Deploying AI and other new technology in employee engagement while respecting privacy and considering individual human factors requires a thoughtful approach. What strategies can leaders employ to effectively convey the purpose, objectives, and advantages of using data for gauging employee engagement? How can organizations establish effective feedback loops to encourage continuous improvement and gather valuable insights from employees?
Panelists:
Moderator:
Lydia Dishman, Senior Editor, Growth and Engagement, Fast Company
Note: This content originally appeared as part of From Day One’s virtual conference “Employee engagement: Gaining better insight into worker needs and ambitions” on August 7, 2024.
The panel discussed the practical and ethical implications of using AI in employee engagement and development. They emphasized the importance of experimenting with new tools, addressing privacy concerns, and ensuring transparency in data usage. Speakers also highlighted the potential of AI to enhance talent development and acquisition but cautioned against the risks of confirmation bias, unconscious bias, and weaponizing feedback. The panel stressed the need for organizations to establish trust through clear communication to prioritize meaningful dialogue and understanding of the data.
Lydia Dishman 00:00
Thank you so much, Steve, and thanks to Sean. Ahead of time, I love to hear about how we can improve our listening skills. I think that that is one of the unsung soft skills, which I like to call power skills. So I was really appreciative of being able to listen in on that session and also thinking about the topic of this panel; thank goodness we don’t have to guess how employees feel about their jobs. There are all manner of tools and tactics to measure engagement, and technology has boosted the ability to gather data and its impact on the workforce, something that I discovered. McKinsey found that companies utilizing AI for engagement measurement and intervention saw a 12% improvement in employee retention rates compared to those who weren’t using AI. A study by PwC companies leveraging AI for personalized engagement initiatives reported a 30% increase in employee satisfaction and a 20% boost in productivity. That’s all swell. It’s not without challenges, not the least of which is getting buy in to deploy these new tools and to get people up to speed learning and using them.
But additionally, Forrester found that 40% of employees expressed concerns about privacy with AI driven engagement tools, and another 18% of organizations using AI for engagement measurement faced challenges related to algorithmic bias, which impacted the effectiveness of their insights. So there are problems, but there are also benefits. So our panelists are here to dive into the practical side of using tech to gain these insights. I’m going to ask them to introduce themselves briefly and tell us how you personally approach learning to use the last new technology your company implemented. So Patrick, let’s start with you.
Patrick Hyland 02:08
Great. Thanks. Lydia, hi everybody. My name is Patrick Hyland. I’m an organizational psychologist. I’ve worked in the employee research field now for 25 years. I cannot believe that it’s it’s been that long, but I work at Remesh, and Remesh is an AI-powered employee listening platform. It’s really revolutionizing the way that we do employee research. They are constantly coming out with new technology, and what I find as an organizational psychologist with 25 years of experience is that I really need to experiment with the technology. I need to try it, and I need to verify it, and I need to really suss out the strengths of the technology and how that squares up with my traditional methods, and really figure out where the synergistic point is. So I think that that’s a really important perspective as we jump into this world of AI and employee research; we really need to be, I think, both open to it, but we also need to have a healthy level of skepticism to see where the potential issues are and pitfalls are.
Lydia Dishman 03:13
Well, thank you for your balanced approach. I appreciate it. Chris, how about you go next?
Chris Coultas 03:21
All right, thanks, Lydia. So, Chris Colvis, I’m also an organizational psychologist. I work at McKesson. McKesson is a Fortune Nine healthcare services organization, so I’m the Senior Director of Performance and Engagement at McKesson. I’m also the founder of a talent management consulting firm called Reason Talent Solutions. At McKesson, what we will be doing is providing services across all of the healthcare industry. In my role, I am over performance management. I’m also over engagement and also and really all of our organizational listening practices in terms of how I typically learn, I would kind of echo what Patrick is saying is, I’m more of a dive in Google it, test it, try to break it. Kind of person we the one we most recently have rolled out was an internal kind of AI chat, GPT type thing. And so it’s just use that compare it to what’s publicly available. So it’s just experimentation.
