Vibe Teaming: How HR Turns AI Experiments into Real Adoption

[00:00:01] You're listening to the HR Mixtape, a podcast for leaders who want to understand people, strengthen culture, and navigate change with clarity. Today's conversation starts now.

[00:00:15] Nicole Bradford, AI and Human Intelligence Executive in Residence at the Society for Human Resource Management. She is a futurist, investor, and executive with more than 20 years in technology and human potential who shapes how HR professionals worldwide approach AI and human intelligence. Nicole Bradford, AI Adoption and Workforce Wellbeing.

[00:00:45] Nicole, thank you for jumping on the podcast with me today. Nicole Bradford, AI, HR Oh, it's my pleasure. It's so nice to see you again. Nicole Bradford, AI, HR We were just talking before we jumped on that we got to have one of your inaugural interview conversations around AI back when SHRM first launched their AI HI conference, and it was wonderful. Nicole Bradford, AI, HR And, you know, one of the things that I was thinking about while we were chatting about that is what a difference two and a half years ago your peers makes. Nicole Bradford, AI Oh my goodness. Yes.

[00:01:14] Nicole Bradford, AI is changing organizations and the way we have had more experience with AI now. And so it's just sort of like thinking about where people were then and what we talked about that and then where we are now. It's pretty dramatic. Yeah, it's one of the things in our industry that is moving so fast. And you've talked about before, HR leaders really becoming that AI catalyst in their organization.

[00:01:43] Can you help define for me, though, what does that mean practically? Because I think a lot of HR leaders like fundamentally understand that but are not really sure what they should be doing. Yeah. So an AI HR catalyst is a person who's helping their organization really move from where we are now, which is people have been doing a lot of experimentation. And how do we move from there into actual responsible adoption? And so that's the transition.

[00:02:10] Now, when you and I talked about this a few years ago, it was just like getting people fluent. But now it's really about taking these experiments, these pilots, and then actually turning it into adoption. Do you think that there's still a group in HR that doesn't necessarily understand AI in general and doesn't know what to do with it? I think we're getting a lot of expectations from our business leaders. Introduce AI, put AI in everything.

[00:02:40] And that isn't always the best solution, right? So how does HR figure out what the most important piece is that they need to think about when it comes to AI? Well, so you asked two questions. The first question is about where are people sort of at? And there's a range. I mean, even though it feels like it's all we've been talking about for the last couple of years, it still is at the beginning. So for anyone out there listening, if you feel like you're behind, you're not.

[00:03:08] We're just we're at the very beginning of this transformation. And so what we see, especially across our SHR membership, is there's a wide range. There's people who are just beginning. And then there are people who are, you know, really advanced in building agents and a bunch of other things. But the people who are just getting started, you're not late. But it is time to get started. And secondarily, the question about what is the most important thing to focus on?

[00:03:36] I would say that it's very individual. Because like what we've seen is that the return on investment is a combination of context. So what's going on in your organization, culture and customer. And those can be internal customers. So a big part of it is what's the thing that is bothering you and your team the most? What is the thing that's sort of like the biggest challenge? And it doesn't mean it's the most sophisticated challenge.

[00:04:05] But it's sort of like what is the rock in your shoe? What is the thing that if this were solved, you know, your organization and your team would work much better? That's the place to start. And just do one thing. And do that one thing. And learn from that. And then pick the next thing together. And then do that thing and learn from that.

[00:04:25] It's far better to do step-by-step problem solving that solves real problems for the organization than to do, especially if you're just getting started, than to do sort of like a sweeping, you know, abstract transformation that then day-to-day people are like, okay, how is this making my life better?

[00:04:47] And then also the other thing I would say is like once you do a few of those where it's a real problem for regular people in your organization and it gets better, then that inspires people to find other places to change the work. But then you have momentum. And those early things can actually help you get the kind of buy-in that allows you to have a real transformation. And we do this already in our HR careers with all the other programs we manage. We do test pilots.

[00:05:17] This isn't any different. It might feel different. And I like that you mentioned you're not behind. I don't know where I read the statistics, so shame on me that I can't quote the actual source. But it talked about the use of AI and only about 20% of the population of the world is actually using AI. So, like, we've got time. We can't ignore it, but we've got time. So I appreciate you saying that.

[00:05:41] You know, one of the biggest things that SHRM has talked about and you've also talked about is that human potential alongside the AI capabilities. How do you make sure that AI is adding rather than replacing that human potential?

[00:05:56] Yeah, I mean, a great deal of it is what are the things that when humans are augmented will allow us to really support and change and drive our businesses?

[00:06:12] And so, you know, a part of it is like, have you seen or heard of email exchanges where everyone's AI, you know, whether they're on co-pilot or whatever they're on, like, like they're just talking to each other. You can see sort of all of these automated exchanges and it's sort of, you know, doesn't actually accomplish the goal. And so there's a bit of that working its way out of the system.

[00:06:42] But one of the things that, you know, you'll see is that I think people really want to solve problems together. One of the things that I'm really excited about is I've been looking a lot at what does it look like when teams use AI together? And that, I think, is the most interesting sort of what's next.

[00:07:04] So we spent the last, you know, two-ish years really thinking and we're learning a lot about how individuals can be more productive with AI. So that's where you've heard about different forms of prompting or, you know, whenever I'm starting something complex, I actually use dictation and I just talk about what kind of problem I'm trying to solve, et cetera.

