930 Serena Huang:

Melinda Wittstock:

Coming up on Wings of Inspired Business:

Serena Huang:

Your company philosophy around AI and how are we going to protect the users of AI? That includes our employees, of course, and in some cases the customers too. I have a responsible AI framework that’s got 10 different elements people can leverage if they want to get started. But at the end of the day, you have to think about fundamentally privacy, things like, you know, how is data protected and how is it going to be used? Because people will opt in as long as they trust that you won’t do anything evil with their data.

Melinda Wittstock:

AI is changing everything about…everything. And companies large and small are hard at work figuring out how best to implement across all areas of operations. Like any technology, AI is both a tool and a weapon, so how we choose to use it is a critical question for our time. Dr. Serena Huang a leading expert in data, AI, and workplace inclusion. Today, candid stories about overcoming adversity, the challenges of championing inclusion, and why Gen Z’s expectations are changing the game for organizational leadership, plus actionable insights for measuring inclusion, fostering empathy in the workplace, and responsibly implementing AI.

Melinda Wittstock:

Hi, I’m Melinda Wittstock and welcome to Wings of Inspired Business, where we share the inspiring entrepreneurial journeys, epiphanies, and practical advice from successful female founders … so you have everything you need at your fingertips to build the business and life of your dreams. I’m all about paying it forward as a five-time serial entrepreneur, so I started this podcast to catalyze an ecosystem where women entrepreneurs mentor, promote, buy from, and invest in each other. Because together we’re stronger, and we all soar higher when we fly together and lift as we climb.

Melinda Wittstock:

Today we meet an inspiring entrepreneur who is revolutionizing how organizations approach talent, well-being, and DEI through data and AI. Dr. Serena Huang is the CEO and Founder of Data with Serena, and the author of the bestselling book The Inclusion Equation: Leveraging Data & AI For Organizational Diversity and Well-being. Serena has spent more than a decade leading measurement and analytics strategy for DEI and ESG at iconic brands like GE, Kraft Heinz, PayPal and LinkedIn as an AI expert, people analytics executive and chief data officer. Also a guest lecturer at top MBA programs, including Wharton, Haas, and Kellogg, Serena’s on a mission to help businesses worldwide actualize a new vision of work where employee well-being and belonging are prioritized alongside profits. Today we get into all things DEI and AI and how companies can effectively measure inclusion and well-being within your teams, leverage AI responsibly, and foster a culture of empathy and innovation that drives real performance and profit.

Melinda Wittstock:

Serena will be here in a moment, and first:

[PROMO CREDIT]

Eight years ago, I started this podcast because I wanted to help women succeed as entrepreneurs. Over the years, I’ve driven more than $10 million in sales to the women I’ve featured on this show, and this year I’m taking my investment in female founders to a whole new level as a venture partner of the new firm Zero Limits Capital, where we’re dedicated to investing in highly scalable seed stage startups founded by women and diverse teams – a mission more important than ever as the Trump administration cracks down on anything and everything DEI. We’re looking for innovators across sectors – healthtech, edtech, fintech, climatetech and beyond with exciting new applications of AI, Blockchain and other emerging technologies that make a social and sustainable impact to change the world. Is this you? If it is, take a moment and tell us about your opportunity at bit.ly/ZLCintake – that’s bit.dot.ly/ZLCintake – capital ZLC lowercase intake.

Melinda Wittstock:

Are you a Costco shopper or a Target shopper? I ask because both companies took a radically different approach to DEI recently, and guess who got the big bump in revenue, earnings and share price? Costco refused to capitulate to President Trump’s demands that it abandon its diversity, equity and inclusion policies, while Target abandoned DEI only to become a target of a boycott. By any measure, companies that champion DEI outperform all others.

Melinda Wittstock:

So, when Dr. Serena Huang published her bestselling book, The Inclusion Equation: Leveraging Data & AI For Organizational Diversity and Well-being, late last year all the hateful trolls came out. But nevertheless, she persisted. Yes, nevertheless, she persisted, because her work on the role of data and AI in reshaping the future of workplace diversity, inclusion and wellbeing is groundbreaking and vital for any company wanting to succeed.

