Inclusive by Design: Using Data & AI to Help Humans Thrive

What if your DEI and well-being problems aren't just failing but costing you talent, productivity, and innovation? And you have no idea. In today's hypercompetitive world, if you can't measure it, you can't improve it. And if you can't prove it, well, you can't fund it. And that's what we're talking about today on Experience Leadership.

Welcome to Experience Leadership, a podcast That challenges small business owners and entrepreneurs just like you to dare to be the exception. Join our host, customer experience expert Marc Haine, as he uncovers relevant and timely content to help you script and direct your business and teams to create jaw-dropping experiences your customers and staff deserve. Here is the host of Experience Leadership, author of Lights! Camera! Action!, customer experience expert Marc Haine. Hello, Marc.

Welcome to this episode of Experience Leadership. I am your host, Customer and Employee Experience Strategist Marc Haine. And today, my guest is data and AI whisperer for employee thriving, Dr. Serena Wong. Today, we will be pulling back the curtain on how to turn your people data into powerful insights that drive inclusion, boost well-being, and create a workplace where everyone and your bottom line can thrive. So, stick around with us. You know that moment when you roll out a new inclusion and wellness initiative? You cross your fingers and hope that it's going to make a difference. Well, hope is not a strategy. The truth is most business leaders have no idea whether their efforts around inclusion and employee well-being are actually working at all. They're throwing spaghetti at the wall and hoping it sticks. with no idea if it is working, nor if there is a true return on investment. And that brings me to our Question of the Day. So, are you investing in your people? Or are you just using common sense and guessing what they need? Go ahead and share your experience on social media. Share the link to this episode. Put in your comments, your feedback on this question, and hashtag it #ExperienceLeadership. I'd love to make you a part of this conversation. As I mentioned, my guest today is someone who spent her entire career translating soft people stuff into hard numbers that even the most skeptical CFOs can love. And we all know how hard it is to make CFOs love anything. She is a global keynote speaker, a LinkedIn Learning instructor, and a veteran of companies like GE, Kraft Heinz, and PayPal. She helps organizations turn employee experiences into a competitive advantage using data and AI to build cultures that perform just as well as they care. She is the author of the book, The Inclusion Equation: Leveraging Data and AI for Organizational Diversity and Well-Being. Dr. Serena, welcome to the show. It's so great to have you.

Thank you so much for having me. Great to be on.

Yeah.

You're doing my introductions from now on. I'm just bringing—

I can record it for you, and you just roll it.

Okay.

Okay.

Love it.

Serena, why don't you tell us a little bit about who your customers are and how you serve them?

Yeah, of course. So, I'm Dr. Serena Huang, and I'm the founder and CEO of Data with Serena. I serve companies in multiple industries. These days, a lot of financial services, Healthcare and food and FMCG. So that's based on my prior experience, of course, most recently head of people analytics and HR technology at PayPal, prior to that, Kraft Heinz. So naturally, a deep understanding of employees and business in those industries. And now I get to help them with really fun problems like, why don't my employees use the AI tools we're rolling out? Why can't we get insights out of this massive amount of data? Or why do employees say they love it when we initially survey them, and after the rollout, they didn't even want to use it?

Right.

So that's what I do these days with my data and AI whispering.

I love that. Let's go really grassroots to start with. A business owner, operator, people with, you know, maybe 5 to 15 or 25 employees, why should they care?

About which part?

About the whole. Taking diversity inclusion, we've heard that as a catchphrase for many years now. And now, of course, we're hearing things like governments are rolling back on DEI and companies have started to drop their departments and so on. And so, we have all this idea of how do we create this inclusive environment that's not DEI because I think everybody's taken DEI meaning to mean kind of like that The hiring practices where you're gonna hire anybody but white, which is not what DEI is.

No.

But then, this whole thing about wellbeing and people feeling like they belong within an organization.

Yes. I think a lot of companies, regardless of size, do have to care about productivity of their employees, right? Because most companies are still made of people. Yes, bots are coming, but it's still, we still, at least as it stands today, we need people to operate. And when people are— your people are not healthy, when they're not thriving, you don't have productivity out of them. And any business owner, regardless of size, have to care about that, whether it's the 2 people that you have or a giant team organization of 300 to 500 or more. And what helps with productivity? You have to be healthy, right? You have to be mentally and physically healthy. And what I found in the interesting research I got to do over the course of last year was that a sense of inclusion is so critical to your mental and physical health. And without being included, you are not going to be healthy and you're not going to be productive. And if you're not going to be productive, the business won't make money. That's it. It's that simple. So I, you know, I tell people, regardless of where you are on the political spectrum, inclusion is actually an important requirement. Because think about the last time you felt excluded at work in a professional setting. Maybe it's just a happy hour you didn't get invited to. Maybe it's a really important meeting where they assigned you a project that you didn't like. You probably didn't feel very good that week, and you started to wonder at some point, if it happened multiple times, what's wrong with me? Why wasn't I included? Is there something happening to my job? And that goes on. So it definitely impacts people's well-being. mental health when they feel excluded. So yes, I think from a business perspective, if you care about revenue, profitability, innovation, you have to care about wellbeing and inclusion.

