E 779 — Leapsome: Agentic AI Built on 10 Years of Data

WEBVTT
00:00:00.017 --> 00:00:03.987
Ten years, five of them without a single year of venture capital.
00:00:04.155 --> 00:00:10.227
Reportedly $10 million in annual recurring revenue before the first outside
00:00:10.227 --> 00:00:12.985
money, then $16 million in one round.
00:00:13.217 --> 00:00:19.126
A core product rebuilt from scratch in 2024 in the middle of the AI boom.
00:00:19.277 --> 00:00:24.740
And today, AI agents running HR for more than 2,000 organizations, while
00:00:25.065 --> 00:00:34.428
new public data says one HR person can support 299 employees at the frontier against a median of 53.
00:00:35.189 --> 00:00:42.207
This is a story of how agentic HR actually gets built and why the agents are
00:00:42.207 --> 00:00:44.123
the last step, not the first.
00:00:54.467 --> 00:00:59.849
Hello and welcome everybody. This is StartupRate.io episode 779.
00:01:00.621 --> 00:01:04.446
Startups, tech and venture capital from Germany, Austria and Switzerland.
00:01:04.585 --> 00:01:09.303
I'm Joe Manager joining you from Frankfurter Main. My guest today is Jenny von
00:01:09.303 --> 00:01:12.463
Porter-Wils, co-founder and co-CEO of Leapsom,
00:01:12.840 --> 00:01:17.193
one of the few founders in Europe who bootstrapped a software company for five
00:01:17.193 --> 00:01:22.366
years, raised exactly one round, 60 million US dollars led by inside partners
00:01:22.674 --> 00:01:29.274
and then rebuild the entire product from the ground up before putting AI agents on top.
00:01:29.721 --> 00:01:36.989
Since July, those agents run core HR programs for more than 2,000 organizations,
00:01:37.285 --> 00:01:41.000
including Notion, Spotify, and Mercedes-Benz.
00:01:41.476 --> 00:01:46.463
And this week, Leapsom published research on how the 100 fastest-growing tech
00:01:46.463 --> 00:01:48.680
companies actually run HR
00:01:49.162 --> 00:01:56.695
my thesis today agentic HR is one on data foundations not on agents let's test it
00:01:57.234 --> 00:02:05.698
Jenny welcome back to episode 779 exactly three years after we did together episode da da da da da 401
00:02:06.685 --> 00:02:08.949
Yes, it's great to be here. Thank you for having me.
00:02:09.245 --> 00:02:11.880
Do you feel like 400 episodes older?
00:02:13.089 --> 00:02:17.149
I'm clearly feeling like 400 episodes older. A lot has happened.
00:02:18.949 --> 00:02:25.429
Okay. We talked in 2023 because you were one of the winners of the German Startup Awards.
00:02:25.841 --> 00:02:30.590
And three years ago on this show, you said most performance management fails
00:02:30.915 --> 00:02:35.605
because it is backward looking. three years and one platform rebuilt later?
00:02:36.029 --> 00:02:40.191
Was Leapsome itself getting wrong in 2023?
00:02:40.511 --> 00:02:46.327
Yeah, great question. I mean, I think we're always smart in hindsight, aren't we?
00:02:46.490 --> 00:02:52.659
And I think at the time when we spoke, I would have to go back to when exactly
00:02:52.659 --> 00:02:55.989
we spoke, what we might have not made or were just in,
00:02:56.491 --> 00:03:02.309
the process of taking the decision to go from vertical, best-in-class talent,
00:03:02.889 --> 00:03:06.318
software to becoming a broad platform.
00:03:06.638 --> 00:03:14.213
So we took the bold strategic bets to consolidate the markets to some extent from the other side.
00:03:14.929 --> 00:03:19.349
A lot of platform, core HR players have moved out into talent,
00:03:19.349 --> 00:03:22.683
into recruiting, into adjacent categories.
00:03:23.499 --> 00:03:29.370
But no one had yet successfully shown that you can go from the depths and talent
00:03:29.643 --> 00:03:36.446
to actually becoming that strong, broad HR platform layer that all of HR ultimately runs on.
00:03:36.858 --> 00:03:43.099
And yeah, that's the decision we took. And maybe we should have taken it even a year earlier.
00:03:44.199 --> 00:03:48.179
But yeah, again, you're always smarter in hindsight. And I can share later why
00:03:48.179 --> 00:03:50.127
exactly we decided to take that move.
00:03:51.108 --> 00:03:53.367
Mm-hmm. I see.
00:03:56.212 --> 00:04:02.640
You bootstrapped for five years, reportedly to around 10 million ARR,
00:04:04.757 --> 00:04:08.048
while you as rivals raised hundreds of millions.
00:04:08.931 --> 00:04:15.110
Discipline or necessity, and what did it force into the product that funded
00:04:15.110 --> 00:04:16.634
competitors never built?
00:04:18.027 --> 00:04:23.310
I mean, I think two things. I would say, why did we do it was focus.
00:04:23.890 --> 00:04:30.903
We were never dogmatically bootstrapped but we rather always asked ourselves what ultimately,
00:04:31.373 --> 00:04:35.630
is the core priority right now and for us the core priorities were understanding
00:04:35.630 --> 00:04:40.350
customer needs and building product that solves those needs better than anybody
00:04:40.350 --> 00:04:45.235
else has solved them so that was kind of the rationale for bootstrapping it didn't distract us,
00:04:45.682 --> 00:04:49.420
um in in terms of having to to go fundraise.
00:04:49.420 --> 00:04:51.463
We're radically focused on customers and product.
00:04:51.933 --> 00:04:57.616
At the time when we were bootstrapped, we were growing as fast as VC-backed companies.
00:04:58.086 --> 00:05:02.631
And so focus was the main driver of bootstrapping.
00:05:03.363 --> 00:05:08.080
And why did we then take a round? We took a round because we wanted to expand
00:05:08.080 --> 00:05:13.800
into the US and that requires some upfront investment. We already got some pull demand out of the US.
00:05:14.320 --> 00:05:18.030
So to capture that more effectively, compete in the best software market in
00:05:18.030 --> 00:05:20.401
the world, which makes you a better software player.
00:05:20.964 --> 00:05:24.770
And at the time, we were actually planning to more upmarket,
00:05:24.770 --> 00:05:28.240
but also means like longer sales cycles. You just need to fund more of your
00:05:28.240 --> 00:05:29.700
go-to-market motion upfront.
00:05:30.542 --> 00:05:37.256
And what has that, what DNA do we maintain because of our bootstrapping times?
00:05:37.256 --> 00:05:43.346
And I would say it's that strong focus on the customer because we needed to
00:05:43.346 --> 00:05:47.177
build things that actually generated revenue in the early days.
00:05:47.722 --> 00:05:54.426
And that DNA of really closely listening to our customers, predicting,
00:05:54.426 --> 00:05:58.366
foreseeing what are the next needs that are starting to emerge that no one has
00:05:58.366 --> 00:05:59.733
actually solved really well.
00:06:00.203 --> 00:06:04.446
Right now, for example, AI transformation, like the role in HR department,
00:06:04.446 --> 00:06:08.046
people, leaders play in that transformation is something that isn't solved.
00:06:08.046 --> 00:06:09.520
It actually plays to our strengths.
00:06:09.903 --> 00:06:15.646
But that's really core focus on the customer needs and closing the gap for the
00:06:15.646 --> 00:06:18.042
customer. That's kind of part of our bootstrapping DNA.
00:06:21.287 --> 00:06:24.445
Being focused on customers is usually a pretty good thing.
00:06:24.729 --> 00:06:30.216
But I was wondering, when you stay out of the funding market for half a decade,
00:06:30.216 --> 00:06:35.066
what did that staying out of the market for so long cost you?
00:06:35.146 --> 00:06:37.826
I mean, it's a good question.
00:06:40.396 --> 00:06:44.081
So, I mean, I think the upside is focus.
