Episode Transcript
[00:00:07] Hello everyone. This week I'm going to start a series of podcasts on HR technology providers as part of this whole podcast series. And it's a particularly interesting week because we just finished the big HR tech conference in Vegas and then there's another one in Europe next week and there were 500 vendors in Vegas. And as all of you know, the HR technology market is wildly confusing, frantically changing all the time, and very difficult to make sense of. And so what I'm going to do in these podcasts now and then is talk about what's going on in different vendors and highlight what I call the trailblazers. And what I mean by trailblazers are these are vendors that are developing things that are particularly unique, particularly innovative, and give you ideas of functionality and technology that you can use in a unique and innovative way to change your company and HR. And we have reached a point in HR, and you'll see this in the systemic HR research, that technology is a requirement. And if you have a poor or inconsistent or difficult to use technology environment, you're going to have a tough time running your HR department. And your employees are going to be complaining because they're pretty busy and they don't want systems or tools that get in the way of their work. And as work changes and we become more dynamic and virtual and part time and gig and remote, we need better and better and better tools. We need to be easier and easier and easier. And by the way, that's really the story of AI. So what we did this week was I've spent a lot of time the last three or four months and really the last 20 years looking at technology. And we are in the middle of the early stages of a massive transformation in HR technology. I mean, the introduction of AI is going to make all of these tools much, much easier to use, much more personalized and relevant, and much simpler. And that is something we're desperately looking for. And the vendors are struggling to figure out what their role will be because they have existing systems that were not built on AI. So the analogy that I've been using with a lot of companies, including vendors and people buying stuff, is what happened when we had the mobile phones. When the iPhone became popular and people started to build apps. Most software companies felt that they could build an app as a sideline to their core system. Their core system was on the web, and then they had a slimmed down version of it on the mobile. And over time, as the tools got better, the phones got better, there were more capabilities in iOS. We realized, wow, there's things we can do on the phone that we can't do in the computer. And by the way, people spend more time on the phone than they do in the computer anyway, so maybe we should focus on building the app first and then the web system second. But that took a while. I mean, that took five, seven, eight years to get to that point where we are today. And I see in our website during the day, we get significant amount of traffic through the web. In the evenings and weekends, it's almost all mobile. So all of the providers are going through this. AI is the same way. AI looks like a small feature right now. It looks like a bunch of little generative tools, generate a job description or something. It's going to change everything. And what it's going to do is make these systems simple, easy to use, conversational, and very, very personalized. And you're also busy, as am I, as is everybody else, that we don't want systems that are hard to use. I think one of the reasons Uber is successful is that app is amazingly functional. I don't think there's a single HR technology that's even close to Uber in its ease of use, or Duolingo in its ease of use. So we're going to learn a lot over the next couple of years about these new systems, even to the point that personally, I think most of the way we're going to interact with HR technology is going to be through chat, through voice, and we're not going to be clicking around on screens, looking for buttons, looking for pages, unless we need to. So what I've been doing, and I highlighted this at the conference, is I highlighted a series of trailblazers, and I'm going to talk about a couple of them this morning. And I want to talk about Four in particular. I'd like to talk about Eightfold. I'm going to talk about paradox. I'm going to talk about success factors, and I'm going to talk about Vizier. And each of these companies have some really trailblazing AI that I think you can learn from. Eightfold, as most of you know, is a company that I met them a long time ago. And what their mission was and is, is to create the best job in the world for everybody on the planet. And the way they did that, the founding team is they amassed essentially what I consider to be the Google of people. They amassed almost 2 billion employee records, profiles. They're all anonymized, but they're real people with real jobs and real job histories. And they started to build AI. It's a completely AI based system that would run models to identify skills, job title trends, career paths, mobility pass. And if you think about the complexity of that database, it is really fascinatingly, interesting. And by the way, LinkedIn didn't do this. LinkedIn didn't have that kind of sophistication when it was founded. So LinkedIn doesn't really have the capabilities that Eightfold does. And so what Eightfold does is it uses AI to identify and create models, to look for people with certain skills, to look for people with certain experiences, to look for career paths, to look for job title changes. And we use it a lot. We use it for our GWI research. And it's incredible the amount of data that's in there and the intelligence of it. Originally, Eightfold came to market as a sourcing tool, which everybody wants. They want to find diverse candidates that have the right skills without looking at their credentials or their degree or their gender or anything like that. So it's very, very good for that. And I talked to a lot of companies that found out that it was really kind of groundbreaking at that and what they found as they rolled it out and implemented it was also good at career planning. It was also good at internal mobility. It was also good at job architecture design. You can go into Eightfold, for example, and look at your job titles and the skills associated with those job titles, and you can collapse the job architecture. They have a tool for that. You can do contingent workforce planning. I mean, there's a whole bunch of cool things you can do there that use the AI that you really can't do very well