Episode Transcript
[00:00:00] Speaker A: The biggest lesson that I say is rather than following a traditional hierarchy, and this is something we have learned in let's say you are actually going to do something with finance or procurement rather than going and setting up a meeting with a VP of procurement, VP of finance like that, go a little deeper and understand from the financial analyst what work are they actually doing and connect at that level and get the validation from them.
[00:00:31] Speaker B: Hi, this is Josh Burson. Welcome to the what Works podcast where Josh Burson company analysts talk with innovative HR and business leaders about what's really working in talent, technology and the future of work.
[00:00:45] Speaker C: Welcome to the what Works podcast. My name is Kathy Enderas. I'm the SVP of research and global industry analyst at the Josh Burson Company and today, today I have a real treat for you all. I'm here with Vijay, the CEO of a company called Drop. And Vijay will tell us all about what Drop does, but then more importantly how he works with clients on this most important topic that every HR team is working on. And that's how do we redesign work for the age of ei? Vijay, as we kick off, tell us a little bit about yourself and about Drop.
[00:01:19] Speaker A: My name is Vijay Swamy. I'm the co founder and CEO of Drop. We've been focused on multiple dimensions of talent intelligence work for the better part of nine to ten years now. We brought in lot more data transparency, raw data discipline, analytic discipline into the field of talent intelligence. So thereby we're the pioneers to bring in peer intelligence, data sets, signals, data sets global in a unified way of ontology. How do you bring in the various labor boards data?
One topic that Josh and I always used to talk about is is this it for talent intelligence, Is this IT for workforce planners or can we actually do more?
When AI started coming in, that's when we quickly realized that talent intelligence workforce planners can actually do a lot more.
And the three limitations that talent intelligence had was translating what they are doing in terms of labor market analysis into a broader enterprise story. In general, SWP has always struggled in and number two is really how do we tell how do we work more with finance people and tell the use case of build by borrow. So how do we actually translate that bill by borrow to a finance leader and say this is what it means. And number three, really get closer to technology Stack. SWP planners have not necessarily gotten closer to tech Stack. They will always leave it to it or not talk about it much.
AI brought all of us out of that comfort level and that's why the last two and a half years, we actually set up an AI lab in San Francisco. We now have combination of PhDs and Y combinators. Along with our Bangalore team, we are, we have built this product called Etter, which is lot more focused on work redesign. And that's what I've been focused on. Heads down on various R and D aspects of that product.
[00:03:15] Speaker C: And Vijay, so many things to talk about, but first I want to have you explain what's behind the name of Drop and what's behind the name of Etta and why these. These names.
[00:03:26] Speaker A: I'm a big fan of Norwegian mythology. Both are from there. So drop actually is derived from the word draupnir, which means it's like a magic ball that. Oh, if you place one magic ball in a plate today, tomorrow it will be two, next day it'll be four. Like it runs in geometric progression. Etter means again in Norwegian mythology, it means actually in pursuit of.
You're always in pursuit of an AI transformation, work redesign. It's never going to be. It's never going to be done, at least in the foreseeable future. So there's going to be multiple progress that you have to make and keep redesigning. That's why I picked the name Etter for the work redesign product.
[00:04:05] Speaker C: We hear a lot from the companies that we work with. And when we talk with Chros, for example, they say, well, will we ever be done? And I don't know if you'll ever be done because this is less of the going from here to here, but a constant redesign and constant motion of constantly looking at how do you need to redesign how people actually work? Because AI is changing all the time too. Let's talk about how you actually do that work redesign. Because I know everybody is talking about jobs are changing, work is changing, but how do you get your arms around this constant reinvention? How do you help clients do that?
[00:04:41] Speaker A: Yeah, there are five large use cases that customers are wanting to solve. Like yesterday. One is at an enterprise level.
What is my AI potential? AI opportunity When I look at work as a whole, am I looking at a. For a hospital customer? We actually sized it to be about, okay, your potential opportunity is between 2.6 to $2.8 billion. So that we did a lot of work to get there. I'll explain how we did that, but that's what we call as a 1A strategy. Strategy 1B is you go, so now we know that potential opportunity that you have vetted with your CFOs and the CEOs through CHROs so what you then typically customers seems to be doing is, hey, let's take two, three immediate opportunities in a hospital setup, that could be an imaging department or in a bank. It could be how we are doing something like underwriting, how we are doing something like a commercial sales process, whatever those bigger opportunities are. If 2.5 billion to 3 billion is the overall opportunity, it's a 200 million opportunity here. How do we actually plan for that? Tell me exactly how to do it. So that question is also never come to SW planners before, but that's now coming. Okay, we have technologies like Palantir, we have technologies like EPIC, technologies like ServiceNow, AI. All these technologies are there.