00:06:44.371 --> 00:06:51.033
The downside is some competitors have more resources to build positioning,
00:06:51.033 --> 00:06:54.861
awareness, have GTM scale in the market.
00:06:56.643 --> 00:07:05.664
At the same time, I think resources are one ingredient in winning the go-to-market game.
00:07:06.140 --> 00:07:11.673
The other is exceptional product, clear differentiation, and to some extent,
00:07:11.673 --> 00:07:20.797
finding the right levers and hacks to grow and start that flywheel of customer referrals.
00:07:22.323 --> 00:07:30.443
And if you look at our category, HR software is also strongly bought by understanding
00:07:30.443 --> 00:07:33.655
what peers use, what peers are starting to use.
00:07:33.986 --> 00:07:38.933
So, that is something that is also important to us and that,
00:07:38.933 --> 00:07:43.285
again, is also grounded in do you understand your customers' needs and solve
00:07:43.627 --> 00:07:46.292
their problems better than others in the market.
00:07:46.628 --> 00:07:55.123
Jenny, walk me through the two or three decisions that turned a feedback tool into a platform.
00:07:55.803 --> 00:08:02.424
When exactly did you know the point solution was a dead end and what triggered it?
00:08:03.567 --> 00:08:10.875
Great question. So the first phase of LeapSim, we were a talent software, right?
00:08:10.875 --> 00:08:15.215
We brought together strengths and depths in performance management,
00:08:15.215 --> 00:08:19.043
feedback, OKRs, meeting management, things that drive alignment,
00:08:19.351 --> 00:08:22.645
as well as leveraging data to build better organizations, for example,
00:08:22.645 --> 00:08:24.651
through engagement surveys.
00:08:24.999 --> 00:08:29.945
So that's been the site where we started. Vertical, absolute best in class for that talent side.
00:08:30.648 --> 00:08:38.449
And sort of into 2023, we saw more and more consolidation pressures in the market.
00:08:38.745 --> 00:08:44.245
So a lot of pressure to reduce tooling, to consolidate tooling,
00:08:44.245 --> 00:08:49.755
to some extent to have like one hub or central platform that a department truly
00:08:49.755 --> 00:08:52.293
runs on. That was one side of the coin.
00:08:52.641 --> 00:08:58.645
The other side, on the opportunity side, is seeing where AI and generative AI
00:08:58.645 --> 00:09:00.757
in particular was heading.
00:09:01.715 --> 00:09:08.505
We also were very clear that owning the data layer, not just in a particular
00:09:08.505 --> 00:09:12.145
subpart of a function, but actually across a whole department,
00:09:12.145 --> 00:09:14.520
is going to be something that's incredibly valuable.
00:09:15.118 --> 00:09:21.138
Owning the workflows truly end-to-end in a particular department to then leverage.
00:09:22.115 --> 00:09:27.495
AI, I mean, now increasingly genetic AI across the entire department grounded
00:09:27.495 --> 00:09:32.305
in that data and that contextual understanding is something that's incredibly valuable.
00:09:32.305 --> 00:09:35.610
So there was both an opportunity, a technological opportunity,
00:09:35.947 --> 00:09:42.715
as well as a market involvement that we saw that ultimately decided that we.
00:09:43.815 --> 00:09:48.949
How we decided that we actually want to become that platform player and evolve the strategy.
00:09:49.565 --> 00:09:55.997
And at the same time, third reason, that no one actually has the strengths on the talent side.
00:09:56.577 --> 00:10:00.647
And while there were all these consolidation trends in the market,
00:10:00.966 --> 00:10:04.748
we had customers telling us, like, we need that strength on the talent side.
00:10:04.748 --> 00:10:07.598
We cannot actually rely on the
00:10:07.598 --> 00:10:11.068
much weaker, much more superficial talent capabilities of other vendors.
00:10:11.068 --> 00:10:15.408
Like, why don't you actually expand into the broad platform space?
00:10:15.408 --> 00:10:18.518
So that's the third one, like getting very direct customer feedback to some
00:10:18.518 --> 00:10:20.993
extent, like really directly asking us to do that.
00:10:21.568 --> 00:10:26.878
And I think that is even becoming more and more important at a time where being
00:10:26.878 --> 00:10:31.988
a strategic partner to your organization, to your leadership team as an HR function
00:10:31.988 --> 00:10:34.838
is paramount as we speak about the AI transition in the workplace.
00:10:36.185 --> 00:10:44.565
So with this reason, you for the first time took in $60 million from Insight Partners in March 2022?
00:10:45.302 --> 00:10:50.688
So at the time, the main driver behind the decision to fundraise was,
00:10:50.688 --> 00:10:55.468
I mean, one was the milestone we had kind of surpassed the 10 million ARR.
00:10:55.808 --> 00:10:58.778
And I think it's always good to ask yourself, what got you here?
00:10:58.778 --> 00:11:03.414
Will that also necessarily get you there? So what's kind of what gets you from 10 to 100 million?
00:11:04.023 --> 00:11:08.069
So that's kind of was one of the drivers where we decided to go fundraise.
00:11:08.690 --> 00:11:14.524
The second one was we wanted to or were getting ready to open an office in New York.
00:11:14.925 --> 00:11:18.348
And expand our footprint in the US markets.
00:11:19.268 --> 00:11:22.498
That is an endeavor where all other founders tell you it takes longer,
00:11:22.498 --> 00:11:26.523
it is harder, it's more cost intense than you can even foresee.
00:11:26.970 --> 00:11:32.799
So funding the US market entry was the second driver for the funding decision.
00:11:34.938 --> 00:11:42.524
I'm curious, just on a side quest, how big was the competition between the employees
00:11:43.404 --> 00:11:45.085
who can open the New York office?
00:11:46.624 --> 00:11:50.434
Yeah, good question. I mean, there was a lot of interest. We actually sent a
00:11:50.434 --> 00:11:53.904
launch team of, I think, seven people at the time to open the U.S.
00:11:53.904 --> 00:12:03.534
Office, like a mixture of sales, customer success, and had someone from our leadership team.
00:12:04.010 --> 00:12:09.264
And ops and yeah, it was obviously a fun, sexy opportunity for a lot of people,
00:12:11.124 --> 00:12:17.784
and we tried and have ever since had also a lot of travel back and forth between the offices.
00:12:17.784 --> 00:12:20.972
Everybody loves to spend some time on the other side of the Atlantic.
00:12:23.440 --> 00:12:32.960
In October 2024, he launched with, how do you pronounce it? H-R-I-S or HIRIS?
00:12:33.075 --> 00:12:34.097
Yeah, H-R-I-S.
00:12:34.642 --> 00:12:41.823
H-R-I-S, okay. Built from scratch into market, personally, Workday, and ADP already owned.
00:12:42.154 --> 00:12:48.603
Building boring HR infrastructure in the middle of an AIM boom looks a kind of a bit contrarian.
00:12:49.091 --> 00:12:55.006
Why was it the precondition for everything you now call agentic?
00:12:55.239 --> 00:13:00.591
Yes, great question. So we took the decision to move from,
00:13:01.148 --> 00:13:07.234
best-in-class tenant to becoming the single source of truth slayer that owns
00:13:07.234 --> 00:13:11.208
all of the data and all of the processes in an HR function.
00:13:12.015 --> 00:13:19.104
And there's two reasons why we believed and continue to believe in that strategy
00:13:19.104 --> 00:13:24.704
one is you actually have the data across the entire department so you have a
00:13:24.704 --> 00:13:26.353
lot of the contextual data,
00:13:27.464 --> 00:13:31.554
in HR actually because of our depth and talent much beyond around the strategy
00:13:31.554 --> 00:13:36.754
the alignment the manager effectiveness the performance of a team how an organization
00:13:36.754 --> 00:13:42.264
works but we could connect this data and can connect this data with a lot of the compliance data.
00:13:43.644 --> 00:13:46.164
The employee attributes, et cetera.
