Turning Data into Action, with Qualtrics' Matt Evans

[00:00:00] Welcome to PeopleTech, the podcast of WorkforceAI.News. I'm Mark Feffer. There's a subtle but important shift going on in how employers analyze and use data. Vendors aren't only talking about analysis anymore, they're talking more about actually using it.

[00:00:29] Among them is Qualtrics. They help their customers develop experiences for employees and customers based on what they can learn and predict from data. A lot of data.

[00:00:40] I went to Qualtrics Experience Management Summit during the first week of May, and while I was there I talked with Matt Evans. He's the company's head of employee experience product science.

[00:00:50] We talked about data obviously, but really focused on what you can accomplish when you pair it with AI. Listen in on this edition of PeopleTech. Hi Matt, thanks for talking with me.

[00:01:03] Qualtrics obviously is very involved with data. You've been sort of at the forefront I think of developing a lot of this technology which in some ways has kind of evolved into AI.

[00:01:15] I'm wondering what you're thinking is right now about how the HR community and the HR tech community is feeling about AI in general. Are they accepting of it? Resisting it? They get it, they don't get it?

[00:01:30] I think it's a great question and I'm going to go back in time to my days of being a sleazy squishy consultant and say a little bit of both.

[00:01:40] So what we find when we speak with HR leaders as well as business leaders is a mixture of concern as well as excitement. And I'll start with excitement first.

[00:01:57] When it comes to excitement, people are enthusiastic about the opportunity for AI to eliminate mundane, tedious tasks that they may not want to do themselves. And this will vary by function.

[00:02:14] So for a recruiter in HR, this may include something like sending out the LinkedIn introductions to potential candidates to fill a role or writing up the same job offer letter for roles that are very frequently filled.

[00:02:35] I would also say though that there are some concerns. Those are things to be excited about. The concerns become, my goodness, if I start to automate myself out of these sorts of tasks, does that mean they're still going to be a job waiting for me?

[00:02:53] If I am a person who has a specialized skill set that has historically been done by a human, be it some type of data analytics, be it design, be it any of these things that have historically been done by a human and have helped people obtain a high level of status in their organization,

[00:03:16] if people start to see those elements of their job at risk of being replaced by AI, those are the times when people get concerned. That's when the concern starts to outweigh the excitement.

[00:03:32] I think about some of the work of Daniel Kahneman when it comes to loss aversion. What Kahneman says is that people essentially hate losses more than they like gains.

[00:03:45] So all things being equal, I would argue that if I'm excited about two things about AI making my life better and I am concerned with AI making two parts of my job worse or go away or be a threat to my status,

[00:04:02] the concerns are honestly probably going to outweigh the excitement just based on how our brains are wired to fear loss more than like or be optimistic when it comes to gains.

[00:04:17] So think about two sets of people. There's the executives and business leaders who polling shows they're very excited about AI, and then there's the people who are doing the work who aren't so excited about AI. They're much more leery.

[00:04:32] How do you bridge that gap but also how important is it for executives and business leaders to face the fact that there is a gap?

[00:04:44] That's a great question and I'm glad that you pointed that out. Our Qualtrics research shows exactly what you just pointed out. It's that executives are far more enthusiastic when it comes to the promise of AI than are the rest of the workers.

[00:05:02] I would say that there's a lot of things that are unbridled optimism. Some might say if you're being pessimistic about it.

[00:05:11] What I would say is that one, it's important for executives to understand that that gap exists like you said but then the question becomes what is it that you ultimately do about that?

[00:05:23] Okay, I would offer two suggestions to leaders to bridge that gap. The first would be to think about those folks who are between top level leadership who are very enthusiastic and the majority of the employee population who are individual contributors who are the most reticent when it comes to AI.

[00:05:46] Who's in between those? Who's the bridge? I forgot your middle managers and you got your managers. As in I would argue any type of effective change management plan, you need to get those folks on board to act as change agents, to act as champions of the change.

[00:06:06] Those are the folks who ultimately individual contributors are going to take their cue from to listen to or that people directly in their chain of command.