in a transactional system. And if you read our white paper, you'll see that an AI system is different architecturally than a transactional system. So hats off to Eightfold for creating a market. And we certainly help them in talent intelligence. By the way, just to let you know, we are not here to sell vendors. We are not promoting vendors. We don't get kickbacks from vendors. We don't get commissions from vendors. We advise vendors, of course, as we do all of you guys, but I'm giving you unbiased information here. Based on what I have learned from these companies and their customers, we are very careful not to put together what I consider to be ridiculous quadrants or magic quadrants or grids of vendors. I just don't think that's worth doing, because every vendor is different, and they all have strengths and weaknesses in different areas unique to their businesses. The second one I want to talk to, that's a trailblazer, is this company you may not know if you're not in recruiting, called Paradox. So Paradox is a vendor that has become by far, the leader in conversational applicant tracking systems. Now, you probably don't know what that means, but basically what it is, it's a system that's been built over a decade to allow job candidates, recruiters, and other people in the talent acquisition function to interact with the recruiting process through a chat. And it is not a chat bot, by the way. A chat bot is kind of a low level concept. This is a sophisticated system, workflow system with all sorts of functionality in it that manifests itself in a chat and a natural language interface, which basically, by the way, is where all of these tools are going. Workday, oracle, SAP, ADP, all of that stuff. Five years, ten years from now you're not going to be clicking around on websites. That's my prediction anyway. So the reason Paradox is a trailblazer is they figured this out a long time before everybody else. I sat next to a woman who works at FedEx. We've interviewed McDonald's. They can transform recruiting in a way to reduce 80% of the work because the conversational interface is so easy for candidates, so easy for recruiters. And you can streamline and automate the process and make it so easy for people applying for a job, for people scheduling interviews, for people doing background checks. All of that stuff is done through this conversational workflow. And even if you're not in the recruiting market looking for recruiting, I think you ought to just look at what Paradox has done to give you a paradigm education on where AI is going. So that's the second one I want to mention. The third one I want to mention is success factors. Now, I'm about to write a big article. It's going to come out this week on Success Factors. Success Factors is a fascinating story of really managing complex software applications in a strategic way over a long period of time. Oracle is very good at this, by the way. Success Factor was acquired by SAP more than a decade ago, almost 15 years ago, as I remember, and was a first generation integrated talent management suite. It was positioned as a system of execution. Back in those days, we weren't so much worried about employee engagement and well being and hybrid work. We were worried about getting people to fill out their goals and do the things they said they were going to do. So Success Factors was a really well designed talent management system that was built on a performance management base. At that particular point in time, everybody was trying to get into the cloud. So they were acquired by SAP for a pretty big sum at the time. Now it doesn't seem so big, but it was at the time. And SAP used the Success Factors infrastructure and expertise to build out its entire suite of cloud based applications for HR, which are now available. So there were lots of fits and starts along the way because there's a lot of architectural work that had to be done. But here we are now in 2023. And at two weeks ago at their conference, they introduced a sort of massive new release of Success Factors that's not only highly integrated and really easy to use, but completely infused with AI. One of the things they demonstrated was a product called Juul, J-O-U-L-E. That is a conversational interface to the entire suite of SAP applications. Again, a little bit like Paradox. It changes the paradigm of how you use these systems. It makes them conversational, and you don't have to learn where every button it is to do something. And they are ahead of the curve on that. They've also introduced generative AI within the recruiting product generative AI in the capability center to do learning and training in the talent marketplace, which they call their opportunity marketplace and their talent intelligence hub. So I think they're a trailblazer because for a company of that size to make that big of a transition in their architecture really is hard and it really takes focus. I'm not saying workday and Oracle aren't going to do the same thing they are, but I think success factors from a standpoint of the technology is definitely in the lead here. The fourth one I want to mention is a company called Vizier. Vizier is probably not as well known. They are a people analytics platform. Very, very powerful system to pull together data from all your heterogeneous HR applications and it turns it into reports and analytics and history and charts and graphs and really incredibly powerful tool. And I do think a lot of times companies replace their back end systems when they really didn't need to. They could have bought busier and left their back end systems in place. So I encourage you to think about it that way as a layer, which I call systemic analytics that sits on top of all of the systems you have, the ATSs, the LMS, the payroll systems that you have in the company. Because even though you might try to clean all that stuff up, every time you clean it up, there's another initiative that happens and then there's something else to clean up. So anyway, visitor sits on top of all that. They're a very successful company. However, as you know, it's hard to implement analytics. It's hard to get people to use it. The average manager doesn't want to learn how to go through a dashboard and click here and there to figure out how many people they have in their group or who's about to have a birthday or whatever. So managers don't use these things very much. They tend to be used by analysts and maybe HR business partners. Well, Vizier knows that. So they built a Generative AI chatbot front end. And I don't want to call it a chatbot, it's really a conversational system called Vee. And you can literally