Oh, come on. Why does anybody think of those things? Profit and profitability and productivity.

I don't know.

It's really interesting because it is kind of like self-explanatory in many ways, right? I mean, if, you know, I think Marriott back in what, the '50s or '60s said, if you take care of your employees, your employees will take care of your customers.

Right.

And yet here we are now in 2025, and we're still fighting these battles of creating this, this sense of connection and inclusion into our businesses. So why are so many companies still struggling to truly move this needle on inclusion and wellbeing?

Because there's no data that tells them where they really are. And for the longest time, companies didn't even measure these fluffy, supposedly fluffy things like inclusion and wellbeing. So yes, we actually, we barely know how to measure productivity if you think about it, because every role is different inside the company. So how you measure the productivity of finance person versus an HR person and an IT person and a salesperson is completely different.

Mm-hmm.

And so if we, you know, we are still arguing about that, right? Are we more productive at home or in the office? The whole return to office debate still goes on. When the data seems to be pretty clear that the commute time is what people really dread. And there's no amount of snacks in the office that solves the commute problem or childcare, right? And I think there's a disconnect between a lot of times senior leadership I see and employees who really need additional help. And the company sometimes gives them the help that they don't need. And I'll give an example of virtual yoga that is free. Remember those? And some companies still have them, but nobody was coming to these virtual yoga sessions, not because they don't want to do yoga or take care of their health, because it started to feel like yet another meeting I have to attend. And gosh, if you just asked me, I would have asked for, please take off 2 meetings from my calendar and save your money on the yoga that you're hosting. But nobody was given that choice. So I think there's the lack of data, but also just lack of curiosity sometimes in asking those questions. A lot of times, data that I ended up using were the ones that I had to gather, and it wasn't in a system somewhere already. Most HR systems don't have a column that indicates employees' health, right? And certainly not anything that would be real-time, right? So, and our health fluctuates from a day-to-day basis. So that really requires a different type of strategy if you want to care about how your employees are doing, which helps you understand where they need help and how to make them more productive, therefore helps your business.

I love that. And the fact that, you know, you mentioned HR a couple of times, I get a feeling that for many different organizations, this becomes HR's job. Like the whole thing about, oh, are my staff happy? Or what's their mental health like? Or can you put something together to make them more productive? It gets lobbed into the HR person's lap who is, you know, a lot of times just relegated to doing payroll, benefit management, and so on. And now you're asking somebody who's not actually dealing with employees day to day to create those policies.

It doesn't make a ton of sense in many situations. I think in very large corporations, right, when you have tens of thousands of employees, that probably makes sense. You have to have more structured people and There are folks with expertise at that scale, but oftentimes the manager actually knows their employees the best. They're the closest to them. In fact, a survey shows the person who has the biggest impact on our mental health, number one, is our direct manager. And on par with that is our significant other, so spouse, domestic partner, what have you, boyfriend, girlfriends. And imagine that, right? Your direct manager has as much impact on your mental health as your partner in life. So that's— I think that speaks a lot to the role of a manager. And I'm not saying that managers should necessarily be responsible for taking care of their employees' mental health 100% of the time, because there are times that they are not working, and there could be other factors that impact someone's mental health that's beyond the manager's control. But I think the manager should be accountable for the part that they Contribute to, right? Things like workload, things like how the onboarding event went, things like providing resources, things like having regular check-ins and ask them, how are things going? Getting to know them as a person and understanding everyone's capacity can fluctuate on a weekly basis, depending on what else is going on, and having that empathy to step in if needed and to understand at least. So, You know, the manager's job is harder now. I think with COVID everyone has started to see what's behind the screen in everyone's real life. We also have different expectations around what managers should be able to do, and things like empathy has increased in importance since COVID and I hope it continues to be important because we are all humans. Again, we might eventually employ lots of bots, but But still, the human element is so important when it comes to employees' mental health.

It was interesting with the whole COVID, with people working remotely. I think it all of a sudden gave managers and leaders an insight into the fact that their employees are human beings. Like all of a sudden you started seeing, you know, their pets running around and you saw their kids running around and you saw that maybe, you know, the room behind them wasn't pristine. And I think it gave us that kind of insight into Oh, there are more to people than just the 8 hours that they spend in our business. However, to your point, the managers who see like— I can't tell you how many times I've talked to a manager and they say, oh, what's wrong with Deborah? Oh, she's just having a bad day. And we kind of sweep the problem under the carpet expecting it to go away. But there's danger to that, isn't there?