[00:06:14] Not from leadership, not from leadership who is at the top of the organization who is five levels above them and there's very little in common between what an IC does on a daily basis and what an executive does on a daily basis.

[00:06:28] There are those folks who sit in the middle, the executives can leverage as people who are going to help transfer that executive enthusiasm down to individual contributors.

[00:06:40] The next thing I think that can be done is to start thinking about how can we take this big blob of ambiguity around AI and what it ultimately means and turn that into a set of discrete tasks and activities that are actually performed by people.

[00:06:58] I think about this primarily because in my role of Qualtrics I'm largely interacting with an HR and an employee experience audience. I think about this in terms of an HR function. So if I think about an HR function like let's take hiring, take talent acquisition for example.

[00:07:17] We have a series of tasks that anybody in a talent acquisition function is going to be responsible for from authoring a job offer to conducting screening interviews to ultimately deciding who's going to move forward to get an offer and then negotiating.

[00:07:37] Each of these different tasks and there are obviously many more if we're talking about talent acquisition they're very busy people. There are a series of tasks and some of those tasks are in fact fully automatable.

[00:07:50] And I would argue that those tasks that are fully automatable are often going to be the ones that people who got into recruiting enjoy that human to human connection, enjoy the conversation, enjoy the sales element of recruiting

[00:08:01] are going to be happy to get rid of that initial blast out to people you are sourcing on LinkedIn to say hey please apply for this job. Crafting a job description based on some messily written job requirements from director in the business.

[00:08:19] These might be the types of things that we look to either fully automate or heavily augment with AI. From there we look at those tasks would become a human machine combination effort.

[00:08:31] So organizations that are hiring a large amount of people into similar role time after time like take a large insurance company hiring people into a call center. By large it's going to be a very similar screening process for folks who we're hiring into those types of roles.

[00:08:50] That could be an opportunity to think about incorporating AI when it comes to the screening process whether it is authoring screening questions whether it is doing video interviews and using AI to monitor body language

[00:09:03] and the words that are coming out of people's mouths to help essentially analyze the interview. That is the type of thing that we can see being augmented by AI. Humans still in the loop looking at the results of what AI is doing for us to make decisions.

[00:09:18] And then we get to the other end of the spectrum which is those things that we don't really want AI to touch either because AI cannot ethically nor effectively do certain things like making a hiring decision like facilitating a hiring committee like negotiating a job offer.

[00:09:39] These are really areas I would say at least here in April of 20 May of 2024 as we are as we are talking today. Those are tasks that need to be done by humans. When you think about that gap or actually people in general are their perceptions of AI realistic?

[00:10:02] I mean are the executives really looking at a dream and are the workers having fear fantasies or are they being kind of even handed in understanding what's really going on? Yeah, I think that's a good question.

[00:10:16] And what I would say is that there is a little bit of both and I think it largely stems in both audiences from not fully understanding what we can expect from AI.

[00:10:31] If I think about the perspective of the employee, I think this is where the fear element comes into it quite a bit.

[00:10:41] An individual contributor or logs in to LinkedIn, they may see in their feed person after person getting these wild certifications and things that didn't even exist six months ago.

[00:10:56] They may see every other thing that they are reading being AI, AI, AI and talking about it in complex difficult to understand terms that doesn't make too much of a sense to them. And so they may think how is this ever going to impact me?

[00:11:13] All I'm seeing here is something that may be coming for me to change my job or take my job.

[00:11:20] The executives who may have a different lens on when it comes to the information that they are ingesting when it comes to AI may look at very specific use cases and say to themselves,

[00:11:33] yes this can work for us, yes this can work for us, let's do this and let's do this and let's do this and let's do this and let's do this.

[00:11:39] Without actually taking into account going back to that gap that you referenced earlier, without taking into account that gap that exists between where they are at and where employees are at.

[00:11:49] I mean I think history proves out that those companies that are able to efficiently adopt new technologies or those companies that ultimately win in the market and I think that's what gets executives excited.

