People Tech with Mark Feffer and Pragya Gupta on AI’s Role in HR

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This guest podcast, originally aired on People Tech, features Mark Feffer and Pragya Gupta, Chief Product and Technology Officer at isolved, as they explore AI’s growing impact on HR. In this episode, they discuss shadow AI, automation, and the ethical considerations shaping today’s AI applications in HR. Pragya shares insights on how AI is transforming daily HR tasks, enhancing personalization, and streamlining strategic decisions across organizations.

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Transcript:

Mark Feffer  00:00

Welcome to People Tech, the podcast of workforceai.news. I’m Mark Feffer. Today I’m joined by Pragya Gupta, the chief product and technology officer at isolved, they’ve been leveraging AI for some time. So we’re going to talk about the state of AI today. Shadow AI. AI’s role in developing HR, in the expectations around implementation and adoption, all that and more on this edition of People Tech Pragya, welcome. It’s good to see you again, and thanks for being here.

Pragya Gupta  00:45

Always good to see you, too, Mark, thank you for having me.

Mark Feffer  00:48

Sure so I wanted to talk about the state of AI. You know, it’s been a couple of years now since chat GPT showed up and sort of got everybody to pay attention, and solutions providers have been working pretty hard to include AI features. I’m wondering, you know what? What progress have you seen since, say, 2023 where the capabilities of AI and and also the adoption by customers.

Pragya Gupta  01:21

Yeah, that’s a great question Mark. As you stated, it was a couple of years back when with the advent of chatGPT and particularly LLM models, AI became far more pervasive than it used to be. AI has been around for a while, but it wasn’t so pervasive, especially in the SMB and mid market spaces, and now it has become widely available. In fact, you know, one of our we did a study, and according to our HR leaders report, 47% of HR leaders believe that AI it can benefit the employee onboarding process as an example. So it is, you know, we are seeing a lot wider adoption. We are seeing a lot wider curiosity. You know, HR, HR leaders, when we speak with customers, when we speak with prospects, that’s one of the biggest questions on their mind. How can, how can AI change the way I do business. How can it make me more efficient? In my view, business leaders should consider solutions like, you know, HR chatbots, which can give, for instance, HR teams, time back in their day to day, so they can be more strategic. They can use things like predictive analytics, which can drive business decisions. We have launched, and I know many, many vendors in this space are thinking about job description generation using AI, candidate job matching to stream, streamline the recruiting efforts. Another example that comes to mind is payroll anomaly detection to ensure accurate payroll every time, right. So there are so many solutions that that are out there, and HR users practitioners should be thinking about them, if not already.

Mark Feffer  03:14

What are you finding? Adoption is like? Are most of your customers using it or expressing a desire to use your AI solutions, or is it sort of an active minority?

Pragya Gupta  03:29

What we are seeing is that they’re very they’re very curious, right? As I said, they are. They’re asking us questions. Because what is happening is internally, most organizations are having discussions about, how can we become better with the use of AI? Some Some organizations are asking themselves questions, should we maybe have less people because we have AI? So it’s not about reducing your workforces, but making your workforce do more with less or be become more strategic, focus on things that are driving the business forward, rather than just the very midosh tactical work. And which is why HR leaders are asking us the question, What? What can we do to become more efficient, more process driven? And in AI platforms, they can, you know, they can drive employee education, training, benefits, and with, with the advent of technology, all these, all these tools and technologies, they are becoming more and more. They’re becoming better. And also the models are learning from the plethora of data available to them. So we are seeing a lot of a lot of inquiry, inquiry. The other thing I would say is that, you know, for small and mid sized business businesses, we are seeing that, AI, it’s really driven by internal experts, right? There’s like. A pocket of individuals who are more curious than others, so they are asking the question, and they’re propelling the organization forward, whereas in large enterprises, it’s also about scale. How can we build the scale to be able to grow more so it is. It is a little little bit nuanced, if you will. But there is a lot of anticipation, interest and discovery that has gone on.

Mark Feffer  05:24

Are you finding that the employers and their employees are aligned on the use of AR? I’ve been reading a lot lately about shadow AI, where people are basically bringing their own AI solution to their their jobs, and it seems like there is a set of employees who are just very, very enthused about the tool, and may have sort of passed by their company or moved ahead of their company. What’s your experience been?