Absolutely. Well, you can only sweep it for so long. And what if you are the reason she's having a bad day? I think sometimes we forget that we have such a huge impact on our colleagues' life in general and how they're doing, that it really matters. So I encourage leaders to just ask. Of course, as a data and AI person, there's a lot of data that you can gather, surveys, and even things in the system that can help you understand behaviors and so on. But for smaller businesses, it probably doesn't need to be that complicated because you can simply talk to them. Those data analytics methods are very powerful to understand when you have a super large organization and you can't talk to everyone. But if you do have the time to interact with almost every single person, then you can simply ask and check in to see how they're doing and treat them as humans like they would treat you as human.

Yeah.

So, basically what you're saying is you can't policy this away. There has to be a human element to that. Are there signs that a company's inclusion or well-being programs just aren't working?

I think, yes, definitely. It depends on how you define working, right? Because sometimes we expect a program to work really quickly and say, hey, we'll give you a meditation app, and then tomorrow you'll feel very relaxed. That's probably not going to happen. So what amount of time is reasonable depending on the program that you're implementing and the size of the organization and how severe the problem was to begin with, right? If everyone is burned out because there's too little resources and there's too much work, adding that meditation app is probably not going to do anything for 3 years, and you can spend 7 figures on a really nice app. it won't give you much return. You have to solve the core problems. So I think that's a common challenge that I see, which is not knowing, not doing the analysis on what the root causes are even at the beginning, and just start implementing programs that supposedly helps because my peers in another company in the same industry are doing it, or a news article mentioned this particular solution, and let's just implement it. It'll all be better. Let's give them Friday afternoon off. Let's do no-meeting Wednesdays. And yes, it might work for some companies, but one, what KPI are you trying to influence? Do you know that? And 2, are you tracking after the implementation? Did that KPI get better? Most organizations don't do any of that, and all they track is things like how much it costs and how many people used it. So usage and cost. Not the actual effectiveness.

And there's also an ego component to it, isn't there? When they implement it in there, they're like, oh, well, I implemented this strategy, this strategy, and this strategy this year.

Oh, yes. Yes. I've been on the receiving end of that where I was asked to help them justify and quantify the effectiveness after the implementation. I said, look, I can only do so much magic. When you call me afterwards, it's too late. We need some sort of baseline so we know the before. And yes, and I think there's a lot of science that we could, maybe not even a lot of science, a little bit of science that we can introduce into this process to just start measuring before and after. If you're trying to improve inclusion, for example, are you doing that with a simple survey asking if people feel included in decisions? What is the metric? Are you actually looking at some more passively gathered data, like do people get invited to the same meetings, right? Because with things like email, Outlook, right, in Outlook, you can see who gets invited, meaning you can also see who gets— who doesn't get invited to a meeting. And you can quantify the behavior type of exclusion using data, but you have to really do that type of exercise before and after the program implementation. to know if it's working. So, I think it's not that a lot of programs haven't worked. I think companies don't even know if it worked or not.

Right.

I think they just know they spend a lot of money. And frankly, a lot of the wellness apps got a lot of backlash from CFOs in particular because they couldn't even demonstrate the usage. And yes, and usage doesn't even mean they feel better. It could just mean that maybe they spend a lot time logging in because they're having a hard time. Maybe the app doesn't work, and that's why I'm on the app. So who knows? Is it troubleshooting, or am I actually using it for something that improves my well-being? So there's a lot of measurement problems. And I think if you think about your CFO and keep them in mind at the beginning of the implementation, it would be a much better conversation than trying to address it at the end to justify. A renewal budget, for instance. Right.

And I think that's part of kind of the strategy of doing the initiation properly is that you're going to say, so what do we want to get out of that? What is going to be our measurement for success? How are we going to measure that? As opposed to just saying, let's just throw money at it. I talked to a DEI specialist. Actually, she's been on my show a couple of times, Tina Verghese. And we were talking about how with the political structure now and all these companies kind of dropping their DEI departments out of their HR, all of a sudden how much this was just more kind of lip service than it was an actual kind of commitment to a culture that would make them more productive. And so now what, what I'm seeing— in fact, I just had— I just got off a conversation with a colleague of mine where she said that she has an employee, uh, his name is Muhammad, uh, brand new with the company, but he doesn't want to be known as Muhammad. He wants to be known as Barry. And so, because he's got, you know, he, he comes into North America now, he's been probably in the country for about 8 months, but he sees kind of the rhetoric around what people think of these Eastern names and the ones from Middle East especially. And, and so he doesn't want to be associated with that stereotype. So this idea of covering people's identities within an organization or even people who cover up their capacity? Because I do know in past experiences that there are some really brilliant people who kind of dumb themselves down because maybe they don't want the extra workload or they don't want that. How does all this impact performance and retention? How can managers kind of mitigate that?

First of all, who wants extra work?

Exactly right.