Lydia Dishman 04:22
Well, experimenting is always good. I like the idea of try to break it, hopefully you don’t break anything too important, I was not successful.
Lydia Dishman 04:31
That’s probably a good thing. Thank you for that. Rebecca, how about you go next?
Rebecca Warren 04:37
Hi there. Rebecca Warren, I am not an organizational psychologist. I work at Eightfold. I’ve been at Eightfold for about three and a half years. Former TA practitioner who ran TA teams and departments for several large restaurant retail and CPG organizations, I moved over into customer success at Eightfold. I helped build out our customer success department, and now I’m over in marketing, in a new practice that is focused on talent transformation, basically helping the organization move. Organizations move to a skills-based approach, focusing on people rather than jobs or racks. I’m based outside of Phoenix, Arizona, so I’m really enjoying the air conditioning today, as I have been all summer. So, my approach is very similar. I was going to say we’ve rolled out a couple of new things, two new apps, glean and Genesis. The question is, do you want the answer that our training and product teams want to hear, or do you want to hear how I actually do it? I’d much rather, yes, actually.
Rebecca Warren 05:44
I’m the same way. I’d much rather get in, play around, break it, punch the button, and see what happens once I feel like I have an understanding of it, and then I go and search out best practices or maybe watch the video that they sent us or something like that. So I’m much more of a test it and try it. I’m the one who’s not paying attention when you roll out something because I’m already in there digging around.
Lydia Dishman 06:05
So I think this could be a topic for a second discussion that our organizational psychologists can help us with. Is it like, does this experience you had as a young student translate to how you operate and learn things in the workplace? But sorry, that’s that’s a side topic. Last but not least, Sarah,
Sarah Waltman 06:21
thank you so much. Sarah Walton, I am Dental Roma’s Vice President of health management. We’re the world’s largest manufacturer Covid professional dental products and technologies. And I was going to give a tangible way that we’re using AI right now, and it’s kind of similar to the sandbox, like, let people get it and break it with our performance review process. This year, we actually are working with a company that has created a tool that allows managers to go in and give practice conversations with an AI persona and throws up challenges for them, like having a difficult conversation, or, you know, how do you manage a high a very high performer with, you know, high expectations? And how do you have a development conversation so they can get around, get in there, and get around and break it before they have the real conversation, and then it gives them coaching and advice on how they can go forward? So that’s just one tangible way that we’re using as part of our development offerings now.
Lydia Dishman 07:24
Yeah, well, I love to hear that people are experimenting. That’s that’s really terrific. The other side of that is that sometimes there just isn’t enough time to experiment, and I’m particularly thinking of our frontline and middle managers, who have been tasked, particularly over the last four years, to juggle their own work, their direct reports, as well as all of the business initiatives that are coming at them. And so we’ve seen unprecedented levels of burnout with middle management. So, I’m always thinking about how AI is supposed to help us and work alongside us. So Chris, I want to come to you with this question. Talk to me about how AI can be used to support these managers who are trying to measure team engagement in addition to juggling everything else, like the cat in the hat.
Chris Coultas 08:22
Yeah. So, there’s a bunch of different ways, and we’re doing some of them and we’re not doing some of them, you know. So, in terms of surveys, I call surveys active listening. So because you’re actively doing something to get to get that data. So if you’re actively listening. You know, AI can automate the administration of the surveys. I can also, you know, I might be out of a job someday because it can often the development of survey questions pretty well. So, so that’s pretty helpful. So that’s more on, like, the administration and development side. But really, I think the big, the biggest value is in understanding and acting on the data that you get. So it’s not so much in the execution of the measurement but more in the understanding and action. So open field, you know, parsing those comments and whatnot, understanding employee sentiment, we get like, 40 something 1000 comments in our surveys. So, you know, nobody can read all that. And so AI can be a huge help there, also identifying patterns and key cuts of data. You know, we have our people analytics team, and I’ve done that in the past, but you know, there are millions of different ways to slice and dice the data, and so AI can do that a lot faster than many, than many people analytics teams, or can certainly augment the efforts of people analytics teams like that. It can also integrate different data streams. And so, you know, we’ve got those active data streams, but you. Also got passive data streams, like social media, internal and external, and this is the stuff that we we don’t do a ton of that because then you get into some big brothers type stuff, but you can’t, yeah, well, we’re
Lydia Dishman 10:12
gonna hit the privacy thing later. Yeah.