00:13:46.164 --> 00:13:52.068
So that gives us a really rich contextual data pool, a talent graph we could build.
00:13:52.625 --> 00:14:01.654
And we have the ownership of workflows across an entire department.
00:14:01.654 --> 00:14:04.844
If something happens here that can trigger a whole lot of additional things
00:14:04.844 --> 00:14:09.464
across the entire employee life cycle across the organization and having that
00:14:09.464 --> 00:14:12.432
connection in the data and in the workflows and now,
00:14:13.264 --> 00:14:18.519
in the genetic capabilities that enable that is something that generates real value to our customers.
00:14:18.604 --> 00:14:23.775
So ultimately power is how an organization or HR organization and a company,
00:14:24.474 --> 00:14:26.985
interacts with data and people processes.
00:14:27.677 --> 00:14:39.115
Hmm? You rebuilt. I was wondering what broke during the rebuild and what would you not do again?
00:14:41.536 --> 00:14:46.778
I mean, I think it's what's broke.
00:14:46.911 --> 00:14:51.379
I mean, I think there's a lot of things you know. You do really diligent customer
00:14:51.379 --> 00:14:54.475
research, user research, product research.
00:14:54.980 --> 00:14:59.059
And then there is maybe the things you don't know yet.
00:15:02.887 --> 00:15:05.739
And I think the part that,
00:15:08.059 --> 00:15:14.899
I, in hindsight, probably would have invested into earlier is changing the perception
00:15:15.239 --> 00:15:16.981
of the platform in the market.
00:15:17.335 --> 00:15:21.629
If you enter the market with a new product, you have the benefit of the curiosity
00:15:21.629 --> 00:15:24.179
of like, oh, who are they? What do they do?
00:15:24.632 --> 00:15:29.101
And if you are an established and actually even like beloved brand,
00:15:29.583 --> 00:15:33.509
the challenge is like, how do you get people to reevaluate and rethink what
00:15:33.509 --> 00:15:36.688
you actually do and change the perception of who you are?
00:15:37.077 --> 00:15:42.659
So that is actually something that is something that, again,
00:15:43.159 --> 00:15:46.929
requires some time, resources to re-educate the market.
00:15:46.929 --> 00:15:50.839
So I think that is something in hindsight I would have started potentially even
00:15:50.839 --> 00:15:52.182
earlier alongside the build.
00:15:53.379 --> 00:15:58.199
Yeah that's always a good idea because then uh people call you hey look i'm
00:15:58.199 --> 00:16:00.919
interested in your product uh we're doing something
00:16:01.319 --> 00:16:06.699
really different now oh i didn't know that's usually a sign your way past that
00:16:06.699 --> 00:16:11.855
but i do believe a lot of companies out there start to communicate
00:16:12.244 --> 00:16:17.218
something like a pivot like a change of strategy way too late to the outside
00:16:17.480 --> 00:16:21.299
they're waiting for the big moment And then from one moment to another, you just
00:16:21.984 --> 00:16:23.569
tap the switch and that's it.
00:16:23.569 --> 00:16:29.268
But it really doesn't work like that, especially in the minds of the clients, right?
00:16:29.999 --> 00:16:33.989
Especially if people have like, they already think they understand what you
00:16:33.989 --> 00:16:38.104
do, right? So it's almost like re-education is harder than initial education.
00:16:39.084 --> 00:16:43.432
Yeah, people don't like to unlearn, yeah.
00:16:45.016 --> 00:16:51.251
In a minute, the public numbers say one HR person can support 299 employees.
00:16:51.698 --> 00:16:58.124
And why Jenny's own research says most companies buying AI see nothing like that. Stay with us.
00:17:03.401 --> 00:17:09.722
Jenny, since July 5, you hosted AI agents run on the platform.
00:17:09.856 --> 00:17:17.125
Name one HR program an agent executes end-to-end today, like every step.
00:17:17.125 --> 00:17:19.597
And tell me where the human signs off.
00:17:20.874 --> 00:17:28.635
I mean, I think a favorite one of mine is we actually see when we speak to HR
00:17:28.635 --> 00:17:33.555
operators that there's still so many manual processes running in an HR department.
00:17:33.555 --> 00:17:39.215
Like people move things from A to B or they remind other humans to like complete
00:17:39.215 --> 00:17:43.378
a step, send something, et cetera. So there's a lot of manual work.
00:17:44.063 --> 00:17:51.534
And to some extent, a lot of that could have already been automated by workflows before.
00:17:51.889 --> 00:17:57.185
However, setting up a workflow takes a bit of time. Maybe some people are also
00:17:57.185 --> 00:17:59.225
like afraid, how do I actually do that?
00:17:59.665 --> 00:18:03.515
What are the right triggers? What are the right events? Can I get it wrong setting
00:18:03.515 --> 00:18:06.045
this up? And one of my favorite.
00:18:08.065 --> 00:18:12.862
Agentic capabilities is actually Leapsome is building the workflows for you.
00:18:13.431 --> 00:18:19.805
So if you have, I mean, either the platform can also help you to draft the process
00:18:19.805 --> 00:18:23.115
you actually want to run, or maybe if you already have a process you want to
00:18:23.125 --> 00:18:24.831
run, let's say an onboarding process.
00:18:25.144 --> 00:18:29.446
There's a whole bunch of things that need to happen, and tool lock-ins that need to be created.
00:18:31.332 --> 00:18:35.545
Equipment that needs to be set up, access right that needs to set up,
00:18:35.545 --> 00:18:39.585
but also maybe the more cultural things like onboarding, maybe a buddy in the
00:18:39.585 --> 00:18:43.813
team, a check-in conversation with a manager, meaningful peers.
00:18:44.565 --> 00:18:48.515
If you have that onboarding process set up, you basically drag and drop it into
00:18:48.515 --> 00:18:53.515
our agentic workflow builder, and the platform builds the process the way you
00:18:53.515 --> 00:18:57.617
have envisioned it and from a technical perspective in the tool. So it's,
00:18:58.447 --> 00:19:01.394
basically from your process, understand what needs to happen on which day,
00:19:02.474 --> 00:19:07.104
who are the respective countable person, what are the rules behind that,
00:19:07.104 --> 00:19:09.721
what are the steps that need to be set up,
00:19:10.834 --> 00:19:14.654
what are some of the steps, can they happen in parallel, do they need to happen,
00:19:15.214 --> 00:19:20.274
in a particular sequence, and all of that is set up immediately and.
00:19:21.634 --> 00:19:26.483
With a much lower technical barrier because everyone can drag and drop a document, right?
00:19:27.261 --> 00:19:31.614
I still believe there's value in the human kind of double-click,
00:19:31.614 --> 00:19:36.964
double-checking if the way it's being set up actually is exactly the way they
00:19:36.964 --> 00:19:39.155
wanted to set it up to quickly audit that,
00:19:39.814 --> 00:19:44.212
but then they can activate this workflow and it's running on autopilot as well.
00:19:44.722 --> 00:19:49.474
But I think this is a really nice example where natural language,
00:19:50.034 --> 00:19:55.688
everybody can define a process in natural language is then being set up to run,
00:19:56.394 --> 00:20:01.654
in an automatic way so much easier than we used to be able to do that in the
00:20:01.654 --> 00:20:03.467
past. That's, I think, one example.
00:20:04.361 --> 00:20:09.237
I could give you others, but I think that's one that's solving a real manual pain point in HR.
00:20:09.521 --> 00:20:15.464
Actually, I also love the same thing. For example, a lot of people already noticed
00:20:15.464 --> 00:20:21.885
that we have a semi-automated way with agents right now to post on social media for Startup Radio.
00:20:22.628 --> 00:20:27.064
They notice and they like it, but you know what my first experience was with
00:20:27.064 --> 00:20:30.006
an agent? I wanted them to post on social media.
00:20:30.273 --> 00:20:36.754
It turns out no post arrived at the end because they just wrote it in a ledger.