I guess it depends on is it with a bonus immediately or without, right? We have to be clear. But Yes, covering is a concept that maybe a lot of times people haven't heard as much about, but it's actually very common. Most recent research show that 60+% of employees cover some part of their identity at work. And so that's more than half. If you look around, the person next to you might be covering if you aren't, and you might be covering something without knowing. So things like, I think your example of Mohammed and the name and the stereotype is a really good one.

Yeah.

And there might be something that's more, that's not so explicit in terms of covering. It might be a parent who doesn't want to show up with talking about their children. Maybe they don't want to display the family picture on their desk. That could be a covering of caretaking responsibility because they're afraid that they will seem less committed to the organization if they do that. And they might not be eligible for promotions and they are not thought of when big projects come up because, well, you know, you have to pick up And so that's a common example. Another might be covering up on behalf of someone. So maybe they have a child who's trans, and they're actually an ally, but they don't talk about it out loud. And, you know, when someone says something that isn't great to the community, they are afraid to sort of speak up because they didn't want other people to know the reason behind that is because their child is trans. So there are lots of examples of covering and even things that may not seem as negative or positive. Let's say someone's immigration status. For example, I am a 1.5-generation immigrant of the US. That means I came here as a child, and I may or may not bring that up all the time, right? People see my last name, they sort of assume that, well, I'm not European at least, or Caucasian probably. And so, you know, there's like only the extent of hiding that I can do and they see my face. But then, you know, but I often, and other Asian Americans as well, run into this really dreaded question of, where are you from? And right when people ask that, and probably to any immigrant, so you sort of start to do the math of, oh, what does this person really mean? And things like that. So there could be different ways of covering. And what we know about covering is that a lot of people do it because they think it will put them in a better position at work for more success, more money, and more likely to be accepted by the group. And that's the whole reason, right? Because I don't want to be different from you. That's why I didn't want to mention this part of my identity, because you may not understand. And that has a detrimental impact on our mental health. Because if you think about that, you know, the part of you that you have to keep hiding, imagine a mother or father has to hide the fact that their child is trans. That's a toll that is just, it's really hard to quantify, but it's definitely there. And if someone has to devote mental energy to think about what I shouldn't say at work constantly, they're not going to thrive. That's just a very difficult place to be. And of course, I show examples of more marginalized communities, but it doesn't have to be. There are lots of other identities white men can cover just as much. In different ways. Maybe they don't want people to know they are married or they're divorced or they have health issues and invisible disabilities, what have you. Lots of reasons that people can cover. And so when you don't know, when you don't even know people are covering and you don't create an environment where people can be relaxed and not have to worry about that in their head, they might say the wrong thing.

Hmm.

You won't get them, you know, you won't get the productive and thriving employee out of your workforce.

When you say that, it really makes me sad that we're contending with this in this day and age. It really does make it, you know, because I remember, you know, growing up in the workforce in the early '70s, you know, and having friends that were gay, but it was like, don't tell anybody, right? And they were constantly kind of mitigating, you know, when people are saying, oh, Oh, let's go out for drinks. Or, you know, or even, you know, somebody who's maybe, who's maybe in AA, right? Who doesn't want to let people know that they had a problem. They're dealing with the problem stuff, but they want to keep it private. And so, it's always this self-editing of, you know, and the stress must be incredible.

Absolutely. Yeah.

So, I'd really love to get to the core of kind of some strategies that we can use to leverage things like data and AI to create a healthy and inclusive environment for every employee within our business to thrive. And we'll get to that right after this.

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I am speaking with Dr. Serena Huang. And like, I'm loving this conversation, but Dr. Serena, you yourself have a book. Why don't you tell us a little bit about it?

Yes.

And it is The Inclusion Equation: Leveraging Data and AI for Organizational Diversity and Well-Being. It's a book helping organizations, managers, How to use data and AI to create an environment where everyone can thrive.

This idea of using AI and data, let's talk a little bit about how this idea of using that, because you said there's lots of data, but I also know at the same token, there's lots of small businesses that don't know how to tap into the data they have. They might be using payroll systems that are third party. They might be using employee engagement and onboarding processes that are third party. So for people who don't know what kind of data that they Have, what should they be looking out for?

For smaller businesses, there's actually a wealth of data in things like absenteeism for employees. So, if you think about, that's one of the metrics I personally use to study how healthy an organization is, and especially when you look at hourly workforce, right? They, when anyone that has to clock in and clock out, And you have very detailed sort of patterns when they're at work, when they're not at work, and why they are not at work, you can start to see things like, hey, there may be a particular unit that has a lot of absenteeism problems that are unplanned, meaning people are out sick and things like that. So again, not to— of course, we have to be very careful with data privacy and don't want to get into why a particular individual didn't show up and what kind of medical issues they have. But overall, you can start to see pockets within the organization. Maybe you have a manager that seems to have a lot of problem with absenteeism and dive into that, right? Dive into why. And you can look at, was it a particular type of employees that are more likely to be absent? And is there any patterns to that? We have jokes in the US, like the day after Super Bowl, people probably won't show up to work. Like, that's no predictive analytics required. We just kind of know that, right? It's like the day everyone calls in sick. And so, you sort of know that, but then what else? Because when the business knows what it has the ability to predict when employees are not going to show up, it can be huge cost savings.

