TRANSKRIBEERI 🔗
WEBVTT
00:00:00.160 --> 00:00:01.280
Hello, everyone.
00:00:01.439 --> 00:00:06.559
Welcome back to the Now To Next Podcast.
00:00:07.280 --> 00:00:09.919
I am so excited that you are back.
00:00:10.160 --> 00:00:15.759
And uh we have had a run of amazing guests, and please keep all your comments coming.
00:00:15.839 --> 00:00:16.320
I love them.
00:00:16.480 --> 00:00:18.480
Was just an event in San Francisco yesterday.
00:00:18.719 --> 00:00:21.280
Had a great few great comments from listeners.
00:00:21.600 --> 00:00:25.519
And I think it's always funny what people are doing while they listen to podcasts.
00:00:25.600 --> 00:00:28.079
So uh keep sharing that with me as well.
00:00:28.320 --> 00:00:32.799
I'm so excited for our guest today, uh Laura Albert.
00:00:33.039 --> 00:00:46.479
And I will let Laura introduce herself and uh her background, and then we're gonna jump into probably one I think one of the most important topics that we can be talking about today, and that is entry-level jobs.
00:00:46.640 --> 00:00:52.240
And just at the nature of saying entry-level jobs, you're like, that's not very exciting.
00:00:52.799 --> 00:00:58.560
But impact on humanity, impact on students, impact on our kids, things like that.
00:00:58.719 --> 00:01:00.159
That's what we're talking about today.
00:01:00.320 --> 00:01:01.520
So so excited you're here.
00:01:01.600 --> 00:01:05.120
And Laura, I'm gonna let you introduce yourself and shut up quick.
00:01:05.840 --> 00:01:07.439
Never, never shut up, Jason.
00:01:07.519 --> 00:01:08.400
Thanks so much for having me.
00:01:08.480 --> 00:01:09.359
I'm Laura Albert.
00:01:09.439 --> 00:01:12.799
I'm the chief marketing officer at SAP Success Factors.
00:01:12.959 --> 00:01:24.959
Um, I'm a true marketer by heart, 20 years experience helping companies of all different types and sizes, global enterprises to scrappy startups to really deliver and drive marketing strategies.
00:01:25.120 --> 00:01:28.560
I've been in the HCM space 10 years now.
00:01:28.799 --> 00:01:37.280
Um, and in this current role, I have an amazing team that is focused on really driving revenue growth and increasing adoption of our solutions.
00:01:37.359 --> 00:01:48.079
And the key way in which we do that is to make sure that everyone, internally and externally, is unmistakably clear when it comes to our value proposition.
00:01:48.239 --> 00:01:50.400
So, what is our product innovation all about?
00:01:50.560 --> 00:01:53.920
What is the value that our solutions really deliver for customers?
00:01:54.079 --> 00:01:56.879
Um, and what industry expertise do we bring to the table?
00:01:57.040 --> 00:02:00.079
So I consider myself to be in a role that's a whole lot of fun.
00:02:00.159 --> 00:02:04.159
And on the personal side, I have a couple of college-age children myself.
00:02:04.239 --> 00:02:06.719
And Jason, I think you're entering that era as well.
00:02:06.799 --> 00:02:20.080
And so this conversation about early talent is not only relevant from a work perspective and the space that I'm in, but also from a personal perspective given where my kids are and the fact that they'll be entering as early career talents very soon.
00:02:20.479 --> 00:02:25.360
That's got to be scary and exciting at the same time, as it is for me, right?
00:02:25.680 --> 00:02:27.280
A hundred percent, yes.
00:02:27.599 --> 00:02:42.560
So I love the fact that we have a head of marketing from a technology provider on talking about a topic that I'm gonna call it a little bit esoteric from the standpoint of how is this related to software?
00:02:42.719 --> 00:02:57.439
And and I love how you're I love your framing there about thinking about how do we help our customers get more value from the tools that they have, realizing that a fool with a tool is still a fool.
00:02:58.080 --> 00:03:12.080
But your job, sorry for that weird quote, but your job is to, and I hope I didn't offend anyone, your job is to educate people on what's happening in the world of work so that they can use your tools to help with some of those issues.
00:03:12.479 --> 00:03:12.879
100%.
00:03:13.280 --> 00:03:14.800
We don't want any fools out there.
00:03:14.879 --> 00:03:16.719
We want everyone fully educated on.
00:03:16.800 --> 00:03:18.560
I mean, look, it's an exciting time, right?
00:03:18.639 --> 00:03:23.680
There's never been a time in the workplace where technology has played such a big role.
00:03:23.759 --> 00:03:28.319
And yet it's played a significant role for as for as as long as we can all recall.
00:03:28.479 --> 00:03:32.639
But in this era of AI, I mean, there's just so much opportunity.
00:03:32.800 --> 00:03:46.159
And yet, let's face it, so much uh uncomfortableness as everybody gets fully up to speed as to what this means, both from an employee standpoint, an HR standpoint, a workplace and workforce standpoint.
00:03:46.240 --> 00:04:04.800
And so, yeah, I would like to say think that our role is more important than ever to sort of net that out for customers that are evaluating all different types of HCM technology and really technology that they're gonna put in the hands of employees and managers each and every day, hopefully to drive tremendous value and to make life easier and to drive better outcomes.
00:04:04.879 --> 00:04:06.240
But we'll No, I love that.
00:04:06.319 --> 00:04:08.000
I I love that, and thank you for sharing.
00:04:08.080 --> 00:04:14.800
I I think that's such a value-added position to have from a software vendor as well as a leader in the space.
00:04:15.120 --> 00:04:24.800
I want to jump into this big, hairy change that we're seeing and we'll around entry-level jobs.
00:04:25.040 --> 00:04:31.360
And you know that I have the great opportunity to teach at a number of different institutions.
00:04:32.000 --> 00:04:36.399
I have had the great opportunity of getting pressured by my parents to go to college.
00:04:36.560 --> 00:04:36.879
Why?
00:04:37.120 --> 00:04:39.040
Because
Load full transcript
it's gonna promise me a good job.
00:04:39.279 --> 00:04:42.879
I've had the great opportunity of pressuring my kids to go to college, why?
00:04:43.120 --> 00:04:45.040
Because it guarantees you a good job.
00:04:45.360 --> 00:04:52.800
And all of a sudden, I feel this, and maybe it never was, but I feel this that's not the promise anymore.
