly to a business result, not an HR result.

And I agree that the knitting it together gets harder, because you do have a depleted workforce in HR. And certainly, that was one of the sentiments that I brought back from some of the conferences I've been with recently, is the HR function is very much still in a wait and see area, in many, as opposed to leading with this is impacting the way we work. This is impacting our workforce.

It is fundamentally something that we should be--

OK, and I'm going to say something that might be controversial, but it might not be. We'll see. For someone old, like me, that's been doing this for a long, long time, there has never been a moment- if air is waiting for an impetus like, dude, sorry. Like it is right there in front of us with flashing, neon lights, saying, this is your time. This is your time. If it's waiting for permission or if it's waiting for some massive thing to change in the world, you may want to think about a different profession.

Well, I think, experimentation as you mentioned earlier, and education, is absolutely key. And people are in different stages. I think there's an appetite and an interest but getting off that starting block appears to be where many are just having some challenges. I wonder if you could share just some practical steps.

Maybe we can take that second point around AI. I mean, AI, certainly generate AI, hype is everywhere. Everyone is wanting to figure out what their strategy should be. Everyone's worried that they are being left behind on the topic. What's your advice, and is there some Bright spots or practice that you've been seeing?

Oh, my gosh. So, I mean, you know, I think you know Kate, I do 5 to 10 of these workshops every week. Yesterday morning was with a huge airline there in the UK. And the thing that they took away is this isn't a thing that should be thought of as another separate technology thing. It's a component that needs to be brought into our overall digital strategy.

Oh, and by the way, we don't have a digital strategy. We have a technology implementation plan. So what we're seeing with generative AI in those organizations that are truly, I'll call it sprinting, right now, is for 2024, to say what is our digital strategy and how do we look at generative AI as a component to infuse into that digital strategy, instead of saying, here's our talent acquisition strategy. Here's our learning strategy. Here's our comp and reward strategy.

Oh. And by the way, here's our gen AI strategy. Gen AI is an infuser into all of those things. It's not its own, separate piece. There's some education, which is what we're doing right now with so many organizations and the AI network, to start to get people up to speed. But the goal is that it needs to be part of the overall digital strategy, not its own separate thing.

And that raises some really interesting questions around how we design HR. Because I think sometimes our operating model can get in the way, as does I think that tech's essential crisis that you and I have chatted about many times, just the amount of different tech that's being rolled out, without that kind of knitted together, connected focus on the experience. How are companies picking some of that and thinking of fresh at how they can create an employee experience that does fuse human and digital a bit better?

Well, Kate, we used to focus a lot on technical architecture. And one of the things about technical architecture, which is cool yet geeky slash people say, well, is how systems, how data flows. That's really the goal of technical architecture diagrams, that look like spaghetti charts, is people, how data flows.

We then get into solution architecture and we're doing a lot of work with solution architecture, Kate, helping organizations understand that they've got, on average, 72 places to store data. And truly, they need 32. And by the way, the more they have, the harder it is to create an experience and the harder it is to prepare yourself for a world of artificial intelligence.

So there's the solution architecture work. And then probably most importantly, back to your question, is the experience architecture, which is how- and the biggest difference between all of this is how do we want things to work versus how do we want things to feel, the F word. How do we want things to feel? OK.

The experience architecture, by nature, is how do we want things to feel. And for organizations, they have to make decisions as to how much do they want to be high touch human versus high touch digital. And in many cases, the answer, in almost all cases, the answer is both. But the question is where on the journey do I go from digital to human?

OK. And that requires design. It requires design, not around me, Kate, as an HR person, but it requires design around my workforce. And listening to how they work. How do I put these tools into the flow of how they work, so they don't have to go to another place to do this. They don't have to go to another place to do this. They don't type in the internet, I'm having a dependent.

They actually can type into Teams, I'm having a baby. What should I do as an employee here? So does that make sense? It's really thinking about those--

Absolutely. And I think generative AI is doing a good job in translating some of that technical languages, whether it's HR technical language or medical technical language, into language as humans can understand. And it's a shame that we have to do that, but I think in many domains, that is occurring.

And I love what you're saying about that connected employee experience, so it feels more intuitive for the individual. I wouldn't mind just talking about the back end, because one of the challenges I hear is I've got skills data in one system over here, I've got information about my contingent workforce over here, I've got pay information over, here and, Oh, we did a separate analysis over here on pay equity.

What are company is doing to bring some of their data together and how do you think AI might change the approach for that?