Sure.

And imagine, well, first of all, you probably can't run a a place if nobody shows up. But at what point does it really impact your operation? Is it 10% absenteeism? Is it 20%? Is it 30%? So if you know that and think about being able to predict, you know, when it reaches that level and patterns in the seasonality so you can get backup staff, that helps the business in really practical ways. And so, you know, I give that as an example because we think about health us like this in-the-cloud concept sometimes, but really, if we're not healthy and we call in sick, that's cost to the business in the way of lost productivity, lost revenue even. And if you're making widgets, if you're making pasta, you can't make those things when you don't have the people to run the machines, for example. So, that's one data I will look at. And the other piece is around demographics. So, not so much about the hiring and diversity side of things, But think about, do you have a multi-generational workforce? Is that creating difficulty for the person who's leading that team? Is that person really, you know, has the leadership training to do that well? Or is that person only hanging out with people from their generation and completely ignoring the Gen Z who need help? And, you know, Gen Z is all the rage, right? And young millennials and wanting different things. But like that kind of demographic data, A lot of times organizations tend to ignore. It's like, oh, you know, we see them every day, they show up. But if you knew that your organization is, you know, say maybe 35% Gen Z, that paints a very different picture than one that is 5% Gen Z. So you might need different strategies, different leaders, different manager training for you to be successful as a business. I'll give those 2 as examples to just get the wheels turning here, because there's a lot of data, and what I've seen, especially in smaller companies is so underutilized and it's an absolute goldmine for insights.

Yeah. And again, to your point about, you know, I believe that, well, since COVID I think I've come to the conclusion that we really need to redefine the role of a manager and start really thinking about the role of a manager as being more a role of a coach. I think, you know, in my— I grew up in the '70s in the workforce. I'm the last of the baby boomers where it was like, just get her done. And you just did whatever you needed to do, including working Working 15-hour days or whatever, right? And with that whole mentality of, you know, if you're not well, just get over it. If you're unhappy, just get over it, right? And so, you have a portion of the demographic that's like, I don't understand what the big hoopla is about wellness. You just go to work and you do what you gotta do.

Right.

But at the same token, I see that managers now— back then, they used to get a lot of stuff they had to do. And so, the managers were just as busy as the employees but with maybe a different thing. They weren't really necessarily leaders. And but now, I'm really seeing that this idea of going from the industrial age to the kind of knowledge worker age to now more of this idea of relationship age where it's more relational everything that we're doing. That now, managers need not only to be trained as leaders, they need to be trained as coaches as well. Is that a safe bet?

Absolutely. And not everyone wants to be a coach.

No.

And not everybody wants to be a manager.

Yes. And not everybody should.

Yeah, right.

Yes. Yeah, I think we can sign off. I think that's a perfect bow.

Yeah, there you go. That's it.

Not everyone wants to be a coach.

Isn't that interesting though? Because a lot of the leaders, I call them incidental leaders because they get promoted without the training because they were good performers. And we've talked about this endlessly in 200 and Plus shows, right? But the habit keeps on going. And so, now that we have data that you're saying that we can tap into, and now we have AI as well, how can we leverage AI now in helping us shape what I like to call the employee experience?

Yeah. Well, AI has helped me personally a lot with text data. Now, if you think about the surveys that whether it's customer survey or employee surveys, there's always text, right? You know, maybe at the end or maybe after each question, you ask the person to provide more context, or is there something that we didn't ask but we should know? And that massive amount of data is really hard for human brains to digest and analyze without some data analytics skills, right? You can read comments all day, you can read 1,000 of them, but by the time you get to 900, you have forgotten the first 200 because we're human. And AI is really good at going through tons of text and summarizing the takeaway, summarizing the themes, and it goes far beyond, but you can think about like really sophisticated word clouds, right? So what are people talking about on the survey? What is on their mind? If they mention burnout, tired, exhausted 50 times, right, or meaning 50 people mention it, maybe that's something to pay attention to. And so using AI for that has helped a lot of companies start to save time in understanding the context and feedback that we previously weren't able to without some really expensive software or a data scientist who's really smart. And now anyone can do it, you know, as long as you have access to a secure AI that you trust. But you can dump that text in and figure out what to do. You can even ask it to not just summarize, but make recommendations. What should I do? Right? And give me the top 3 problems or top 3 areas we're doing well, top 3 that we need to improve on. It can do all kinds of things that used to take a really long time. And so I think there's tremendous potential there in understanding data using AI. The other piece I'll mention is things like wearables, right? When we think of AI in healthcare space, for me at least, Apple Watches and other wearables come up. How can we use that type of data to get a Real-time or more real-time understanding of how people are really doing and feeling and where they need help. And again, privacy, of course, we have to be very careful along with how is that data used and ethical usages of such data. But think about it. If you didn't sleep well the night before, you are not going to be 100% today, right? And if we knew the entire team didn't sleep well the night before, because we only see aggregate data, it's anonymized, We probably know this team is going to suffer a bit this week. So that can be very helpful data. And again, I think if we get into things like medical care, medical conditions, that's just too scary. And I definitely wouldn't recommend that. But things like, things as simple as sleep, frankly, I think we have a sleep epidemic as well.

