Leapsome: From Bootstrapped HR Tool to Agentic HR Platform
What Is This About?
Every HR software company in 2026 claims AI agents. Almost none can show the decade of work that makes agents useful. In this episode, Jenny von Podewils — co-founder and co-CEO of Leapsome — walks through the ten-year path from a bootstrapped Berlin feedback tool to an agentic HR platform serving more than 2,000 organizations, and through the new public data on how the 100 fastest-growing tech companies actually run HR. This episode is sponsored by Leapsome; the questions, the pushback, and the analysis are ours.
The video goes live on Tuesday, September 29th, 2026
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The audio podcast goes live earlier the same day.
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Executive Summary
Leapsome was founded in Berlin in 2016 by Jenny von Podewils and Kajetan von Armansperg and ran for five years without outside capital — reportedly reaching about $10M in annual recurring revenue — before raising a $60M Series A led by Insight Partners, with Creandum and Visionaries Club, in March 2022. In October 2024 the company rebuilt its core as an HRIS; in July 2026 it launched a unified platform with five EU-hosted AI agents and a recruiting ATS in early access. Its 2027 Workforce Trends Report, published September 16, 2026, analyzes 100 high-growth tech companies from public data and finds a median of one HR professional per 53 employees — against 1:299 at ElevenLabs and 1:243 at OpenAI. The spread between those numbers, the Density Gap, is the cleanest measure of what an AI-native organization means in practice.
Key Takeaways
Agentic HR is won on data foundations, not on agents: Leapsome built its unified employee record for eight years before the first agent shipped.
Five bootstrapped years forced product discipline that funded rivals never needed — and left the founders with exactly one funding decision in ten years.
The Density Gap: the median high-growth company runs one HR professional per 53 employees; the AI-native frontier runs 1:243 to 1:299, according to Leapsome's analysis of public data.
Of high-growth companies with available data, 81% already put AI into People functions — yet most companies do not realize the productivity gains they expect from AI.
Only 30% of AI labs' own People-function job postings mention AI skills, the lowest of 11 industry segments measured — the companies building AI do not yet hire for it in HR.
Five Bootstrapped Years (2016–2022)
Leapsome started in 2016 as a performance-management and feedback product, built on the conviction that the talent practices of the best organizations should be available to every HR team. What makes the company unusual in the German startup landscape is not the product category — it is the capital structure. For five years the founders took no outside money, reportedly reaching around $10M in annual recurring revenue before their first round. When the round came, in March 2022, it was a single large one: $60M, led by Insight Partners with Creandum and Visionaries Club, at a point when the company already served more than 1,000 customers including Spotify, Unity, and Mercedes-Benz. The US expansion followed within months, and the company now says the US is its largest market. Jenny von Podewils first joined Startuprad.io in episode 401 (November 2023), the year she was named Female Entrepreneur of the Year at the German Startup Awards.
Her 2023 conversation is still online: listen on Apple Podcasts or Spotify.
The Rebuild (2024)
The decision that defines the second half of the journey looks contrarian in hindsight: in October 2024, in the middle of the AI boom, Leapsome shipped an HRIS — employee records, absences, payroll preparation, document workflows. Boring infrastructure, built from the ground up, into a market that Personio, Workday, and ADP already occupied. The logic only becomes visible from 2026: AI agents can execute complete HR programs only when recruiting, records, performance, and compensation data sit in one connected system. The rebuild was the precondition for the agentic layer, not a detour from it.
The Agentic Layer (2026)
In July 2026 Leapsome launched its unified platform: five EU-hosted AI agents covering onboarding, absences, payroll preparation, review and calibration drafting, and recruiting screens, plus Model Context Protocol connectivity and a new applicant tracking system in early access. Leapsome states the platform is EU AI Act-compliant, ISO 27001-certified, and GDPR-compliant, with every agent action logged and human sign-off wherever judgment matters — compensation, calibration outcomes, terminations. Across its customer base, Leapsome reports an average of 40% administrative time saved and up to 20% higher employee performance; the company itself is careful to note these are customer outcomes since adopting the platform, not an AI before-and-after — a distinction we hold it to on air.
The Density Gap
Talent density — the concentration of high performers in a team — is the metric Leapsome has organized itself around. The 2027 Workforce Trends Report makes it measurable: across 100 high-growth tech companies, assessed on more than 20 public data points each with no surveys, the median company employs one HR professional per 53 employees. ElevenLabs runs one per 299 with a four-person People team; OpenAI one per 243, according to the report's public-data analysis. We call that spread the Density Gap: the distance between median people-operations leverage (about 1:53) and the AI-native frontier (1:243–1:299). It is the operating-leverage measure of AI-era organization design — and the number that decides whether an HR platform is a cost line or a multiplier. The metric has critics: on professional networks, raising talent density is increasingly read as a polite word for layoffs. We put that criticism to Jenny von Podewils directly in the episode.
Where the Productivity Gains Die
The report's most uncomfortable finding cuts against the industry selling it: 81% of high-growth companies with available data already integrate AI into People functions, yet most companies do not achieve the productivity gains they expect. And in the strangest result of the dataset, only 30% of AI labs' own People-function job postings mention AI skills — the lowest of the 11 segments measured. The gap between buying AI and reorganizing around it is where this episode lives: hiring, management spans, skills, and trust have to change before the economics do. The human side of that gap comes from Leapsome's earlier, survey-based 2026 Workforce Trends Report (2,400 employees and HR leaders): 33% of employees doubt their skills would meet the demands of a new role in the AI era, 60% of HR leaders say disengagement is lowering performance, and 1 in 4 employees stay in their jobs out of fear of change rather than satisfaction — against a global cost of disengagement the report puts at roughly $8.8 trillion a year, about 9% of global GDP.