Melinda Wittstock:

Today on Wings we get a little nerdy on all things data and AI, with a focus on how companies can leverage AI in an ethical and responsible way to ensure they create and support workplace cultures focused on the wellbeing of their employees. We explore actionable ways for companies to use AI, including how to measure efforts at inclusion, and how to create lasting performance gains. We also discuss the challenges and controversy surrounding DEI, and why companies simply can’t avoid it if they want to succeed. But as Serena says, “data alone won’t change minds—stories do”, which is why her work for major corporations, startups and elite universities alike focus on sharing personal experiences of what exclusion feels like so leaders can build empathy and momentum for change.

Melinda Wittstock:

Let’s put on our wings with the inspiring Dr. Serena Huang.

[INTERVIEW]

Melinda Wittstock:

Serena, welcome to Wings.

Serena Huang:

Thank you so much for having me.

Melinda Wittstock:

I’m excited to talk to you about how data and AI is really changing the way we approach talent, well-being and such through the workplace. This is your whole body of work and obviously it’s an interesting time with the war on DEI and such. So, like, you know, talking here now in May of 2025, where does all this stand?

Serena Huang:

Oh, starting off with the tough question, huh?

Melinda Wittstock:

Why not?

Serena Huang:

Oh. Oh, good. It’s been really interesting journey, for sure. And if I just take us back a little bit. My book went to the printer in November and right before the election results came out. And then there was a point when I wonder, wow, is this the right time to talk about inclusion? And of course, I come from a data background, so it’s the data application and how AI plays a role in this topic. Nonetheless, I was fully aware this could be quite controversial. And then I think, I didn’t realize how controversial it was going to be until probably January.

Serena Huang:

My book launched at the end of January, which, as you can imagine, is a very interesting time. And as I celebrated the launch, there’s a lot of noise around. Does this matter? And I remember a moment where, gosh, I, I had received a lot of interesting messages, totally unprompted for, from people who said I should not be talking about this topic and that’s inappropriate, and some other less nice things I won’t say on air. Um, but then I had, and I had openly shared this on LinkedIn. I said, look, it’s. It’s been a really challenging time. And yes, I do wonder if I should continue to do this and if this is the right time, if there’s even audience for it, if anybody even cares. And then I got this lovely message from a woman who bought my book.

Serena Huang:

I didn’t know her, but we were connected on LinkedIn from many years ago, probably met at a conference and she sent me a picture of my book on her desk, and she said it was. With all the noise happening right now, your book was the only thing that got me through the week. So, thank you. Please don’t stop doing this work.

Melinda Wittstock:

Yeah, please don’t. I mean, it’s important. And yet there are a lot of loud voices that would intimidate you.

Serena Huang:

Yes.

Melinda Wittstock:

And it sounds like all the haters out there. And yet for all of those, there’s a, I believe, a quiet majority that, that are offended by the attack on DEI. We live in a diverse society.

Serena Huang:

Yes.

Melinda Wittstock:

If I put on my hat as a venture partner, all these different companies, the companies that do, do champion inclusion and do champion, you know, like diversity and in all forms, not just, or gender, but, but of experience and such, outperform by any metric. The companies that don’t like, hands down, it just, it makes business sense, and the data just proves that it does.

Serena Huang:

And then if we even think about the new generation in our workplace, Gen Z, it’s the most diverse generation we have seen and about 50% are nonwhite. And so, I even saw a survey from World Economic Forum saying most of them won’t take a job in the organization if they don’t see diversity at the top of the house, at the most senior level. And I think that makes a lot of sense. It’s not that Gen Z is somehow radical. They want to be activists. It’s none of that. If you think about their demographic being 50% non-white, that makes a lot of sense for them to want to see someone like them in leadership roles. And they won’t take, you know, the current reality.

Serena Huang:

They want to see something better and they’re not afraid to say it. So, I think those are all important data points to keep in mind as we want to attract and retain the next generation who look a little bit different than the generations, you know, 40 years ago. And my personal opinion is that companies that are doing this right and continuing to invest in the area will ultimately be able to retain and attract Gen Z a lot better than others.