[00:12:05] Where I think some executives may fall short, at least in the conversations that I have, is that we're not necessarily thinking through some of the very real human change implications that come with this level of major transformation.

[00:12:22] And this is, no mistake about it, a significant, significant transformation in terms of the world of work. Probably the largest, I shouldn't say probably definitely the largest since the onset of the digital revolution of the 80s and 90s.

[00:12:37] How can people, again talking about executives, but how can business leaders get ready to make a purchase? I mean if they're, the notion of AI is becoming so ubiquitous.

[00:12:51] I've got to imagine almost everybody is like looking at are they going to implement Qualtrics or somebody else. How can business leader who's not a data scientist stay out of trouble?

[00:13:05] I think it's a very good, I think that's a very good question. I think the first thing that I would say is that they need to rely on a cross-functional team of folks that certainly has to include HR.

[00:13:17] This is not just an IT decision. This is not just a business decision when we are talking about this kind of technology because of the very real and perhaps outsize human impact that adopting artificial intelligence can actually have.

[00:13:36] So that's the first thing that I would say. In IT purchasing, you have the notion of an integrated product team where procurement comes together with the IT leader and the CIO and the security person and the business that's actually looking to purchase things.

[00:13:54] I think I would double down on that when it comes to AI and ensure that there is a party in the room who has the human interest in mind, and then that party makes very logical sense for that to be HR.

[00:14:07] I think the second thing that I would point out or suggest is that we should get very specific on the actual uses and use cases that we are looking for when it comes to AI.

[00:14:20] Much like when we are considering purchasing any kind of technology, I think we need to get very specific around the business problem that it is that we are trying to solve and not just be swayed by the potential bells and whistles that some of us have.

[00:14:37] Certain products and certain technologies can purport to offer. I think by focusing on the job to be done, the problem to be solved, we can help focus and prioritize those technologies that are being acquired and those technologies that are being used.

[00:14:55] I think we are largely seeing that to a degree. What we see at Qualtrics and the way that we have tried to build is that in our conversations with executives, HR leaders, frontline managers and even employees, what we have found is that there are a certain set of jobs to be done within the listening space

[00:15:16] that are very much open to AI, whether it is in the research design. The idea that AI can support people when it comes to question and questionnaire design through to analytics when it comes to helping people understand their qualitative data better

[00:15:37] and moving from descriptive to predictive and ultimately suggestive analytics to providing science backed recommendations for managers and leaders to take based on what their specific results are telling them.

[00:15:51] So these are the types of things at Qualtrics that we have heard are areas where we can really help companies save time. That is what we have built for and just bringing it back again to what leaders should be looking for when it comes to acquiring technologies

[00:16:06] Because when we talk about acquiring AI, very often we are talking about a set of AI features embedded in another kind of technology. They should be thinking about the problems that they are trying to solve that the AI features incorporated into a product can actually address for them.

[00:16:22] I want to shift gears a little bit and talk about Qualtrics.

[00:16:26] Qualtrics it seems to me over the last few years has been paying more and more attention to the employee experience and to HR in general. It also seems to me that a lot of the products that you develop for consumers pretty much directly or very close to what employers need to know as opposed to someone they are trying to sell to.

[00:16:50] So how do you leverage that? How does Qualtrics sort of determine that this feature is good in both places? How do we tweak it or revise it to make sure it's doing the right thing in the right way.

[00:17:07] So I think you are completely right. As an experience management company, and I'll just primarily talk about customers and employees here as I go forward, people are looking for are largely the same things.

[00:17:23] They are looking for a company to essentially do what they say they are going to do. They are looking for a company to live up to the brand promise that they purport to deliver either as an employee or as a customer.

[00:17:41] So it makes perfect sense that the technologies that are used on both the employee side of the house and on the customer side of the house really mirror each other.

[00:17:51] When we acquired Clare Bridge a couple of years ago now and have incorporated that into product, it is a very similar deployment on the employee side of things as it is on the customer side of things.