Quote Highlights
"A lot of the bad traditional performance management has been mainly backward-looking." — Jenny von Podewils, Startuprad.io episode 401 (2023)
"The pattern is hard to miss: the companies pulling furthest ahead all gave HR a genuine seat in deciding how the business runs, not just how it operates." — Jenny von Podewils, 2027 Workforce Trends Report
"Reeducation is harder than initial education." — Jenny von Podewils, in this episode, on repositioning Leapsome from talent tool to platform
"At the time when we were bootstrapped, we were growing as fast as VC-backed companies." — Jenny von Podewils, in this episode
"The most AI native and AI forward companies don't even necessarily speak about AI in the broad terms anymore because it's so table stakes already." — Jenny von Podewils, in this episode
"In next year's report, the employee to HR persona ratio is going to be stable … same size of team, very different priorities and focus and impact." — Jenny von Podewils, forecast on record for the 2028 report
FAQ
What is Leapsome? Leapsome is a Berlin- and New York-based HR platform founded in 2016 by Jenny von Podewils and Kajetan von Armansperg. It combines an HRIS, a talent suite, recruiting, and EU-hosted AI agents, and serves more than 2,000 organizations including Notion, Spotify, and Mercedes-Benz.
Who is Jenny von Podewils? Jenny von Podewils is the co-founder and co-CEO of Leapsome. She holds a B.A. in economics and international relations from the University of St. Gallen and an M.Sc. in environmental change and management from the University of Oxford, and was named Female Entrepreneur of the Year 2023 at the German Startup Awards. Investors and operators can reach her via her LinkedIn profile.
What is talent density? Talent density is the concentration of high performers in a team. In economic terms it measures how much output an organization gets per person — and it rises when hiring, development, and people decisions improve faster than headcount grows.
What is an agentic HR platform? An agentic HR platform is software whose AI agents execute complete HR programs end to end — onboarding, absences, payroll preparation, review drafting, recruiting screens — with humans signing off where judgment matters, rather than software that only reminds people to do the work.
What is a good HR-to-employee ratio? Leapsome's 2027 Workforce Trends Report finds a median of one HR professional per 53 employees across 100 high-growth tech companies, while the leanest AI-native companies run one per 243 to 299, based on public data.
What is the 2027 Workforce Trends Report? A Leapsome study published on September 16, 2026, analyzing how 100 of the fastest-growing tech companies run HR, using more than 20 public data points per company and no surveys.
Get the Report
Leapsome's 2027 Workforce Trends Report — the data behind this conversation — is available at leapsome.com/2027-report. The survey-based 2026 Workforce Trends Report — 2,400 employees and HR leaders on engagement, AI readiness, and trust in HR — is available at leapsome.com/2026-report.
Leapsome is Hiring!
Leapsome is also hiring across several teams, including engineering, product, marketing, sales, customer success, and business operations. You can find current openings on the Leapsome careers page
Disclosure: This episode is sponsored content — a paid partnership with Leapsome. The questions, the pushback, and the analysis remain with Startuprad.io.
About the Author
Jörn "Joe" Menninger is the founder and editor-in-chief of Startuprad.io, covering startups, tech, and venture capital in Germany, Austria, and Switzerland in English since 2014. He has more than 15 years of experience in management consulting and writes from Frankfurt am Main. Bio.
Machine-readable index: https://startuprad.io/llm
Created with the assistance of AI.
Automated Transcript
E779 — Leapsome: Cleaned Transcript
Jenny Podewils, Co-Founder & Co-CEO, Leapsome
Startuprad.io — Episode 779
[00:00:00] Jörn "Joe" Menninger
10 years, 5 of them without a single year of venture capital. Reportedly $10 million in annual recurring revenue before the first outside money, then $60 million in one round. A core product rebuilt from scratch in 2024 in the middle of the AI boom. And today, AI agents running HR for more than 2,000 organizations, while new public data says one HR person can support 299 employees at the frontier, against a median of 53. This is the story of how agentic HR actually gets built and why the agents are the last step, not the first.
Hello and welcome, everybody, this is Startuprad.io episode 779. Startups, tech, and venture capital from Germany, Austria, and Switzerland. I'm Joe Menninger joining you from Frankfurt am Main.
[00:01:07] Jörn "Joe" Menninger
My guest today is Jenny Podewils, co-founder and co-CEO of Leapsome, one of the few founders in Europe who bootstrapped a software company for 5 years, raised exactly one round: $60 million, led by Insight Partners, and then rebuilt the entire product from the ground up before putting AI agents on top. Since July, those agents run core HR programs for more than 2,000 organizations, including Notion, Spotify, and Mercedes-Benz. And this week, Leapsome published research on how the 100 fastest-growing tech companies actually run HR. My thesis today: agentic HR is won on data foundations, not on agents. Let's test it. Jenny, welcome back to episode 779, exactly 3 years after we did together episode 401.
[00:02:06] Jenny Podewils
Yes, it's great to be here. Thank you for having me.
[00:02:09] Jörn "Joe" Menninger
Do you feel like 400 episodes older?
[00:02:12] Jenny Podewils
I'm clearly feeling like 400 episodes older. A lot has happened.
[00:02:19] Jörn "Joe" Menninger
Okay, we talked in 2023 because you were one of the winners of the German Startup Awards. And 3 years ago on this show, you said most performance management fails because it is backward-looking. 3 years and one platform rebuild later, what was Leapsome itself getting wrong in 2023?
[00:02:40] Jenny Podewils
Yeah, great question. I mean, I think we're always smarter in hindsight, aren't we? And I think at the time when we spoke I would have to go back to when exactly we spoke. What we might have not made, or we're just in the process of taking the decision to go from vertical, best-in-class talent software to becoming a broad platform. So we took the bold strategic bets to consolidate the market to some extent from the other side. A lot of platform kind of core HR players have moved out into talent, into recruiting, into adjacent categories. But no one had yet successfully shown that you can go from the depth in talent to actually becoming that strong, broad HR platform layer that all of HR ultimately runs on. And yeah, that's the decision we took. And maybe we should have taken it even a year earlier.
