Mark: This is PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer.
Today I’m speaking with Geoff Webb, the Vice President of Solutions Strategy at isolved. The company recently launched compensation analytics and talent intelligence solutions, both as a follow up to their benchmark insights. The products are designed to help companies of all sizes take advantage of enterprise-grade analytics. We’re going to talk about the technology needs and demands of small business and look at what AI means to them. We’ll also talk about how vendors are trying to address those needs on this edition of PeopleTech.
Hey Geoff, welcome. Now, isolved recently said that it plans to incorporate new tools into people cloud and their aim is to help small and medium-sized businesses. And I’m curious about what those tools do and what’s the thinking behind adding them.
Geoff: Oh, yeah, sure. So I mean obviously we provide both the technology and we provide a lot of services as well and advice and things. But from the technology point of view, we’re always moving forward the capabilities of the platform because the demands and the needs of our customers and our partners continue to evolve too, as and are driven by the demands and needs of their employees and the businesses that they serve.
And so for us, we’ve been looking at, how do we reduce workload? How do we make it easier to find and keep good people? How do we provide better information to HR teams and business leaders to make good decisions? So we’re including a lot of capabilities, and I hesitate to use the phrase artificial intelligence, but we are genuinely, it has its uses and we can certainly talk about what we mean by those things, but there’s a lot more intelligence in the platform, especially in the roadmap moving forward, we’re continuing to develop intelligence in the platform for very good reasons, better analytics and just more capacity to automate and simplify the workflows.
But if you think about the objective here, a lot of the things we’re building out are, let’s just make it easier for HR people to focus on what they care about and let’s make it easier for them to make good decisions and feed good decision-supporting information and data into business leaders so that they can make good decisions about how they develop their organization and what is the future shape of their business going to look like.
Mark: When you say you’re adding more intelligence to the platform, what do you mean by that? What kind of thing is it going to be able to do now?
Geoff: So if I think about the way that we are seeing the whole role of machine learning and artificial intelligence emerging in HR technology, I tend to think of it in three buckets, three groups of capabilities, and we’re really addressing all three at various points, both recently and over the course of the next few months and into next year. I would say, first and foremost, the AI engine in the background, there’s things in the flow of work where artificial intelligence can be used to better identify trends, it can be used to simplify work, it can be used to spot anomalies.
So I’m thinking of a good example here. There’s a certain change in your payroll in some area that looks weird. Maybe there’s been a mistake made, maybe there’s, heaven forbid, fraud or something else going on, it’s the kind of thing that might get missed by very, very busy people. It’s a perfect use case for something like an AI, or basically a machine-learning model to go, “Well, hold on. Normally it would look like this. That’s a weird anomaly. You should probably go look at that.” So I mean that’s the one area. So in the flow of work, doing things in the background to simplify and streamline and help run things.
I think the second area is really around the use of AI to provide more personalization and more intelligence into the interactions with people, so things like learning what questions get asked regularly, enabling people to more easily find information that they want, personalizing and identifying, “You’re here in your job, the next things that we would normally expect you to go learn how to do are this, this, and this. You should probably take these courses.” So that sort of personalization engine that we were very comfortable with in a lot of places in our day-to-day lives starts to feed into work.
And then I think the third area is interaction. So you’ve got an engine running things, you’ve got some personalization and tuning capabilities, and then you’ve got an interaction engine capability that, things like chatbots, for example, are a great example of that that make it very, very easy for people to self-serve and just find stuff. Tell me how many days vacation I have. Is this a holiday? Who are my benefit recipients? How do I do this? Where would I find that information? And that just moves those, I hesitate to say, but very transactional conversations away from an HR professional who’s trying to get other stuff done but answers the same questions over and over again. It moves it to an automated process that actually can be much more responsive and can learn in the interactions too. So you’ve really got those three, and we’re really looking at all three of those as areas where we can just continue to build and infuse the platform with more intelligence as we go along.
Mark: These are all great examples of how small business software for HR is getting more powerful, which usually means it’s getting more complex. Given the dynamics of SMBs, are business owners and managers able to keep up with all this in understanding it, if not using it?