00:04:53.040 --> 00:05:00.720
The promise isn't going to college is going to guarantee you an entry-level job at A, B, or C.
00:05:00.959 --> 00:05:04.879
And you've done some amazing research to start to back this up.
00:05:05.040 --> 00:05:15.680
And I'd love to have you jump into a little of the research A, and then let's talk to the people that are listening about what they can be doing, both at work as well as at home.
00:05:16.319 --> 00:05:18.000
Yeah, let's jump right in.
00:05:18.079 --> 00:05:19.120
This is going to be a lot of fun.
00:05:19.279 --> 00:05:25.040
And I think, you know, your point about guarantees, like the the world of guarantees is is probably over.
00:05:25.120 --> 00:05:29.519
You know, it's probably something quote, unquote, the world of guarantees is over.
00:05:29.759 --> 00:05:37.040
I mean, or there's let's just say there would need to be a lot of caveats around any terminology that suggests a guarantee.
00:05:37.120 --> 00:05:40.639
But as we talk about early talent, so there's a couple of things that we're seeing.
00:05:40.720 --> 00:05:48.639
And like you said, we've done a lot of research on this topic, specifically with CHROs who are really in the driver's seat, I would argue.
00:05:48.879 --> 00:05:59.199
And what we do know is that AI is absolutely changing what entry-level work looks like, but it's not necessarily eliminating the need for early talent.
00:05:59.360 --> 00:06:04.079
I mean, I think what we're seeing is more of a redesign of the career ladder.
00:06:04.319 --> 00:06:10.160
So we all know entry-level workers have often in the past learned through repetition.
00:06:10.319 --> 00:06:12.560
And now AI can automate so much of that.
00:06:12.639 --> 00:06:19.120
And so the opportunity for early talent then is to be able to do much more strategic work earlier on.
00:06:19.360 --> 00:06:32.079
And that's why one of the findings of our research was quite interesting in that 87% of CHROs expect new hires to be completely comfortable with AI tools on day one.
00:06:32.319 --> 00:06:39.839
And so that what that tells us is that being ready for work today looks very different and means something very different.
00:06:39.920 --> 00:06:46.000
It means combining AI fluency with, of course, those human capabilities that are that are pretty unique.
00:06:46.079 --> 00:06:47.759
And so Did you say 87%?
00:06:48.399 --> 00:06:48.959
87%.
00:06:49.439 --> 00:06:49.920
87%.
00:06:50.560 --> 00:06:56.639
I I would ask C if CHRLs if their organization is 87% ready for AI.
00:06:56.959 --> 00:06:57.759
Well, exactly.
00:06:57.920 --> 00:07:06.639
It's that coming in comfortable
using AI, which tells you that their expectation is that new hires are becoming comfortable before that first job.
00:07:06.800 --> 00:07:19.199
You know, as students, as interns, doing gig work, contract, what have you, before they land that first entry-level role, 87% in terms of an expectation of of new hires being comfortable.
00:07:19.360 --> 00:07:20.399
So pretty staggering.
00:07:21.040 --> 00:07:22.879
That is a big number.
00:07:23.680 --> 00:07:27.040
Now, I I'd love to actually peel this.
00:07:27.199 --> 00:07:46.639
I mean, let's take one step backwards and ask if entry-level jobs were ever designed right, and if the reason that they're changing is because of AI, or if AI is the accelerant to actually rethink what these jobs look like.
00:07:47.279 --> 00:07:49.279
I think it's very much an accelerant.
00:07:49.439 --> 00:07:54.560
I mean, at the end of the day, like this whole era of AI is about increased productivity.
00:07:54.720 --> 00:07:58.480
And so that isn't at the exclusion, though, of humans and of people.
00:07:58.560 --> 00:07:59.920
It's in concert with.
00:08:00.000 --> 00:08:07.040
So then the question becomes if AI can, through automation, can accelerate productivity on one front, then what does that mean for people?
00:08:07.199 --> 00:08:18.879
And it also becomes an accelerant of what people are then doing in terms of communication skills, you know, applying judgment, um, being able to navigate ambiguity, things of that nature.
00:08:19.120 --> 00:08:25.360
All of a sudden, you can you can see that early talent is gonna be able to add a lot more value earlier on.
00:08:25.519 --> 00:08:30.800
Now, that does bring concerns in some form or fashion, which I'm sure we'll get to in a minute.
00:08:31.040 --> 00:08:45.200
But in theory, AI is 100% an accelerant of what people can do at work and what we're gonna see early talent doing now as a result of AI in the mix.
00:08:45.600 --> 00:08:49.360
So I love how you're using the term early talent.
00:08:49.519 --> 00:08:53.039
And, you know, for me, and once again, old.
00:08:53.759 --> 00:08:55.039
Come on, Jason.
00:08:55.279 --> 00:08:55.679
Sorry.
00:08:56.000 --> 00:09:07.600
You're old, I'm old, so we're both old Career ladder, career lattice, whatever you want to say, entry level job has always been a label.
00:09:07.919 --> 00:09:08.320
Yep.
00:09:08.879 --> 00:09:10.960
And you're saying early talent.
00:09:11.120 --> 00:09:13.519
I'm not sure those are the same thing.
00:09:13.840 --> 00:09:21.519
And uh I'm curious if that concept of entry-level job maybe is just gone.
00:09:21.919 --> 00:09:23.840
And maybe we just stopped saying it.
00:09:24.639 --> 00:09:32.799
I think we have to define it differently because I think for years, uh for really the test of time, early talent has been associated with an age.
00:09:32.960 --> 00:09:40.799
Like early talent meant someone that was two years out of university or three years out of university or a single year out of
university.
00:09:40.960 --> 00:09:43.440
But now, I mean, I had a friend of mine last week.
00:09:43.600 --> 00:09:57.120
She spent, you know, 25 years in a classroom essentially as a teacher, and now has been going to, you know, 20 podcasts and workshops a week because she wants to take a completely new direction with her career.
00:09:57.279 --> 00:09:59.360
And so she'll be an early talent.
00:09:59.440 --> 00:10:05.759
In fact, she wants to go on the tech side, which is pretty interesting, with some educational software companies that are doing interesting things.
00:10:05.919 --> 00:10:10.399
And so she'll be an early talent as she enters that next phase of her career.
00:10:10.559 --> 00:10:13.519
So I think I don't think the notion of early talent is going away.
00:10:13.600 --> 00:10:18.080
I just think the definition has certainly evolved to, you know, according to what it went's way.