[00:03:44] Jenny Podewils
But yeah, again, you're always smarter in hindsight. And I can share later why exactly we decided to take that move.
[00:03:52] Jörn "Joe" Menninger
I see. You bootstrapped for 5 years, reportedly to around $10 million ARR, while US rivals raised hundreds of millions. Discipline or necessity? And what did it force into the product that funded competitors never built?
[00:04:16] Jenny Podewils
I mean, I think 2 things. I would say, why did we do it? It was focus. We were never dogmatically bootstrapped, but we rather always asked ourselves, what ultimately is the core priority right now? And for us, the core priorities were understanding customer needs and building product that solved those needs better than anybody else has solved them. So that was kind of the rationale for bootstrapping. It didn't distract us. In terms of having to go fundraise. We were radically focused on customers and product. At the time when we were bootstrapped, we were growing as fast as VC-backed companies.
[00:04:58] Jenny Podewils
And so focus was the main driver of bootstrapping. And why did we then take a round? We took a round because we wanted to expand into the US, and that requires some upfront investment. We already got some pull demand out of the US, so we wanted to capture that more effectively, compete in the best software market in the world, which makes you a better software player. And at the time, we were actually planning to more upmarket, which also means like longer sales cycles. You just need to kind of fund more of your go-to-market motion upfront. And what has that— what DNA do we maintain because of our bootstrapping times? And I would say it's that strong focus on the customer because we need to— like we needed to build things that actually generated revenue in the early days. And that DNA of really closely listening to our customers, predicting, foreseeing what are the next needs that are starting to emerge that no one has actually solved really well. Right now, for example, AI transformation, like the role in HR department people, leaders play in that transformation is something that isn't solved.
[00:06:08] Jenny Podewils
It actually plays to our strengths. But that's really core focus on the customer needs and closing the gap for the customer. That's kind of part of our bootstrapping DNA.
[00:06:18] Jörn "Joe" Menninger
Being focused on customers is usually a pretty good thing. But I was wondering, when you stay out of the funding market for half a decade, what did that staying out of the markets for so long cost you?
[00:06:36] Jenny Podewils
I mean, it's a good question. So, I mean, I think the upside is focus. The downside is some competitors have more resources to build positioning awareness, have GTM scale in the market. At the same time, I think resources are one ingredient in winning the go-to-market game. The other is exceptional product, clear differentiation, and to some extent finding the right levers and hacks to grow and start that flywheel of customer referrals. And if you look at our category, HR software is also strongly bought by understanding what peers use, what peers are starting to use. So that is something that is also important to us And that again is also grounded in, do you understand your customers' needs and solve their problems better than others in the market?
[00:07:46] Jörn "Joe" Menninger
Jenny, walk me through the 2 or 3 decisions that turned a feedback tool into platform. When exactly did you know the point solution was a dead end and what triggered it?
[00:08:03] Jenny Podewils
Great question. So the first phase of Leapsome, we were a talent software, right? We brought together strengths and depths in performance management, feedback, OKRs, meeting management, things that drive alignment, as well as leveraging data to build better organizations, for example, through engagement surveys. So that's what— that's been the side where we started, vertical, absolute best in class for that talent side. And sort of into 2023, we saw more and more consolidation pressures in the market. So a lot of pressure to reduce tooling, to consolidate tooling, to some extent to have like one hub or central platform that a department truly runs on. That was one side of the coin. The other side, on the opportunity side, is seeing where AI and generative AI in particular was heading, we also were very clear that owning the data layer, not just in a particular subpart of a function, but actually across a whole department, is going to be something that's incredibly valuable. Owning the workflows truly end-to-end in a particular department to then leverage AI, I mean, now increasingly agentic AI across the entire department grounded in that data and that contextual understanding, is something that's incredibly valuable.
[00:09:32] Jenny Podewils
So there was both an opportunity, a technological opportunity, as well as a market evolvement that we saw that ultimately decided us— decided that we— how we decided that we actually want to become that platform player and evolve the strategy. And at the same time, third reason, that no one actually has the strengths on the talent side. And while there were all these consolidation trends in the market, we had customers telling us, like, we need that strength on the talent side. We cannot actually rely on the much weaker, much more superficial talent capabilities of other vendors. Like, why don't you actually expand into the broad platform space? So that's the third one, like, getting very direct customer feedback to some extent, like, really directly asking us to do that. And I think that is even becoming more and more important at a time where being a strategic partner to your organization, to your leadership team as an HR function is paramount as we speak about the AI transition in the workplace.
[00:10:36] Jörn "Joe" Menninger
So, was this the reason you for the first time took in $60 million from Insight Partners in March 2022?
[00:10:45] Jenny Podewils
So, at the time, the main driver behind the decision to fundraise was, I mean, One was the milestone we had kind of surpassed the $10 million ARR. And I think it's always good to ask yourself, what got you here? Will that also necessarily get you there? So what's kind of what gets you from $10 to $100 million? So that's kind of was one of the drivers where we decided to go fundraise. The second one was we wanted to, or we're getting ready to open an office in New York and expand our footprint in the US market. That is an endeavor where all other founders tell you it takes longer, it is harder, it's more cost intense than you can even foresee. So funding the US market entry was the second driver for the funding decision. And the third one, at the time when we went fundraising in early 2022, we were still a vertical talent management play at the time. And in order to go from SMB mid-market into sort of enterprise, We just needed the funds also to pre-fund that going-up-market GTM extension.
[00:11:55] Jörn "Joe" Menninger
I'm curious, just on a side quest, how big was the competition between the employees who can open the New York office?