Pragya Gupta  05:54

That’s a very, very interesting insight. And actually, I 100% agree with you. You know there are as as humans, some of us are more curious, or, as you said, enthused, than others. So there are a lot of individuals who are coming up with smart ways to explore and use AI, right? Even even with chat GPT as an example. As you start using it, you understand that your prompts drive so much of the output, right? So the better prompts you use, the better output you get out of it. Are outcomes you get out of it. So definitely, there are, you know, cheer, there are people in the organization who are ahead of others in their use of AI and exploration and discovery of AI. And in my view, you know, organizations need to embrace those individuals and ask them to be the torch bearers for their departments. They in the in the beginning, there was, about a year ago, I think there was a lot of you know, some people were scared. What does this mean for me? What does this mean for for my job? But with with time, especially with those, some of the leaders who are, you know, who want to explore more, those are essentially paving the way for others in the organization, and we are seeing a lot of that

Mark Feffer  07:23

Now You’ve been both you personally, but also isolved has been immersed in this pretty much from the beginning. So what have you learned in the last year or so about AI and its use by HR,

Pragya Gupta  07:40

yeah, definitely. So according to recent research, and particularly from the HR professional survey, you know, 37% of employees, they still feel threatened in some ways, that AI can can replace their job and and what we are seeing, what we are seeing from the surveys that on an average, AI is handling 34% of HR related tasks in the organizations we surveyed. So 34% is a big number, if you think about it, because this means that that time is now being freed to go do more strategic things. And, you know, just HR. I’m so happy to see that HR is driving a lot of, you know, embracing of AI. And what we’ve seen in our survey is that 77% of HR professionals believe that AI training is critical to enhancing their roles. So, you know, we’ve gone from people being threatened, but ultimately, like 77% are saying that we want to learn, give us more. How can AI make us better? And also, that’s, that’s a really good stat that happy to see HR professionals jumping on the on the band wagon, so to speak. And and HR professionals that are using AI, they are saying that automating those routine tasks. You know, I talked about HR chatbot as an example, like, imagine how many times HR is just being pinged day in, day out. What is my PTO balance? What is the tuition reimbursement policy? What is travel policy? These are questions that can be answered by Bots as an example. So now, now what we are seeing from these professionals is that 81% of these professionals that are using AI, they are saying that automating these routine tasks has allowed them to focus on building more meaningful connections, more strategic discussions and discovery with the employees in the organization, rather than just that answer back and forth, answering sessions.

Mark Feffer  09:56

It strikes me that implementing AI is kind. A nuanced thing from the human point of view, from the user point of view, there’s been some research done that shows employees look at their employer as the source of training. Basically, they’re relying on their employer to train them in its use. Do you find that employers actually get that and they’re supporting their their employees when they implement an AI platform,

Pragya Gupta  10:27

I do, I think you know, it comes down to, if you you know, if we as organizations, we don’t train our employees, there is a skill gap, right? And it is always going to be harder to, you know, to get we could just say, Okay, let’s go outside and get more, get employees who know, AI, but then you lose all the organization context, all the growth that has happened in the employees. So it’s, it is better for organizations to train their existing population, whether that is sending them to relevant courses or, you know, helping them enroll in learning programs, which is what we’re seeing quite a bit of it is better for organizations to do that. Invest in their employees, let them get to the point where they are not scared of AI anymore. AI is becoming an asset. And what you could do, what five individuals could do in 10 days now, they can probably do that in two days, which doesn’t mean that, you know, you don’t need as many employees. What that means is that they can go do more. So organizations have this ability to propel themselves forward, and that is where the training is extremely important and and find building those relevant curriculum, sending them to those relevant programs, is pretty critical for organizations to embrace.

Mark Feffer  11:52

So where do you think this is all heading? Where do you think AI is going to be especially useful to HR, we all talk about automation and such inefficiency right now, but are there other things that it can bring to the table?