Chris Coultas 10:16
I mean, you can do all that kind of stuff that can help automate some of the passive listening type things, but also just in the flow of work. You know, if I’m having a one on one with my people, and I’m taking notes, and I have a nice long list of notes over the past year, dump that into AI and pull out insights over the past year as well. So, I mean, you can do that as well. And the final thing is really just an action, and this is something that I’m really excited about, because we’ve done this recently, is, if you we, as a kind of a COE, have calendarized how managers should be acting throughout the year. And you know, it’s July, you know, you’re two months out from the last survey, and you’re two months away from the next survey, you know, what should you be doing to keep driving the momentum? Well, we can give you tips. We can also give you AI prompts to keep the conversation going and suggest recommendations for action. And again, look at the data and not only identify those insights but say, based on what I’m seeing here, I would recommend you do that so you almost have your own engagement coach.
Lydia Dishman 11:23
Yeah, that’s definitely but go ahead, Patrick, you want to add something, yeah?
Patrick Hyland 11:29
Well, I just wanted to pick up on what Chris is saying. And what we find is that if you blend good research, right? So Theresa I Molly has done research and found is the best days at work are days where we’re making progress against meaningful goals, right? So that benefits the employee, it benefits the organization. The question is, how do we do that at scale? And I think the technology, and AI like Chris is saying, is allowing us to start doing that. So what we’re able to do at Remesh is engage employees in team-based conversations after action reviews, Team reflexivity conversations, and as they put in really those open-ended comments, those suggestions, those issues, they’re actually able to endorse and upvote and review what their colleagues are saying and say, look, I feel very similarly, and that’s all powered by AI on the back end. And so it’s taking this overwhelming amount of data and turning it into employee-led, participant-led suggestions for here’s how we can make more progress. And so I think it’s that combination of employee voice, solid research and then technology powered by AI that can really create more momentum and accelerate change. And I think that’s the key to engagement and performance.
Lydia Dishman 12:51
Yeah, absolutely performance, very important. And I want to, I want to talk about that a little bit in terms of developing professionally. Sarah, talk to me about how to measure where employees are professionally and where they can be developed so we can have more of those good days that Theresa was talking about. I think you’re on mute. Yes.
Sarah Waltman 13:18
Sorry. I clicked it. There are so many ways; I think what I’ll say is that there’s not a one-size-fits-all, and what we have to really do is put a lot of effort into the development plan and then have a broad, easily accessible range of ways people can develop mostly focused on through on the job or actually doing things because we know that, you know with those 7020 10, that the majority of that is going to come from experience. So, I think AI can help us develop a breadth of content and a depth of content. And I think that it can also help us in giving them practice spaces, especially when you’re in an organization that’s moving in the tech direction, having more things that are protect driven, having those individuals have more sandbox-like opportunities so that they don’t break the company or nor the one that turned off the other week turned off the Microsoft Security.
Lydia Dishman 14:26
Yeah, poor guy.
Sarah Waltman 14:29
So we really want to have more spaces where they can practice trying different skills because we know that they’ll get the most return from that. So I really see AI is uplifting the talent community. So those of us who are professionals in HR and allowing us to think more about the strategy and allowing AI to do more of the work, let AI be the trainer, let AI develop content. There’s not many things that AI can’t do as far. Tactical which gives us the chance to pull up and think about the strategies and what types of things they would want to map out for their development plan, versus actually making the things.