[00:12:07] Jenny Podewils
Yeah, good question. I mean, there was a lot of interest. We actually sent a launch team of, I think, 7 people at the time to open the US office, like a mixture of sales, customer success, and had sort of like someone from our leadership team and ops. And yeah, it was obviously a fun, sexy opportunity for a lot of people. And we tried and have ever since had also a lot of travel back and forth between the offices. Everybody loves to spend some time on the other side of the Atlantic.
[00:12:44] Jörn "Joe" Menninger
In October 2024, he launched with, how do you pronounce it, HRIS or “high-res”?
[00:12:53] Jenny Podewils
Yeah, HRIS.
[00:12:55] Jörn "Joe" Menninger
HRIS. Okay. Built from scratch into market, Personio, Workday, and ADP already owned. Building boring HR infrastructure in the middle of an AI boom looks like kind of a bit contrarian. Why was it the precondition for everything you now call Agentic?
[00:13:16] Jenny Podewils
Yes, great question. So we took the decision to move from best-in-class talent to becoming the single source of truth layer that owns all of the data and all of the processes in an HR function. And there's 2 reasons why we believed and continue to believe in that strategy. One is you actually have the data across the entire department. So you have a lot of the contextual data in HR, actually, because of our depth in talent, much beyond around the strategy, the alignment, the manager effectiveness, the performance of a team, how an organization works. But we could connect this data and can connect this data with a lot of the compliance data, the employee attributes, etc. So that gives us really rich contextual data. Pool, a talent graph we could build.
[00:14:13] Jenny Podewils
And we have the ownership of workflows across an entire department. If something happens here, that can trigger a whole lot of additional things across the entire employee lifecycle, across the organization. And having that connection in the data and in the workflows and now in the agentic capabilities that enable that, is something that generates real value to our customers. So, ultimately powers how an organization, HR organization and a company interacts with HR data and people processes.
[00:14:50] Jörn "Joe" Menninger
You rebuilt. I was wondering what broke during the rebuild? And what would you not do again?
[00:15:02] Jenny Podewils
I mean, I think It's what's broke. I mean, I think there's a lot of things, you know, you do really diligent customer research, user research, product research, and then there is maybe the things you don't know yet. And I think the part that I, in hindsight, probably would've invested into earlier is changing the perception of the platform in the market. If you enter the market with a new product, you have the benefit of the curiosity of like, oh, who are they? What do they do? And if you are an established and actually even like beloved brand, the challenge is like, how do you get people to reevaluate and rethink what you actually do and change the perception of who you are? So that is actually something that is something that again requires some time, resources to reeducate the market. So I think that is some, something in hindsight I would've started potentially even earlier alongside the build.
[00:16:14] Jörn "Joe" Menninger
Yeah, that's always a good idea because then people call you, hey, look, I'm interested in your product. We're doing something really different now. Oh, I didn't know. That's usually a sign you're way past that. But I do believe a lot of companies out there start to communicate something like a pivot, like a change of strategy, way too late to the outside. They're waiting for the big moment, and then from one moment to another, you just tap the switch and that's it. But it really doesn't work like that, especially in the, in the minds of, of the clients, right?
[00:16:50] Jenny Podewils
Especially if people have like— they already think they understand what you do, right? So it's almost like re-education is harder than initial education.
[00:16:59] Jörn "Joe" Menninger
Yeah, people don't like to— don't like to unlearn. Yeah. in a minute, the public numbers say one HR person can support 299 employees, and why Jenny's own research says most companies buying AI see nothing like that. Stay with us. Jenny, since July, 5 EU-hosted AI agents run on the platform. Name one HR program an agent executes end-to-end today, like every step, and tell me where the human signs off.
[00:17:41] Jenny Podewils
I mean, I think a favorite one of mine is, We actually see when we speak to HR operators that there's still so many manual processes running in an HR department. Like people move things from A to B, or they remind other humans to like complete a step, send something, et cetera. So there's a lot of manual work. And to some extent, a lot of that could have already been automated by workflows before. However, setting up a workflow takes a bit of time. Maybe some people are also afraid, how do I actually do that? What are the right triggers? What are the right events? Can I get it wrong setting this up? One of my favorite GenAI capabilities is actually Leapsome is building the workflows for you. Either the platform can also help you to draft the process you actually want to run, Or maybe if you already have a process you want to run, let's say an onboarding process, there's a whole bunch of things that need to happen, tool logins that need to be created, equipment that needs to be set up, access rights that need to set up, but also maybe the more cultural things like onboarding, maybe a buddy in the team, a check-in conversation with a manager, meaningful peers. If you have that onboarding process set up, you basically drag and drop it into our agentic workflow builder.
[00:19:11] Jenny Podewils
And the platform builds the process the way you have envisioned it from a technical perspective in the tool. So it basically, from your process, understands what needs to happen on which day, who are the respective accountable person, what are the rules behind that, what are the steps that need to be set up, what are some of the steps, can they happen in parallel, do they need to happen in a particular sequence, and all of that is set up immediately and with a much lower technical barrier because everyone can drag and drop a document. I still believe there's value in the human double-checking if the way it's being set up actually is exactly the way they wanted to set it up to quickly audit that. But then they can activate this workflow and it's running on autopilot as well. But I think this is a really nice example where natural language, everybody can define a process in natural language, is then being set up to run in an automatic way so much easier than it used to be able to do, than we used to be able to do that in the past. That's, I think, one example. I could give you others, but I think that's one that's solving a real manual pain point in HR.
[00:20:30] Jörn "Joe" Menninger
Actually, also love the same thing. For example, a lot of people already noticed that we have a semi- automated way with the agents right now to post on social media for Startup Radio. They notice and they like it. But you know what my first experience was with an agent? I wanted them to post on social media. It turns out no post arrived at the end because they just wrote it into a letter. So when you have the bugs figured out and you have it made very simple, you have a framework and understanding what it does, it makes it easier to have agents in the running. But I do love the idea with other companies, with you for example, also Moss, is there no complete automation but the human in the loop? Because there's some processes like your salary where you don't want an AI agent to go crazy, or your performance review. Or other, well, are there non-important processes in HR? I don't think so.