Melinda Wittstock:

Yeah, this is vitally important. I mean, I just have noticed in all the social media and whatnot of the past few months the difference between, say, Costco saying yes.

Serena Huang:

Right.

Melinda Wittstock:

Sticking with our DEI policies.

Serena Huang:

Yes.

Melinda Wittstock:

And then a company like Target, a direct competitor going in the opposite direction. Look what’s happened to their share prices.

Serena Huang:

Yes.

Melinda Wittstock:

What happened to customer loyalty. Costco is wildly outperforming Target. Like Target is actually the target of a boycott as a result of the state.

Serena Huang:

Absolutely.

Melinda Wittstock:

That they, that they took. So, you work with a lot of corporations, brands like GE and Kraft, Heinz, PayPal and such. So how do you help them at this particular moment in history navigate through all this? Because the data tells the story. You can just see it in stock performance, in startup performance, all of it. But yet they’re getting all this pressure, you know, on social media and, and from the Trump administration itself. So how, how do you navigate that? How do you advise them?

Serena Huang:

Yeah, great question. And what I’ve learned as a quant myself and really worked in data for decades Is that the harsh truth is data itself won’t change minds. I’ll repeat that data won’t change minds. But stories do. Something that I’ve gotten really good at doing in my career, and I credit that to the promotions I’ve gotten, is I’ve been able to tell stories with the data. And that can help with influencing people’s decisions and getting them to open their eyes to something different. Because ultimately we don’t like to be told what to do as humans and we only want to change when we want to change. You can throw data in someone’s way all day and say DEI is great for you or you need to hire differently, and companies outperform and whatnot.

Serena Huang:

If they don’t want to change their mind, they’re not going to. It doesn’t matter what the data says. But what I seeing is as I start to share more stories in these conversations with especially senior leaders on getting them to be more empathetic. And one of the, one of the things I’ve done in all my speaking engagements this year is have people think about a time when they have felt excluded. When they have felt excluded even as a child, a birthday party they didn’t get invited to, or now as an adult at work where they didn’t get invited to the meeting or a happy hour or a decision that impacted them, but their boss made it for them. Those are moments of exclusion that everyone can relate to. In all the rooms that I’ve been in, I’ve done about 300 speaking engagements at this point. Somewhat good sample size.

Serena Huang:

And about 95% of the people in the room will raise their hand that they have experienced exclusion at some point in their life. This is an experience people do understand. And once I’m able to get them to think about what that means and what that means for your performance, your health in general, when you feel excluded, especially at work, you’re not going to feel great the rest of the day. That opens up a different conversation that I’m able to have.

Melinda Wittstock:

You mentioned one word. I’m just going to key into that empathy. I mean, sometimes we just live in this society that’s so…

Serena Huang:

Yes.

Melinda Wittstock:

…separated from one another and like…

Serena Huang:

Right.

Melinda Wittstock:

…put themselves in other people’s shoes. And it’s a real problem. It’s a problem in business; it’s a problem in marketing. How can you be an effective marketer if you don’t have empathy for your customers? I mean, it’s logical to me.

Serena Huang:

Yes, exactly. And…

Melinda Wittstock:

And yet, and yet our society is so broken in that sense of like.

Serena Huang:

Yeah.

Melinda Wittstock:

Connecting people back to that. How do you do that? You really inspire. I mean, you gave an example of how you did it. But, but I could see all these people raising their hands. I can imagine all of that and getting all that. But then how do you start to get them into a practice of really implementing that or maintaining that or really understanding it as, as they go about, you know, how they hire talent or how they, how they market, how they do all the things that businesses have to do to succeed.

Serena Huang:

Right. I think one, one of the first things I would get them to do is to recognize that you can’t improve what you don’t measure. And if you can’t improve what you don’t measure, then inclusion is something that you have to start measuring along with employee well-being. And there are easy ways to do that through surveys and there are more complex ways to do it. But if you don’t know how you’re doing on those seemingly fluffy or difficult to quantify metrics, you won’t know making progress. So that’s step one. And then second is really then starting to think about the current systems in place. Are we actually rewarding people who are showing empathetic, inclusive behaviors? Are we rewarding leaders who prioritize employee well-being? Or our incentive is more likely to be aligned with people who are ask everyone to work on weekends and ignore work life balance.