00:20:39.460 --> 00:20:45.325
So, when you have the bugs figured out and you have it made very simple,
00:20:46.180 --> 00:20:53.528
you have a framework and understanding what it does, it makes it easier to have agents in the running.
00:20:53.847 --> 00:20:58.224
But I do love the idea with other companies, with you, for example, also Moss,
00:20:59.220 --> 00:21:03.570
is there no complete automation, but the human in the loop, because there's
00:21:03.570 --> 00:21:09.360
some processes like your salary, where you don't want an AI agent to go crazy
00:21:09.360 --> 00:21:12.400
or your performance review? Or
00:21:15.700 --> 00:21:19.417
are there non-important processes in HR? I don't think so.
00:21:19.846 --> 00:21:27.360
No, I think it's more about getting the aspects that the agent can do well and
00:21:27.360 --> 00:21:30.860
the human kind of collaborating most effectively together.
00:21:31.260 --> 00:21:34.480
So the agent can quickly set something up.
00:21:35.160 --> 00:21:41.600
They can see maybe if we think about payroll preparation, maybe see an unexpected,
00:21:44.332 --> 00:21:49.130
pattern they would expect differently. But then the human looks at the data,
00:21:49.130 --> 00:21:53.300
is the workflow set up or looks at the interface of the workflow set up correctly?
00:21:53.672 --> 00:22:01.670
Or if there is an anomaly in a payroll, maybe there's a reason behind it that the human knows, right?
00:22:01.670 --> 00:22:06.112
So I can, it's a signal that helps me, like it's auditing, but then it's also
00:22:06.466 --> 00:22:12.480
giving me the signal where as a human, I might want to duck deeper and build
00:22:12.480 --> 00:22:16.340
additional context or start an additional sort of review process,
00:22:16.980 --> 00:22:20.072
that I think is the best of combining the best of both worlds.
00:22:21.239 --> 00:22:25.490
In preparation of this interview, I talked to a lot of lovely people on your
00:22:25.490 --> 00:22:29.685
team and they insist Leapsom is a platform, not a tool.
00:22:30.120 --> 00:22:34.380
Strip away the language. What is the operational difference for,
00:22:34.380 --> 00:22:36.000
let's say, a nine-person team?
00:22:37.446 --> 00:22:43.992
I mean, I think it's Leapsam as a platform is becoming the central layer that
00:22:43.992 --> 00:22:50.624
enables a small HR team to run like a much more mature, much bigger,
00:22:51.883 --> 00:22:53.323
people or HR team, right?
00:22:53.723 --> 00:23:00.887
A lot of the things that don't have to be done manually can be delegated to the platform.
00:23:01.212 --> 00:23:07.912
The platform also gives the team the insight and the data points where to double click on.
00:23:07.912 --> 00:23:12.472
Or let's make that really specific. We have resilient signals in the platform.
00:23:12.472 --> 00:23:18.942
They give you a signal, a data point where you maybe want to dive deeper to
00:23:18.942 --> 00:23:20.761
have a retention risk there.
00:23:21.006 --> 00:23:25.432
It's a conversation starter. it's orienting my attention as a human to double
00:23:25.432 --> 00:23:28.522
click on something and figure out like what's the backstory to his do i need
00:23:28.522 --> 00:23:30.346
to focus some of my attention there
00:23:30.677 --> 00:23:34.812
also like it's solving real pain points right um i have a board meeting coming
00:23:34.812 --> 00:23:38.442
up the next day all of a sudden i'm getting a data request that i would have
00:23:38.442 --> 00:23:40.712
been scrambling to to respond to
00:23:41.052 --> 00:23:43.221
in the past but now i can use
00:23:43.581 --> 00:23:46.432
the functionality the genetic functionality in the platform and vibe code a
00:23:46.432 --> 00:23:50.922
dashboard to actually pull together the right information in the moment to answer
00:23:50.922 --> 00:23:53.432
that question even like a nice polished,
00:23:53.892 --> 00:23:58.018
report and dashboard right that's solving a real pain and that enables the team to act.
00:23:59.732 --> 00:24:05.762
Much more strategically have much more impact in an organization and save themselves
00:24:05.762 --> 00:24:11.954
a lot more of the sort of like pain the scrambling the manual work and thereby operate on on this layer,
00:24:13.552 --> 00:24:18.159
across all of the data, across all of the processes, the workflows in a really effective way.
00:24:18.362 --> 00:24:22.676
You're actually, during your rebuild, you built an EU-hosted,
00:24:22.914 --> 00:24:29.253
an EU AI Act compliant to a platform from the start.
00:24:29.560 --> 00:24:37.130
A constraint that slowed you down or do you see it as an asset now selling in the US?
00:24:39.922 --> 00:24:47.308
I'd actually say it's it's i mean we are working with highly sensitive data
00:24:47.308 --> 00:24:52.328
compliance is essential as paramount it's the baseline we operate on and,
00:24:53.468 --> 00:25:00.488
the regulation we have in europe i think is is guiding us to provide and build
00:25:00.488 --> 00:25:04.488
at that level of security at that level of um,
00:25:05.788 --> 00:25:07.568
of care and,
00:25:09.191 --> 00:25:13.091
be intentional about also the agentic and AI products we build.
00:25:13.625 --> 00:25:17.746
And I think this is something that we also bring into the US market.
00:25:18.153 --> 00:25:23.453
And I think it also gives us a competitive edge, right? We can,
00:25:24.388 --> 00:25:31.690
and it's actually part of also how we educate the market that with maybe traditional software.
00:25:33.727 --> 00:25:39.578
Some of the judgments your vendors put into how they build authentic capabilities
00:25:39.578 --> 00:25:44.038
are actually really, really important, but you don't even necessarily see them on first side, right?
00:25:44.038 --> 00:25:51.358
So understanding kind of the product principles of your technology partners,
00:25:51.358 --> 00:25:55.879
understanding how they build technology is really, really essential,
00:25:56.468 --> 00:25:59.420
especially at this day and age.
00:25:59.705 --> 00:26:05.808
So, being a vendor with a strong European and a strong U.S. presence,
00:26:05.808 --> 00:26:09.740
I think, is something that can actually speak to the diligence and the care,
00:26:10.668 --> 00:26:13.961
that we put into our capabilities across the board.
00:26:16.503 --> 00:26:21.168
We're also talking about the report...
00:26:23.382 --> 00:26:32.151
You made. Can you define talent density in economics, not cultural terms for
00:26:32.151 --> 00:26:36.165
us, and what does it change on a company's P&L?
00:26:37.064 --> 00:26:44.379
So what does talent density mean? Talent density means the share of your workforce
00:26:44.802 --> 00:26:50.391
that is truly effective in the context of your organization, right?
00:26:50.391 --> 00:26:54.061
Different organization, run slightly differently, have different preferences,
00:26:54.061 --> 00:26:55.693
have different operating rhythms.
00:26:56.058 --> 00:27:02.966
So talent density is about finding to some extent the recipe of who is successful
00:27:03.326 --> 00:27:04.801
in your particular company.
00:27:04.801 --> 00:27:09.311
What do you understand about them? How do you make sure you hire more people
00:27:09.311 --> 00:27:13.421
that will be that level of effective in your organization? And how do you actually
00:27:13.421 --> 00:27:14.511
make it more successful?
00:27:16.161 --> 00:27:22.453
Tangible also to your existing workforce of what exactly your expectations are
00:27:22.453 --> 00:27:23.876
for them to be effective.
00:27:24.943 --> 00:27:28.663
So it's very much about managing or setting clear expectation,
00:27:29.063 --> 00:27:33.146
but then also managing against those in hiring and with the existing team.
00:27:33.820 --> 00:27:39.023
And why does it matter? Because in the end, that means you'll have a higher
00:27:39.023 --> 00:27:41.883
performing, more effective team.
00:27:42.183 --> 00:27:49.232
And in many organizations we work with talent and humans are still a lot of the deciding factor um.
