Knitting AI into HR Platforms, with Darwinbox's Rohit Chennamaneni

[00:00:00] Welcome to PeopleTech, the podcast of Workforce AI.News, I'm Mark Feffer. Today I'm speaking with Rohit Chenamaneni. He's the co-founder of DarwinBox. They're an end-to-end platform that helps customers automate and understand the value of their HR processes.

[00:00:29] He's having to address changing customer demands as well as the growth of AI. That's what we're going to talk about on this edition of PeopleTech. Hey, Rohit. Welcome. So first tell me about DarwinBox. What do you do? DarwinBox is a new age end-to-end HR technology platform.

[00:00:51] There's a lot of competition out there doing what you're doing. So what makes DarwinBox stand out? So I think one of the most important things which we started building out DarwinBox

[00:01:03] for Mark was we wanted to build a new age system which has the employee at the center of whatever we're building. So we over-indexed on making sure whatever we built, the end user, the employer finds it extremely easy to use and finds value out of it.

[00:01:21] So simple examples could be how to make this easily available in the flow of the employee might be using Slack, Teams, WhatsApp, whatever they're using. Can they do most of the activities that they need to do on a HR platform through these channels?

[00:01:36] Apply for a leave on Teams or write a performance review on Slack and it sits out there or approve notification or a requisition on WhatsApp, whatever that is.

[00:01:46] So this is just one example of how we thought about making life easy for the end user while using a system. And when we started, what we had realized was most of the HR technology systems

[00:01:59] and this is one system used by everybody in the org were mostly built for the administrators rather than the end users and we wanted to change that. So that became the core of what we built as a HR system.

[00:02:14] The second important thing that we solved for was we wanted to build something that makes large organizations agile. So we wanted to simplify changing processes, needing new things, adding new features, etc. So that large organizations need not be boxed into saying, hey, this is how my system runs.

[00:02:36] So my process also has to follow it rather the system has to follow the process. So these two are the reasons we exist. These two are the reasons we have 900 plus customers globally. We compete with the likes of SAP SuccessFactors Oracle Fusion Workday.

[00:02:54] A lot of the existing players, we've been able to replace a lot of them also because we've been able to clearly differentiate on the end user experience and the agility we bring to these enterprises. Now, if memory serves me right, you were founded in 2015

[00:03:13] and you've had a steady path moving up throughout the business. But then last year all of a sudden generative AI showed up. And how did that impact not just your growth but your plans? Are you having to think about things differently now?

[00:03:33] So I think the fundamental premise of the way we built DavenBox didn't change much, right? But how we deliver that significantly changed. And I can talk about it in two parts. One, of course on product.

[00:03:47] When I set out to build or when we set out to build the product we said hey, we should be easier for the easier usage for the end user or higher agility for the enterprise. Now how does AI enable that?

[00:04:01] And we had to be at the forefront of doing that because then otherwise we would become irrelevant in terms of a product that does that. So the way we think about AI in the product is three-fold. One is transactions, right? So how do you make transactions easier?

[00:04:17] So this could be for example, there are a lot of policy and process documents that organizations have. And most of the times employees are struggling to figure out what is applicable for them and what is not. Right? And organizations also build things as one size fits all

[00:04:33] because you can't have documents for individuals. But now we are able to actually give answers to individuals based on their specific questions while reading out of a lot of documents and giving that. And this all exists on the product today, right?

[00:04:47] And that we couldn't do this before the explosion of Generative AI because it just was extremely difficult to understand, especially language-wise, because different employees have different languages to ask the same question. They could position it differently. Their problem statements could be expressed differently.

[00:05:08] But Generative AI actually helped understand what their positioning was and then give the right answers to them from the loads of documents and reams of papers that existed which are digitized. So that's at a one level. The second level of solution was more on the talent management piece.

[00:05:26] Again, if you look at the cross-section of jobs, resumes and roles, right, or skills, how do you bring all of those things together? Especially again coming from a place of jobs are described in different way. You're a sales representative in one place.

[00:05:47] You're an account executive in one place. You're a sales director in one place, right? Resume descriptions are very different based on, hey, I come from this college and the course is called this versus I come from a different college. The course would be called something else

[00:06:02] and or an experience in a SaaS company versus a services company or something else. And on the skills front, right? Communication skills or a programming skill could be called different things in different places. How do we bring all of this together

[00:06:17] where we can find the perfect match internally when we are looking for a role or externally through recruitment was something we solved very well through generative AI because again, it gave us a common language on saying, this means this in most other contexts.

[00:06:34] I've looked at the web and this is what it means and it brings all of that together saying maybe this person applying to you might not say it in the same way but probably is the right fit for the role on the skills front, right?

[00:06:47] So again, that was a big area of focus for us as we built out and now we have like a whole marketplace of this. The other advantage we had Mark is Microsoft happens to be an investor in Davenbox and as a strategic investor,

[00:07:05] they are using us as the partner to build out a lot of co-pilot use cases on the HR front. So we're getting access to a lot of new innovations which is still not out in the market where even for Microsoft

[00:07:19] they need great use cases for the technology that we are building and for us, we get access to certain technology for the problems you are solving. So as a combination of this, we are able to bring out a lot of features around recruiting, around talent management,

[00:07:34] around all the operational internal support issues, etc. That's solving through AI, right? So of course, this acceleration will mean that how the product looked for nine years that we existed will be very different from how it will look for the next few years that we exist

[00:07:53] or many years that we exist and that's a big shift that we are going through. This is more on the product side and again on the support side as well when we are answering queries, when we are building some things for customers, etc.