Yeah.

People's sleep quality is really poor. And I once had a manager who just couldn't sleep, and everyone was watching out for this manager's mood every morning depending on how sleep went. And it's a real problem because a lot of people have issues with sleep. And so, I bring that up as, you know, what if we live in a world where we can get access to just the aggregate insights on how individual teams or different functions are doing on something like sleep? What if we get it on a weekly basis? What if you can correlate that with some other productivity outcome and KPIs where we know, you know what, when this team only has 4 hours of sleep on average, our customers are really mad. Our customers are going to call us and complain all week, and they need additional help, right? So what if we can do that more proactively instead of solving those customer complaints? What if we just knew that Poor sleep today? Okay, let's get them some backup right away, as opposed to, all right, I guess we'll staff up the customer service line and wait for complaints. And that's— so I think there's a ton of potential with wellbeing-related data from employees, but it must be used the right way to answer very specific questions. And ultimately, it should also be good for employees, right? It's like ultimately, we don't want to punish anyone who is not sleeping well.

We want to give them additional And again, it goes back into that mindset now that managers as coaches must be there to be of service. It's how do you figure out how you can be the best service so you can get the best from your people and you can get them proud to be part of your team. This is an absolutely fascinating conversation. Could you let everybody know how they can get in touch with you, Serena?

Yeah, of course. So, you can follow me on LinkedIn. I share insights on data, AI, well-being, sometimes my puppy. On LinkedIn. So, uh, it's, uh, it's just my name you can search for, or my company website, datawithserena.com. And I hope to see you there and stay in touch.

You know, it's interesting what you were saying about kind of using AI to leverage some of the comments and stuff, because I have seen, you know, I used to do a lot of secret shopping reporting. I used to go into businesses and with trained personnel coming out with the reports, and all we did was just indicate observations. No judgment, right? We would say if there are fingerprints on the windows, no judgment. If the menus in the restaurant were torn, no judgment. If the aisle in the grocery store was there, we just said, this is what it is. And it was amazing to see when I sat down with managers and owners and stuff, the ego and the emotion that comes into interpreting that data. And so, I imagine a lot of times when people get survey responses back and they see and people are complaining about stuff, you know, the manager is just like taking a stand and going, ah, it's just, you know, 5% of my staff are just bellyachers. They just— nothing I can do is ever going to make them happy, right? And so, I love this idea of being able to leverage AI because AI really does take the emotion out of it, doesn't it?

Yes, at least we hope so.

Yeah, well, for now.

For now.

Until we program it to use you. So, this is this idea of being able But there's also, you know, we've been seeing a lot with generative, uh, with some of the talk around generative AI that sometimes it can be heavily biased depending on the prompts we're using and depending some of the tone we're using. How do we make sure that AI is there and helping us reduce that bias instead of reinforcing it?

Oh, that's, uh, that's a million-dollar—

Just a small question. It's just a small question.

Yeah, just Just a small question. We'll solve world hunger after this too, right, Marc? Yeah, so yes, exactly. In those cases, AI can be biased, but I'll take it a step back even. The training data AI has access to itself might be biased.

Right, right.

Meaning that perhaps you're just trying to build a really simple bot that answers questions like an FAQ machine. Using available resources, but because you're using your previous customer interaction data, and that is heavily biased towards, well, one, people who are really angry, and then two, perhaps some demographics, right? If you didn't look at it, perhaps it's more male, perhaps more female, who knows? And that bias is already there. And so going forward, you build a bot and And it's only able to spit out, or it spits out answers that's very similar to whatever that's more appropriate for the demographic in your training data. And then when people not like them start to ask something in a different tone, something in a different style, something very different, then the bot is confused. And what it does? Well, then it hallucinates. And then you have this group of individuals who are not served because your data is biased. do was answer simple questions, or so you thought.

Right.

And I give that simple example to show you that bias is inherent, and it can be there even with the best intentions, right? Even with the best intentions, you're thinking, let me incorporate the biggest complainers, right? The biggest problems, and then it's going to be great. But it takes humans to analyze, and I would call it like the testing process, to see What does it— what is it able to answer? What is it unable to answer? When is it making things up as it goes because it doesn't have anything in the training data or other reasons? And that takes human skills to analyze and to detect and eventually prevent, hopefully, that bias in the data. And as users of, you know, really large language models, it's very hard for us to know because we can't possibly understand what goes into, you know, OpenAI's model, right?