00:27:50.207 --> 00:27:56.363
As to how successful companies actually are and um if we want to kind of like
00:27:56.363 --> 00:28:02.136
go into the reports um what we actually did is we analyzed um in our 2027,
00:28:02.983 --> 00:28:06.331
workforce trends report that you also find on Leapsom's website,
00:28:07.023 --> 00:28:16.203
we analyzed data from the 100 most successful, fastest growing companies across
00:28:16.543 --> 00:28:20.338
a lot of publicly available data points.
00:28:20.704 --> 00:28:26.853
We complemented them with some in-depth calls to kind of complement some of
00:28:26.853 --> 00:28:29.923
the sort of broader data points we analyzed.
00:28:29.923 --> 00:28:36.413
So it was a larger data analysis. Historically, we've actually often done these survey-based.
00:28:36.413 --> 00:28:41.233
This one we've done data-based, about 5,000 data points we analyzed,
00:28:41.233 --> 00:28:46.809
partially with AI with a lot of manual human facts validation.
00:28:47.680 --> 00:28:52.179
And I think what's interesting, if we want to go into those directly,
00:28:52.179 --> 00:28:58.564
and basically the question we ask ourselves is, how does HR in these best companies actually work?
00:28:59.087 --> 00:29:04.379
And I think the three things that really stood out was the HR teams weren't
00:29:04.379 --> 00:29:09.639
actually necessarily getting smaller, which is interesting because we often
00:29:09.639 --> 00:29:13.598
ask ourselves the question, does AI actually kill jobs?
00:29:14.010 --> 00:29:19.509
So in these companies, we actually, with two outliers, haven't really seen that.
00:29:19.509 --> 00:29:23.913
The HR teams were roughly the same size as they are in kind of the industry average.
00:29:24.558 --> 00:29:31.689
However, what work is done in these organizations has shifted and roles have shifted.
00:29:31.689 --> 00:29:37.433
And they shifted away from the more sort of like mundane administrative tasks
00:29:37.695 --> 00:29:42.965
to the more talent-oriented strategic tasks of building the right organizations.
00:29:43.383 --> 00:29:47.737
Retaining, well, like hiring the right talent, retaining the right talent,
00:29:48.097 --> 00:29:51.029
making managers effective, building the right org structures.
00:29:51.359 --> 00:29:57.379
So it's the automation of the core layer has enabled the more strategic work
00:29:57.379 --> 00:30:04.454
that ultimately, again, creates to some extent that flywheel of more talent, more success.
00:30:04.907 --> 00:30:09.847
And what's also been interesting is that in these organizations,
00:30:10.259 --> 00:30:17.498
HR tends to be – or the HR leadership tends to be set higher up in the organizations.
00:30:18.349 --> 00:30:19.229
Which is also interesting.
00:30:19.229 --> 00:30:21.794
So HR actually has a more strategic seat at the table.
00:30:22.258 --> 00:30:27.105
And those were the most fundamental changes that we've seen in that data set.
00:30:27.425 --> 00:30:32.219
I've also looked into the report. By the way, I do believe we will link the
00:30:32.219 --> 00:30:36.989
website you'll share with me after the interview. And we'll link it where everybody
00:30:36.989 --> 00:30:38.791
can find it, at least on our blog.
00:30:39.133 --> 00:30:42.407
And go straight from there to you and download it there.
00:30:43.441 --> 00:30:47.121
Sorry for the detour. Your report puts the median...
00:30:47.783 --> 00:30:59.136
At 53 employees per HR professional and 11 labs at 299 with people team of just four.
00:31:00.709 --> 00:31:08.807
Can a company outside of an AI lab reproduce this one to 299 for a normal company,
00:31:09.080 --> 00:31:15.239
or is it just an AI lab anomaly that just happened?
00:31:16.696 --> 00:31:18.838
I mean, it's an interesting question. The two outliers were,
00:31:18.838 --> 00:31:21.348
as you mentioned, 11 labs and the other one was OpenAI.
00:31:22.528 --> 00:31:31.220
That was where like the massive outliers was like 250 to 200 or 300 employees per HR headcount.
00:31:32.213 --> 00:31:38.458
I mean, I think at this stage, I would say these are rather outliers and the
00:31:38.458 --> 00:31:44.648
98 other companies in the data set, again, behaved a lot more like the standard.
00:31:47.288 --> 00:31:50.658
Company in terms of numbers, not in terms of what people do.
00:31:50.658 --> 00:31:54.487
Again, like very different, important differentiation.
00:31:55.050 --> 00:31:59.348
I mean, I think neither of us has a crystal ball, right?
00:32:00.388 --> 00:32:04.370
But I think what we're actually rather seeing from the conversations we're having
00:32:04.719 --> 00:32:11.615
with people leaders in our community, in the markets, in our ecosystem is.
00:32:13.077 --> 00:32:18.884
HR's role has actually become more important in becoming a strategic partner
00:32:18.884 --> 00:32:23.613
to the C-level, the CEO in navigating companies through the AI transformation.
00:32:23.810 --> 00:32:28.304
It's partially a technological transformation, but it's also very much a people transformation.
00:32:28.773 --> 00:32:35.134
So what we're seeing is HR is not just tasked to rebuild their own department
00:32:35.134 --> 00:32:41.484
and make HR truly like agentic and set up for the,
00:32:42.564 --> 00:32:46.574
AI era in the narrow sense, like that's the own HR department strategy, right?
00:32:46.574 --> 00:32:49.404
That's what we empower with SleepSense agentic HR platform.
00:32:49.758 --> 00:32:53.814
But beyond that, it's also a key partner to the rest of the business to navigate
00:32:53.814 --> 00:32:55.954
the rest of the organizations through the change, right?
00:32:55.954 --> 00:33:00.804
We've seen massive change in engineering organizations. Like one of the fastest
00:33:00.804 --> 00:33:07.718
evolutions of a job that we've seen in this decade, like massive change of how people work.
00:33:08.333 --> 00:33:14.864
And HR is still in many organizations the most experienced change management
00:33:15.304 --> 00:33:16.600
professional that can be.
00:33:18.624 --> 00:33:21.914
The partner to a CTO to manage their organization through that change.
00:33:21.914 --> 00:33:27.004
How do we rethink hiring? How do we rethink management? How do we rethink compensation,
00:33:28.124 --> 00:33:33.014
at the time where the job of an engineer has changed so much and maybe converges
00:33:33.014 --> 00:33:35.414
with product roles and design roles, right?
00:33:35.414 --> 00:33:39.074
So I think that's kind of the whole change management track record or change
00:33:39.074 --> 00:33:40.458
management work stream.
00:33:40.963 --> 00:33:44.334
Another one we've seen is like, how do we need to rethink org designs?
00:33:44.334 --> 00:33:46.924
Are teams becoming flatter?
00:33:47.304 --> 00:33:51.324
Are we increasing the span of control for managers, right? This is again one
00:33:51.324 --> 00:33:57.454
where the strategic partner to the leadership team is often the HR persona.
00:33:57.454 --> 00:34:00.449
They're the ones that also then implement these changes.
00:34:00.786 --> 00:34:05.232
So all of this really essential strategic talent work and org work,
00:34:05.907 --> 00:34:11.240
And it's also something that has become even more valuable, even more urgent,
00:34:11.240 --> 00:34:15.900
even more in demand, and again, also needs to have the resources and also the
00:34:15.900 --> 00:34:18.068
tooling to get this job done right.
00:34:18.411 --> 00:34:26.490
So this is why I would see these, I would probably right now predict that the
00:34:26.490 --> 00:34:29.910
11 labs and open AI ratios are more of the outliers.
00:34:29.910 --> 00:34:34.920
And because all of these challenges are real and a lot of work,
00:34:34.920 --> 00:34:41.752
I would expect the HR team sizes to be staying relatively similar,
00:34:42.770 --> 00:34:47.645
but with a radical change in where the priorities lie and what expertise is actually in demand.