[00:08:07] Things are a lot more accelerated which means we can actually deliver a lot more for the customers. But you still face the problem of having to push through all of the buzz that's out there. I mean, it seems like everybody in their mother

[00:08:24] is coming out with an AI-powered platform. So that makes a big competition in terms of marketing. How do you make yourself stand out when you're trying to get your message across? So I think it's a great question, Mark because it's definitely a challenge around getting the message across

[00:08:48] because there is so many people talking about it, so many funded folks talking about it. But I think the biggest advantage in this AI way is for players who already have existing data and content. So application software that already exists

[00:09:04] are in the best place to show results to customers. So for example, if I go back to one of my customers who's been with us for the last three years, four years and layer my AI application on top of it, they'll be able to see outcomes today

[00:09:20] versus any new platform that's coming in today will have to generate enough training data to make it possible for the customers to be able to show impact six months from now, one year from now. So that's the head start we have as a product

[00:09:36] or most application software have as a product which have been around for a while. So I think we are very well placed to capture this AI wave because we can actually show results immediately for our customers because we have training data, et cetera

[00:09:51] and we will also be able to sift through a lot of noise what AI can do and cannot do because we'll actually have real examples. So I think that's the advantage that we're playing on as a platform and as a company, right?

[00:10:05] But I would still say the noise around the marketing that's happening on AI of course is a tough one to break through but I think the biggest difference that we'll bring in is we'll have real life use cases where companies or customers have benefited from this

[00:10:19] which will be the biggest marketing that we can ask for. And what are you hearing from customers or prospective customers about not just your tool but also all of these AI features in general. I mean, they're really getting snowed by it in terms of messaging.

[00:10:42] What's the feedback that you're getting? I think it's very interesting Mark that right from board rooms which we are a part of as founders of companies, right? The investors are asking these questions the public markets are asking these questions

[00:10:56] which leads to the CEOs asking it to their management team on saying what are you doing on AI which leads to management team asking individual teams on saying what are you doing on AI, right? So a lot of pressure is coming top down

[00:11:09] because of all the noise that's there. I think what we are seeing a lot more is functional leaders, right? And this could be HR leaders, this could be finance leaders this could be sales leaders going into the experts, right? And these could be consultants

[00:11:25] or these could be product companies like us and saying, right, which parts can we leverage, right? And it's our responsibility also to educate them back on saying there are these areas where you'll see significant impact going forward but there are some areas where, right

[00:11:46] there's a lot more to be done for example on bias a lot more to be done in terms of hallucinations, etc with our limitations of the capability of the technology where you might not have an immediate answer and that's something that needs to be played back

[00:12:01] to the management teams, to the CEOs and to the boards because there is definitely immediate impact you can see but not in all areas, right? So and that I think is the part where we have to keep our counterparts honest

[00:12:16] on the client side, but the ask is a lot, right? Every quarterly business review with my customers I am in I get asked on saying, hey, what are you doing on SA and now we're almost preempting it AI so we are almost preempting it to say, hey,

[00:12:32] these are the things we're doing on AI but hey, we also have a webinar which talks about where you can see impact but where you cannot because we also believe given our access to Microsoft we know the cutting edge of technology on AI

[00:12:47] so we can actually be ahead of the curve in terms of telling you what's relevant and what's not and what's coming. Are the demands the customers are making changing because of AI? In other words, are they asking for new capabilities that they think AI can deliver

[00:13:04] or are they just asking for the same type of solution which they assume will be done better? Yeah, I think the needs don't change significantly, Mark. I mean, as HR functions are looking at attracting the best talent, retaining the best talent

[00:13:22] and making sure this talent have a great experience while they're in the organization. I think those things don't change but the effort that goes into doing that, for example they're looking at AI to help reduce that effort significantly. So on recruiting front because

[00:13:39] when you're able to define very clearly what you need from the market thanks to help from AI or thanks to help from searching the internet and figuring out what's most available and what's not and who are the best set of folks

[00:13:52] who we need to speak to instead of speaking to the whole world. I think they're looking at how does work get easier the same set of outcomes that you're looking for become easier with AI or faster with AI or cheaper with AI

[00:14:06] but I think the fundamentals don't change in that sense. But there is impatience given as I said, right? Like there's a lot of top down push on saying how are you using AI to improve your function and they're almost being pushed to give an answer

[00:14:22] on saying next year I'm going to do this 2x faster or at half the cost or so much better because I've been accessing AI or using AI to do this and I think that's where the push is coming from. Thank you very much.

[00:14:38] It was great to meet you and great to talk with you and I hope you'll come talk again. Absolutely Mark and these are great questions. Thank you. My guest today has been Rohit Chinnamaneni the co-founder of DarwinBox and this has been PeopleTech the podcast of Workforce AI.News

[00:15:05] To keep up with AI technology and HR subscribe to Workforce AI today we're the most trusted source of news in the HR tech industry find us at www.workforceai.news I'm Mark Feffer