Which process is going to be useful for what process?
And how have you come up with that assumption? And if you don't have an answer for that, you're going to lose credibility real, real quick. Which is why in Etter we have built this AI sandbox which can actually let you simulate the entire process and say that hey, for opening the customer data sets, you can use Genesis if it is a contact center. But processing that medical data, you need a tool like EPIC or source now AI type of products.
And then let the user actually simulate and understand where the bottlenecks will come, how much tokens you are expected to spend. Because one of the challenges is are we calculating this through one dimension? Meaning are we only calculating the savings without understanding what the investment going to be? So if I'm going to reach $2.6 billion in savings, but by the way, you have to spend 2 billion for that, that's a different meaning than is it an. Is it a total net savings or total savings? So we get into the aspect in 1C. 1D is a typical LND part because a lot of there is a lot of skills changes. So we map current skills, future skills, what are all the future products? How do you actually connect the root skills to core skills to soft skills? Understanding that connectivity between root skills and core skills, and more emphasis on soft skills. Josh talks about this quite a bit as well. That is the dynamic skills architecture use case that we deal with. And number five is actually business model changes. And this is where I see a completely new opportunity for all workforce planners and predictive analytics people. Because whatever we are doing, it's going to have to be evaluated through the lens of how the industry itself is changing. The three major companies that have disrupted this thinking is obviously SpaceX, Blue Origin and type of companies they are, they are the Googles and the metas. And the most Innovative companies of the previous era. Now these companies I've told that you can't operate in your domain alone, you have to diversify. So basically Volkswagen, you might have read it last week, they are going to get more into defense for helping German Germany become more self sufficient.
In that angle, if you look at similarly IT services, they are becoming the likes of Accentures and the Capgeminis, they are becoming more AI services company because IT services is getting disrupted by AI. If you look at Biopharma they are all getting into large molecules, more gene based work. And also they are rethinking their medtech modules because the devices are coming in into medical cure. So there is a change in the way how industries are going to evolve. And that's another big use case many companies are wanting to evaluate. And these are the five use cases we typically support. The way we do it is we have built a very powerful system which lets us discover what work is actually being done by the enterprise. So that's a combination of job descriptions, all the process maps, KPIs, SLAs and strategic documents. We read all that through our AI and also we integrate with multiple systems, whether it is workday successful factors, Oracle, erp, Cellonis, type of systems, work IQ type of systems which sits on emails and understand okay, they are setting up, spending a lot of time and setting up interviews, coordinating all that. We translate into what is called an ontology, an etrontology.
The beauty of the heterontology is if you look at a typical taxonomy that goes into occupation, job, family, group job, family, job, role, tasks, skills, we conveniently miss out the most important thing that a business needs which is actually processes and workflows. So for the first time we have actually brought that in and we call that the etrontology.
And we actually built what is called an slm, which is a small language model that helps us crunch what should be the what are the task times? So what is the task time with automation and without automation and then run multiple simulation scenarios based on the technology that is selected.
In the end we identify what job roles can be automated by how much, what type of new skills should be considered by job role. And last two weeks ago we launched an employee module by employee we can say who in finance can be helpful for agent take finance, who in procurement can help in agent take procurement process.
[00:10:55] Speaker C: We've been talking about industry convergence for a number of years. We've seen that actually coming. And you're discovering that too with your clients and with your technology too, that the industry boundaries are blurring. And companies can't stay in their industry lane anymore because all of these industries are just converging from a business model perspective, from a customer perspective and even from a workforce perspective too. So a client would then feed in whatever is in their HCM system like their job architecture, everything that they have basically in Workday or SAP or Oracle in any of their ATM systems. Do you also need to integrate with the systems where the actual work happens? They might have Salesforce or Jira or any of like the work systems too. How do you get the work perspective into that?
[00:11:44] Speaker A: We have a model called repository modeling process. That's a process that kickstart start the journey with a customer.
We do read start with reading static documents as a starting point. Then we basically ask have you done any process mapping studies, six sigma process in the past? We'll use that as a starting point.