Rebecca Warren 15:13
Yeah, and I can tag onto that as well. I Sarah, I think that’s exactly right. And I think when we use with the amount of information that’s out there and all of the options out there. When we’re able to create that culture of learning and development, it becomes less about us trying to serve up the right content to the right people at the right time. When we use tech to understand what people want, where they’re at, what do they need, we’re developing that culture of continuous learning, and so it becomes proactive instead of reactive, right? We’re not serving things up to people and saying, Take this training. And people are like, oh, like me, I don’t want to take that training. Go do something else, right? But if we, if we serve it up in a in a way of saying, here are your options. And then even give that not just a continuous learning culture, but a sense of here’s customized content for you. Here are the things that you need Sarah, which is very different from what Patrick needs or what Chris needs. When you feel like somebody pays attention to you and you want to learn because you want to give to the organization, it’s a whole different way of engagement, where employees feel like it’s part of my job to learn, as opposed to, oh, they’re forcing me to do this.
Sarah Waltman 16:26
Yes, and very much integrated into their job. I see a future where, you know, I’m getting ready to do a task, and AI knows that that task has a potential that I could, you know, breach confidentiality of the country or the company. And so it brings up, you know, a short training on that for me to think and consider, versus, why am I taking this confidentiality training right now? It doesn’t, it doesn’t feel like, you know, and you got the speed person trying to go through it. So I see a world where it can start being more part of the job instead of something separate?
Lydia Dishman 17:00
Yeah, absolutely. And I’m also thinking, Rebecca, I want to come back to you for a second here when something you said at the beginning about building transformation based on people and skills. And a few years ago, all we were talking about was technology to make sure that the talent pipeline was what we needed it to be. So now that we’ve sort of evolved, if you will, into using more and more of these tools, how can technology be used to ensure that the talent pipeline is really inclusive, particularly when it comes to people from non traditional backgrounds?
Rebecca Warren 17:43
Yeah, I love that question, and I am one of my background in, where I’m at is very non traditional. I my undergrad is actually in youth and family ministry, and I ended up in HR, so it’s very odd, right? But because people before, yeah, before we had the tech, people were doing that manually, right? Like hey, I think when you’ve got folks who are really good at sussing out what people are good at, not just what a background looks like, they are able to manually do that. I think now with tech and becoming more inclusive, we’re in a really good spot to use it to help folks become and Sarah, you mentioned this, right? How do we allow folks to be more strategic? How do we let our TA professionals develop relationships with the different departments? How do we use tech to take away that non value added work? It’s important, but it’s non value added for recruiters and for TA professionals. How do we move that away? So one of the things that AI can do, and eight fold does this is bias detection, right? Helping your job descriptions become more inclusive, helping you when somebody applies, looking at that profile and masking information that might cause unconscious bias, taking those pieces out so folks are able to look at what’s important, which is the skills and the things that folks have done, as opposed to, oh, well, they’ve went to this school which I don’t like, or they have a background which doesn’t feel like it makes sense. Tech can also help open up that pipeline, making that a wider reach, getting the information out to more people, and also helping people share it more easily, thinking about virtual interviewing. You know, we weren’t doing a lot of it, or if we were, it was a little clunky. Virtual interviewing now is commonplace, and it allows folks who may not have the ability to get into an office to interview. It allows them to be able to be included into the process and not be excluded because of transportation or other barriers, and, of course, skills based hiring, right? And I mentioned that a little bit in the bias piece. But how do we look at the skills that somebody brings to the table and also take into consideration some of the things that they may want to do, as opposed to what they don’t? There’s a lot of folks who are good at things but they don’t necessarily want to do them. I joke and say that a resume is a list of all the things I never want to do again, because I’ve already done that. I don’t want to continue doing that. So how do we, how do we cull out what those skills are that allow you then to move into different spaces? And again, mine has been more manual before I got to eight fold, and then looking at using the technology, moving out of who would have thought right, going from talent acquisition over to customer success over to marketing and developing this new practice, right? But the skill sets were able to be transferred. And I think one other thing I wanted to mention, too, and Chris you talked about this, those data driven insights, using tech to collect and analyze data on the diversity of the pipeline, and not just diversity in a common use, right, but also thinking about neuro divergence, thinking about background, thinking about location, thinking about all of those things outside of just gender and ethnicity. So using tech to collect and analyze that data and then help organizations identify areas where they may need or want to improve. So I think there’s a lot of things tech can do to make this a much simpler process, but the adoption is the challenge, right? AI is a little scary, and tech is taking my job so
Lydia Dishman 21:18
well there, there is that? And I’ve been hearing that, but just to, just to connect the dot here, because the reason I asked that question was because I really do think that engagement starts before somebody actually comes into the company, because they are engaging with the employer ahead of time. So it stands to reason that if you are using these tools before they’re even through the door that you will see the engagement later on if you are being a truly inclusive practitioner. So thank you for that real good thing. Let
Rebecca Warren 21:50
me just share one thing on that. Sorry, but that transparency also, when you think about where people apply, if you’re transparent about you being inclusive on all areas, it makes folks a lot more open to actually applying to different organizations if they don’t see that transparency, or if they don’t feel like they may be acknowledged or recognized or supported, they may not even get in the door. So that transparency of that inclusive environment is one of the most important things in your EVP to put in place, to say, hey, we already are known for that. So again, it’s proactive instead of reactive.