00:10:18.320 --> 00:10:22.240
I don't think early talent's going away, but what about the entry level job?
00:10:22.960 --> 00:10:25.679
Yeah, well, one of the things, Laura, I'm sorry, you're interrupting.
00:10:25.840 --> 00:10:29.360
One of the things that people always say to me is they're like, Jason, you're a futurist.
00:10:29.440 --> 00:10:30.799
And Laura, you're a futurist.
00:10:30.960 --> 00:10:33.440
And I kind of say I'm a nowist.
00:10:33.759 --> 00:10:34.799
I'm not a futurist.
00:10:34.879 --> 00:10:36.480
Like I actually think this is happening now.
00:10:36.639 --> 00:10:39.840
This isn't like something I'm saying is going to happen in the future.
00:10:40.159 --> 00:10:43.840
I think the entry-level job has changed.
00:10:44.159 --> 00:10:44.480
Changed.
00:10:44.720 --> 00:10:44.960
Already.
00:10:45.200 --> 00:10:45.519
Yeah.
00:10:45.919 --> 00:10:46.799
It already has changed.
00:10:48.000 --> 00:10:49.600
It has not completely disappeared.
00:10:49.679 --> 00:10:52.320
It is changed, it has changed and it is changing.
00:10:52.399 --> 00:10:53.759
It's continuing to evolve.
00:10:54.000 --> 00:11:06.320
You know, you mentioned the the ladder a few minutes ago, and we typically think of that career ladder as, you know, where growth is much more linear and you have these, you know, distinct rungs and it's somewhat hierarchical.
00:11:06.399 --> 00:11:12.240
And I think they had like weird time frames of you can't even climb the next ladder unless it's 18 months.
00:11:12.320 --> 00:11:14.240
Otherwise, don't even think about climbing the ladder.
00:11:14.559 --> 00:11:14.799
Exactly.
00:11:14.960 --> 00:11:16.159
They were very time-bound.
00:11:16.240 --> 00:11:33.759
And I think if we think about now the career ladder, especially for an early career talent, think of it as much more of a jungle gym where the it's not so linear and where growth is much more associated with it's much more skills-based, it's much more dynamic.
00:11:33.919 --> 00:11:40.799
There's not a path, there's not an expectation, and there's opportunity left, right, and center.
00:11:40.960 --> 00:11:56.879
And so, and and that really speaks to a lot of what early career talent need to be, need to be embracing in terms of a sense of adaptability and things of that nature to be able to really take advantage of what that jungle gym presents.
00:11:57.039 --> 00:12:15.840
But I I think going back to what we were saying a few minutes ago, I mean, the the challenge is that some of the learning reps that historically have been in place to help people build confidence early on in their career, in some cases, those are being compromised.
00:12:15.919 --> 00:12:19.039
In other cases, they might be entirely skipped.
00:12:19.200 --> 00:12:35.360
And so that raises a real question, I think, for leaders that if AI is handling all of these, you know, foundational tasks, how do people still develop that foundational business judgment and stakeholder management skills, critical thinking skills, things of that nature?
00:12:35.519 --> 00:12:38.799
And what are companies going to do to create those learning opportunities?
00:12:38.960 --> 00:12:41.840
Because they're not going to automatically happen as they have in the past.
00:12:42.159 --> 00:12:46.320
I think it's such a fascinating discussion that literally I could do this all day.
00:12:46.480 --> 00:12:53.120
We're not going to, but I could do this all day because like even just what you said confidence.
00:12:53.279 --> 00:12:54.799
Let's just use confidence.
00:12:56.159 --> 00:13:07.600
Like I'm hiring people that are 21, 22, 23 that are more confident than 40 and 50 year olds today.
00:13:07.919 --> 00:13:10.159
Now they're not confident in everything.
00:13:10.720 --> 00:13:11.120
Correct.
00:13:11.440 --> 00:13:18.799
And what they know actually and and what the 40 and 50 year olds are confident in, maybe they shouldn't be confident in anymore.
00:13:18.960 --> 00:13:23.279
That's another whole discussion around inertia and change and things like that.
00:13:23.440 --> 00:13:26.399
But that that word confidence is so interesting.
00:13:26.559 --> 00:13:31.039
I'm curious as to how confident are you in this shift?
00:13:31.200 --> 00:13:37.840
Are you seeing other signals that the the 87% around AI is interesting?
00:13:38.080 --> 00:13:50.720
What else are you seeing from a your customer standpoint or from a CHRO standpoint that that give you confidence that this isn't just a passing fad, but this is really the future?
00:13:51.200 --> 00:13:53.279
Definitely do not think it's a passing fad.
00:13:53.440 --> 00:13:56.960
I mean, the the shift is already pretty visible in many ways.
00:13:57.200 --> 00:14:10.399
So another thing that our research pointed out is that 79% of CHROs said said that early career employees are already receiving enterprise AI tools within their first month.
00:14:10.639 --> 00:14:12.080
So talk about confidence.
00:14:12.240 --> 00:14:23.840
If you're handing individuals the tools that we now have have hung so much promise on,
right, that have accelerated productivity, then obviously there's going to be increased uh confidence.
00:14:24.000 --> 00:14:27.440
But there's other things beyond that, besides just giving people AI tools.
00:14:27.600 --> 00:14:30.399
Yeah, I was gonna say the tools are one thing, but do they know what to do with them?
00:14:30.879 --> 00:14:47.679
Already we're seeing a lot of our customers are completely rethinking things like onboarding, things like learning and development, even performance expectations, career pathing, all in light of this notion of AI accelerating productivity, because the expectations of people have changed.
00:14:47.840 --> 00:14:49.440
Goal plans are now evolving.
00:14:49.600 --> 00:14:52.080
Career pathing has got to be completely reworked.
00:14:52.159 --> 00:15:03.120
And so I would say across the board, aside from just giving people a set of tools, like a lot of these HR processes that are all about uh related to talent strategy are evolving in real time.
00:15:03.279 --> 00:15:06.639
So yeah, do you when you say reworked?
00:15:07.679 --> 00:15:12.320
Sorry, I sorry, I jump around, but I love I love words like that.
00:15:12.559 --> 00:15:23.759
Do you think that right now because I have a lot of people that I talk to that talk a bit to me about coming up with a new skills library or coming up with a new way to do performance?
00:15:24.080 --> 00:15:30.639
Like, do you think that we're at a point where we can confident, let's use that word again, rework?