Any contracts, customer contracts that you can share with us so we understand like what type of project work you are bidding, what are you actually selling, KPIs and components like that. Then when it comes to the dynamic systems integration, we integrate not only with scms but which we are certified at workday, SAP and Oracle. That's where the work actually happens. You mentioned Salesforce. Any such tool we either go through an intermediate system or native systems directly go into the native systems and the third layer is actually an evidence layer. The evidence layer is Microsoft work IQ product which actually sits on all teams, slack emails and then in a PII redacted way it gives you okay, this recruiter is going to spending a lot of time on setting up appointments, things like that. That data many companies won't easily give because they are very worried but we have the ability to do that. Then we've been working quite a bit with the C suite office to say hey, what is your strategic plan? Is something are you looking at additional capabilities through the AI lens or new technology lens Depending upon the types of industry, types of companies they will have certain things prioritize and certain things not prioritize. So they may be going in alignment to the industry or they may actually be thinking a little different.
What that that will give us understand what futuristic skills are required. And once we do that, we basically make an agentic understanding of what we call as a data health module. The data health module. One thing I realized Cathy is like it's much less about output in these companies.
It's a lot more about how can we help them understand what data gaps they have. And we realized that when we modeled their R&D department, the skills were not very granular. They had intentionally kept it very broad. And that actually has an impact on how recruiter interprets the job and even tell the story to the potential hire. So when we highlighted all that and say out of another auto company that we work, like we said, like 40% of your job description looks like it is really old, even though you say you updated it last month. But when I read the content, look at you are trying to hire an IT manager. There is no reference of anything about AI. But if you hire IT manager today and they come in next month, 90% of their work is going to be either doing something with AI or evaluating AI. So you should at least have that layer. And so lot of the ROIs and the benefits and the interesting things seem to come from correcting the enterprise data that's there in different systems. Another thing I realize is the HCM systems have been completely disconnected from the ERP systems and other actual systems where work is happening. But they are actually saying they have that skill. But when you go and look at the hcm, that looks like very traditional skills, almost industrial age skills, there is a gap that HCM is already sustaining than where the work is happening in the ERP systems or a Salesforce system. Can we actually pick an intermediate system like databricks or a snowflake and then say hey, you push all this data there and rather than us connecting to 20 different systems, can we just read it from there? And your job is to kind of keep these databricks or snowflake updated. You don't have to update real time every day. We see that quite a bit in cybersecurity where the existing cybersecurity roles are not capable to handle AI based cybersecurity attacks, for example. But the IT may have put some processes there, but they may not have mapped the skills and given that to the HCM people to actually say this is where cybersecurity is moving. So we're finding a lot of these fascinating things when we start looking at the enterprise as a whole.
And we have built something called Workforce Twin that helps you simulate all these different scenarios in a more logical way. So that's kind of how we've been going after an understanding customer's tech stack and say, hey, if you already have Snowflake, if you already have databricks, you just push the data to that. And quite often there is quite a bit of buy in there because if they have something like that, they are already working on a project like that. So that's how we've been approaching that.
[00:16:37] Speaker C: I give you access maybe to my hcm, but not to my salesforce, not to my work systems and all of that because I don't want to build these integrations obviously with somebody that seems to be more in the HR area for it. What you're doing is seen probably more in the HR bucket than they say. We only need access to the HCM system. Double down on the work redesign. How that's a new skill that HR hasn't historically done a lot of. Because HR always said we own the job descriptions and we own the skills, but we don't own the work because the work of course we can't understand really how the work flows. And so now as HR and workforce planning and the chro and HR business partners, quite frankly are all asked to help with this AI transformation from a work and workforce perspective, it's a new muscle that we need to build. How does your tool help do that?
[00:17:34] Speaker A: One of the challenge that I do see is in many times the HR leaders are they do not have a clear understanding of what is the work differentiating data assets or reasons why a company exists and makes money. So this is so that's number, that's number one. Number two is rather than telling the story by if you ask a chro, say six, seven months back, we always tell the story of oh it there is five job families, 20 job roles. And then when the tasks came we say okay, we are doing 5,000 tasks. We're trying to change that story to say that okay, there are 10 processes that your IT is doing or your finance is doing. And out of that four processes are AI is heavily compressing. Two processes are AI cannot do anything like a Treasury process, for example. You still have to have a lot of human control if it is finance. And then there are new processes that have been introduced because in finance, for example, we have to evaluate all the AI models and see which one will cost much token. That's how we are approaching this.
[00:18:51] Speaker C: Because it can be very limiting. If you just start with these are the tasks and we just cut tasks out.
Because sometimes you're just doing things completely differently rather than just cutting little tasks out.