Lydia Dishman 22:28
Yeah, absolutely. You mentioned the limitations, and one of them being, you know, the rate of adoption. I’ve been covering AI and HR now for almost 10 years, and the rate of adoption was really low for a really long time, and there was a generalized sense of fear around it, and also because HR is definitely one of the more regulated departments in a company, there are those things to think about as well compliance, but What I mentioned at the beginning was something about the the promise and the peril, and so I want to just make sure that we talk about this, Patrick, I’m going to lay this on you right now. Talk to me about the limits of technology when it comes to measuring engagement.
Patrick Hyland 23:17
Well, look, I traditionally the biggest barriers to engagement in organizations, I think, are a couple of things. I think lack of momentum is a big issue, and I think lack of action that’s that’s what we’ve talked about, and I think that technology and AI are allowing us to open up these listening channels and make sense of data faster than ever. That’s the good news. But if that process is not increasing dialog and relationality and action within the system, if it’s not increasing reflection for an individual leader or a team, increased empathy, removing barriers, then this is not going to benefit organizations, and in fact, it’s probably going to create more distance between employee and manager and leaders. So I think what we got to think about as we’re deploying technology within organizations is, yes, we need to be data driven, but that data, that insight, needs to lead to these really strong dialogs about, what does that data mean, and what should we do with that? That’s where the human element is is so critical, so important. And I think organizations really need to think carefully about that, researchers, employee, listening, leads, leaders, managers, employees, striking that right relationship with the technology we are using is really important. And. And so technology really great to open those channels, to allow more voices to come together and make sense of that. But from there, we really then need to be developing this shared understanding of what does this data mean, and what can we actually and practically do with it. Otherwise, it’s not going to help. It’s not going to be a panacea.
Lydia Dishman 25:21
Absolutely. Chris, you want to add, yeah,
Chris Coultas 25:24
I just wanted to piggyback a little bit off of the notion of action and dialog. You know, I think one of the one of the risks and limits of technology is, you know, it can we talk about bias a little bit? I think it can also drive confirmation bias just within the behavior and actions of just day managers, right? I, you know, I dump everything into the system and it spits out something that seems legit, and I just, I want to run with it, right? It speeds things up, and it gave me the right answer. And, you know that’s, that’s not even true, even if you read it all yourself, right? And that’s one of the things that we always encourage people when they’re looking at their own their own data, is, you know, try to understand it, but then go back to your team and have a conversation, and the first step is check your understanding right? This is, this is not. You should not be going back to your team and saying, This is what you told me, and this is what I’m going to do. You should be going back to your team and saying, This is what I think you’re saying. This is, this is what I’m hearing so far. Did I get that right? And I think that AI can break that chain a little bit, because it it can feel so confident
Lydia Dishman 26:46
Absolutely. I wanted to get back into the, you know, privacy and personalization part of the conversation, because we had touched on that earlier. Sarah, I want you to start off with with this one. How are you managing the privacy of sensitive information or any confidential employee feedback?