ask it a question like who are the highest turnover teams in sales? Which individuals are most likely to leave? Who's been through training in the last six months? Who hasn't been through training in the last six months? Show me the compa ratio of all the people in my team. Show me the distribution of the performance ratings of all the people in this group, et cetera, et cetera, et cetera. And you can connect it to business data. So you can say of the people that made their quota this last period, what is their tenure? Of the people that made their quota this last period, how much training did they have versus the people that didn't make their quota? I mean, you can imagine the kinds of things you might want to do as an analyst that you can now do narratively. And this is another one that is going to be just groundbreaking. You're all probably using chat, GBT or bard. I am. And you can ask good questions that in a minute or less will save you an hour of playing around with Excel or some other tool to get the answer. And V does that. And I encouraged people to go look at it at the conference because when you look at it, you sort of change your thinking about what HR software should do. Now, there's other vendors on the list. I won't go through all of them, but let me go back to kind of where the market is at. And I had a lot of conversations. I talked to Siemens, I talked to Hewlett Packard, a whole bunch of companies while I was down there. The challenge that we're in is that most software companies have a product. They have a user interface. They have product managers. They have a product roadmap. They have a technology stack. They can't just replace it overnight. So what they're going through, pretty much everyone is they're looking at Generative, AI, they're looking at the NLP and a Chatbot. They're looking at the large language models. And they're going to make decisions on how they're going to implement it to transform their product. One of the vendors I spent some time with was UKG. You all know UKG, very big company, very successful in the hourly market. They've developed a very interesting strategy to leverage AI to improve the employee experience in hourly workforce workers, which, by the way, are the ones who are on strike. So they've got plenty of issues. So there's lots of opportunity for them there. Other vendors are going to personalize their career pathing. If you look at Gloat, if you look at Fuel 50, they're going to make it easier and easier and easier for you to find a new job inside of the company. Other vendors are going to do what paradox does. They're going to make it easier and easier for job candidates to find a job without having to filter through a job site. There are tools being developed for managers to make it easier for managers to manage teams, to get coaching on how to have performance conversations. So these are using technologies that are only in the market for a year. So when the iPhone was announced, I think it was 2008, it took a year or two, or maybe longer, for vendors to figure out what do we want to do and how are we going to do it and how do we build a team and what products are we going to add into this and what is it going to replace? An obsolete so that's why the market is under so much change, is that even though many new vendors sound like they've started with AI from scratch, they don't have the functionality of the larger vendors. So the larger vendors are going through this engineering process to figure out what's next. I think from the standpoint of you listening to this, there's two things that you should do, and I talked about this yesterday on another interview. Number one is you need to familiarize yourself with what this stuff can do, because you will pretty soon be selecting products based on their AI capabilities. Look at demos, read the stuff we've done, take some of the courses. We have an AI course, actually a couple of AI courses in our academy and play around with it. We actually are building our own AI. So I'm very comfortable and familiar with it because I've had a chance to actually talk with engineers about how it works. That's number one. Number two is to focus on the problem. And I don't want to beat this issue to death here, but these are very sexy things, and I'm sure the first time you saw an iPhone app, you thought, let's buy that. Well, a lot of the apps that came out on the iPhone when the iPhone was launched are gone. They don't even exist anymore. They were cool at the time, but they weren't built out into real use cases and problem solving tools. Now the sophisticated mobile apps like Uber or DoorDash or whichever one you use are very useful. Well, that's the same thing here. If you don't know what problem is presenting the most opportunity for you to invest in, then you might get kind of interested in the snazziness of one of these apps. You might buy it and then find out people don't really want it because they're not really worried about that issue. So just as with all technology projects, if you don't understand the business problem you're trying to solve, the ease of use may not matter if people don't sense that that's where they want to spend their time on. So if you're growing like crazy like FedEx is, and these other retail and hospitality companies, then streamlining recruiting is a huge thing. In fact, in the case of FedEx, if they can't hire enough package handlers, that's a revenue generating problem. So everything they can do to speed up the hiring is a top line solution. So clearly, lots and lots of interest in that. If you have a huge turnover problem, if you have people leaving the company because they can't find new careers, please, you can't transform the organization and open up opportunities internally, that might be a big problem. If you're trying to reskill a bunch of your scientists, that might be a big problem. So my point is that in addition to getting to know how the technology works, talk about this as a team. We know how to do this. We have workshops on this all the time on where are the most critical talent issues that the company agrees are worth spending time on, and then you can focus your AI efforts and investigations there. So I'm going to continue doing this periodically on these podcasts and I'll tell you more about some of these other vendors next time. Last week was in Vegas. I hope you all enjoyed it. I am personally going to Europe this weekend and we're going to be in Paris for Unleash next week. So I'll tell you more about what's going on in Europe in the following week and please keep in touch. I always like to hear from all you guys. Thanks a lot.