So what's so amazing about that question is that what organizations are doing is they're coming up with a strategy that says, basically, how do I want people- because all of that data, the fact that it's in lots of places isn't a problem, until you actually think about what you're trying to do with it. OK. Does that make sense?

Like, hey, it's totally cool that I've got 82 systems. But when all of a sudden, I start to think about what's my measure of success and what am I trying to get out of them, that's when I come into a problem. So what's really important here is to think about many people think about AI, and I encourage this all the time, as a second brain or a copilot. Like could you imagine if the co-pilot on an airline, there's the captain and then there's the copilot, can you imagine if the copilot was in a different room?

And you had to figure out how to get up, go find the co-pilot, ask a question, go back to the cockpit, and then fly the plane. It wouldn't work. If you think about this concept of a second brain, imagine if I had a second brain right. Now I wouldn't want to say, hey, second brain, come see me in 30 minutes because I'm going to have a question. I want it right there.

So designing these tools in a way where I have access to the power of AI to deliver second brain or copilot, or whatever term we want to use, is key. And by the way, it's key not just, once again, from the HR point of view, from the employee point of view. Remember, they want a second brain that doesn't speak HR because that's not what they do. That's not what we hired them to do, HR.

So from an intentional design standpoint, it's how do I get my data sources aligned? How do I make sure I've got clean data so that we don't have too much hallucination. By the way, if we have time, we should talk about hallucination in a second. But we don't have too much hallucination in what AI produces, so that these tools can be of value as minimum lovable product, not minimum viable product, on day one.

You know, I always love it when you use that phrase. I think it, honestly, to me, it just unlocks how many littered projects that haven't got off the ground. Because through the financial negotiations, internally, that minimal viable product didn't meet anyone's needs. And minimal lovable, I think really hits the mark.

But I do agree with your comments there. And it's been fascinating to see. I think the more people get exposure and experience of doing their own prompts and training, some of the generative AI tools that are out there at the moment, so ChatGPT four, or what have, you they're beginning to learn what are the biases. They're beginning to recognize what it's actually not doing, search for us. So it can't answer that question so it's making the question up.

But I'm always fascinated to ask different populations, how many people are using, let's say, ChatGPT in their personal life or in their professional life? And over the last few months, I've seen a real shift that when you ask people how many are using it in their personal life, pretty much the majority are now putting their hand up. When you ask how many people are using it to inform their work, 2/3 of them put their hands down.

And so I do think understanding of some of these topics becomes a lot easy when you've got experience of using it. What do you think we need to do to get more people thinking about its application in the workplace?

You know, Kate, I don't want to get too technical, but I'm going to get technical for a second. I recorded--

I love that, so don't worry about it.

I recorded a podcast about a month ago on what is a large language model, OK. And LLM is a technical term. But one of the things that builds trust is understanding. And the reason I recorded a podcast for HR people on large language models, was so that at least they understand what's going on behind the scenes, so that it doesn't seem like this magic box.

Because if it's a magic box, just like we question how does how do comp reviews get done or who's doing my overall succession planning, we don't trust it. So we have to build trust. And we have to have enough of an understanding as to how these tools work, to say, Oh, that's why that did that.

Instead of saying, I give up. This technology is terrible. OK. And that, to me, that's the first step.

The second step is once I understand that, then what are some use cases that I can use to actually learn? OK. As I said, the organization I was with yesterday has some amazing people in it who are really doing their own experimentation. This is an organization that doesn't have any internal large language model, doesn't have anything behind the firewall. They're doing stuff outside in ChatGPT and Claude.

Of course, with a barbed wire fence to keep them safe. But they're like, Wow, this is going to change the world. And the sooner that we can see that, the sooner that we can envision that, and then start to say, how is it going to impact our jobs, the better we're going to be.

That doesn't mean, that doesn't mean stop doing everything and change your whole agenda to gen AI, OK? Because guess what? You'll crash. Not technology wise, but your crash as a function, because this is a balance.

The reason we talk about standing on the shoulders of robots is it's a balance. It's not just the robots. It's a balance of the technology.

And I agree with you on that point about we've got to have trust. I also think sharing use cases. You know, I think when we share something, hey, this worked for us. It really does inspire other people to do that. I think sometimes there isn't enough time and attention around inspiring people to do some of that stuff. I wonder if you--

I'll tell you, Kate, just really quickly on that topic, our AI network and our AI forum, we have over 200 people right now, 200 companies, that do nothing but get together every other month and share what they're doing. And by the way, none of us, none of us know this stuff, to the n-th degree.