00:15:31.120 --> 00:15:38.879
Or are we like living breathing in a lab, a petri dish of trying different things based on the changes going on?
00:15:39.039 --> 00:15:49.919
Uh because I think that's I think haven't changed for 50 years, and then all of a sudden we've got things changing so fast that I'm not sure we can count on one specific change.
00:15:50.320 --> 00:15:55.360
Well, I think, I mean, look, a lot of these HR workflows, they've they've looked the same for many, many years.
00:15:55.440 --> 00:16:02.000
And I think initially there was this promise of like, oh, we can automate these workflows and become much more efficient.
00:16:02.080 --> 00:16:04.720
And I think the light bulb has gone off, especially.
00:16:04.879 --> 00:16:21.759
I mean, I sit in the room a lot with our customers, and they are saying, like, we have to go beyond simply automating the way things have been done for X amount of years and really lean into assistants and agents and all things that Agentec AI offer to do things differently.
00:16:21.919 --> 00:16:32.559
And so that may be tailoring, like having many, many different versions of workflows based on different profiles, different characteristics, different triggers or what have you.
00:16:32.720 --> 00:16:41.919
But it also, I mean, it I mean, even just the notion of like people manually not having to be involved to do so many of the steps, that alone changes the workflow.
00:16:42.080 --> 00:16:52.000
So, yes, I do think
when it comes to onboarding and learning and development and um and career pathing, I mean, first of all, it is sort of grounded in like how good are your analytics, right?
00:16:52.159 --> 00:16:58.159
In terms of understanding what people need, where are their gaps and being able to respond to that.
00:16:58.320 --> 00:17:10.400
But assuming that those capabilities are in place, yes, I do see customers every day looking at how to completely revamp some of these, what I would say are traditional HR processes that have been in place a long time.
00:17:10.799 --> 00:17:13.359
So, Laura, you work with some of the biggest companies in the world.
00:17:13.519 --> 00:17:14.640
And thankfully.
00:17:14.799 --> 00:17:15.839
Thankfully, yes.
00:17:16.559 --> 00:17:23.279
I I I'm curious as to how ready you think HR is for this.
00:17:23.519 --> 00:17:28.799
I work with some of the biggest companies in the world also, and they're still stuck in the transactional oftentimes.
00:17:28.960 --> 00:17:36.400
And we're talking about massive redesign of things, you know, and I still have to keep the lights on.
00:17:36.720 --> 00:17:43.279
So I'm curious when you're working with your customers, like what are you how are you guiding them right now?
00:17:43.680 --> 00:17:45.039
It look, it runs the gamut.
00:17:45.200 --> 00:17:47.279
I mean, we have thousands and thousands of customers.
00:17:47.440 --> 00:17:56.000
I wish I could say, you know, all customers sort of fit this profile, and therefore we can, you know, be in service of them in some sort of similar manner.
00:17:56.160 --> 00:18:14.559
The reality is we have very forward thinking, forward-acting customers who are on the forefront who are not only leaning into AI capabilities within our success factor suite, but are way ahead in terms of building their own agents and assistants and sort of leading the way.
00:18:14.720 --> 00:18:16.880
And then we have customers who are middle of the road.
00:18:16.960 --> 00:18:23.039
And then we have those who are still, you know, in our roundtable discussions, they're still in a bit of wait and see.
00:18:23.119 --> 00:18:31.440
You know, they still are stuck on like trying to get business cases and getting internal buy-in and worried about change management, how to drive adoption at scale.
00:18:31.599 --> 00:18:34.960
And so, I mean, to answer your question, I think we see it all.
00:18:35.200 --> 00:18:38.960
You know, we are trying to meet customers where they are as a market leader.
00:18:39.119 --> 00:18:52.240
Obviously, we need to make sure the capabilities are available, but that doesn't mean that we expect all thousands of customers to flip a switch and be, you know, having revamped their processes in a in a way that takes full advantage of AI.
00:18:52.400 --> 00:18:54.480
It's going to be a journey and it's going to take a look at the phone.
00:18:54.640 --> 00:18:56.240
Yeah, no, no, I completely agree with that.
00:18:56.319 --> 00:19:02.079
And now
you just use a phrase, and I want to make this phrase into a pain, if I could.
00:19:02.240 --> 00:19:04.000
The phrase was wait and see.
00:19:04.160 --> 00:19:12.319
And I don't want to tie the pain to the enterprise, but I want to tie the pain to parents, students, and hiring managers.
00:19:12.880 --> 00:19:15.920
Because those three can't afford to wait and see.
00:19:16.240 --> 00:19:16.559
100%.
00:19:17.279 --> 00:19:19.039
Like HR, wait and see.
00:19:19.119 --> 00:19:19.839
I I get that.
00:19:19.920 --> 00:19:30.160
And there's a lot of uh as much as we would love for them not to wait and see, like the pace of organizational change versus the pace of human change is different.
00:19:30.880 --> 00:19:43.440
So when we think about parents and students, and we're like, how like based on the changes we're talking about with early talent and entry-level jobs, how do you position yourself?
00:19:43.599 --> 00:19:49.759
And as a hiring manager, what should you be looking for in a world of ambiguity?
00:19:50.160 --> 00:19:50.559
Yeah.
00:19:50.799 --> 00:19:57.039
I mean, there's no one that's going to want their college-age children off the payroll faster than me.
00:19:57.279 --> 00:19:58.720
So, yes, I have lots of.
00:19:59.119 --> 00:20:00.480
Wait and see doesn't work, right?
00:20:00.640 --> 00:20:01.200
Wait and see.
00:20:01.440 --> 00:20:03.200
Wait and see doesn't work for sure.
00:20:03.359 --> 00:20:08.400
I mean, look, from a student perspective, a couple things come to mind.
00:20:08.559 --> 00:20:19.759
I mean, number one, my my advice to students is don't measure your value or articulate your value along the lines of what AI can do much faster and better.
00:20:20.079 --> 00:20:23.839
So really think about how you're positioning your strengths.
00:20:24.000 --> 00:20:29.599
Because if they are strengths that honestly AI can win at the ball game, that's not a winning strategy.
00:20:29.759 --> 00:20:34.079
And so in the world say that again, because I think that's really important.
00:20:34.400 --> 00:20:34.640
Yeah.
00:20:34.720 --> 00:20:40.240
I mean, what I'd like to tell students is don't measure your value according to what AI can do.
00:20:40.319 --> 00:20:44.400
That's in a much better way, faster way, more effective way.