Serena Huang:

And that creates really the opposite of what you want. So, measuring and then thinking about what systematic or process changes need to be in place to make that work. Because without incentive changes, it’s very hard to maintain.

Melinda Wittstock:

Yeah. So, getting into the data and the measurement is tricky because people don’t really know. I think a lot of people maybe think that these things are sort of nebulous or hard to measure. Right. They’re not. But like, give me some examples of ways in which a company can actually measure this and measure their progress. What kind of metrics can people best use?

Serena Huang:

Yeah. So, we’ll start with the survey route because most companies do have employee engagement surveys and it could be as simple as introducing a couple questions into the current survey and ask about whether or not they believe that their opinions are valued, that everyone has a voice that they are able to speak up, even when their opinions are different from others in the room. And things like that really get you some of the psychological safety that is really key to having a positive and healthy environment for employees around employee well-being. You can’t exactly ask people about mental health challenges, but you can start to ask about, are we supporting you in a way where if you need time off, you are okay to ask, do we have the resources to support you as a person and ask about essentially things that the company can do and those are easy ways to get started. And just on a regular basis, keep pulsing the employees on how they’re doing and most importantly, you want to take action on the results. So, if there’s a big group of people who say, hey, the benefits are not really great for my needs, then do something about it. And the worst thing you can do is do a survey and then gather data and not do anything with it. I would say I mentioned there are different ways of measuring.

Serena Huang:

A more sophisticated way that is data heavy is actually looking at communication and collaboration patterns along with maybe introducing wearables for employees to measure their, their, their health in more real time. Now that might sound like a lot, but there are actually companies who have started to do this because they, they want to know, and they want to take actions more quickly. So, surveys have the benefit of being easy to implement, but it’s also very slow. Right. You can’t do daily surveys without burning people out. So, so things like looking at the metadata, who’s talking to who in email and chat and meetings, who is not included in a meeting, which demographic is more likely to interact with each other, all of that provides really great examples of how you can get a sense of what’s really happening in the organization without doing surveys. And then wearables, as long as you keep it transparent, have people opt in instead of requiring anyone to do it and only look at the aggregate data at the team level. It can be very insightful to know, hey, do I have a part of my organization where people are sleeping really poorly, or people are in back-to-back meetings and on those days they seem to have elevated heart rates.

Serena Huang:

Again, of course we have to keep privacy legal considerations in mind, but those type of data at a more advanced level can give you insights that you can’t get on surveys.

Melinda Wittstock:

This is really important, and I can see AI playing a big role. I mean, you mentioned wearables and that’s interesting. You have to persuade your employees to actually do that and that you’re going to be a responsible steward of that and prove that to them in some way. Let’s get back to that in a moment. How you do that, but also the role of AI.

Melinda Wittstock:

So how do you see that rolling out effectively without bias. I mean, there’s a big debate that AI could really be used as an exclusionary tool, depending on the data set, depending on how it’s used, right? Or it could be inclusive, but we don’t know. It depends on the company, it depends on the, the, their, their approach. Like for instance, AI is training on data that’s historic, so it could easily be introducing biases into it. Yeah, this becomes a big kind of issue. So, you know, for the companies that you work with that are actually using AI in this area for HR and for inclusion purposes and for employee well-being, what are some of the do’s and don’ts? What are, what are, what are some of the concerns they have? Who’s doing it well, who’s not? And this is a big, big topic.

Serena Huang:

Yes. Yeah, absolutely. And one of the first things I asked them to consider is what is your philosophy, your company philosophy around AI and how are we going to protect the users of AI? That includes our employees, of course, and in some cases the customers too. I have a responsible AI framework that’s got 10 different elements people can leverage if they want to get started. But at the end of the day, you have to think about fundamentally privacy, things like, you know, how is data protected and how is it going to be used? So, we want to protect the data people provide if it’s a wearables or even surveys. But then also how are you going to use it? Because people will opt in as long as they trust that you won’t do anything evil with their data, right. So, if I opt in and then on days when I’m really tired, I don’t get good quality sleep, my manager says, hey, take the morning off, you know, let’s have someone else cover your workload, let’s chat about what’s going on.