So the reason that people say, hey Jason, you're a gen AI expert. I'm a gen AI expert because I'm learning every single day from what others are experimenting with and just trying to consolidate together, in a way to help a larger mass of people.

I agree. And that's why I think it's so exciting. Every time we have an opportunity to present or to be in a forum, just sharing some of those examples. It's one or two examples that spark an idea or hey, if I could apply this into another space, what could be. I mean, it's such an exciting time, I think-

And the beauty, the beauty is that it's really an art. It's really an art. There's a science underneath it, but it's really an art. So being able to go to an art exhibition and see everyone's creativity shine, that's really what we're doing today, is watching that.

Now, you also get an opportunity to go to many conferences, and I know that you are at HR tech recently, and I think you're gearing up for unleashed, here in Europe. I'm curious, has there been any particular AI applications that have got you salivating. That you've gone, Oh, that's pretty cool. I haven't seen that before. Or, Wow, the possibilities of that, when it scales, is going to be pretty impressive.

The biggest thing for me, Kate, is the power of generative AI and it's, and the computing power behind the scenes, with the use of a human interface. We try not to use the term user interface, but human interface, conversationally, that actually lets me understand things from a data point of view that I'd have no way of finding out before.

So example, I am watching turnover and I'm watching employee engagement in a few organizations when it comes to return to office. Now, think about that. How many miles is it that someone's commuting, how many days of the week are they going in, how engaged are they, and how's their performance? Not on a performance review basis, but on a sales or NPS net promoter score result.

The human brain can't take all of that data and put it together, to be able to create a prediction and then a prescription as to what I should do. What gen AI tools are doing, because basically the 12,000 by 12,000 side Rubik's cube, is basically doing that calculation for me, doing that understanding of the data for me, and telling me, or let me say that differently.

Giving me advice as how to look further into the data. So as much as the use cases on writing job descriptions are cool, and as much as the use cases on, hey, someone's going to be able to do their performance management form so much faster even though they still hate doing it, they're just going to do it faster. We could go, or we could go in lots of different directions on this topic.

What I'm most excited about is the unleashing the power of our data, to be able to make work better. Because these tools, AI has the power to see things that humans can't. And, to me, the power of one plus one here, human plus machine, is going to equal 1,000.

Totally agree with you. And I'm so excited about this future that is ushering in. It's funny, I agree with you, there's been so much talk about AI can write a policy for you. It can write a job description.

And now we're having some debate about, well, should they actually be writing the performance reviews for my team or not, and how effective is their coaching or isn't their coaching? But I think the real power comes when it's making recommendations. And it will be making recommendations because it knows us really well and our data, and it has the energy to look at all those policies that sit behind those open enrollment decisions.

I think that's the really exciting piece.

Right. And Kate, just if I could, just a real quick example. If my CEO doesn't like time to fill as a ratio, because all the time to fill is doing is saying how long it takes to fill a job here. And even if I show improvement, that's cool, but that's an HR measure of itself. OK.

So don't just take an old measure and now say, Oh cool. Now I can do it with AI and have a conversation with it and come out with the same thing. Take it one step further. And this is the difference between a technology transfer- excuse me a technology transition and an HR transformation.

A technology transition basically allows me to do the same thing I was doing before, in a new technology. That's what gen AIU waste of time would be. OK. Digital transformation means I'm going to work different. My measures of success are going to be different, and I'm going to digitally transform the function to be able to do things it was never able to do before.

That's where generative AI is going to play a huge role. Don't just apply it to the things that we've been doing before. Because, I mean, that's good. I'm not saying that's bad. Because guess what, you can get some efficiencies, you can get some productivity. But go further than that.

I agree we have such a unique opportunity now, to step back. And think the biggest danger is taking the old world of HR and putting it on a new platform.

And, Kate, one more thing on this topic. This is not an IT topic.

Do you know, I was just about to make the same point. I was also going to say, we're all novices here, as well, because yes, it's not an IT problem, challenge, and we should just be waiting for that digital rollout. It firmly sits in the HR space.

But since we're all learning together and experimenting together, I think really being human centric is going to be working, not with personas, but actually people in those target populations. I think they're going to have some of the best ideas about how we can do it differently.

Jason, you started off today talking about you got into this field because you wanted to usher in a better future of work for your own kids. As a key influencer in the HR tech space, I think you're well on the road to that. But my question is another one that often comes up when we think about kids. Because the advice that we give to them, the recommendations about what skills they learn, do impact futures.