[00:21:40] Jenny Podewils
No, I think it's more about getting the aspects that the agent can do well and the human kind of collaborating most effectively together, right? So the agent can quickly set something up. They can see maybe if we think about payroll preparation, maybe see an unexpected pattern they would expect differently. But then the human looks at the data, is the workflow set up, or looks at the interface, is the workflow set up correctly? Or if there is an anomaly in payroll, maybe there's a reason behind it that the human knows. It's signal that helps me, it's auditing, but then it's also giving me the signal where, as a human, I might want to duck deeper and build additional context or start an additional sort of review process. that I think is the best of combining the best of both worlds.
[00:22:42] Jörn "Joe" Menninger
In preparation of this interview, I talked to a lot of lovely people on your team, and they insist Leapsome is a platform, not a tool. Strip away the language What is the operational difference for, let's say, a 9-person team?
[00:22:58] Jenny Podewils
I mean, I think it's Leapsome as a platform is becoming the central layer that enables a small HR team to run like a much more mature, much bigger people or HR team, right? A lot of the things that don't have to be done manually can be delegated to the platform. The platform also gives the team the insight and the data points where to double-click on, or let's make that really specific, right? We have resilience signals in the platform, right? They give you a signal, a data point where you maybe want to dive deeper. Do I have a retention risk there, right? It's a conversation starter. It's orienting my attention as a human to double-click on something and figure out, like, what's the backstory to this? Do I need to focus some of my attention there? also, like, it's solving real pain points, right? I have a board meeting coming up the next day. All of a sudden, I'm getting a data request that I would have been scrambling to respond to in the past. But now I can use the functionality, the agentic functionality in the platform, and vibe code a dashboard to actually pull together the right information in the moment to answer that question, even like a nice polished report and dashboard, right? That's solving a real pain, and that enables the team to act much more strategically, have much more impact in an organization, save themselves a lot more of the sort of like pain, the scrambling, the manual work, and thereby operate on this layer across all of their data, across all of the processes, the workflows in a really effective way.
[00:24:39] Jörn "Joe" Menninger
You're actually, during your rebuild, you built an EU-hosted, an EU AI Act compliant to a platform from the start, a constraint that slowed you down, or do you see it as an asset now selling in the US?
[00:25:00] Jenny Podewils
I'd actually say it's, it's— I mean, we are working with highly sensitive data. Compliance is essential, is paramount. It's the baseline we operate on. And the regulation we have in Europe, I think, is guiding us to provide and build at that level of security, at that level of care, and be intentional about also the agentic and AI products we build. And I think this is something that we also bring into the US market. And I think it also gives us a competitive edge, right? We can— and it's actually part of also how we educate the market that with maybe traditional software, some of the judgments your vendors put into how they build agentic capabilities are actually really, really important, but you don't even necessarily see them on first sight, right? So understanding kind of the the product principles of your technology partners, understanding how they build technology is really, really essential, especially at this day and age. So having— being a vendor with a strong European and a strong US presence, I think, is something that can actually speak to the diligence and the care that we put into our capabilities across the board.
[00:26:37] Jörn "Joe" Menninger
We're also talking about the report you made. can you define talent density in economics, not cultural terms, for us? And what does it change on a company's P&L?
[00:26:57] Jenny Podewils
So talent— what does talent density mean? Talent density means the share of your workforce that is truly effective in the context of your organization, right? Different organizations run slightly differently, have different preferences, have different operating rhythms. So talent density is about finding, to some extent, the recipe of who is successful in your particular company. What do you understand about them? How do you make sure you hire more people that will be that level of effective in your organization? And how do you actually make it more tangible also to your existing workforce of what exactly your expectations are for them to be effective? So it's very much about managing or setting clear expectations, but then also managing against those in hiring and with the existing team. And why does it matter? Because in the end, that means you'll have a higher-performing, more effective team. And in many organizations we work with, talent and humans are still a lot of the deciding factor as to how successful companies actually are. And if we want to kind of like go into the report, what we actually did is we analyzed in our 2027 Workforce Trends Report that you also find on Leapsome's website. We analyzed data from the 100 most successful, fastest-growing companies across a lot of publicly available data points. We complemented them with some in-depth calls to kind of complement some of the sort of broader data points we analyzed. So it was a larger data analysis.
[00:28:53] Jenny Podewils
Historically, we've actually often done these survey-based. So this one we've done data-based, about 5,000 data points we analyzed partially with AI, with a lot of manual human fact validation. And I think what's interesting, if we want to go into those directly, and we basically the question we asked ourselves is how does HR in these best companies actually work? And I think the 3 things that really stood out was The HR teams weren't actually necessarily getting smaller, which is interesting because we often ask ourselves the question, does AI actually kill jobs? So in these companies, we actually, with 2 outliers, haven't really seen that. The HR teams were roughly the same size as they are in kind of the industry average. However, what work is done in these organizations has shifted and roles have shifted and they shifted away from the more sort of like mundane administrative tasks to the more talent-oriented strategic tasks of building the right organizations, retaining or hiring the right talent, retaining the right talent, making managers effective, building the right org structures. So it's the automation of the core layer has enabled the more strategic work that ultimately again creates, to some extent, that flywheel of more talent density, more success. And what's also been interesting is that in these organizations, HR tends to be, or the HR leadership tends to be set higher up in the organizations, which is also interesting. So HR actually has a more strategic seat at the table.
[00:30:43] Jenny Podewils
And those were the most fundamental changes that we've seen in that dataset.