Serena Huang:

And there’s a safe environment to do that. That’s, that’s very different than a company that says, wow, you’re not hitting your goals and looks like you are not sleeping well as a team. Well, something is wrong with you, and it looks like you’re not performing. Let’s take the good projects away from you. Those extreme examples that I just shared highlight how you react, how you use the data makes all the difference. It’s not about the gathering, it’s what you do with the data. And there’s no, you know, there’s no AI without data. So, so that’s really key.

Serena Huang:

And think about the impact that the AI has on employees, on customers, and be very clear before any Implementation on what your principles are, what, what are you not going to do with AI and what are you going to do with AI? So, what are those lines for you as a company and in the employee side space? A lot of companies will draw the line and say, we will not use this for performance evaluation purposes, that we will use it to recommend new courses, but if we discover some behaviors and so on, you will not get punished because of this data. And that’s where they draw the line. And I think that’s really healthy to debate for an organization because that’s where then you can have a transparent framework that you share with employees on how their data will be used when AI is in place. As far as biases, I’m a AI optimist, so I think AI is very helpful to discover biases actually in organizations. And I personally used it for, for those purposes with different clients. One of the examples I, I will share is AI is really good at understanding text, right? We have, we’ve known this for a couple of years now with ChatGPT and Gemini and other models. So how can you use AI to understand biases? Well, you can feed things like performance evaluations, right? There’s a lot of text or even customer feedback, employee feedback and so on. Do you see a difference between the feedback from a, let’s say a male manager versus a female manager? Do you see a difference between the feedback that a young employee gets versus a more senior employee? Those are all examples where you can use AI really quickly to identify problem areas where you might have biases in your organization and then use AI to identify them and create a plan to remove them.

Serena Huang:

So, I think there’s a lot of opportunities for AI to actually identify and remove bias. If used appropriately.

Melinda Wittstock:

Yeah, if you use appropriately. And there’s a steep learning curve for companies large and small, right, to figure out how to do this, how to implement it, where to even begin on such an endeavor. And you could just imagine a lot of mistakes being made, you know, out of, out of ignorance or such. So where do we stand? Like if you look at kind of corporate America, I guess, right? And the use of AI, how’s it going so far given the context and the framing you just put around what needs to happen?

Serena Huang:

Gosh, I see such a variety right now. I think there’s a lot of AI, you know, AI first, companies where they call themselves, who are with all the tools, all the models, and they’re asking employees to use them as much as possible and training everyone on it, and then on the other Spectrum, we have companies who don’t want to be the first movers in AI, and they’re saying, we’re going to wait to see what my peers in the industry are doing to before I invest in it. But at this point, every single company I’ve talked to, their CEO knows AI is here to stay. It’s not going anywhere. And your strategy around AI can make all the difference. I definitely see a variety, even within very conservative or highly regulated industries like healthcare, financial services. Some companies are all in on AI. They might even build their own AI, while other companies are sort of waiting and seeing.

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Melinda Wittstock:

And we’re back with Serena Huang, CEO and Founder of Data With Serena and author of the book The Inclusion Equation: Leveraging Data & AI For Organizational Diversity and Well-being.

[INTERVIEW CONTINUES]

Serena Huang:

And what I do know is their employees are not waiting and seeing most of the employees, even if the company hasn’t invested in upskilling efforts or they haven’t bought the fancy AI for people to use, a lot of employees have started to upskill, you know, upskilling themselves by taking courses, watching YouTube videos, and just learning how to use AI because they know this is a critical skill going forward. And I think a gap that concerns me right now is there is not, there’s not really a clear structure on where AI leadership sits in the organization. So, some companies have started to hire chief AI officers. Right. And that sounds great and all, but at the end of the day, sometimes these leaders come in and they don’t have the resources, they don’t have the budget, they don’t have the decision powers, and they decide to leave in a short amount of time. And, and we don’t really know what the chief AI officer is supposed to do either. So, there’s a lot of learning that that needs to continue to happen. And also, just as a side data point, nobody knows how much these people should be paid either.