[00:19:04] Speaker A: Correct. When you actually go, you made a brilliant point. If you do a task level estimate of this task can be automated. That task that helps really well from a 1A perspective, okay, I have, I do 10,000 tasks in my enterprise. Out of that, 2,000 tasks can be potentially done by AI. 8,000 or 10,000 is my opportunity. It helps in that level. But as you grow closer and closer, deeper and deeper, you need to know when. If that is 1A, when you go to 1B, you need to know what are all the broad technologies I'm going to bet on it could be Palantir, it could be anthropic, it could be workday AI, whatever those are. There is a context that we need to bring in depending upon whether that review and study is a radiography versus a finance. You have a completely different automation potential. That's where we bring in that workflow and say, okay, this task in this workflow is 10% automatable, but the same task in this workflow is 50% automatable because it's a simpler workflow. That's where the third model is also very important. Yes.
[00:20:16] Speaker C: The other thing that I really appreciate about your model and your approach to that is how methodological you go basically from the biggest picture all the way down to what are the skills that we now need for employees and how do we build them, how do we buy them, how do we. The 3B or we call it the 4R framework. Basically same thing. Because that whole skills scenario has been a little bit confusing for many HR people to say we start with the skills, but you can't really start with the skills if you don't know what the work is going to be. So you got to start with the work and then work your way towards the skills, not the other way around. Otherwise you always miss skills to what ends. Right.
[00:20:56] Speaker A: We actually are writing on paper. Maybe I'll share that with you in the next couple of weeks. But one of the interesting thing that we are seeing is the root skills. So we broke the skills into four layers. So root skills, core skills, tech stack and soft skills. So the root skills are the foundational skills. Core skills are very attached to the specific job.
And Tech stack is just technology, products. Soft skills is problem solving, creativity and things like that.
Now what we noticed was there has been lot of confusion between root skills and soft skills and people were mixing both.
Something like statistical thinking is really not soft skills. That's more a root skill. Because using that you have to do ANOVA variance analysis. If you are a finance analyst, that ANOVA variance analysis, a core skill, statistical analysis is a root skill. Like we mapped all that in a more granular way. And then what is the soft skill associated with that? Articulation of data, storytelling, all that on the soft skill. And then you can use a SaaS like product or an SPSS type of tool for doing that. Like we're trying to bring that network view of skills and that's another huge work we are doing and we are very close to implementing that model.
[00:22:24] Speaker C: Where are you taking the ETTER product and overall what you're doing here on work redesign and that bucket that we've talked on next?
[00:22:32] Speaker A: The whole ETTER is moving into what we call as a visual factory modeling where you can literally see a function and then understand, hey, there is six processes, 10 workflows here, double click that, that see the task and then say, okay, I'm going to delete this task. And in general just expand the boundaries of SWP and talent intelligence because now is the time for all of us to we always want to align more with business and AI has come in to force us to do that. So that's kind of the other aspects of building the muscle of talking to the CFOs and explaining that how we have done the model and how they should use that input. And rather than stopping the conversation the minute someone from finance enters the conference room,
[00:23:22] Speaker C: the last question I have for you is what's your word of advice to our listeners as they want to learn more and get more into this constant work design. We call that dynamic work design world. What should they do? How do they get started?
[00:23:36] Speaker A: The biggest lesson that I say is rather than following a traditional hierarchy, and this is something we have learned in, let's say you are actually going to do something with finance or procurement rather than going and setting up a meeting with a VP of procurement, VP of Finance like that, go a little deeper and understand from the financial analyst what work are they actually doing and connect at that level and get the validation from them. And that helps in two ways. One is we know the true nature of work that's happening, the true shape of work. If we know that your backend system in finance is PeopleSoft, you actually cannot select Gemini, for example, for automation because they don't have a connector that flows well. They're still building it, their version of it, but it's not optimal. So some of the models don't connect to the legacy systems, then you have to look for some intermediate products that will connect well to your legacy system. So you are having all this null and if you are actually as making an assumption that hey, we have built this agent, this will actually save us two FTEs. But when you take it to them it's like, okay, it cannot because it can crunch the number. But how will it Update to the PeopleSoft? There is tons of benefits in that, but I would say have a 1A, 1B on 1C. But equally important, that's what my learning has been.
[00:25:08] Speaker C: Really appreciate your time and your insights, Vijay. Thank you. What an interesting conversation with Vijay Swami, the CEO of Drop. We talked about the latest offering aeta and how it helps companies transform their business and the workforce around AI in five big use cases. They're all centered around making more informed workforce and work decisions.
Everybody's talking about how work is changing and here's Vijay and his team supporting this with really granular insight instead of guesswork.
Thanks for listening to the what Works podcast. Until next time, keep exploring what works in your world.