00:34:48.428 --> 00:34:54.520
Jenny, your team was careful to tell us that 40% admin time and 20% performance
00:34:54.520 --> 00:35:00.080
numbers are customer outcomes since adopting Leapsome,
00:35:00.080 --> 00:35:01.930
Not an AI before or after.
00:35:01.930 --> 00:35:09.221
Honestly, respect for that. So what is the AI before or after inside Leapsome?
00:35:10.213 --> 00:35:13.310
Inside Leapsome as an organization, right? Not for our customers,
00:35:13.310 --> 00:35:16.970
but for us as a company ourselves. I think it's...
00:35:18.509 --> 00:35:26.128
We actually hired a new VP, People and Strategy, earlier this year.
00:35:26.128 --> 00:35:33.198
I think she started in March, who is also operating from a very AI-first mindset.
00:35:34.178 --> 00:35:37.918
And it's really exceptional. She's actually going to run a masterclass,
00:35:39.918 --> 00:35:45.618
for a cohort of customers in Q1. So I think kind of the six months to Gentic,
00:35:46.258 --> 00:35:53.581
HR. So, we have a people leader in place who's really exceptional on rethinking HR.
00:35:54.400 --> 00:36:02.108
And we have been very, I mean, to some extent, we're always our customer number
00:36:02.108 --> 00:36:03.879
one on everything we build anyways.
00:36:04.291 --> 00:36:11.658
So, what has changed is we have introduced a whole lot of.
00:36:13.816 --> 00:36:17.636
Or we've reinvented a whole lot of processes that used to be slow,
00:36:19.676 --> 00:36:27.940
manual, painful. And I think we haven't done a great job to sum up of what all of these compound to.
00:36:28.398 --> 00:36:32.619
But I think if I give you specific examples, for example, our calibration process
00:36:32.979 --> 00:36:39.364
used to be something that was probably a net one week lift for the people team.
00:36:39.851 --> 00:36:47.056
That is probably now in the late last form with AI-generated pre-reads probably
00:36:47.056 --> 00:36:50.976
condensed down to less than a day of work.
00:36:50.976 --> 00:36:56.249
So cutting that from one week to one day is like a very.
00:36:58.476 --> 00:37:05.066
Very significant lift in how that process is actually now much easier.
00:37:05.066 --> 00:37:08.496
And I think calibration is one of the really, really valuable parts,
00:37:10.516 --> 00:37:15.036
of a good performance process and not every organization is able to do it and
00:37:15.036 --> 00:37:19.876
run it because it used to be quite resource intensive, right?
00:37:19.876 --> 00:37:26.906
And I think this is to the point I made earlier, a way how a small people team
00:37:26.906 --> 00:37:33.026
can now actually act like only the best, most mature people team could in the past, right?
00:37:33.026 --> 00:37:37.748
Because I now have technology as partner that can help me get these lifts done well.
00:37:38.956 --> 00:37:43.596
And I think this is something we're feeling internally.
00:37:43.596 --> 00:37:47.656
It leads them across calibration, across,
00:37:48.476 --> 00:37:53.456
coaching managers to make merit and performance cases, to prepare them right,
00:37:53.456 --> 00:37:58.146
to challenge them right, where they aligned with the principle of what grants
00:37:58.146 --> 00:38:00.456
a promotion, what grants...
00:38:02.331 --> 00:38:05.896
A merit. It's making sure we really,
00:38:07.971 --> 00:38:14.591
automate as many of the internal processes, make it even easier to be self-service,
00:38:15.991 --> 00:38:20.121
as the user, as the manager, to have a sparing partner to prepare me for a hard
00:38:20.121 --> 00:38:23.879
conversation or to make a conversation more effective.
00:38:26.201 --> 00:38:30.793
I think for our managers, preparing reviews,
00:38:31.281 --> 00:38:36.521
has probably like I always used to take me about 45 minutes to prepare a really
00:38:36.521 --> 00:38:41.511
strong feedback conversation it's probably now down to like 15 minutes,
00:38:41.971 --> 00:38:47.631
using sort of leveraging pulling context through AI more effectively challenging
00:38:47.631 --> 00:38:53.371
myself on where I have provided enough example I haven't so again I think that's a 3x lift,
00:38:54.291 --> 00:38:58.101
on my time so there's all these very specific ways.
00:38:58.101 --> 00:39:05.621
It hasn't just made the lives of our HR team simpler, but actually also significantly
00:39:05.621 --> 00:39:12.570
for our managers while increasing the transparency and the fairness for employees.
00:39:13.046 --> 00:39:16.350
And also the ability to own your own development to some extent.
00:39:16.733 --> 00:39:26.909
I also found that 81% of companies with available data, put AI into people functions.
00:39:27.286 --> 00:39:34.618
Yet your research says most fail to get the expected productivity gains.
00:39:35.152 --> 00:39:41.671
Where does the gain actually die? Is it in hiring, in management spans, skills, or trust?
00:39:43.748 --> 00:39:54.876
I would say it's because we use AI to compartmentalize, like in many organizations,
00:39:54.876 --> 00:39:58.236
it's still like a chat here, a quick,
00:40:00.996 --> 00:40:07.366
LLM conversation there. But I think the biggest lift comes from making sure
00:40:07.366 --> 00:40:13.436
you really have a strong connection of all of the relevant contextual layer.
00:40:13.836 --> 00:40:17.476
So, for example, all the context in Leapsam, Leapsam integrates through an MCP
00:40:17.476 --> 00:40:19.076
with Slack, with Notion, right?
00:40:19.076 --> 00:40:25.676
Like connecting all of the data that is needed to give a most relevant,
00:40:28.036 --> 00:40:32.365
or use AI in the most relevant ways. And then really rethinking,
00:40:35.087 --> 00:40:37.686
processes and being more ambitious
00:40:37.686 --> 00:40:42.566
around what the AI or the identity capabilities should be doing for you.
00:40:42.566 --> 00:40:49.842
So moving maybe more away from like piecemeal to having a strong technological setup with,
00:40:50.980 --> 00:40:58.956
access to a broad range of processes and deep contextual data that is then the unlock for higher,
00:41:01.673 --> 00:41:05.278
productivity, but actually also like ultimate impact.
00:41:12.400 --> 00:41:20.271
Only 30% of AI Labs' own people team job postings mention AI skills.
00:41:20.271 --> 00:41:21.508
I found this pretty funny.
00:41:21.816 --> 00:41:25.404
The loss of the 11 segments you measured.
00:41:25.729 --> 00:41:29.212
The companies building AI do not hire for it in HR.
00:41:29.897 --> 00:41:31.574
What does that tell you?
00:41:32.062 --> 00:41:35.945
I think that was an interesting one. And we deep dived into it.
00:41:36.567 --> 00:41:40.461
And allow me a quick diversion.
00:41:41.603 --> 00:41:45.621
Do you remember the ads I don't know what was that in the 90s of,
00:41:47.121 --> 00:41:50.341
am I in bin ich drin for internet,
00:41:51.321 --> 00:41:57.201
providers and I think at the time like it was such a like novel thing to like
00:41:57.201 --> 00:41:59.771
go onto the internet so we celebrated
00:41:59.771 --> 00:42:03.401
right and we would say I go onto the internet now I shop on Amazon
00:42:03.801 --> 00:42:08.981
I listen to music on Spotify I watch I don't know shows on Netflix right So
00:42:08.981 --> 00:42:11.284
we don't actually talk about the internet anymore.
00:42:11.655 --> 00:42:13.432
It's such a like table stakes.
00:42:13.908 --> 00:42:19.351
And to some extent, what we're seeing there is the most AI native and AI forward
00:42:19.351 --> 00:42:24.761
companies don't even necessarily speak about AI in the broad terms anymore because
00:42:24.761 --> 00:42:26.510
it's so table stakes already.
00:42:26.917 --> 00:42:30.847
What they specifically mention and look for is the right kind of judgment.