00:20:44.559 --> 00:20:49.920
Because at the end of the day, if you're if you're articulating your value, it and it's kind of a losing game.
00:20:50.240 --> 00:20:51.119
It's a losing game.
00:20:51.279 --> 00:20:52.079
Right, exactly.
00:20:52.240 --> 00:21:07.359
And so think about those experiences that you've gained as a student where you've had to navigate change, where you've had to lead, where you've had to demonstrate empathy, where you've had to problem solve.
00:21:07.519 --> 00:21:11.519
Those are skills that AI aren't gonna win at, right?
00:21:11.680 --> 00:21:29.599
And so that's my advice is like look for those opportunities, even when you're doing basic summer jobs and internships and
what have you, like look for those opportunities so that on the back end you can speak to those unique human capabilities that you've fine-tuned as a result of those experiences.
00:21:29.839 --> 00:21:32.160
I think on the parent side, I mean, this is a big one.
00:21:32.240 --> 00:21:33.599
And you and I have talked about this.
00:21:33.759 --> 00:21:48.640
I mean, the the days of sort of, you know, coaching your children or students on a particular path as though that's gonna stick for 10 years or 15 years or 20 years, I think those days are over.
00:21:48.799 --> 00:21:54.160
I mean, the best secret sauce that somebody can have in today's age is adaptability.
00:21:54.319 --> 00:21:59.759
And so, as a parent, going ahead and talking about how things are changing so rapidly.
00:22:00.079 --> 00:22:05.599
And how careers are going to continuously evolve and change and take directions right and left.
00:22:05.680 --> 00:22:20.640
I think you're then setting the groundwork for your student or child to expect change and to embrace it, to be ready for it, and to be able to talk through how they can more successfully navigate it versus maybe other candidates.
00:22:20.960 --> 00:22:27.200
You know what's so fascinating, Laura, is that my dad worked for the same company for 48 years.
00:22:27.599 --> 00:22:28.400
48.
00:22:28.640 --> 00:22:29.039
Wow.
00:22:29.359 --> 00:22:35.680
And you know that his dad, my grandpa, worked for that same company for 49 years.
00:22:36.000 --> 00:22:37.039
You are kidding.
00:22:37.680 --> 00:22:38.720
But that wasn't rare.
00:22:38.880 --> 00:22:45.759
I'm sure that was not a 97 years in the between the two of them in the same insurance company.
00:22:46.000 --> 00:22:47.279
And then you broke the mold.
00:22:47.519 --> 00:22:48.799
I broke the mold, A.
00:22:48.880 --> 00:22:52.319
But B, I want to, do you think that's even reasonable?
00:22:53.200 --> 00:22:54.960
Do you even think you should do that anymore?
00:22:55.519 --> 00:22:57.759
I mean, I I don't I don't think it's realistic.
00:22:57.920 --> 00:22:58.720
Let's just say that.
00:22:58.880 --> 00:22:59.839
I mean, could it happen?
00:22:59.920 --> 00:23:00.559
Yes, of course.
00:23:00.720 --> 00:23:06.319
Particularly if you have a family-owned business and that, you know, you have a legacy that you're continuing, you're growing it, what have you.
00:23:06.400 --> 00:23:14.400
But I think the notion of like going into banking for 20 years or even even getting a law degree and and practicing a similar type of law for 25 years.
00:23:14.559 --> 00:23:20.640
I I mean, we see a lot of doctors that are, that are entering different, you know, they're not just in a performing surgeries.
00:23:20.720 --> 00:23:34.000
I think it's just unrealistic to think or to even coach someone into like, you're going to get this degree, you're going to have these skills, and those skills will allow you to run course, a similar course for 20 years.
00:23:34.079 --> 00:23:36.480
I don't think that's the world we're in anymore.
00:23:37.279 --> 00:23:46.880
In the world that we are in, and this is someone asked me this question during a keynote that I gave yesterday, you know, and they they asked me if I was familiar with a racy.
00:23:47.599 --> 00:23:50.559
And I'm like, yeah, I'm familiar with a racy.
00:23:50.960 --> 00:23:57.519
And they're like, so you're saying that entry level or early talent jobs have to change.
00:23:58.319 --> 00:23:59.440
Who's got the R?
00:23:59.759 --> 00:24:03.920
And it was fascinating because they're like, our HR department's not going to change that.
00:24:04.400 --> 00:24:09.599
Like our HR department takes job recks that people tell us what they need and we fill them.
00:24:09.920 --> 00:24:11.440
But we're not going to change them.
00:24:11.759 --> 00:24:19.359
And then we have people in the business who say, well, we're waiting for HR to change what those entry-level jobs look like.
00:24:19.599 --> 00:24:21.359
I'm curious if you're seeing that.
00:24:21.440 --> 00:24:29.920
And I'm who in your world, who's got the R here to actually redesign and who has that responsibility to do that?
00:24:30.880 --> 00:24:34.480
Yeah, I don't think it actually sits on any one shoulder.
00:24:34.640 --> 00:24:38.720
I mean, we do it, we've done a lot of work with smart recruiters, which as you know, we acquired in the fall.
00:24:38.799 --> 00:24:47.519
And I was just doing a session with them last week, actually, where someone asked, like, are you seeing job descriptions completely change?
00:24:47.759 --> 00:24:54.720
Like, do they look entirely different than they did for what might be a similar type of role?
00:24:54.960 --> 00:24:59.680
And the question was, again, in some cases, yes, and others no.
00:24:59.839 --> 00:25:08.640
I mean, it kind of depends on the maturity of where companies are in that particular instance from a recruiting standpoint, right?
00:25:08.799 --> 00:25:22.799
Of there's so many AI capabilities, quite honestly, that in most, a lot of cases, candidates are using AI almost to then, it's also forcing organizations to say, like, oh, wait a minute, uh, we should be, we should be taking advantage of this as well.
00:25:22.960 --> 00:25:35.920
But I mean, it in that case, it it, yes, HR plays a role, but also the capabilities that you put in the hands of recruiters and and hiring managers and candidates, quite honestly, becomes equally as important.
00:25:36.160 --> 00:25:38.240
So there is a bit of a pull and a push.
00:25:38.319 --> 00:25:52.319
And I think within organizations that's looking, that looks different, depending on whether HR from the business is saying, okay, to align with our business strategy, we are going to, you know, drive change management and all these sort of new AI capabilities.