Geoff: Yeah, I think you’ve hit the nail on the head right there, which is the trick here is not just to put tools out there, it’s not just to throw technology at problems, it’s not just to give more and bloat technology platforms with more and more capabilities. The trick here is to embed them in a way that makes them not just useful, but really, to a degree, almost built into the fabric of the workflow. And I know that sounds really jargon-y, but basically the idea is, look, we don’t want to have people to deal with AI. It shouldn’t have to. I don’t think about dealing with AI when I’m asking my phone to go find me the closest, quickest route to the pizza store. I have a problem, I want some help with it, some technology can help me go find that.
And so the approach should always be, how do we embed more intelligence into tools to make the tool easier to use and to make the tool able to deliver more value and more capabilities? Not add additional buttons to push and levers to pull so that people become overwhelmed, and then actually start to shy away from it. And the good news is what we’re seeing overall in the industry is there’s a good move to embedding more intelligence in these tools in a way that is easier to consume for people that just don’t have the time to spend figuring out and tinkering under the hood. But it’s a dangerous path to go down to start to think, “Well, we’ll just make it smarter and smarter and smarter and it becomes more intelligent and harder to use.” No, that’s not the job here at all.
Mark: You’ve actually just teed up my next question because I’m always wondering about users, employers, and employees, do they really care if something is AI or are they really thinking about what can I do with this product or what can I do with that tool?
Geoff: I think it’s exactly right, and it shouldn’t matter to them. I mean the point should be that, and I actually say this, I talk to a lot of our HR customers and our partners and I tend to frame up the importance of AI in these technologies is really this; it is, look, on one hand, it can help answer questions for you. So in other words, if I have a question, the tool, the technology can help me go find the answer more easily and more quickly, and that’s something that they care about. But I think the thing that really separates where we’re headed with this technology from other approaches from just raw automation is that AI can not only help you answer a question more easily, but it can help you identify the questions that you should be asking yourself.
In other words, again, go back to that example, I’ve seen a change occurring here that looks a little odd. You should probably look at that and understand why, because it looks like it’s sort of out of bounds. There’s something odd going on here. Take a closer look. You’ve suddenly changed your hiring practices, you’ve suddenly changed how much you’re spending to bring these people in. You’re suddenly seeing a change in the diversity of this particular department, and it’s in a weird way, maybe just take a look at that. And that they do care about. They care about getting to focus on the things that are important to them, whether that’s, again, retention or quality or higher productivity or whatever it happens to be, generally building a good employee experience.
Those things they care about, the how you get there is and should remain essentially irrelevant to them. That’s our job as a technology provider and supporter of these organizations to figure out how to infuse the tools they use with more intelligence to make them easier to use. I don’t care about how the engine in my car manages its fuel efficiency, I just want to make sure that when I press the accelerator it goes in a good way. And that’s really how we should be thinking of this. Technology should be getting easier to use, not more difficult simply, but if it’s getting more sophisticated, that’s a good thing.
Mark: You mentioned, a few minutes ago you were talking about the flow of work, and I know that there’s details about your approach to the flow of work in your roadmap and other vendors are also talking about the flow of work. It’s what people are paying attention to. So if users are accessing tools through, let’s say, I don’t know, for lack of a better word, satellite apps or things that are directly packaged in with isolved, what’s that mean for your product and your product design?
Geoff: So I think a couple of things. I would say, from a design perspective, you have to have a great degree of consistency in the way you interact and you have to build in from a design perspective, simplicity and ease of interaction. So I think of examples of we have customers who would have, they don’t have a whole bunch of people sitting in offices necessarily working on a PC or on a laptop or something chugging away doing things. They have people out on the road, they have people out on construction sites, they have a nursing staff who are driving around between different locations. They want to be able to very easily get access to information, do things. They want to be able to do it at the weekend when they’re not necessarily, there’s nobody back in the home office. They want to do it from a mobile phone that might be several generations out of date, like mine.