Pragya Gupta  12:12

So I think most definitely automation efficiency, but also the personalization of experience, is something AI can do another, another thing that is very relevant is, for instance, in think of in the benefit space, right for as long as we’ve been in this space, even when we have to enroll in our own benefits, you know, should I use should I choose HMO? Should I choose PPO? What is a hospital indemnity plan? Is this the right plan? What should I use? HSA. How should I contribute to HSA? Did I select the right plan or not? These are questions that we as individuals are ask ourselves every day, every time we have to do open enrollment. Now, as much as you know, we’ve talked about efficiency, we’ve talked about automation, if we can give individuals the right tools, you know, ai, ai recommended assessment of benefits, or, you know, AI enabled recommendations when you’re when we are choosing our benefits plan as an example that now is taking it to a whole different level. It’s not only about productivity, it’s not only about automation, it’s about personalization, and it’s about in helping AI make us make better choices. Or, for instance, you know, can we creating models that can be trained on data, on plethora on a lot of data? For instance, someone who is in my demographic. You know, I’m two children, you know, maybe more doctor visits. This type of plan is better for you now. That is powerful information that helps me make a good, good decision, good health decision, good, good financial decision for my family. So that that ties into financial values. Another example we touched on earlier was payroll, payroll efficiency, or, you know, make, making, ensuring that payroll is correct. You know, whenever we do a survey of employees, what is their number one reason for leaving payroll is actually still in this day and age, a big reason for why employees leave the organization. It could be incorrect payroll or and are not feeling adequately compensated. There’s a lot of factors that come into play. But you know, whenever I talk to payroll payroll admins, all of us have at some point that with the stress of a payroll Thursday, right processing, or there were changes. You know, some employees was doing part time hours, they’ve been there now doing full time hours, but we have to make sure that the HR classification has been done so we don’t have compliance issues. You or we don’t have financial implications. So think of payroll assist using AI as as a smart assistant in your car right drivers assist in your Smart car, you clearly know how to drive, but when you are barreling down that inner state and your car is goes left or right, or there is an there is a discrepancy in your driving pattern, the car tells you, hey, you’re not driving well, go take a break. Similarly, you know, perfect we’re working on this model. It’s called perfect payroll. Essentially, it’s enabling the payroll admin to make the right decisions ahead of processing payroll, not after, not after, payroll has been processed, and then the employee says, hey, my hours were incorrect, or I wasn’t classified from a part time to a full time, or whatever it is, you can proactively take care of these anomalies. You can proactively address them. So it’s so I mean, it is the payroll example that the one I just gave. It’s still a little bit of productivity and efficiency, but now it’s not only productivity and efficiency, it also is experience, because now my my receiver doesn’t have to call me and tell me there was a discrepancy. You took care of it ahead of time.

Mark Feffer  16:15

Now, AI is obviously a big part of the vendors messaging right now, and I’m curious about the customer’s expectations. When they start the discussion with you, are they believing all the hype that’s out there and setting goals that aren’t realistic, or are they taking a more pragmatic approach? What? What’s it like out there?

Pragya Gupta  16:42

Absolutely That’s a great question, because you are absolutely right. When we talk to prospects, when we talk to customers, they ask, you know, as much as they they are happy to hear about productivity, efficiency and experiences. They are also very much focused on data privacy, ethical practices. They are asking that, how, how are you making sure that your models are not intrusive? So they are thinking about these things, and you know, like, are is? Are we? Are we going from just prediction and guidance to oversight and control. What does the ease of use of these AI tools look like? So, so, so customers are are focusing on not only what the efficiencies, but they are asking us the right questions, especially around biases, misalignment with company values, intrusion, human oversight, they are asking us questions about them, and rightfully so.

Mark Feffer  17:47

Are they satisfied? Do you think with where we are right now?

Pragya Gupta  17:50

Yeah. So how I how I think about it? Let’s take one example. So let’s maybe take example of ethical, ethical practices in AI right when? When we build an AI model that is like, that’s maybe take example of candidate matching, right? So when, when we match the skills of the candidate with the skills of the job. So think of when you post a poster, a job opening, right? Job posting, you get 1000 resumes. Now, instead of manually going through each of these resumes, we have built an ai, ai based candidate matching model, which will say that these top 10, these top 10 candidates, are the best fit for the job based on their skills, based on their their experiences, as an example. Now, what a customer or a prospect wants to make sure is that there is no bias in this model. There’s no bias based on gender, there’s no bias based on age, there’s no bias based on race. That’s what they want to make sure, right? So as the developer of these models, what we what we do is two things. First, whenever we are building this model, we strip the, you know, the data exchange of any of these personally identifiable information. So then, when we are training our models, our models are not trained with anything personally identifiable. So then the models are skills and experience based only as an example. That’s one thing. The second thing that we have to do is there are ai, ai and ethical consortiums that, you know, do audits. They we participate in in in forums that make sure that our our models are continuously being audited, our models are meeting the best practices that that have been set from these. Forms so so that that’s something that every vendor should be thinking about. Every vendor should be proactively self screening themselves.

Mark Feffer  20:10

Well, Pragya, thanks very much for for talking with me. It’s always great to talk with you, and I appreciate your time.

Pragya Gupta  20:18

Wonderful. It’s always good to speak with you mark as well. Have a great day.

Mark Feffer  20:22

You too. My guest today has been Pragya Gupta, the chief product and technology officer at isolved, and this has been People Tech, the podcast for workforceai.news. We’re a part of the WRKdefined Podcast Network. Find them, http://www.wrkdefined.com and to keep up with AI technology and HR, subscribe to workforceai today, we’re the most trusted source of news in the HR tech industry. Find us. http://Www.workforceai.news. I’m Mark Feffer.

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