Serena Huang:

So, if I look at, and you’re welcome to look this up on LinkedIn too, on those AI roles, that the compensation is all over the place. I mean, I’m talking ranging from 95,000 to $1 million. So you probably won’t get someone who’s really good at AI for 95, but not everyone can afford a million dollars either. So, there’s a lot going on. It’s exciting times.

Melinda Wittstock:

No, it really is. Just even in terms of everything from evaluating the tools to actually understanding what are the best applications of it, and then how to finesse those applications. I think something you said moments ago that was intriguing to me is what’s your policy on it? What’s your philosophy? Should you have sort of like an AI code of ethics or whatever right in your company? And so that thinking At a high level, but also I would imagine involving employees at all levels of a company in that discussion.

Serena Huang:

Yes.

Melinda Wittstock:

Would be inclusive and important. Yes, do that. So, there’s an opportunity to build that in a way from the ground up rather than having just some top-down AI kind of adjunct comes in who’s, yeah, every tower, you know what I mean? But not really in the organization.

Serena Huang:

Yes, it’s, it’s so important to involve employees not only because it gives them a voice, but you also don’t know best. Employees likely know better, and they can share their concerns on, on day one and one in, in a couple of the organizations I work with actually help stand up ethical data councils internally that will have a cross functional team including legal, technology, HR of course. And I think that’s something to think about how even if you’re a data and AI expert, you may not know all the other aspects of governance that is needed to have that conversation. So, one of my favorite principles of AI responsible AI is do no harm. I think that’s got to be number one. And in most applications, whether it’s for employees or for customers, and that’s actually very hard to do. It’s not easy. You have to monitor the AI on an ongoing basis to know whether or not it’s doing harm or not.

Serena Huang:

And many people forget it’s sort of, we set it up and then it’s up and running. It’s producing great results. But we forget to check are different groups being impacted by this technology and you have to check back on it 100%.

Melinda Wittstock:

So, say let’s just dig into AI policy. So yeah, do no harm. I think back to the old Google days of like, you know, ‘don’t be evil’ or whatever was their slogan, right? But, but yes, they held onto that entirely. Maybe a bit patchy on that. So, so there’s do no harm. What are the other aspects of a good AI policy?

Serena Huang:

Autonomy is a really good one, meaning people can opt in or opt out, it is their choice. And, and if that sounds too easy, you can actually think about all the, all the different times you might have gone through an airport where there’s facial recognition technology and yes, there’s a sign saying you can opt out. But then you also know, hey, it might be faster if I don’t opt out. So, think about those, you know, is that, how do we think about autonomy when it comes to employees and customers in our, in our workplace, on them opting into, let’s say you use some sort of Resume AI screening. Right. And you let candidates opt out, but does that mean they then go to a different pile that no one looks at? Or, or it just takes a long time that the job is already filled by other candidates who didn’t opt out of AI? And all those are really good things to think about. And. Yeah, and like customer royalty programs, for instance, you can opt in and get discounts, for example, and the AI then start to recommend amazing products for you if you want.

Serena Huang:

And once you opt out, does that mean you get worse pricing because we don’t have your data. I think those are all concrete examples that people can think about in terms of autonomy.

Melinda Wittstock:

Yeah. This is such, such an important point. There’s a lot of things, I mean this is going to be going on for a long time and I think something that you mentioned before, if you’re not measuring it, you don’t know how you’re doing. So just getting, getting the measurements in place, like if you’re going to try something, be transparent about it, involve everybody in it. But, but also understand how you’re going to measure it. Like what is, what is success and how.

Serena Huang:

Yes.

Melinda Wittstock:

And, and how do you know if you’re succeeding?

Serena Huang:

Right. Yeah. And of course, at every company it depends on what you want to do with, with that. And for some companies it might be, we want to do generate new revenue, we want to make more money from this. And it’s coming from new markets, it’s coming from personalized product recommendations. Think about that end goal. And for other companies is more about cost savings. It’s more about removing processes that are too slow, replacing them with AI.