And so you have to make it so that there’s a great degree of consistency and accessibility to everything that you build. And so you have to start from that perspective, which is what’s the right approach. We took a design decision to not build things like a dedicated mobile app simply because they always lagged behind, they were always harder to maintain. There’s always another interface to deal with. So we built, without getting into too much of the technology details, the approach we built was using what’s called a progressive web app, which again, I won’t go any more technical than that, but the objective here is that the way it’s delivered is completely consistent across every platform. And by the way, other companies are looking at this too, it’s not that we’re the only people who’ve spotted this as an approach, but that’s always been our approach.
And so what it means is you get comfortable using the technology so you can self-serve, which means that it’s always available, always consistent, there’s a lot less work for everybody involved. And so you build that in to the way you design the interface. And then you layer on top of that the things we were talking about just now around greater intelligence, interaction with chatbots, natural language processing so that, in other words, the objective here is I don’t have to, even as easy as it is, navigate through to some menu to say, “Tell me how to do this.”
I can literally just ask the system and the system understands enough of human interaction and language to be able to go get me the answer that I need, and then even more importantly, take the next step, which is to say, great example, “You asked how many vacation days you have left or how many hours you have left this month, here you are. Does that mean you’d like to book a vacation day? If so, let me know and I’ll run that system.” And go automate the steps in the background. “I’ll pass from the interaction layer back to the engine in the background. I’ll go do that for you. Let your boss know. Tell them. When they’ve said yes, I’ll let you know and you’re good to go, all the records are up to date.” That’s building intelligence both into the interaction but also into the core flow of work so that everything just comes much more intuitive and natural in the way that we interact with technology.
Mark: There was something else in your roadmap that caught my eye. It talks about zero error payroll, and my first reaction was, “Really?” But can you talk about your thinking there?
Geoff: Definitely, yeah. It’s an objective, right? I mean I think one of the first things we see, when we start working with organizations, we’ll look at, we have what we call a maturity model, an HCM maturity model. And that model really lays out the journey that many or many organizations have already been through. We’ve been doing this for a long time, and as a result, we’ve seen that journey occur very often. And so we have a model that enables us to have a common frame of reference, a conversation with an organization that we’re engaging with to say, “Well, okay, we see you’re here. This is the things that we see happening. Here’s the stuff you’re saying you’re seeing in your organization. That means you’re probably here, which means that the next set of steps should be this, this, and this.”
One of the first places we usually find is that people say, “Payroll is so painful. It’s so difficult to do. It’s an extremely manual process. We often have issues with just getting accurate information on time. We’re constantly having to go back and fix things.” We know that there are studies out there that show that even minor errors in your payroll accumulate very rapidly to cost businesses lots and lots of money, both in time to fix it, but actually in things like overpaying people. And so it’s an area that we usually see first of all on that journey of, “Help me reduce the workload of running payroll and getting it right because errors cost time and money and just nobody’s happy,” right? Nobody’s happy if they’re underpaid. The business is not happy if they’re overpaid. And the HR payroll people are not happy in either situation. They’d like to get it done.
So, what we’re talking about here is, well, obviously the first step we typically put in place is the automation of that process. Let’s automate payroll, just makes it easier. But in addition, that backend engine, that thing that’s running is capable then of saying, well, the objective here is to say, “Well, again, I see a weird anomaly in your payroll from a business perspective.” In other words, “You’re running payroll, something’s happened, go check because I think an error was made there, something went wrong.” Or alternatively, from the perspective of the employee to be able to alert them to say, “By the way, your payroll just weirdly changed this month. Were you expecting that change? Because you should probably check in on that.” Both of those do a couple of things.
Both of them obviously give you more confidence that things are running correctly, they’re going to reduce errors, they’re going to reduce cost, but they also improve the experience of both sets of people. They improve the experience of poor HR payroll people who are trying to run stuff and get it right, and they improve the experience of the employee enormously because now I’m looking at this going, “Hey, there’s somebody actually checking my payslip for me, making sure I got paid the right number of hours and the right overtime and for the right projects and so on. And that gives me more confidence that these guys are on my side.” So, zero error payroll, perfect payroll, however we’re phrasing it, that’s the objective here. And the way you get there is you infuse machine learning and artificial intelligence into the process to just keep an eye on stuff. And it’s the thing that AI is really good at.
