How To Create A Sense Of Belonging & Use AI To Remove Bias

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Vijay Pendakur: Like anything, it's a question of intentionality. So once we establish clarity on what the forest is and what the trees are, then it's the companies that are doing a good job with this have actually sometime in the last 12, 24, 36 months started to reskill their team leaders to lead for belonging.

David Rice: Welcome to the People Managing People podcast. We're on a mission to build a better world of work and to help you create happy, healthy, and productive workplaces. I'm your host, David Rice. 

Today, we're going to be continuing to part two of my recent conversation with Dr. Vijay Pendakur, author of The Alchemy of Talent. We're going to dig into this idea of a crisis of belonging and take a look at how much AI is changing things in the talent acquisition landscape. We're going to pick it up right where we left off, so we'll just jump right into it. Enjoy! 

You go on to talk about a crisis of belonging in the U.S. workforce, and I'm curious what you feel the biggest driver of that is and, how it's sort of shifting. Because I think that belonging is a term that we'll be hearing a bit more of in the coming years. And before, as we said, that as people start freewheeling it with these terms, it starts to take on a new meaning over time, right?

I'm wondering what you think the biggest factors are to creating this sense of belonging as we understand it right now. And what are some of the examples you've seen of companies doing this well? 

Vijay Pendakur: Sure. Yeah. Wow. This is a meaty question. 

David Rice: I think it was three questions. 

Vijay Pendakur: Yeah. Right. Right. Right. It's a, it's the triple meat sandwich. It's a which is one of my favorites. So I think understanding the crisis of belonging first takes us applying a little bit of rigor to what it means to belong at work. Just like psychological safety or trust, from our previous discussion, the word belonging has everyday usage and then technical usage.

And for me, as an employee experience expert, it's important to establish some technical rigor here. I really like the framework that Coqual developed in their research on belonging at work, and I use this in the book and shout out to Coqual. But people feel a sense of belonging on their team when they feel seen, connected, proud, and supported.

These are factors that are much more actionable and in aggregate when people have these things, they report a high sense of belonging. So strong validity in this social science approach. Just to break these terms down for listeners that are newer to this body of research and want to put some of this into action with their team, being seen is largely an inclusion factor.

Right? Am I understood and valued for who I am on this team? Am I connected? Do I get to have meaningful interactions with people that I work with? Am I proud? Do I feel like I'm on a team where I can be successful? Is my team successful? Is my team valued at this company in this organization? Do I understand how my work contributes to a bigger win?

These are elements of workplace pride, even all the way through to is my company or is my organization a place that I feel proud to work in terms of the work that they're doing in the world or the way they treat their employees? And then finally, supported. That's that fourth factor. Do I have a job here?

Or do I have a career? This is one of the my litmuses on whether people feel supported or not. Am I on a trajectory? Can I learn and grow? Or is this transactional? Is this just a moment in time? And when each of those four factors, when the volume is turned up on each of those four sub factors, people report a strong sense of belonging.

So, having brought a little bit of a more technical heuristic to what it means to belong, let's dial back from that. Think about this whole conversation, David. Think about the last four and a half years. All of those disruptions that we talked about at the top of the podcast, the disruption in terms of the way we work, remote, hybrid, economic downturns and resulting layoffs, promotion freezes and offshoring as organizations and companies look for least cost location strategy. 

Companies reducing benefits spend in the last 12 to 18 months or ending social purpose programs, the sense of disposability that comes to employees who didn't get rift in cyclical layoffs, but now are still at the company, have a survivorship guilt and are wondering, am I next?

All of that's what I hear when I'm engaged with the workforce and with team leaders, and it represents the crisis of belonging. Because how do you come to feeling seen, connected, proud, and supported when that's the thrash, the churn you're living in? I think that you also asked in your three part triple meat sandwich of a question, what can companies do, right?

Well, who's doing a good job with this? And I think that like anything, it's a question of intentionality. So once we establish clarity on what the forest is and what the trees are, then it's the companies that are doing a good job with this have actually sometime in the last 12, 24, 36 months started to reskill their team leaders to lead for belonging.

By giving them a mental model awareness and a toolbox skills and behaviors to actually turn up the volume on the sub factors for belonging. So making sure that your leaders have basic inclusion skills, making sure that you as a company have created plenty of opportunity for your hybrid, remote and on site workforce to find a meaningful connection with their colleagues.

To have career pathing and transparent job, family architectures and leveling so that people are like, okay, I get it. If I do these things and I gained these competencies, I think I can get to the next spot in this company. Or maybe it's my next role isn't on my team, but there's really great nonlinear career pathing in this organization.