And I'd just be curious, as we start to see AI redistribute tasks between humans and technology, what advice are you giving to your children to help them prepare for that future?

So my advice is a little bit different than traditional education. Traditional education, at the moment, sees generative AI as cheating. And we won't talk about- we could go in another whole podcast on the world of education and K through 12 plus post-secondary education. We won't do that right this second.

But what's really important is the skills that people will need going forward are changing. They're changing drastically. And what's most important for my kids is to be watching what that is. And you, know calculators can be so, once again, get old. You know, calculators that was cheating.

I remember.

Yeah, Excel, that was cheating. OK. All of these productivity tools are designed to make us, as humans, better. OK.

So the question becomes, as these move forward, what do I need to learn? And there's some great quotes out there from many, many leaders that say AI is not going to replace jobs, but it's going to replace the jobs of people that don't know anything about AI.

And I completely agree with that. Completely, completely agree with that. If I don't know how to use these tools, and then as a business or as an enterprise, I've done a good job of infusing these tools for my workforce into how they work, I'm going to be at a severe disadvantage, severe disadvantage.

So it's going to be a common thing in a job interview and a job discussion is, do you know how to use these tools? But, I mean, you shouldn't have to say, do I know how to use a calculator. Notice, I mean, if you remember, Kate, you and I are remember this there used to be this thing called want ads in the newspaper, that people would say PeopleSoft experience required or ADP experience required.

Like I, we shouldn't have to say gen AI experience required, OK. But if we don't actually take this on and learn how these tools work to make us better, once again, there's going to be a very big line. And once again, we're not going to go into social impact on this because that's another discussion we could have, about economic impact of these types of tools.

But just really quickly, what are the skills I'm going to need? And what we're watching really quickly, is the skill shifting back to soft skills. Kate, we break things down in working with clients on hands work heads work and hearts work. Hands work will be automated and eliminated, guarantee. Which means the skills that my kids need are the ability to take data and tell stories about it and the ability to empathize with people, from a soft skills standpoint.

Jason, I love hearing you, but it's also making me wonder whether you are priming your children to go into the same field. Because isn't that what we want for our best HR leaders, those that can empathize and tell a great story with data.

That's what we want from our best leaders.

It is. And Jason, I might actually have you back and do an education edition. I feel as passionately on it. My nine-year-old and my 10-year-old are on ChatGPT all the time, and it's been fascinating to watch how it builds their skills in one area but maybe atrophies some of their learning in the other area. Hopefully, educational systems will catch up as quickly as they have when they started to look at calculators and Excel and we won't be having this conversation in a few months time.

Jason, thank you so much for sharing your insights today. I know that you've got quite a few events that are happening around the globe that you are speaking at. Do you mind sharing just a couple of them, for our listeners today?

Thanks for that question, Kate. Two big events, HR tech, which is the week of October 12th in the US, and then the following week, Unleash, which in Paris, the week of October 19. Those are probably the two biggies coming up. Along with every single HR tech vendor is having conferences between now and then. So watch for new innovations.

So Jason, if I'm going to have you back on the call, I should book you in now. Otherwise, it's going to be another six months. And it's been so much fun to welcome you on here.

You shared many of my favorite topics. I always love it when you talk about minimal lovable products. That has stuck with me, ever since I first heard you talk about it. The importance of building trust and trust in some of these digital tools, but making sure that we don't get distracted by these apps and tools and really do have a digital strategy, rather than a tech strategy.

Educate, experience. I can't remember what the third was, but for me, I'm going to put energize. And just that new world that we're going to usher in around prediction and prescription and what does that mean to how we think about our data and how we work across some of the traditional HR functional silos.

There was a lot more that you shared, but there were some of the things that lodged in my mind. And loved your final comments there, about how the world of work would change. How we'd be doing less of the hands, work more of the heart work and more of the head work.

[INAUDIBLE] thank you also for tuning in today. If you're interested in all things related to digital HR, please do check out Jason's Friday digital meetup. And Jason, I know you've also got a podcast series, I think, on the now of work, if I'm not, if I'm correct, is that right?

As well as gen AI, yep.

Great. And you also mentioned that you're also bringing a network together, of people who are experimenting on gen AI in the field. So please reach out to Jason, if you'd like to be part of that.

As, always if you're interested in hearing on other topics associated with the new shape of work, please do visit our interview series on mercer.com. And thank you all for tuning in. Wishing you all a great rest of day. Jason, thank you so much for joining us today.

Thanks for having me, Kate.