[00:30:48] Jörn "Joe" Menninger
I've also looked into the report. but by the way, I do believe we will link the website you'll share with me after the interview, and you will link it, where everybody can find it, at least on our blog, and go straight from there to you and download it there. sorry for the, for the detour. Your report puts the median at 53 employees per HR professional, and ElevenLabs at $299 million with a people team of just 4. Is— can a company outside of an AI lab reproduce this 1 to 299 for normal company? Or is it just an AI lab anomaly that, that just happened?
[00:31:37] Jenny Podewils
I mean, it's an interesting question. The 2 outliers were, as you mentioned, ElevenLabs, and the other one was OpenAI. that was— were like the massive outliers with like 250 to 200 or 300 employees per HR headcount. I mean, I think at this stage, I would say these are rather outliers. And the 98 other companies in the dataset, again, behaved a lot more like the standard company in terms of numbers, not in terms of what people do. Again, like very different, important differentiation. I mean, I think neither of us has a crystal ball, right? But I think what we're actually rather seeing from the conversations we're having with people leaders in our community, in the market, in our ecosystem is HR's role has actually become more important in becoming a strategic partner to the C-level, the CEO, in navigating companies through the AI transformation. It's partially a technological transformation, but it's also very much a people transformation.
[00:32:49] Jenny Podewils
So what we're seeing is HR is not just tasked to rebuild their own department and make HR truly like agentic and set up for the AI era in the narrow sense. Like that's the own HR department strategy, right? That's what we empower with Leapsome's agentic HR platform. But beyond that, it's also a key partner to the rest of the business to navigate the rest of the organization through the change, right? We've seen massive change in engineering organizations, like one of the fastest evolutions of a job that we've seen in this decade, like massive change of how people work. And HR is still, in many organizations, the most experienced change management professional that can be the partner to a CTO to manage their organization through that change. How do we rethink hiring? How do we rethink management? We rethink compensation at the time where the job of an engineer has changed so much and maybe converges with product roles and design roles, right? So I think that's kind of the whole change management track record, or change management work stream. Another one we've seen is like, how do we need to rethink org designs? Are teams becoming flatter? Are we increasing the span of control for managers, right? This is again one where the strategic partner to the leadership team is often the HR persona. They're the ones that also then implement these changes. So all of this really essential strategic talent work and org work is also something that has become even more valuable, even more urgent, even more in demand, and again, also needs to have the resources and also the tooling to get this job done right.
[00:34:39] Jenny Podewils
So this is why I would see these— I would probably right now predict that the ElevenLabs and OpenAI ratios are more of the outliers. And because all of these challenges are real and a lot of work, I would expect the HR team sizes to be staying relatively similar, but with a radical change in where the priorities lie and what expertise is actually in demand.
[00:35:09] Jörn "Joe" Menninger
Looking at LinkedIn right now, raising talent density is being called the polite word for layoffs. Logo density, keeper tests, removing the denominator. Why is your version of that metric not that?
[00:35:27] Jenny Podewils
I mean, in the end, I think we are seeing shifts in organizations, right, of how organizations operate, what competencies are most sought after. So I think, yes, we are in a transition. I think there's also the reality of AI being used as an easy blame for some of the layoffs and drift that are maybe driven by other things, overhiring, etc. But I would decouple talent density because talent density, again, as we discussed before, is ultimately really the question of who are the people that are most effective in your organization? And how do you actually understand who is the right fit for your particular operating rhythm, for your particular priorities as the company? But yeah, it's probably also part of the truth that That at times also means understanding who's— where there's not a good mutual fit. And hopefully, whenever that is happening, it's done in a very good way. But yeah, I think we are in a time of pretty massive shifts in the workforce.
[00:36:53] Jörn "Joe" Menninger
Yeah, don't worry. I know the inside feeling. I was cut from my consulting job and ever since run Startup Radio. it, it doesn't necessarily turn out pretty bad, I gotta tell you guys. Jenny, your team was careful to tell us that 40% admin time and 20% performance numbers are customer outcomes since adopting Leapsome, not an AI before-after. Honestly, respect for that. So what is the AI before-after? Inside Leapsome?
[00:37:27] Jenny Podewils
Inside Leapsome as an organization, right? Not for our customers, but for us as a company ourselves. I think it's— we actually hired a new VP, People and Strategy, earlier this year. I think she started in March, who is also operating from a very AI-first mindset and is really exceptional. She's actually going to run a masterclass for a cohort of customers in Q1. So I think kind of the 6 months to agentic HR. So we have a people leader in place who's really exceptional on rethinking HR. And we have been very— I mean, to some extent, we're always our customer number one on everything we build anyways. So what has changed is we have introduced a whole lot of, or we've reinvented a whole lot of processes that used to be slow, manual, painful.
[00:38:39] Jenny Podewils
And I think we haven't done a great job to sum up of what all of these compound to. But I think if I give you specific examples, for example, our calibration process, used to be something that was probably a net 1-week lift for the people team. That is probably now in the late last form with like AI-generated pre-reads, probably condensed down to more than— to less than a day of work. So cutting that from 1 week to 1 day is like a very very significant lift in how that process is actually now much easier. And I think calibration is one of the really, really valuable parts of a good performance process. And not every organization is able to do it and run it because it is, it used to be quite resource intensive, right? And I think this is to the point I made earlier, a way how a small people team can now actually act like only the best, most mature people team could in the past, right? Because I now have technology as a partner that can help me get these lifts done well. And I think this is something we're feeling internally at Leapsome across calibration, across coaching managers to make merit and performance cases, to prepare them right, to challenge them right, where they aligned with the principle of what grants a promotion, what grants a merit. It's making sure we really automate as many of the internal processes, make it even easier to be self-service as a user, as a manager, to have a sparrings partner, to prepare me for a hard conversation or to make a conversation more effective.