Because it sounds so good. It's got to be right because it sounds so intelligent.

Yes, yes. And that's a huge problem, right? I think that is a huge problem because it sounds so believable. And we didn't have this problem with the less intelligent bots in the past because it was clearly robotic. It didn't make much sense and it sounded like you just pieced together words. But now it sounds really intelligent. It's full sentences. It's even elegant. Sometimes it's even better than some people's writing. So it's very believable. And I found this fun exercise I do with some of the bots I interact with, and I would ask a simple follow-up question, and it says, are you sure? That's it. Are you sure? And sometimes it corrects itself like, oh no, apologies, I made a mistake.

Right. And so, to your point, really what you started off with today was this idea of curiosity.

Yes.

We talked about at the very beginning using curiosity to really look at your teams and start analyzing kind of patterns and behaviors. And that's what we're talking about today more than anything else is just leverage curiosity in every aspect, including bringing AI to the forefront. So, being able to be curious enough to test it by saying, are you sure? Or even pushing back. I did a demo for an agricultural society where the, you know, I asked it something and somebody said something. It's like, oh, okay, let's play with this. And it's like, it seems like what you gave me is not quite true. And it goes, oh, Marc, you are absolutely right. And so in a live demo with a bunch of people running agricultural societies, I could show, I was able to show that. Yeah. If you push back, make sure you push back because that's what we can do as human beings. We don't have to accept it.

That's right. That's right. Yeah, continue to use our critical thinking skills as humans.

Please.

And don't take— yeah, don't take everything AI says as truth.

Right. Yeah. So, as AI becomes more integrated into the workplace, is there anything that leaders and employees need to be adapting to, to kind of understand the dynamics that this itself will have on culture?

It depends on how they use AI today, right? And certainly where the company is going, we're going to start to see different adoption rates, of course, but more human and AI interaction at work.

Right.

And more of AI starting to replace some of the tasks that humans do. And that could be analyzing large amount of text data, like I described, right? It used to maybe take people 20 hours to analyze one survey. response, and now it's 2 seconds, and then you're spending 20 more minutes making a slide deck that is also assisted by AI, and then you're done.

And then you're at the Keurig having your next cup of coffee.

Yes, yes, or maybe a full meal. I don't know. It's a lot of time. Yes. And so, I think the question some people have started to ponder is, what do I do with this extra time? If AI is truly starting to save us time, what do I do with that? And a lot of times, organizations might just pile on more work, right? There's never enough time. But then also, I think a lot of people, maybe Gen Z and millennials in particular, are wondering, what kind of life do I want to live? How do I want to spend my time? And I definitely encourage everyone to think about that, regardless of generation and where you you are in the stage of your career, what is that ideal life that you have right now? Knowing that AI will start to do some of our work, what do you want to spend your time and energy on doing? What really lifts you up? What brings you joy? And what do you want to continue to do? And what do you want to delegate to AI to do? And for managers and leaders to start thinking about what that collaboration looks like going forward. And I think it's a bit too early to tell. We've seen some organizations roll out AI and said, okay, we don't need humans anymore, and then started to roll back and say, oh no, just kidding, actually we do. So no one really has this figured out, but we do know AI can replace some human tasks that nobody wants to do. So I think, yeah, staying curious continues to be really important because we have to Ourselves and how we use AI, how we coexist with AI. But really, instead of competing against AI, learn to collaborate with it so that we can get the most out of it.

Yeah.

That would be my recommendation.

I'd like to get into some pitfalls and cautionaries. I'm always afraid of, like you were mentioning, the people who just throw their resources into this new solution, think it's going to solve all the problems, and then have to roll it all back with the, oh no, we were just kidding. Oh, it was just a test. We didn't really mean it. And so we'll get to pitfalls and cautionaries right after this.

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Welcome back! I hope you are getting tons of value from today's episode. As you can tell, Serena and I are on a mission to support businesses just like yours. So, if you belong to an organization that could use our help, or you or someone you know might be planning a leadership retreat, a conference, some level of people gathering, drop us a line. We'd love to explore how we could be of service to you and your people. So Serena, what's one small step that somebody could take today to start improving this idea of inclusion and well-being that would make a world of difference starting today?

Oh, start by asking the right questions. Start by asking what makes people want to stay in this workplace. What will make them want to leave? Ultimately, when an employee is so burned out, fed up, what have you, they leave, that is your loss. So, ask those tough questions. You know, I call these stay interviews, right? And asking them, what's, you know, what are those conditions that will make you want to leave? And what's keeping you here excited? And have that honest conversation. I think that type of data gathering, let's call it courageous data gathering, really goes a It goes a long way. And it's courageous because not everyone is courageous enough to ask. Not everyone wants to know. Not everyone wants to act on it. And I only have one rule for data collection, whether it's a survey form or a simple question, is if you're not going to do anything with the response, don't ask. That's it.