[00:18:06] Helping customer experience and employee experience leaders understand what it is that is most important to people either as a buyer of a product or as a provider of labor to a company. And what is people's emotion around those specific experiences?

[00:18:25] So for an employee that may show up as I am talking about my manager. My level of effort in dealing with that manager is very high.

[00:18:36] My negative emotion in dealing with that manager is very high. My level of trust with that manager is very low. Hopefully we don't see that a lot.

[00:18:46] And with a customer it is a very similar sort of a thing. How do I feel about a specific product? Or how did I feel about that customer service interaction that I had?

[00:18:56] So I think you are completely right in that there is the opportunity to leverage product features at Qualtrics on both the customer side as well as the employee side.

[00:19:07] One of the other things that we are seeing in terms of growing interest when it comes to our customer base is the idea of linking employee experience data to customer experience data.

[00:19:21] More and more we are hearing from both employee experience leaders as well as customer experience leaders tell us the elements of the employee experience that are having the biggest impact on customer outcomes.

[00:19:35] So customer experience leaders want to know this because they want the highest customer NPS as possible or they want the largest share of baskets as possible or any of these other CX metrics.

[00:19:48] EX leaders want to be able to demonstrate and our research shows this. EX leaders want to be able to demonstrate as high a possible of a return on investment and their EX spend as possible.

[00:20:02] And a way that they can do that is to demonstrate a strong link between improvements in the employee experience and an improvement in customer experience outcomes.

[00:20:12] One of my favorite stories of where we have done this is was for a fast casual restaurant and what we found was that those locations of this restaurant that have more than 2000 locations.

[00:20:25] One locations that had the highest level of perceived employee teamwork also had a higher level of customer experience.

[00:20:37] And if you think about it in a fast casual type of setting where people are working together very quickly behind a line and one person is putting one topping on a sandwich and another person is doing another thing and another person is throwing it in the grill

[00:20:51] and then handing it to the person at the cash register. There's this interaction between folks that's happening over and over and over again and a customer see that and be the better people are working together the quicker I get my sandwich right so this really shows I think the impact that a positive employee experience can have on customer outcome.

[00:21:13] So when we talk about bringing together employee experience and customer experience yes it's on product feature and functionality but it's also about how those data are really interacting together to tell a complete story that's very appreciated and usable by multiple stakeholders in the organization.

[00:21:32] One more question with all of this going on everything in a I Qualtrics over the last few years has your job gotten easier or harder.

[00:21:44] Oh that's a great question. All right so go into a little bit of Matt Evans history for you. So I was hired specifically to build out survey methodologies.

[00:21:57] My background is that of an engagement and culture researcher. So I worked in consulting for a number of years working directly with clients and my current role in product science.

[00:22:09] My team is responsible for developing listening methodologies about two years ago or so I will say we got to a point where we had a pretty full basket of listening methodologies that were survey based what that meant for me and my team.

[00:22:27] My role was that we had to rethink what it means to bring a behavioral science backed approach to what it is that we are developing in product. Now whether that means supporting our user experience and our user experience research folks when it comes to how product feature

[00:22:44] and functionality is ultimately incorporated into product whether it means building out topic models to be used in our natural language processing capabilities.

[00:22:55] Whether it means that we have really stood up in my team and driven across Qualtrics and ethics committee to think about how we are incorporating AI feature and functionality into our product.

[00:23:08] Our group's portfolio has really expanded from what it was just two years ago to where it is today. So my job has gotten more complicated, more complex but more fun.

[00:23:22] Completely fair. Matt thanks very much for stopping by and talking to me and I hope I can lure you back sometime. I would love it. Thank you very much.

[00:23:39] My guest today has been Matt Evans the head of employee experience product science at Qualtrics. And this has been PeopleTech the podcast of workforce AI.News.

[00:23:54] To keep up with AI technology and HR subscribe to workforce AI today. We're the most trusted source of news covering AI in the HR tech industry. Find us at www.workforceai.news. I'm Matt suffering.