Sarah Waltman 27:09
We have not started using AI heavily outside of our engagement survey process. Okay, my experience coming from a company that’s in 100 plus different countries with varying laws. Our workers Council in Germany is a good example. We have to be very cautious about what we’re trying to intake. And so I think one of the cautions I have for my HR, you know, peers across the world would be, this is we’re going where no person has gone poor, right in a lot of areas, and so working very closely with the company and whatever your local workers councils are, and your employees to come up with guidelines and guidance on What would be acceptable to intake and and be very transparent about how the information is going to be used. And I think working very closely with legal and it to make sure that you’re, you’re cautious about that. You know, I use chat GPT a lot, and I’m always trying to get, you know, things pre written for myself. But one of our it, people had brought up, you know, let’s say we had someone from investor relations doing that and dropping in content. It’s going out there. So the same could be true, as if we’re working with employees, and anything you know about performances associated to their name could be going to the company that you know you’re you’re getting support from, or just on, on the general web. So I think that we really need to move forward with great caution. And this would be, I think, for all of us, a good pause to say, Do we have a good procurement it legal review process going forward for AI type technology, and those of us who are in HR need to be particularly careful, just like someone in r, d or investor relations, because we’ve got very sensitive information. So I think coming up with, you know, a checklist, or some boundaries, or, you know, how we’re going to work going forward. And I can’t sit here and say we have it all figured out, because this is so true. Yeah,
Lydia Dishman 29:17
I don’t think anyone really does, you know, even
Sarah Waltman 29:20
thinking in my mind, other than some of the ways we’ve used it, which we’ve not necessarily taken, you know, people’s names or different information from them, you know, there’s a lot of risk involved with it, so I don’t have any all the solutions, but I think we need to really work on it with our internal teams.
Lydia Dishman 29:34
Yeah. And I also think about like, pulse surveys, which, you know, can have anonymized an anonymized component to it, and so people feel like they can say what they want to say without fear of retaliation. But really how confidential is that, and how can employers maintain that? Trust between the leadership and the employee. Yeah, while using these, while using these tools, you were going to say something else,
Sarah Waltman 30:09
no, cautious of not weaponizing the feedback or allowing leaders who might not agree with the feedback to become frustrated. I think it’s very transparent about saying that, and leaders, you know, stepping forward, and this is not something AI can do for them, and showing empathy and understanding even if they don’t agree with some of the feedback they’re getting, makes people more apt to give it, regardless of whether it’s confidential or not.
Lydia Dishman 30:37
Someone else wants to weigh in on best practices. Go ahead. Rebecca, I was
Rebecca Warren 30:42
just going to say in the company that I’ve worked at, there was that issue on saying, Hey, I’m not going to take the employee survey because I don’t trust that that information is going to be kept confidential. And it showed in the results, because entire departments would have zero respondents no matter what you know, folks were trying to do. And so it was an awakening for the organization to say, hey, we need to be very clear about what is shared, what isn’t shared, what’s anonymized, what information does the C suite have access to? What are they doing with that information. So it was an opportunity for the organization to lean in and say, We need to have some conversations, to allow folks to feel more comfortable with the data, and spend some time actually digging into the tech that was used to process the information. Say, this is how it works. We can’t see this. We can see this. Let us show you, it made a big difference with the teams, because they now had a deeper understanding, whereas before it felt like the gossip mill the rumors, right? Like, oh, I’m pretty sure I set that comment in and then that happened. So I’m sure that that’s how you know things went off the rails. So that transparency piece with employees, I think, is really important, crucial.