00:25:52.559 --> 00:26:01.519
But there's also, there's also a pull from within the business of certain roles going, like, I can't do it the old way anymore,
because A, we're not going to have as good of outcomes.
00:26:01.599 --> 00:26:11.759
And B, like, look at the look at some of the stats on like work-life balance and things of that nature in some of these companies unfiltered or, you know, employee sat surveys.
00:26:12.000 --> 00:26:14.400
You know, the burnout situation is real.
00:26:14.480 --> 00:26:23.599
And so in this case, like this can be a positive in terms of really um enhancing what people are doing and how they're doing it, even though it is something, quote, new to learn.
00:26:24.319 --> 00:26:41.039
When you look at the research that you've done and all of the research, and put your parent hat on for a second, is there anything that sticks out to you where you're like, holy cow, I need to really make sure my kids are thinking this?
00:26:41.200 --> 00:26:46.400
Or something that sticks out to you that says, holy cow, what they're doing in college is all wrong.
00:26:46.799 --> 00:26:49.359
Or holy cow, they shouldn't be getting a degree.
00:26:49.519 --> 00:26:51.839
I'm I'm just making stuff up at this point.
00:26:52.000 --> 00:27:00.640
Because I'm not sure there is a right answer, but I'm curious if there's anything else, because I think this research is so deep that that you want to call out.
00:27:01.119 --> 00:27:12.799
Well, two of the stats I think are or insights I think we spoke upon, which was, you know, companies are now expecting people to come in with a high degree of AI literacy.
00:27:13.039 --> 00:27:14.960
And where do you where do you where do you get that?
00:27:15.200 --> 00:27:20.720
And so that that's curious because I mean, there's some colleges that say if you use AI, you're cheating.
00:27:20.880 --> 00:27:23.839
And then you're saying, hey, you're supposed to come in with AI literacy.
00:27:24.079 --> 00:27:28.799
Well, and it's funny, I've actually been asking my college university to I have two kids in university.
00:27:28.880 --> 00:27:31.680
I've just been saying, like, what is the level of coaching?
00:27:31.839 --> 00:27:34.400
What like to what extent are the tools available?
00:27:34.559 --> 00:27:37.359
To what extent are they, are you, are you allowed to use them?
00:27:37.519 --> 00:27:43.440
You know, what governance is in place so that you are so it's crystal clear, like cheating versus like value add.
00:27:43.680 --> 00:27:53.599
And so those are questions, honestly, that every parent should be asking if they have, you know, kids or students in that, in that realm, because their expectation is real.
00:27:53.759 --> 00:27:59.039
Now, the other one about, you know, giving, giving early career talent those tools.
00:27:59.200 --> 00:28:04.559
If if someone was to do a wait and see, they better be ready on day one to take advantage of those tools.
00:28:04.720 --> 00:28:11.200
But I think some of the more interesting things, though, are some of the concerns that CHROs have.
00:28:11.359 --> 00:28:23.279
Like we we talk about sort of all the
positiveness, but there are also concerns that CHROs have expressed through the research that go along with the positive notion around AI productivity.
00:28:23.440 --> 00:28:27.680
So, for example, there's a pretty high attrition risk.
00:28:27.839 --> 00:28:35.039
I think 45% of CHROs cited a higher attrition risk tied to uneven AI enablement.
00:28:35.279 --> 00:28:43.119
And so that alone speaks to the fact of like, I've got to get my training and development in court, you know, in check.
00:28:43.440 --> 00:28:54.799
Because if people don't feel as if employees don't feel as though they are on the cutting edge, they're gonna leave to go to another company that does offer that same onboarding or development.
00:28:55.039 --> 00:29:04.880
Another thing that was interesting was that about 43% of CHROs expressed or reported higher performance pressure for early talent.
00:29:05.359 --> 00:29:12.480
And so what that tells us is, you know, in the interview process, being able to handle increased pressure.
00:29:12.720 --> 00:29:17.759
And, you know, not everyone is a gunner on day one, but you kind of need to vet that out, right?
00:29:18.000 --> 00:29:27.200
Because if all of a sudden the expectations around productivity and performance are that much higher with AI, then all of a sudden you need to have people that can handle that, right?
00:29:27.359 --> 00:29:34.960
They can that can handle the higher cognitive load, that can high handle the increased expectation at month three in the role versus year three.
00:29:35.279 --> 00:29:44.960
So there were some really interesting, there was one more that actually I found interesting, which was around the rise of shadow AI, which I thought was kind of an interesting concept.
00:29:45.119 --> 00:29:52.640
So you have all these employees earlier on that are trying to keep pace with the rising productivity expectations.
00:29:52.799 --> 00:30:04.559
And they don't necessarily know exactly, I mean, what tools they can use, how they should use them, to what extent they can use them, can they rely on them versus just, you know, tap into them.
00:30:04.720 --> 00:30:13.279
And so we had over half of the CHROs say that that said that they when they see shadow AI, they see it because the guidance isn't clear.
00:30:13.440 --> 00:30:17.519
And so what that tells you is that employees need governance.
00:30:17.759 --> 00:30:22.799
They want guidance, they want systems that embed AI into the workflows.
00:30:22.880 --> 00:30:29.119
And if they don't have that, they are going to improvise on their own.
00:30:29.359 --> 00:30:34.400
And then that becomes stressful because employees want to do things like within the firewall.
00:30:34.480 --> 00:30:35.680
They want to do things the right way.
00:30:35.759 --> 00:30:42.960
They don't want to be using external tools that are sort of ungoverned, but yet the expectation for for productivity is there.
00:30:43.039 --> 00:30:43.680
So I thought that was.
00:30:44.000 -->
00:30:50.640
Yeah, no, I think we're going from the BYOD world in college to the BYOAI world in work.
00:30:50.960 --> 00:30:51.039
Yeah.
00:30:51.359 --> 00:30:55.680
And when that starts to happen, I start to have some major security issues.
00:30:56.000 --> 00:31:04.880
But I I and I I just want to wrap us up in in a second here, but I I love and hate that performance discussion.
00:31:05.599 --> 00:31:09.680
And I love it because I think expectations have changed.
00:31:09.839 --> 00:31:17.920
But I think you uh have some people working with a very fast race car, and you have some people working with a horse and buggy.
00:31:18.079 --> 00:31:18.319
Yeah.
00:31:18.559 --> 00:31:26.160
And one of the things that you talked about in the report was this concept of which a little bit slang, but AI brain fry.