[00:40:43] Jenny Podewils
I think for our managers, preparing reviews has probably— like, it always used to take me about 45 minutes to prepare a really strong feedback conversation. It's probably now down to like 15 minutes using sort of leveraging, pulling context through AI more effectively, challenging myself on where I have provided enough example or haven't. So again, I think that's a 3x lift on my time. So there's all these very specific ways it hasn't just made the lives of our HR team simpler, but actually also significantly for our managers while increasing the transparency and the fairness for employees, and also the ability to own your own development to some extent.
[00:41:33] Jörn "Joe" Menninger
I also found that 81% of companies with available data put AI into people functions, yet your research says most fail to get the expected productivity gains. Where does the gain actually die? Is it in hiring, in management spans, skills, or trust?
[00:42:00] Jenny Podewils
I would say it's because we use AI too compartmentalized. Like, in many organizations, it's still like a chat here, a quick LLM conversation there. But I think the biggest lift comes from making sure you really have a strong connection of all of the relevant contextual layer. So for example, all the context in Leapsome, Leapsome integrates through an MCP with like with Notion, right? Like connecting all of the data that is needed to give a most relevant or use AI in the most relevant ways. And then really rethinking processes and being more ambitious around what the AI or the agentic capabilities should be doing for you. So moving maybe more away from piecemeal to having a strong technological setup with access to a broad range of processes and deep contextual data, that is then the unlock for a higher productivity, but actually also ultimate impact.
[00:43:29] Jörn "Joe" Menninger
only 30% of AI labs' own people team job postings mention AI skills. I found this pretty funny. The lowest of the 11 segments you measured. The companies building AI do not hire for it in HR. What does that tell you?
[00:43:49] Jenny Podewils
I think that was an interesting one. And we deep dived into it. And allow me a quick diversion. Do you remember the ads? I don't know, what was that in the '90s of, am I in? Bin ich drin? For internet providers. And I think at the time, like, it was such a, like, novel thing to, like, go onto the internet. So we celebrated it, right? And we would say, I go onto the internet now, I shop on Amazon, I listen to music on Spotify. I watch, I don't know, shows on Netflix, right? So we don't actually talk about the internet anymore. It's such a like table stakes.
[00:44:30] Jenny Podewils
And to some extent, what we're seeing there is the most AI native and AI forward companies don't even necessarily speak about AI in the broad terms anymore because it's so table stakes already. What they specifically mention and look for is the right kind of judgment. It's asking good questions. It's almost like the next layer of showing the clear capabilities and skills that make working with AI most effective.
[00:45:01] Jörn "Joe" Menninger
Let's tease you a little bit about the future of work. What has the AI transition changed about how you personally lead? We already talked about the feedback conversations. where your time goes. Which decisions do you no longer make?
[00:45:22] Jenny Podewils
I mean, especially with capabilities like Cowork, there are some tasks where I previously asked somebody else to do them for me that I can actually much easier set up for myself to run quickly. And that almost means like no passing, unnecessarily having to pass on context, or waiting for someone else finishing that. So I think it allows me to actually get certain things done more simultaneously and faster. I think that's, that's maybe one very specific, change. I think it is challenging me to find time to experiment, explore, learn, just because the space is actually moving so quickly. And I think that's the challenge for any leader. There's not a lot of spare time on the calendar. And at the same time, it is important to really stay on the forefront of what is happening.
[00:46:25] Jenny Podewils
And in terms of how it changes how I lead or how we function on the leadership team, I think to some extent it has meant massive acceleration of velocity across the org. We ship product faster. We can run more experiments in parallel. And to some extent, we also compete with other players that are similarly moving faster. So I think it also generates a new operating speed. We always moved fast, but I think right now it's getting even faster, which means you need to, as a company, need to continue to evolve your own operating rhythm to make sure, like, you both move really fast, but we never want to compromise on quality either. So finding that sweet spot of the right operating rhythm for speed with quality, and also probably being honest that that level of speed might not actually resonate with everyone, and that's maybe also okay. But I think it is also part of the new reality of running a technology company in this day and age.
[00:47:42] Jörn "Joe" Menninger
This day and age, curious, what happens to junior roles? Your own job postings now require automation fluency. Where does the first rung of the career ladder go?
[00:47:55] Jenny Podewils
I mean, I think folks who are entering the job market now have a disadvantage and an advantage. I think the advantage is there's no preconceived learned ways of the old working, which to some extent can be an advantage, right? I try new things. I start directly with grounded in the technology that's available at my fingertips right now. And at the same time, The disadvantage is that I think sometimes you have to do things a certain, a few times yourself to get the pattern recognition, the muscle memory that enables you to be most successful. For example, junior engineers, like it's harder to probably review code when you don't have the same kind of years of experience writing code yourself. So how do you QA some vibecoded code? And I think in the end, it's a matter of pairing these 2 strengths in teams, the folks that come with a wide blank canvas and the folks who have the mentorship, the coaching abilities to still continue to actually advance folks in their careers. So I think it's not an either/or. It's like really combining the strengths of both.
[00:49:19] Jenny Podewils
But if I look at our— us internally at Leapsome, for example, we used to have support roles, right? Now a lot of the sort of first-level support tickets can actually be responded to by AI. So this means the people on the support team actually get to work on maybe the more interesting but also slightly more hairy tickets earlier, which requires there's maybe slightly longer onboarding. However, there's also technologies to support them, but it also actually makes the job more interesting, a higher learning curve. It's almost becoming a product specialist than a support person. So I think there's, again, like there is new challenges, but also like quite a lot of new opportunity.
[00:50:04] Jörn "Joe" Menninger
And now the AI agent can work. Have you tried turning it on and off again?
[00:50:10] Jenny Podewils
Say it again.
[00:50:11] Jörn "Joe" Menninger
And now the AI agent can first ask you before you get to any company, Did you try turning it on and off again?