Patrick Hyland 32:03
Patrick, yeah, well, I mean, what we find is, if you don’t have that psychological safety, no one is going to participate or participate fully. So what really becomes important is, I think the way that we set up these listening experiences with the stakeholders themselves. We find when we go talk to employees that they really do prefer anonymized feedback loops rather than identified feedback loops. You can create more trust in the process with things like common URLs, right? This is not specific to you. You can pass this along to other employees, and I think that can start to build trust. And then, you know, the platform and the processes we use, they really need to be communicating and clarifying for any participants. Here’s what we’re doing with your data, here’s how we’re using it. Here are the protections in place. And so the more of those things, I think that we establish as third party vendors, as HR professionals, the more trust we build in the system. And then I think the final part is with the report out, with the feedback, I think that’s another place where the experts and the leaders and the managers and the stakeholders all need to be practicing really good behavior. So this gets back to the human side. Yes,
Lydia Dishman 33:20
that’s 100% Chris, you want to add? Yeah,
Chris Coultas 33:24
so, so we actually do confidential, not, not anonymous. And for us, I think what it comes back, what it comes and we have, we have great response rates, by the way, and the feedback is, I read the comments. It’s very it’s very transparent,
Lydia Dishman 33:41
so you can actually see who’s who’s writing it too
Chris Coultas 33:46
well. So this, this is, this is my point. So we so it comes down to the systems. And so we are very explicit that only a very like only we say it eight people. There’s only eight people in the entire company, within HR, who, who can see the, you know, de identified data. And so, you know, they get, they’ll get a custom link just for them. Or if they use the URL, like the common URL, they they have to put in identifying information. But we’re very explicit that, you know, only eight people can see it anytime it’s ever used. It’s going to be aggregated in keeping with the and like the system itself. So, like our tech platform has, has very rigorous suppression thresholds and things like that. So you can’t slice and dice to kind of reverse engineer it like, to the point where it’s sometimes obnoxious, right? It’s, it’s like you are suppressing this to the point where I can’t see something that.
Lydia Dishman 34:54
Did he freeze and
Chris Coultas 34:55
and then we’re just very religious with like, Hey, I’m sorry I can’t. Not tell you that because the because the system prevents me, because of confidentiality, okay,
Lydia Dishman 35:08
this is, this is all fascinating. And you know, the follow up question I had to that is, you know, how do you make sure that leaders are gathering this information, either digesting it through AI and coming up with their own strategy to move forward, or whatever, whatever the particular thing is, but making sure that what the result is is not seen as punitive. So if a bunch of people are saying like, you know this isn’t really working for us, and they’re really being honest, how do you even with the best intention, sometimes a leader, a manager, will come back and be like, You know what you said? You wanted more communication. Well, here we go. We’re gonna have meetings like, left, right and center. Sarah, you’re laughing. Tell me. Tell me. What about your experience?
Sarah Waltman 36:00
I think the weaponizing, and I think this is probably at the heart of most organizations, when I look at macro engagement results, is there’s a significant lack of trust between employees and management, or the leadership of companies. I think a lot of that’s driven by all the uncertain, you know, layoffs and things that are going on because of economics. Um, what I would say is, I think this is, you know, we were talking earlier about AI being able to free us up from being in the tactical and entering in data and, you know, spreadsheeting all this stuff. This is where we really need to become partners. This is where HR business partners, those of us in the top management space really need to not just flip over those results without any kind of psychological backing, and I think this is where we need to also tee those things up to say this is the type of opportunity that rebuilds trust, and that’s, you know, based on what they see, how you react, what type of programs or initiatives or different things are put in place and are Do they feel punitive, or, you know, you want communication? Get ready. If that’s the sentiment that is coming, then I think employees will continue to lose trust. They may, they may still, you know, express themselves in their surveys, that the trust gap will get wider and wider. So I think this is where we take on the role of coaches and and help guide leaders through that without just flipping it over.