00:31:27.200 --> 00:31:31.759
And I don't want to get too deep into that because it's truly another whole topic.
00:31:32.160 --> 00:31:48.240
But I don't know about you, but I never have enough time anymore to do the things that I want to do that I want to do, that I want to learn, that I want to get better at, and then I have to do my job at the same time.
00:31:48.480 --> 00:31:51.759
Uh so I think I am getting a little AI brain fry.
00:31:51.920 --> 00:31:54.160
Not I'm not even sure it's negative.
00:31:54.400 --> 00:31:57.279
I just think it's a different mental strain.
00:31:57.759 --> 00:31:59.039
Uh I'd love your thoughts on that.
00:31:59.279 --> 00:32:00.240
Yeah, I mean, a hundred percent.
00:32:00.319 --> 00:32:03.759
And it it does, it it it obviously depends on what role you're in.
00:32:03.839 --> 00:32:12.400
If you're in engineering, if you're in sales, if you're in marketing, finance, obviously, like the expectations and even the the use of AIs varies.
00:32:12.720 --> 00:32:18.240
But I think across the board, I mean, not everyone needs to be an AI engineer, right?
00:32:18.400 --> 00:32:40.319
Not everyone's going to be out there building models, but increasingly every single employee is going to be expected to work alongside AI co-pilots, to evaluate AI-generated outcomes or content or what have you, to be able to ask the right prompt uh questions, use the right prompts, and to solve problems, you know, in some sort of collaborative manner.
00:32:40.480 --> 00:32:58.720
And so, in many ways, AI fluency is, it's not already there, but I do think it's becoming like email or Excel once were, in terms of like not just the specialized technical skill, but more of a baseline workplace capability expectation.
00:32:58.880 --> 00:33:08.400
And that does create for those that aren't on the leading edge, that aren't on their own, sort of further ahead, you know, it does create a, you know, a burn, right?
00:33:08.559 --> 00:33:12.400
To your point, like, you know, how do I stay, how do I stay abroad?
00:33:12.559 --> 00:33:13.680
How do I do my day job?
00:33:13.839 --> 00:33:18.240
And how do I also make sure that I'm that I'm leaning in as much as I should be?
00:33:18.400 --> 00:33:20.880
And so it's gonna be, it's gonna be a ride.
00:33:21.039 --> 00:33:26.720
I think those organizations that come out on top, they're not gonna be the ones that deploy the most AI.
00:33:26.880 --> 00:33:31.119
And they're not gonna be the ones that deploy the best AI, which we hear a lot about.
00:33:31.279 --> 00:33:48.000
They're gonna be the ones that redesign their entire workplaces around humans working alongside AI and putting in place the right training and development and learning experiences that maximize the potential of both.
00:33:48.160 --> 00:33:49.680
And that's just the bottom line.
00:33:50.000 --> 00:33:50.480
I love that.
00:33:50.640 --> 00:33:59.839
Designing the experience with the human at the center still, but able to work with these new tools and new capabilities that makes the human amplified.
00:34:00.079 --> 00:34:00.559
Exactly.
00:34:00.799 --> 00:34:02.480
Because I don't think they're going away, right?
00:34:02.640 --> 00:34:06.640
And anyone that thinks it's to you, you know, you laughed earlier about, you know, is this some kind of fad?
00:34:06.799 --> 00:34:08.320
Like, I don't think it's a fad.
00:34:08.400 --> 00:34:14.159
You know, I think AI is here to stay, and I think there's a tremendous opportunity, but obviously organizations need to be thoughtful.
00:34:14.400 --> 00:34:21.199
These are not just things we can throw over the fence and expect people to adopt them in the right way and and keep doing things the way they always have.
00:34:21.599 --> 00:34:27.760
So, Laura, the the last thing I want to end on is, and I can't, I couldn't do the I wouldn't be doing justice if I didn't ask this question.
00:34:27.920 --> 00:34:34.000
It deals with jobs and you know, the whole job conversation, which to me is fascinating.
00:34:34.159 --> 00:34:36.800
Yeah, it's fascinating It's probably three things.
00:34:36.960 --> 00:34:39.840
It's fascinating, it's frustrating, and it's funky.
00:34:40.159 --> 00:34:54.719
But uh to me, to me, uh I see uh the world that we're in today as a transition period where jobs are being eliminated, quote unquote, because of AI.
00:34:54.880 --> 00:35:00.800
And I by the way, I think that's happened in every industrial revolution we've ever gone through.
00:35:00.960 --> 00:35:06.000
You know, the person that used to run the elevator, you know, that got eliminated.
00:35:06.079 --> 00:35:06.400
Why?
00:35:06.559 --> 00:35:08.960
Because I started to trust the elevator more.
00:35:09.280 --> 00:35:15.920
At the same time, I truly believe that AI is a net positive from a job creation standpoint.
00:35:16.159 --> 00:35:20.639
It's just that the job A and job B are not the same.
00:35:21.039 --> 00:35:22.800
I I'd love your thoughts on that.
00:35:23.039 --> 00:35:25.679
Any validation, A tomatoes you want to throw.
00:35:25.920 --> 00:35:30.079
We're in a turbulent period right now where we need our seat belts buckled.
00:35:30.400 --> 00:35:30.719
100%.
00:35:31.199 --> 00:35:32.159
I mean, a hundred percent.
00:35:32.320 --> 00:35:39.840
I mean, look, at the end of the day, there are still, there are still and will always be tons of tasks to be done.
00:35:40.079 --> 00:35:45.920
And arguably AI can do now some portion of those tasks.
00:35:46.079 --> 00:35:50.480
I don't think anyone is debating that or questioning that.
00:35:50.880 --> 00:36:11.199
But even with all of the automation, every organization is still going to need people who can lead, who can build trust, who can actually oversee and direct and coach even AI co-pilots and and other agentic AI tools can solve problems, make thoughtful decisions.
00:36:11.519 --> 00:36:17.039
And so we have to think of the world of jobs as ever evolving.
00:36:17.199 --> 00:36:27.920
I mean, to your point, it's not, it's not that much different than any of these other industrial revolutions where there was immediate fear, you know, life was going to be over as everyone had known it.
00:36:28.079 --> 00:36:32.079
And then on the other side, it was a net positive to your point.
00:36:32.320 --> 00:36:39.679
And so we just have to think through like, what is it that AI is really good at and should be doing and be okay with that?
00:36:39.840 --> 00:36:44.960
And then say, okay, what are the things that AI is never going to be able to do or or shouldn't be doing?