Just don't bother.

That's a bad question. Just don't. Yeah. If you don't have the resources, the time, or willingness to solve whatever problem comes out of that response, They get it. And I think it's, yeah, that goes a really long way. And as far as wellbeing, I think start seeing people as full human beings and like we did in COVID. But now, you know, now it's almost, I don't know if this is COVID 2.0, but I heard this term that we now live in collective trauma as human race with so many things going on. Every day something is new thrown at us. Yeah. And I don't know if that's too extreme of a word, but I definitely know for a lot of people this year has been super difficult and with so much change, right, the market uncertainty and so on. And so how can you show up as a leader to guide your employees and help them live a better life? Because remember, you have as much impact on their mental health as their spouse, significant other. Yeah, that's not responsibility to take lightly.

So, having said that, avoiding the band-aid solutions, cautionaries and pitfalls. As people start leveraging AI, people start looking at this whole thing of, okay, fine, we don't have a DEI department anymore. It doesn't mean that we don't want people to feel included or feel like they belong. So, what are some pitfalls we should be aware of as we start looking at what can we do to our culture to change it, to get better buy-in, to get people feeling like they're included? to leverage the magic that is around us?

Oh, there are lots of pitfalls, potentially. I think the biggest one we were talking about earlier is really jumping into implementing programs instead of figuring out what KPI is going to change and what you want to improve and having a well-defined measure of success upfront. That's a really big pitfall because programs cost a lot of money. And in uncertain times like this, every dollar counts. So you definitely want to figure out what you want to improve and why that matters at the beginning. And I think there's also the so-called shiny object syndrome that we got so distracted by, whether it's AI or the virtual yoga wellness apps that were going on earlier in the pandemic. Whatever the situation is, really be mindful that those best practices, I'll put in quotes, may not be applicable at all to your employees because your employees were not in those studies. They were not in those research, and you need to figure out what works for them and then spend your money there. That's probably the biggest one because we definitely see a lot of vendors even who sell those programs and apps, right? They say, oh, so-and-so have implemented this. This is when your ears should perk up and ask yourself, so what?

There's no shortage of people wanting to take your money.

Oh my gosh, yes. Now, as an entrepreneur, absolutely, and author.

Yeah, there you go. This has been a fantastic conversation. Could you remind everybody one more time how they can get ahold of you?

Yes. LinkedIn, you can find me there, Dr. Serena Huang, and then datawithserena.com is my website. Please stay connected.

Wonderful. And we will put the link to your book in the show notes as well so that there's an easy access to it. I think that would be a wonderful starting spot, especially if you're limited in resources and you don't know how to get started. I think that book might be the magic, magic key to help you think about it differently.

So— Yeah, it's also on sale, 30% off on Amazon right now. So— Ah, there you go. It's a good deal. Terrific.

As we wrap up, do you have any last thoughts about what we've been talking about today? I know we've covered a whole waft of topics basically around this idea. of inclusion and AI and kind of blah. There's a lot. Any last thoughts?

Yes, I have something that I want to share that's kind of in the wellness field, but more on a personal level. I tell people that I don't manage my time, but instead I manage my energy. As a speaker, author, and now with a book launch and business on 3 continents, I'm On the road a lot and I'm doing speaking engagements all the time and I like absolutely cannot afford to be sick because it's what I have to deliver. And one of the things I started doing was energy tracking. And I started this during COVID because I felt exhausted and I wasn't sure why. And it was the back to back to back to back Zooms. But I also noticed when I started tracking, meaning like after each type of meeting, I would track, was this an energy booster for me or a drainer? And then I would look at my data and then change my week accordingly. So I decide how to have more energy-boosting, uplifting moments and less of the draining moments. And that's how I'm able to get a lot done. So in the whole productivity conversation, I wonder if some people might want to try this instead of managing how many hours you work. Think about what lifts your energy and what drains your energy instead.

I love that. And hopefully your time with us today has lifted your energy and your— and I hope if you're watching, this lifted your energy as well.

Yes, this is what I love doing. So thank you so much.

Terrific.

Thank you very much, Serena. This has been a great conversation. I really do appreciate you bringing your knowledge to the forefront. your passion. Of course, your expertise has been brilliant. So, thank you for doing that.

Thank you for having me.

If you have any questions about today's episode or you'd like to get a complimentary 30-minute brainstorming session with your team, we can bring it all together. Feel free to book yourself on my online calendar. The link is down below. It's the one that's marked meetwith.marchaine.com. As always, I am at your service. And if you haven't done so yet, what are Why don't you go ahead and subscribe to this podcast? Follow me on social media. Lots and lots of content. And of course, you'll see time and time again that I will announce when we bring fresh new perspectives on this show. Thank you very much for joining me today. My name is Marc Haine. I hope you stay safe, stay healthy, and I hope you dare to be the exception.

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