00:42:31.212 --> 00:42:37.610
It's asking good questions. It's almost like the next layer of showing the clear,
00:42:38.661 --> 00:42:42.873
capabilities and skills that make working with AI most effective.
00:42:44.941 --> 00:42:48.156
Let's teach you a little bit about the future of work.
00:42:48.597 --> 00:42:54.333
What has the AI transition changed about how you personally lead?
00:42:54.841 --> 00:42:59.244
We already talked about the feedback conversations, where your time goes.
00:42:59.731 --> 00:43:03.917
Which decisions do you no longer make?
00:43:05.025 --> 00:43:06.561
I mean, especially with...
00:43:08.469 --> 00:43:15.957
Capabilities like co-work. There are some tasks where I previously asked somebody
00:43:15.957 --> 00:43:23.202
else to do them for me that I can actually much easier set up for myself to run quickly.
00:43:23.771 --> 00:43:29.217
And that almost means like no passing, unnecessarily having to pass on context.
00:43:31.417 --> 00:43:35.160
Or waiting for someone else finishing that. So I think it allows me to actually,
00:43:35.735 --> 00:43:39.954
get certain things done more simultaneously and faster.
00:43:41.237 --> 00:43:46.548
I think that's maybe one very specific change.
00:43:47.692 --> 00:43:54.327
I think it is challenging me to find time to experiment, explore,
00:43:54.327 --> 00:43:57.627
learn, just because the space is actually moving so quickly.
00:43:57.627 --> 00:44:01.947
And I think that's the challenge for any leader. There's not a lot of spare
00:44:01.947 --> 00:44:05.787
time on the calendar and at the same time, it is important to really stay on
00:44:05.787 --> 00:44:07.626
the forefront of what is happening.
00:44:08.143 --> 00:44:10.737
And in terms of.
00:44:12.409 --> 00:44:18.951
How it changes how I lead or how we function on the leadership team.
00:44:18.951 --> 00:44:25.668
I think to some extent it has meant massive acceleration of velocity across the org.
00:44:26.132 --> 00:44:27.967
We ship product faster.
00:44:28.931 --> 00:44:32.268
We can run more experiments in parallel.
00:44:32.797 --> 00:44:38.722
And to some extent we also compete with other players that are similarly moving faster.
00:44:39.111 --> 00:44:42.971
So I think it also generates a new operating speed.
00:44:43.411 --> 00:44:46.941
We always moved fast, but I think right now it's getting even faster,
00:44:46.941 --> 00:44:49.374
which means you need to, as a company, need to,
00:44:50.471 --> 00:44:57.061
continue to evolve your own operating rhythm to make sure like you both move
00:44:57.061 --> 00:45:00.751
really fast, but we never want to compromise in quality either.
00:45:00.751 --> 00:45:07.765
So finding that sweet spot of the right operating rhythm for speed with quality
00:45:08.200 --> 00:45:13.161
and also probably being honest that that level of speed might not actually resonate
00:45:13.161 --> 00:45:15.691
with everyone. And that's maybe also OK.
00:45:17.011 --> 00:45:23.293
But I think it is also part of the new reality of running a technology company in this day and age.
00:45:25.220 --> 00:45:30.811
This day and age. Curious. What happens to junior roles? Your own job postings
00:45:30.811 --> 00:45:33.665
now require automation fluency.
00:45:34.008 --> 00:45:37.461
Where does the first run of the career letter go?
00:45:38.273 --> 00:45:46.657
I mean, I think folks who are entering the job market now have a disadvantage and an advantage.
00:45:47.167 --> 00:45:56.954
I think the advantage is there's no preconceived learned ways of the old working.
00:45:57.116 --> 00:46:00.481
Which to some extent can be an advantage, right? I try new things,
00:46:00.481 --> 00:46:06.671
I start directly with grounded in the technology that's available at my fingertips right now.
00:46:07.890 --> 00:46:11.177
And at the same time, the disadvantage is,
00:46:12.432 --> 00:46:17.545
that I think sometimes you have to do things a certain, a few times yourself,
00:46:18.392 --> 00:46:24.142
to get the pattern recognition, the muscle memory that enables you to be most successful.
00:46:24.142 --> 00:46:28.682
For example, junior engineers, like it's harder to probably review codes when
00:46:28.682 --> 00:46:33.342
you don't have the same kind of years of experience writing code yourself.
00:46:33.342 --> 00:46:36.485
So how do you, how do you QA some white coded codes?
00:46:37.472 --> 00:46:42.243
And I think in the end, it's a matter of pairing these two strengths and teams,
00:46:42.620 --> 00:46:45.552
the folks that come with a wide blank canvas.
00:46:46.823 --> 00:46:52.722
And the folks who have the mentorship, the coaching abilities to still continue
00:46:52.722 --> 00:46:56.712
to actually advance folks in their career.
00:46:56.712 --> 00:47:02.762
So I think it's not a nice role. It's like really combining the strengths of both.
00:47:02.762 --> 00:47:09.342
But if I look at us internally at Leapsome, for example, we used to have support
00:47:09.342 --> 00:47:11.309
roles, right? Now, a lot of the
00:47:11.605 --> 00:47:17.474
sort of first level support tickets can actually be responded to by AI.
00:47:17.874 --> 00:47:23.752
So this means the people on the support team actually get to work on maybe the
00:47:23.752 --> 00:47:28.542
more interesting, but also more hairy tickets earlier, which requires there's
00:47:28.542 --> 00:47:30.352
maybe slightly longer onboarding.
00:47:30.760 --> 00:47:33.882
However, there's also technologies to support them, but it also actually makes
00:47:33.882 --> 00:47:37.813
the job more interesting, a higher learning curve.
00:47:38.057 --> 00:47:41.452
It's almost becoming a product specialist than a support person.
00:47:41.452 --> 00:47:46.346
So I think there's, again, there's new challenges, but also quite a lot of new opportunity.
00:47:46.764 --> 00:47:52.182
Your report says AI made the old split, HR managing people, while real decision
00:47:52.182 --> 00:47:55.303
happened elsewhere, impossible to sustain.
00:47:55.698 --> 00:48:02.832
Which companies will the industry study five years from now and what did they do differently?
00:48:04.266 --> 00:48:11.513
I mean, I think what we're seeing in some of the best leading-edge companies is that,
00:48:13.897 --> 00:48:18.053
next to their kind of traditional HR.
00:48:21.368 --> 00:48:26.713
To-dos and roles and responsibilities, there's a lot more
00:48:27.153 --> 00:48:31.453
focus on the strategic side of talent work the right org design the right manager
00:48:31.453 --> 00:48:33.333
effectiveness the right kind
00:48:33.333 --> 00:48:37.993
of understanding of who succeeds should be retained should be hired and,
00:48:38.853 --> 00:48:40.331
then we've actually seen a,
00:48:41.893 --> 00:48:48.723
whole stack of senior HR leaders now becoming also the AI transformation leaders
00:48:48.723 --> 00:48:55.570
again like we're in a massive transformation and shift in work and how we work.
00:48:55.889 --> 00:49:05.853
And part of that is underlying technology, but part of that is a massive people innovation effort.
00:49:05.853 --> 00:49:12.038
Like, do we understand, like, how do we manage the change? How do we build new capabilities?
00:49:12.346 --> 00:49:18.703
How do we redesign design org models, leadership models, compensation models.
00:49:18.703 --> 00:49:22.973
So we see the best companies being in the forefront of that.
00:49:22.973 --> 00:49:27.173
And I think that is the direction and the blueprint that other companies will
00:49:27.173 --> 00:49:31.913
follow, where again, some of the core and the mundane is going to be increasingly
00:49:31.913 --> 00:49:36.546
automated with judgment through humans at the right points.
00:49:37.433 --> 00:49:41.113
But there's going to be more focus and roles on the more strategic.