Lydia Dishman 37:40
Rebecca, yeah,
Rebecca Warren 37:41
maybe I’m a broken record here, but Transparency is key, right? I think you know, you have to share with your organization the what and the why of new tech way before it actually shows up, and tie it to business priorities. If people don’t understand the reason for it, they’re going to not. They don’t want to do it, right? It doesn’t make sense. If folks are moving in a certain path in their job and they feel like this is a disruptor. If they don’t understand the importance of it, they’re not going to do it. So I think the what and the why and the business priorities need to be shared first and then, right? That’s the transformation piece, and then move into change management with all different ways on how and why, so that by the time it actually rolls out, it feels like a no brainer, like it feels like, Oh, we’ve been talking about this forever. Of course, we’re going to do this, and then you have your standard ways of measuring your engagement. Are people in the platform? Are they talking about it? What do we need to do to continue to keep that engagement? But I think if you get ahead of it, it’s much more it’s a lot easier for employees to get on board with it than if they come into work on a Monday and they’re like, hey, guess what? Log into this new tool and create a profile and do this and fill this out. People like, peace out. I don’t want to do that. I got work to do, right?
Lydia Dishman 38:59
Absolutely. I’m glad you said measuring, measuring the engagement, because I want to end, we just have a few more minutes, and I wanted to end on measuring. How do you measure the effectiveness? How and how often? So let’s, let’s just go quick around the room, Patrick,
Patrick Hyland 39:19
to measure employee engagement as a whole. Look, I mean, traditionally, as an employee engagement researcher, we would conduct surveys once a month, and that became once a quarter. I still think if you’re trying to make incremental progress on engagement as a whole, that’s not a bad cadence. That said we’re going to employees want to be engaged in conversation about real life issues on a much more regular basis. And I think those conversations should be happening weekly and monthly, and I think technology allows us to do that at scale. So I think regular feedback that leads to action doesn’t overwhelm and swamp us. Is really the key. Momentum is important.
Lydia Dishman 39:58
Okay? Chris, are we
Chris Coultas 40:04
talking about measuring engagement or measuring the effectiveness of new tech,
Lydia Dishman 40:08
or both, both? Let’s make it really complicated in these last Yeah.
Chris Coultas 40:15
So yeah, in terms of I would, I would echo what Patrick was saying, like measuring engagement, and it really kind of depends on, like, what the level of action that you’re expecting is, right? I mean, if you’re, if you’re trying to drive action at an enterprise level, for, you know, 50,000 employees, you know, measuring it quarterly might actually be a little bit too much. It may be more like, you know, we have a big annual one and then a biannual pulse check, and so that allows you to kind of keep track on the on the on the progress there. But to Patrick’s point at the manager by manager level, anything you can do to keep tabs on that on a more frequent basis is better in terms of measuring the effect of this new tech. I it just depends on what the point of the tech is, and what do you think the expected outcome is right? Is it? You’re trying to make engagement higher, innovation, collaboration, so, you know, define your criteria and determine who is and isn’t using it. Determine how you’re going to actually measure those outcomes. Just, you know, not rocket science, just good, just good science. Sarah,
Sarah Waltman 41:19
I’ll take on it, both Patrick and Chris’s response were brilliant. I’ll add, you know, establishing, you know, what is your ROI going to be like. So what would success look like by deploying this? And some of that might be higher engagement or, you know, higher business results. So I think just establishing that, you know, return on investment strategy expectation early on. And I think since most of the tech would be, you know, external partners that were coming in. I don’t know how many companies are inventing their own, but I think that’s something you can really partner with that, that provider to help you establish like, you know, one year from now, we want things to look like this because we invested in this, that’d be what I would get.
Lydia Dishman 42:00
Yeah, good prescription. Finally, Rebecca, take us home.
Rebecca Warren 42:05
Yeah. So I’ll round it out. All of those things are spot on. I think for me, keeping that human element in there, making sure that employees feel listened to, that they feel heard, that they feel what they’re doing is making a difference, making the organization better, or making their individual lives better. So I am 100% for tech, right? I work for an AI tech company, but I think that the human loop needs to stay connected for that context, for that continuity, and for the folks to feel like it’s ethically done. So that human loop and letting people feel listened to and heard, to me, puts that cherry on the top.
Lydia Dishman 42:42
And that’s the success metric for it sounds like all of you so well that takes us to the top of the hour. I am delighted to have had the chance to spend this time with you. Patrick, Rebecca, Sarah, Steve, and Chris, thank you so much for conversing with me.