Serena Huang:

So, I think those are really good goals to think about. And because AI investments are really expensive, if you have to think about the front and make sure that you can get ROI in a relatively reasonable amount of time. It’s not today, it’s not super-fast, but, but you have to have that goal in mind. Definitely on day one.

Melinda Wittstock:

Okay, so tell me, at, at this moment in time, what are the most important steps companies can take to use AI? Just give us some examples of ways that AI can right off the gate. Easy peasy. Steps to start improving organizational performance.

Serena Huang:

Yeah. 1. You know, I think there’s so much potential with AI, sometimes we don’t know where to start. And I created a 3C framework around where you can use gen AI and the first is communication. How can you communicate better with AI? I think we’ve all learned how to write better emails thanks to gen AI at this point, but it can be using it to understand your customers and respond back differently and actually understand their perspectives. So, communication is one of them. And the next C is collaboration. How can you collaborate better? One of the things that gen AI is really good at doing is brainstorming because it’s got massive amount of data that we don’t have.

Serena Huang:

So, when you ask it to be a brainstorming partner, it does a really good job coming up with ideas that you by yourself won’t, or even you as your small group won’t. I’ve recently implemented a really fun AI plus Human collaboration session where we as a small group come up with ideas and then we ask AI to come up with the other ones that are not, you know, they are not already on the human generated list and then we continue to iterate. And I think that’s gotta be the near future is how AI and human work together in real time as opposed to AI versus human. It’s AI plus human. And then the last C is crystallization of insights, and it’s how do we understand data better? Because data is now everywhere. Well, data has always been everywhere, but now data and AI are everywhere. So how do we get faster at going from raw data to recommendations and insights that will resonate with our stakeholders so that we don’t spend hours and hours in Excel but actually spend more time in the conversation about what do we do with this data. So those are three ways that companies can think about where gen AI can play a role and really accelerate your productivity for different departments, different functions, different parts of the world even.

Serena Huang:

It’s highly relevant and really easy to apply.

Melinda Wittstock:

Yeah. One of the things about data is everybody says they want it, but they don’t necessarily want to wade through it or don’t necessarily understand how to make it actionable. So that’s very important, and that’s interesting because that comes back to the collaborative point. Okay, so we have all this data. What should we learn from this? What are some ways that we can actually take. Take this new understanding and put it into practice?

Serena Huang:

Yeah, absolutely.

Melinda Wittstock:

Prompts and involving everybody in that is. Is a really important thing to do. So, Serena, I’m curious. How did you get into this whole field to begin with? What was the. Tell me a little bit about your entrepreneurial story and what it. Yeah, what, what, what got you interested in this whole area?

Serena Huang:

Oh, my goodness. I think it’s the other way around. I think the market told me that I was really needed in, in the space, so it’s a bit different. I wasn’t someone who, you know, dreamed of being a. An entrepreneur, but now I’m solopreneur in year three, and it’s been really interesting. Probably the most I have learned ever in my life it in running my own business. I really got to a point in my corporate career where I realized I wanted to do something different, and I didn’t want to do another leadership role in data analytics. I love the work, but I felt like something wasn’t there and I wanted to have more impact.

Serena Huang:

So, I asked myself, what really brings me joy? That was my question to ponder for a bit. And I realized the two things that bring me the most joy was public speaking, live stage in particular. And second was content creation of some sort, whether it’s writing, blogging, creating funny videos. But that kind of sharing my voice and knowledge and getting people to engage was something that bring me a tremendous amount of joy. So, I gave myself a mission. I said, okay, you got six months of runway, and we can take a break from corporate. And if nobody hires me to do any of those things I like to do, then we’ll go back to a corporate job. That was my plan.

Serena Huang:

And then, you know, of course, as soon as I did that, I did have a full business plan. I knew the milestones I wanted to achieve in the next three to five years. And to myself, surprised when I announced my company on LinkedIn, I was immediately booked for like six to eight months out with speaking engagements that took me to 30 countries, three continents around the globe, and I haven’t stopped since. So, it’s a little bit accidental, but also I am pretty intentional as a person along the way, so I definitely take the time to plan and strategize.