So I can go from sales on the product for product onto a corporate team, or central team. Companies that are thinking about pride that are saying, gosh, we did spend money in social purpose programs and give money to worthy causes for a couple of years, but we just don't have the margins to do that anymore.

There are really important practices that you can invest in that increase pride. Pay equity. One of the really interesting pieces of research I saw was that companies that invest in pay equity and then transparently report out to their workforce, that they have an annual pay equity review process conducted by a rigorous third party audit and that they make adjustments around fairness so that there aren't unfair pay equity issues at the company. When companies do that, it increases employee pride because employees say, great, I'm really proud to work at a company that cares about fairness. And when there's an unfair situation around pay equity, they fix it.

So there's also sensible business ROI things you can do to increase some of these factors as well. 

David Rice: Absolutely. Now, this isn't about the book, but as somebody who's worked in SaaS companies and tech spaces, I've got to ask you, because I'm curious to get your thoughts on something. 

You mentioned in the book, the words generative AI appear just about everywhere now, and now we're seeing AI get threaded into pretty much every kind of HR software that's out on the market. So I'm curious, what are your thoughts on its sort of ability to weed out bias or help us build more diverse organizations sort of naturally by way of its presence, I guess? 

Because it's something I'm sure you've heard proponents of AI talk heavily about, and I have, I'm curious how you would assess where it's at today and where you think it'll be maybe in five years time.

Vijay Pendakur: Sure, sure. Proponents and critics, right? Everybody's talking about it. I mean, yeah, you cannot be in an HR room right now, right, without, or go to an HR conference without hearing about Gen AI. I mean, first I'll say, I think our time to impact is still, we're not there, right? There's a lot of discussion.

But the product market fit, the value prop, the implementation time, workforce readiness, there's a number of things that have to be addressed in the on ramps. So it's not an overnight switch flip. I think in terms of end user tools, we're seeing, obviously I can have a ChatGPT account or Gemini account and start using it as a form of a copilot.

That's one thing. But in terms of the HR tech stack being fundamentally changed by gen AI, I think we've got some time. In terms of bias, I'm of two minds, right? I have a very bipolar, bimodal response to this. On one hand, I know that human experiences at work are deeply impacted by human errors in judgment.

Either those errors are because of bias or noise, right? Noise being just simple, unwanted errors in human judgment. Bias being the shortcuts our brain takes in a pattern that have a negative downstream effect on employees. And you can see this and everything from performance evaluation to who gets stretch assignments, who gets sponsors, there's so many things that are just not there at work.

They're loaded with bias and noise. I'm hopeful that generative AI gives us a counterbalance that the human machine partnership, the human software partnership, allows for us to overcome our historic reliance on a very biased and noisy mechanism, human brain. And that in the checks and balances of the pilot and the copilot that we land in a better space where our talent and our hard work is cleanly assessed and rewarded at work.

And that would be amazing because right now, I don't think there's a lot of companies that can say that they've cracked the nut on that. On the flip side, my skepticism and my hesitancy also comes from the reality that in the move fast and break things vibe of Silicon Valley, where I spent a lot of time, many companies have trained their large language models using data that is loaded with bias and noise because the data it comes from the human experience. 

They're using the repository of human created data that reflects all of our baggage as a species, as a society. And so I'm nervous because we appear to be baking into the large language models our issues and problems because we're moving so fast. And so I do think that likely that this will be a recursive movement towards progress 2 steps forward, 1 step back, 1 step forward, 2 steps back. And we've seen that with some of the high profile launches and errors and gaps of some of the more famous generative AI tools as the pendulum swings and overcorrect happens in real time in front of live audiences.

It's an area I'm still thinking through, but hopefully my thinking out loud with you, David, is productive for you and me and your listeners. 

David Rice: No, it's great to hear because I think if nothing else, it just continues to underscore the point that we still just don't know so much, that we still don't know more than we know on this one.

So, before we go, there's 2 things we'll need to do that I always like to do to end the podcast. First, I want to give you a chance to tell people more about where they can connect with you, find out more about what you've got going on, where they can buy the book. Go ahead and share with us kind of your promo speech.

Vijay Pendakur: Alright. Well, thank you for the chance to do some self, shameless self promoting. I think anybody who is interested in following me or my work, my website is my first and last name.com. VijayPendakur.com. Super easy to find me on the web. I would encourage you to sign up for my newsletter.

That's a quick way to get a steady drip of what I'm doing, what I'm working on, my actual original writing and research. There's following me on LinkedIn. I have a very active LinkedIn presence, I think is another fruitful way for us to stay connected. And the book comes out soft launch with the publisher in the middle of September.