Mark: Isolved is also expanding your AI ethics policy. And I think you were one of the first companies to have one, but what are you adding to it or changing and what spurred you to do it?
Geoff: Yeah, I think that having a strong AI ethics policy is really, really important. It’s part of almost the social contract between ourselves and our customers and the broader society. We handle an enormous amount of sensitive information for our customers, for our partners. We’re talking about touching the lives of, for us as a business, what’s over 6 million employees around the US now, whose very sensitive information is with us. We want to make sure a couple of things. One, that we’re very clear on how that information is used. So in other words, we’ve entrusted with sensitive information, we want to be very clear, this is how that information is used. It’s used in the ways you would expect and would want us to use it. It’s not used in other ways.
And two, as we use intelligence and artificial intelligence to do more things, to extract or identify information that’s in there that you would want to know, that it is done so in a transparent and ethical way. In other words, we’re not going to be looking for things. We’re not going to be doing things with information that you would not want us to be doing, using that kind of technology.
And then the third thing is the AI itself is not being used in an unethical way, and you reach this frontier very quickly. There are things you can do with AI, and there are so many examples where not having a strong ethical framework and a clear ethical policy and approach can lead you astray very, very easily and very quickly down the dark side of AI. And we don’t want to do that. We want to be very clear that this is the approach we take, we live and breathe on the trust of our customers and our partners and broadly, the market overall. We need to maintain that, being transparent about it’s the best way.
Mark: So we’ve been talking a lot about very sophisticated products and very sophisticated technology, and I think most people, when they hear about products like this, they think of large businesses and the enterprise. They don’t think about small businesses. How do you view the overall landscape? And by that I mean, when you think about the HR technology industry, where does the small business market fit into the overall equation?
Geoff: Yeah, and I love that because it’s an area that I feel so very passionate about myself, which is that you talk to smaller businesses and they say, “Look, we struggle to compete with bigger organizations that can offer more benefits and can attract away our best employees. We have a small team. We need to get so many things done.” One of the really great things about what’s happening right now is that these kinds of technologies, HTM platforms like ours, the HTM platforms generally, the infusion of things like artificial intelligence into the platforms and so on and so on, all the automation capabilities, the analytics capabilities, all those things being built in the right way, what’s happening is the tools that only used to be available to large enterprises that have a big team are becoming very, very accessible and very usable and really becoming tools that are right there in the toolbox of small to medium businesses.
It’s no longer a case that they have to deal with stitching together bits of technology and figuring things out and post-it notes and Excel spreadsheets, that the level of capability is now very much available for small to medium business. And we have learned so many lessons about usability and ease of use and simplicity and elegance in the way that you use this technology. From all the things that have happened at the enterprise layer where you saw these, and I’ve worked at many large enterprise organizations, where you saw this horrific sort of bloat of technology and feature bloat and things that just made things unusable.
We’ve learned the lessons, as an industry overall, and isolved specifically, we’ve always been focused on small to medium business. So what we’re doing is taking the best of that capability and delivering it in a way that we know small to medium business, with one or two HR people, with a payroll person running over here, somebody in finance trying to help out, that small team is capable of utilizing. What it means is for small to medium business, you’ve got an amazing renaissance of capabilities and there’s new technologies available, easy to use, that are just going to give you the same capacity to identify what people care about, deliver the right benefits, tune your compensation, figure out where you are in respect to the market overall, and make sure that you’re able to hire and develop and retain the very best people.
Mark: Geoff, this has been fun. I hope we can do it again. It’s been really great to talk to you.
Geoff: No, I’ve loved it. Thank you. I appreciate it. I always love to talk about this stuff, as you can probably tell, and it’s great to chat with you.
Mark: My guest today has been Geoff Webb, the Vice President of Solutions Strategy at isolved, and this has been PeopleTech, the podcast of the HCM Technology Report. We’re a publication of Recruiting Daily. We’re also a part of Evergreen Podcasts. To see all of their programs, visit www.evergreenpodcast.com. And to keep up with HR technology, visit the HCM Technology Report every day, we’re the most trusted source of news in the HR tech industry. Find us at www.hcmtechnologyreport.com. I’m Mark Feffer.