Melinda Wittstock:

Ah, well, it’s done you. Well, I mean you are one of 2024’s top AI keynote speakers. And I mean, you know, you, you, you taught MBA programs like Wharton and, and such. So, so something’s working there. Well, it’s great when you find your joy. I mean I think so many people who become entrepreneurs, in the end it’s like they have this expertise.

Serena Huang:

Yes.

Melinda Wittstock:

But they can’t entirely be themselves within corporate, which speaks big problem in corporate. But it’s great for entrepreneurship.

Serena Huang:

Yes, absolutely. And I think I was, I just felt like I had so many dimensions of me that I wasn’t, I wasn’t able to use all my skills in a traditional corporate role. And I think that’s very common for a lot of entrepreneurs I’ve talked to. And so, like you know, I did a workshop actually on from insights to income. How do you monetize your expertise? And I share with the group. I, I now have 10 income streams. Right. And, and that means I’m able to use different skills I have, which brings me a lot of joy.

Serena Huang:

But then that also means I’m not putting all my eggs in one basket. So, if something goes wrong with one income stream, I still have nine more. That’s really important.

Melinda Wittstock:

What are all your income streams? That’s, that’s exciting.

Serena Huang:

Yeah. So, so definitely speaking is one of them and it’s probably the main one right now. I also do corporate training and so I come work with different learning departments, help them figure out what to do with their gen AI upskilling strategy and then they design and deliver those workshops. I also have online course courses, just reached 50,000 learners and let’s see, what else. So, I do some teaching in person, different MBA programs, guest lecturing. All of that writing of course gives me a little bit of revenue as well. I’ve also been asked to do like these other ones are a bit more nontraditional. So occasionally I get invites to facilitate senior leadership dinners.

Serena Huang:

So, like people literally hire me to go to a five star hotel, very lovely steakhouse and facilitate a conversation with C suite leaders in the data and AI space. And I think there’s something to say about facilitation skills being so underrated and sometimes hard to polish. It took me several years to get good at it because unlike speaking, that’s more one directional facilitation requires a lot of listening and empathy. Yeah. And I’ve also been hired by tech companies to run wellness retreats for women who are stressed out. And. And I, as a side, know, I’ve been meditating since I was 7 years old and I do a little bit of yoga and Pilates, enough to be dangerous. And I also, I paint, I make music.

Serena Huang:

So, I bring all that together to help people relax. I’m probably missing a few more, but those are the main ones.

Melinda Wittstock:

That’s amazing. Well, congratulations on building all of that. I imagine anyone listening to this podcast that wants to work with you in some way. Certainly, we’ll make sure your book is in the show notes and everybody can grab that. But what’s the best way? Like if you’re, say you run a startup or you’re, you know, or a major corporation or whatever, and you’re listening. Wow, Serena could be really helpful to us.

Melinda Wittstock:

What’s the best way to get in touch with you and work with you?

Serena Huang:

Definitely connect with me on LinkedIn. You can find me easily there. Or my website Data with Serena, one word data with Serena.com and my email is infoatawithsarena.com so I’d love to connect with, you know, different listeners who are interested in partnering, collaborating, or just sharing stories. I. Yes, I also advise some startups, and I think that’s a space that is so needed where, gosh, we need more women. We need more fearless women who are, who are doing this work. And, and it’s also very hard.

Serena Huang:

It’s not easy, easy. And I hope we continue to build more community in that space.

Melinda Wittstock:

Wonderful. Well, Serena, thank you so much for putting on your wings and flying. Great interview. Really enjoyed it.

Serena Huang:

Thank you so much for having me.

[INTERVIEW ENDS]

Melinda Wittstock:

Dr. Serena Huang is revolutionizing how organizations approach talent, well-being, and DEI through data and AI. CEO and Founder of Data With Serena, she’s also the author of the book The Inclusion Equation: Leveraging Data & AI For Organizational Diversity and Well-being.

Melinda Wittstock:

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Melinda Wittstock:

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