00:49:42.873 --> 00:49:48.185
Work and really supporting organizations finding the right operating rhythm,
00:49:49.093 --> 00:49:52.736
in the era of AI or agents and humans working together.
00:49:54.634 --> 00:49:59.141
So, I would take away from that statement that
00:50:00.404 --> 00:50:05.651
the demand for people who also read and correct the footnotes is getting lower
00:50:05.651 --> 00:50:10.141
and the demand for people who could think ahead, even in terms of what other
00:50:10.141 --> 00:50:14.470
people do in terms of the strategy, the demand will increase there.
00:50:15.265 --> 00:50:21.011
I mean, I think it's about asking the right questions, like interpreting signals,
00:50:21.011 --> 00:50:24.981
where to go deeper, where to build better human contacts, taking,
00:50:24.981 --> 00:50:27.175
making those decisions and,
00:50:28.081 --> 00:50:33.691
making sure or making a call in a decision, not just what is good everywhere,
00:50:33.691 --> 00:50:37.541
but what is the right and best for your organization, for your people.
00:50:37.961 --> 00:50:44.335
So it's these human traits, the curiosity,
00:50:46.441 --> 00:50:51.841
the human relationships, the judgments that are really, really paramount,
00:50:51.841 --> 00:50:53.373
and those won't be replaced.
00:50:56.403 --> 00:51:02.231
Jenny, you're already holding up very well here for 55 minutes of recording
00:51:02.231 --> 00:51:03.909
time. Awesome. Thank you very much.
00:51:04.234 --> 00:51:06.347
I have three more questions.
00:51:07.960 --> 00:51:11.705
One on the capital and team, you raised once in 10 years.
00:51:12.099 --> 00:51:17.945
Are you open to talk to new investors or was Series A the last round you will ever need?
00:51:18.873 --> 00:51:22.335
I mean, we're not in an active funding process.
00:51:23.135 --> 00:51:28.115
We are cash efficient. We have a lot of capital in the bank.
00:51:28.955 --> 00:51:33.795
And at the same time, I think it's always good to speak to the right people, get to know one another.
00:51:34.675 --> 00:51:37.275
And at the same time, we're also someone who's very laser focused.
00:51:37.275 --> 00:51:39.758
So I think that's a good German Jain.
00:51:40.795 --> 00:51:46.145
Jain, exactly. By the way, every investor interested go on our blog there is
00:51:46.145 --> 00:51:48.146
your linkedin profile linked and
00:51:49.058 --> 00:51:56.430
who are you hiring right now and what does ai fluency required actually screen for
00:51:56.575 --> 00:52:01.795
Yeah great question um so we're actually hiring across um i think almost all
00:52:01.795 --> 00:52:09.545
teams um so engineering products um marketing sales cs people I feel like I
00:52:09.545 --> 00:52:10.955
forgot something, BizOps.
00:52:11.535 --> 00:52:18.605
So I think there are some open roles across the board. And we are looking for,
00:52:18.605 --> 00:52:22.195
I mean, we're first of all also looking for people who are aligned with our
00:52:22.195 --> 00:52:24.345
operating systems, our culture.
00:52:24.345 --> 00:52:28.455
We care about impact. We care about speed. We care about ownership without ego,
00:52:28.815 --> 00:52:30.202
which is important to learning.
00:52:31.804 --> 00:52:41.215
And we look for folks who are curious and capable in interacting with AI.
00:52:41.215 --> 00:52:45.812
So being curious, asking the right questions, also being,
00:52:48.256 --> 00:52:55.545
careful to evaluate where's the output right, where is it wrong.
00:52:55.545 --> 00:53:01.355
So like that level of caution is also very important. And we actually tested
00:53:01.355 --> 00:53:04.260
through case studies that are now AI enabled.
00:53:05.875 --> 00:53:10.595
So finding mistakes is also important. And then it's important for us that as
00:53:10.595 --> 00:53:15.342
people who are adaptable, who are willing to learn because the technology is evolving so quickly.
00:53:16.575 --> 00:53:21.905
And that level of curiosity, adaptability and a learning mindset is really,
00:53:21.905 --> 00:53:23.253
really quite essential for us.
00:53:24.286 --> 00:53:27.804
Hmm, that sounds pretty interesting.
00:53:28.222 --> 00:53:34.648
Let's close with your forecast. One falsifiable prediction on the record.
00:53:35.165 --> 00:53:43.715
Where is the medium HR professional to employee ratio in your 2028 report?
00:53:43.988 --> 00:53:48.487
And what happens to HR software spending per employee by then?
00:53:53.334 --> 00:53:58.309
Predictions are always difficult, especially concerning the future. I know, I know.
00:53:58.669 --> 00:54:03.969
So, where's the ratio? I actually think in next year's report,
00:54:04.427 --> 00:54:10.395
the employee to HR persona ratio is going to be stable.
00:54:12.207 --> 00:54:20.455
Because on the one hand manual work is going to be reduced and at the same time the importance of,
00:54:21.500 --> 00:54:27.174
talent work and ai transformation work is essential right now for the companies
00:54:27.174 --> 00:54:33.667
for companies to succeed so that's where the the priorities are shifting to so um,
00:54:35.065 --> 00:54:40.366
same size of team, very different priorities and focus and impact,
00:54:41.805 --> 00:54:42.974
and spend,
00:54:44.656 --> 00:54:45.814
on HR tooling.
00:54:46.674 --> 00:54:50.374
I think that's an interesting one because on the one hand,
00:54:52.921 --> 00:54:56.909
I think that I would expect tooling.
00:54:59.720 --> 00:55:03.052
Investments into tooling to increase.
00:55:03.853 --> 00:55:07.854
And at the same time, through especially agentic capabilities,
00:55:08.214 --> 00:55:15.014
that might also at times mean there's less need for external,
00:55:16.594 --> 00:55:21.864
resources, certain consultants, certain recruiters, because more of the work
00:55:21.864 --> 00:55:26.037
can also be done by the internal team with the help of technology,
00:55:26.391 --> 00:55:30.104
which again means there's less loss of context.
00:55:30.104 --> 00:55:34.874
It's faster it's a bit related to the example i also use i can now get more
00:55:34.874 --> 00:55:38.553
done myself with technology so those would probably be my two predictions.
00:55:40.054 --> 00:55:46.344
So that should scare a lot of free hr professionals out there working as recruiters
00:55:46.344 --> 00:55:52.078
Headhunters and so on i mean i think at the same time there is also a lot of,
00:55:52.914 --> 00:55:57.254
work to be done to help organizations make that transition so i think again,
00:55:57.254 --> 00:55:59.294
the expertise isn't lost.
00:56:00.294 --> 00:56:03.805
I think, again, it's going to shift, I think, where it's being deployed.
00:56:06.491 --> 00:56:12.018
Jenny, thank you. The one insight I want everybody to keep, the agents are downstream.
00:56:12.407 --> 00:56:18.355
LeapSum spent eight years building the data foundation before the first agent shipped.
00:56:18.739 --> 00:56:25.667
And the density gap 53 versus 299 employees per HR professional is the number
00:56:25.667 --> 00:56:29.971
that tells you whether AI is actually changing how a company runs.
00:56:30.343 --> 00:56:36.235
You'll find LeapSum's 2027 Workforce Trends report and every source in the blog post.
00:56:36.566 --> 00:56:39.977
Link here down in the show notes. If this episode was useful,
00:56:39.977 --> 00:56:45.238
send it to one founder who still thinks AI transformation starts with buying AI tools.
00:56:45.442 --> 00:56:48.716
Rate us, reviewers, share us. It's genuinely helpful.
00:56:49.046 --> 00:56:51.694
This interview is in partnership with Leapsome.
00:56:52.048 --> 00:56:59.339
This podcast goes live on Tuesday, September 29, 2026, the audio podcast earlier that same day.
00:56:59.710 --> 00:57:03.571
I'm Joe Manager. This was . Thank you for listening.
00:57:04.552 --> 00:57:05.197
Thank you so much.