[00:50:21] Jenny Podewils
Yeah. -huh.
[00:50:21] Jörn "Joe" Menninger
Okay, I see. Your report says AI made the old split— HR managing people while real decisions happen elsewhere— impossible to sustain. Which companies will the industry study 5 years from now, and what did they do differently?
[00:50:40] Jenny Podewils
I mean, I think, what we're seeing in some of the best, leading-edge companies is that next to their traditional HR to-dos and roles and responsibilities, there's a lot more focus on the strategic side of talent work, the right org design, the right manager effectiveness, the right understanding of who succeeds, should be retained, should be hired. And then we've actually seen a whole stack of senior HR leaders now becoming also the AI transformation leaders. Again, we're in a massive transformation and shift in how we work. And part of that is underlying technology, but part of that is a massive people innovation effort. Like, do we understand, like, how do we manage the change? How do we build new capabilities? How do we redesign org models, leadership models, compensation models? So we see the best companies being on the forefront of that. And I think that is the direction and the blueprint that other companies will follow, where again, some of the the core and the mundane is going to be increasingly automated with judgment through humans at the right points. But there's going to be more focus and roles on the more strategic work and really supporting organizations finding the right operating rhythm in the era of AI or agents and humans working together.
[00:52:30] Jörn "Joe" Menninger
So Our takeaway from that statement, that the demand for people who also read and correct the footnotes is getting lower, and the demand for people who could think ahead, even in terms of what other people do in terms of the strategy, the demand will increase there.
[00:52:51] Jenny Podewils
I mean, I think it's about asking the right questions, like interpreting signals, where to go deeper, where to build better human context, taking— making those decisions. Making sure or making a call and a decision, not just what is good everywhere, but what is right and best for your organization, for your people, right? So it's these human traits, the curiosity, the human relationships, the judgments that are really, really paramount, and those won't be replaced.
[00:53:32] Jörn "Joe" Menninger
Jenny, you're already holding up very well here for 55 minutes of recording time. Awesome, thank you very much. I have 3 more questions. One on capital and team. You raise once in 10 years. Are you open to talk to new investors, or was Series A the last round you will ever need?
[00:53:54] Jenny Podewils
I mean, it's— we're not in an active funding process. we are cash efficient. We have a lot of capital in the bank. And at the same time, I think it's always good to speak to the right people, get to know one another. And at the same time, we're also someone who's very laser-focused. So I think that's a good German jein.
[00:54:16] Jörn "Joe" Menninger
Jein, exactly. By the way, every investor interested, go on our blog, there is your LinkedIn profile linked. And who are you hiring right now? And what does AI fluency required actually screened for?
[00:54:32] Jenny Podewils
Yeah, great question. So we're actually hiring across, I think, almost all teams. So engineering, product, marketing, sales, CS people. I feel like I forgot something. Biz Ops. So I think there's some open roles across the board. And we are looking for— I mean, we're first of all also looking for people who are aligned with our operating systems, our culture. We care about impact, we care about speed, we care about ownership without ego, which is important to learning.
[00:55:08] Jenny Podewils
And we look for folks who are curious and capable in interacting with AI. So being curious, asking the right questions, also being careful to evaluate where's the output right, where is it wrong. So like that level of caution is also very important. And we actually tested through case studies that are now AI-enabled. So finding mistakes is also important. And then it's important for us that it's people who are adaptable, who are willing to learn, because the technology is evolving so quickly. And, and that level of curiosity, adaptability, and a learning mindset is really, really quite essential for us.
[00:56:00] Jörn "Joe" Menninger
Hmm, that sounds pretty interesting. Let's close with your forecast. One falsifiable prediction on the record. Where is the median HR-professional-to-employee ratio in your 2028 report, and what happens to HR software spending per employee by then? Predictions are always difficult, especially concerning the future. I know, I know.
[00:56:34] Jenny Podewils
So where's the ratio? I actually think in next year's report the employee to HR persona ratio is going to be stable because on the one hand, manual work is going to be reduced. And at the same time, the importance of talent work and AI transformation work is essential right now for the companies, for companies to succeed. So that's where the priorities are shifting to. So same size of team, very different priorities and focus and impact. And spend on HR tooling, I think that's an interesting one because on the one hand, I think that there— I would expect tooling investments into tooling to increase. And at the same time, through especially agentic capabilities, that might also at times mean there's less need for external resources, certain consultants, certain recruiters, because more of the work can also be done by the internal team with the help of technology, which again means there's less loss of context. It's faster. It's a bit related to the example I also use.
[00:58:09] Jenny Podewils
I can now get more done myself with technology. So those would probably be my 2 predictions.
[00:58:15] Jörn "Joe" Menninger
So that should scare a lot of free HR professionals out there working as recruiters, headhunters, and so on.
[00:58:24] Jenny Podewils
I mean, I think at the same time, there is also a lot of work to be done to help organizations make that transition. So I think again, the expertise isn't, isn't lost. I think it's again, it's going to shift, I think, where, where it's being deployed.
[00:58:42] Jörn "Joe" Menninger
Jenny, thank you. The one insight I want everybody to keep, the agents are downstream. Leapsome spent 8 years building the data foundation before the first agent shipped. And the density gap, 53 versus 299 employees per HR professional, is the number that tells you whether AI is actually changing how a company runs. You'll find Leapsome's 2027 Workforce Trends Report and every source linked in the blog post and show notes. If this episode was useful, send it to one founder who still thinks AI transformation starts with buying AI tools. us, rate us, review us, share us. It's genuinely helpful.
[00:59:25] Jörn "Joe" Menninger
This interview is in partnership with Leapsome. This podcast goes live on Tuesday, September 29th, 2026. The audio podcast earlier that same day. I'm Joe Menninger. This was Startuprad.io. Thank you for listening.
[00:59:40] Jenny Podewils
Thank you so much, Joe.
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