00:36:45.199 --> 00:36:48.960
And then we lean in in terms of putting our best talent there.
00:36:49.119 --> 00:36:51.599
And I'm pretty confident there's room for both.
00:36:51.920 --> 00:36:52.960
I love the way you said that.
00:36:53.039 --> 00:36:55.039
And I said the same, I believe the same thing.
00:36:55.199 --> 00:36:56.400
There's a could and should.
00:36:56.639 --> 00:36:56.960
Exactly.
00:36:57.360 --> 00:37:01.280
What could AI do versus what should AI do?
00:37:01.440 --> 00:37:06.480
And that's up to us as leaders, in my personal opinion, to help design that.
00:37:06.800 --> 00:37:08.719
So, Laura, how can people get a hold of you?
00:37:08.800 --> 00:37:12.639
We're going to put this research in the show notes, but how else can people get a?
00:37:12.880 --> 00:37:15.119
You don't need to give out your cell phone number or anything like that.
00:37:15.440 --> 00:37:17.199
But how do you want people to find you?
00:37:17.360 --> 00:37:20.320
Because all of these generate discussion.
00:37:20.639 --> 00:37:20.880
Yeah.
00:37:21.039 --> 00:37:24.559
LinkedIn is probably the easiest way through a LinkedIn Messenger.
00:37:24.719 --> 00:37:26.159
I'm pretty active on LinkedIn.
00:37:26.400 --> 00:37:31.599
So I've already posted about uh a lot of the research that we've done and certainly talked to a lot.
00:37:31.760 --> 00:37:36.880
I was with uh a bunch of industry folks last week and the week before that with a group of CHROs.
00:37:36.960 --> 00:37:40.159
And so I I'm always game for sharing insights.
00:37:40.320 --> 00:37:50.000
Um, I
don't think we at SAP are ever of the mindset that what we're doing is uh above and beyond what others in the industry are seeing or experiencing or what have you.
00:37:50.079 --> 00:37:52.079
So would welcome that conversation for sure.
00:37:52.400 --> 00:37:58.639
Now, and one of the things I think SAP does so well, I think I missed this last week out in San Francisco, if that's what you're referring to.
00:37:59.119 --> 00:38:09.119
Uh my colleague Jess Von Bank was there, is the you do a great job of working with industry analysts and thought leaders to help bring them along with your thinking as well.
00:38:09.360 --> 00:38:11.039
So it's really important.
00:38:11.199 --> 00:38:19.760
I mean, we have an in-house team of biopsychologists that are studying, you know, what has been the future of work, which we like to say the future of work is now here today and present.
00:38:19.920 --> 00:38:23.760
And we think that's unique relative to some of the other vendors in the space.
00:38:24.000 --> 00:38:43.360
But with that said, we're looking to share our research insights, which are driving our product strategy and roadmap, with other industry advisors and analysts for the sake of tell us where you see alignment and tell us where you might have a different point of view or see things shaping up in an alternative way.
00:38:43.519 --> 00:38:46.559
Because at the end of the day, like things are rapidly evolving.
00:38:46.719 --> 00:38:49.840
There is no one viewpoint that is sort of the way forward.
00:38:50.000 --> 00:38:54.639
And so, to your point, we try to surround ourselves with people that are much smarter than us.
00:38:54.719 --> 00:39:07.280
And that's why we love having conversations with you on topics like this, because you're talking to just as many people and forming your own opinions, and all of that becomes input for how we take our product strategy and how we serve our customers forward.
00:39:07.599 --> 00:39:08.800
And I got amazing feedback.
00:39:08.880 --> 00:39:13.840
I heard that that was the best event people have been at for a long, long time from an analyst standpoint.
00:39:13.920 --> 00:39:14.400
So congrats.
00:39:14.719 --> 00:39:24.000
Well, apparently we didn't do so much presenting and we actually did discussion, which is not it shouldn't be a novel idea, but you know, sometimes I guess people, you know, I can't help themselves.
00:39:24.079 --> 00:39:36.960
But yeah, we had really great discussion and some workshop type activities to try to figure out some of these things versus you know, versus a and I and I make the statement, Laura, that we're all in the movie 2026 together.
00:39:37.119 --> 00:39:39.440
We're all actors and actresses in this movie.
00:39:39.599 --> 00:39:48.559
And what it's gonna look like in 2030 when we go back to watch it is all of these conversations that we're having right now and coming up with these solutions together.
00:39:48.880 --> 00:39:51.280
And who's going to say, like, this is fun?
00:39:51.440 --> 00:39:53.599
I mean, if nothing was happening, let's be honest.
00:39:53.760 --> 00:39:56.159
What we wouldn't even be having this conversation, Jason.
00:39:56.239 --> 00:40:02.239
We would be, you know, okay, like it things have always been, you know, things are moving forward as they've always been.
00:40:02.320 --> 00:40:04.800
Like this is this is an exciting time.
00:40:04.880 --> 00:40:17.920
And I know that sounds trite, but I think I think we'd all agree, like the conversations are interesting and the fact that things are still unfolding and and really makes for, I would say, like interesting careers at the moment.
00:40:18.000 --> 00:40:20.480
So well, thank you for all the work you're doing.
00:40:20.719 --> 00:40:21.039
Thank you.
00:40:21.119 --> 00:40:21.599
I appreciate it.
00:40:21.840 --> 00:40:31.599
Thank you, SAP success factors for being a leader in the space, and thank you for being here because uh I hope at least one person, you know, found this kind helpful.
00:40:31.679 --> 00:40:39.440
And one person, and what I care most about is one parent goes back to their kid and says, Hey, Laura said adaptable.
00:40:39.599 --> 00:40:40.159
Let's work on it.
00:40:40.480 --> 00:40:43.840
Adaptability is your child's secret weapon, secret sauce.
00:40:44.000 --> 00:40:44.480
That's right.
00:40:44.719 --> 00:40:45.599
Yeah, love that.
00:40:45.840 --> 00:40:46.880
Thank you again for being here.
00:40:46.960 --> 00:40:47.920
Really, really appreciate it.
00:40:48.079 --> 00:40:48.719
Thanks, Jason.
00:40:48.800 --> 00:40:50.400
Always a pleasure, and let's catch up soon.
00:40:50.639 --> 00:40:51.039
Take care.
00:40:51.280 --> 00:40:51.519
Take care.
00:40:51.760 --> 00:40:51.920
Bye.