So September 16th through Amplify Publishing Group, and you can go to Amplify Publishing Group's website and order it directly from the publisher. If you want to be a super fan, I would love it starting in the middle of September, or you can hang back and build steam and wait till the Amazon Barnes & Noble's Walmart Books-A-Million go live, which is early December.

We're looking at tentatively December 3rd right now. You're going to see a lot more sort of launch events and media spectaculars between now and then, and in an attempt to celebrate the opportunity of offering people a radically simplified blueprint on doing the best work of their lives and leading high performing teams.

And so that's my shameless self promotion. 

David Rice: I love it. That's good. The second thing is we have a little tradition here on the podcast where you get to return the tables. You get to ask me a question. So I'm going to turn it over to you. Ask me anything you want. It doesn't have to be about the topic today, but it can be whatever you think.

Vijay Pendakur: Well, great. I I'm excited for this because I was looking at your online presence, David, and was really intrigued by your deep background in journalism. Numerous really impressive journalistic roles. You've been in the space a long time. Talk about a space affected by disruption. Talk about a space affected by aggregation.

You have far fewer players in the space than we did when I was growing up in the 80s and 90s, and now we've got Gen AI. So you asked me to look around corners and try and do a little bit of crystal ball on Gen AI into HR space. I'd love to turn that around and ask you to look into a crystal ball. Where are we in five years with generative AI journalism?

David Rice: I think it's gonna be more common. You'll get more and more of the sort of citizen journalist thing going on because the writing aspect and some of the, even some of the editing really becomes much more mechanical. The hard part for those folks will still be knowing the right questions to ask, who to ask them to, how to go find that person.

The tools of a journalist of really doing the work teaches you how to go get information in a systematic way, whereas where folks will struggle and what I worry about getting lost as we start to trust this thing even more and more is that we start to lose that as a muscle, basically, that we can flex in order to tell stories.

So I think that is a concern, but I think what will separate good reporting from bad reporting is your ability to do that. And we'll be able to use Gen AI a little bit to help you write or edit or, streamline a few things here and there, certainly with like things like search engine optimization, because even journalists have to think about it now.

Yes, you can use it for all those things, but it's at the end of the day, you've either got a story or you haven't. It's still, and that's one of the things that's going to continue to be at a premium is your ability to go out and find the right people to talk to, the right examples to tell your story.

Cause Gen AI can help you, but it can't totally make the whole thing. We see it all the time right now. We're actually constantly having this conversation right now about, it'll give you a baseline. It's like, it'll give you a canvas with a line for the horizon, but it won't draw anything on the horizon. 

Vijay Pendakur: Yeah. And you're reminding me of the Sports Illustrated debacle from a couple of quarters ago. Yeah. Like where it's like over reliance on this tool produced some pretty terrible writing and articles that were both counterfactual and also just like, wait, does the article just cut off right there?

David Rice: Yeah, difficult to read, understand what was the intention of this.

Vijay Pendakur: Yes. Yes. 

David Rice: Why did this is illustrious publication? Put this out there. That will continue to be a thing. And so, like, people always say, wow, the future of this job. And I'm like, well, actually, the future of this job is okay. It was damaged a long time ago through the things that you talked about, some of the disruptions that we've had in terms of there aren't that many media outlets.

A handful of companies seem to own everything and everything else is sort of corporate journalism. It's almost like content marketing, and then you've got like a handful of companies that are running the whole thing. And so that is, was really the most damaging impact. I'd say Gen AI, there'll be good things about it, bad things about it.

People are getting better already at spotting it, you know what I mean? So like people can feel when something's written by AI and sort of, they have their own opinions on that. They can determine whether or not they trust it or, and that a trust, we talked about it in an organizational sense, but in, in terms of your customer, which in media is your audience.

You're going to have to keep working harder and harder to build it because people are getting more skeptical of what they see. I mean, that's been going on before AI was a thing. We've got to, we're in the era of misinformation. So that's not Gen AI's fault, but it isn't helping any necessarily. 

Yeah, I take it. It's one of those things. It's a mixed bag. Where are we in five years? It'll be people are using it, but they still have to have the skills to go out and tell a good story.

Vijay Pendakur: Fantastic. Taking us to church, David. I like it. 

David Rice: Well, thanks for giving us some of your time today. I really appreciate you coming on the show.

Vijay Pendakur: It's been a pleasure being here and I look forward to continue this conversation with you offline. 

David Rice: Alright. Well, listeners, thanks for you to join us today. If you haven't already done so, head on over to peoplemanagingpeople.com/subscribe, sign up for the newsletter. You'll get things like this podcast, all our latest articles. We've got a lot of software content coming up. So definitely be on the lookout for that. Get all our tool reviews, things like that. So do sign up for the newsletter.

And until next time, play with AI, but don't take it too seriously.