[00:07:29] Then my very last sentence is and write a prompt for that. I might say write a McKinsey style prompt for that. Write a Fast Company type prompt for that, like sort of like whatever's the feel I want. And then I have it write the prompt and, you know, depending on what I'm doing, I'll use different models. And so we've learned a lot about what does it take for an individual to be productive. I think what's really interesting is what does it take for a team to be productive and understanding that.

[00:07:59] And there's some really wonderful things. There's a methodology called Vibe Teaming. It was developed by Jacob Taylor out of the Brookings Institute. And it's really straightforward. And I raise it because it's really a good example of augmentation. And what you do is you have a structured conversation with a group of people. So say five or six people. And you have one person who's the AI wrangler.

[00:08:26] You have another person who agrees for this conversation to be the facilitator, meaning, you know, maybe you're using a structure sort of like what's the problem? What are the constraints? What are the must-haves? And so it's like a structured conversation. And then that facilitator is ensuring that everyone's communicating, that everyone's putting their thoughts in. And you record that in a transcript.

[00:08:52] Then you take that transcript and you put it into ChatGPT or one of the other models or your corporate model. And you say, organize this, you know, according to the structure. And then the group, this is the key part, you take the output and you put it on screen. And the group talks about the output. What's right? What's wrong? Is that accurate? And you record that.

[00:09:20] And then you add that back in. And so the ability for a group to do very high-level strategic thought. And the thing that's really incredible about it is that in this context, a lot of times when we're working with AI one-on-one, you know, if you're not really careful and because it's been trained on sort of like the corpus of human knowledge, it adds a lot to what you're doing. But in this other structure, AI is not adding.

[00:09:48] It's simply organizing and helping us access the collective intelligence on a team of human beings who know the problem. They know the culture. They know the context. They know the customer. And those conversations and those outputs, in many times, it's superior output.

[00:10:08] The other thing that it does that's really exciting is, you know, there's a lot of organizations where individuals are using AI so much individually, it's getting really lonely. You know, so people have – they have a lot of interaction with the models, but they kind of feel alone at work. And then I do my 70-page proposal and I send it to you and you put it in your AI to summarize it.

[00:10:35] And then you send me your 70-page, you know, counterproposal. And so it's very lonely. But what this vibe teaming does is people actually feel really connected. It gives a regular point of connection, their thinking together, which is the fun part of working together. That's why we work together because it's fun. And it gives a structured output that is superior. So it creates, like, social moments.

[00:11:05] It creates ritual. It creates superior output, allows people to do collective intelligence. It makes the models augmentative. I just made that word up. But it makes it – you know, it supports the humans. It's not a participant. You know, like, it's not a thinker on the thing. It's taking the brilliance of a group of people and helping them pull it out.

[00:11:26] And then the last part that's really important is that over 44 percent of people say that they use AI every day in some of the studies that we've done. But an almost equal number of people say that they encounter work slop, you know, in a given day. And that's the thing where it's just like – I don't even have to describe it. Like, if you've seen a huge report where you're reading and you're like, what did I just read? And you don't know what was in there. That's work slop.

[00:11:54] So having these vibe teaming sessions, like, maybe once every two weeks or so, what it does is it helps the whole team understand what's salient, what's important. And so it actually reduces the work slop because then people are really clear about what's additive to the team and what the team is looking for and what the goals are. So I think that is one of the newest ways to ensure that AI is supporting human potential.

[00:12:25] I love this example, especially because you can do it in person, hybrid, or fully remote because you're basing it on that transcript that you talked about. I love that. I thank you so much for sharing that. Hopefully people will start to implement the vibe teaming in their organizations. Can I give you one quick example? Yeah. So I did it remote with a group of executive assistants. And so it was for assistant day. And it was a group of executive assistants, like eight of them.

[00:12:55] And they were in the room together. And I was on Zoom. And so these are not AI native folks. And they're like, you know, I mean, assistants rule the world. It's one of the things I learned very early in my career. They know everything. But they weren't, you know, they weren't who you might think of when you think about an AI native group of people. And so we did the process.

[00:13:18] And they came up with a way to fundamentally improve the efficiency of how their C-suite works together. And by the end of it, they realized that not only did they have a proposal for what they would do that they could take to their leaders and say, this is how we're going to organize these meetings and some other things.

[00:13:44] But they also had something that they could just start doing without asking permission, you know, in a way to like fundamentally like make a big difference in something that was a rock in the shoe for lots of people. And so it's not, you know, it's not a giant AI overhaul. It's a rock in the shoe that was affecting the efficiency, you know, of a team.

[00:14:08] And they, non-AI natives, used vibe teaming to outline a solution that fits their culture, context, and customers that they were like, yeah, this will work. We're going to try it. And it was something that, you know, if they took it to their leaders, you know, they would have been impressed. But they also didn't even have to ask permission to do it. It was just between them. And vibe teaming helped them see it. And so I think it's wonderful.

[00:14:37] So for the HR leader listening who's like, yep, I'm totally on board. I see the value of AI. I see the value of making sure that we anchor it in human potential. What I don't understand is what guardrails I need to create because we're starting to hear or not starting, continuing to hear chatter around talent acquisition, hiring specifically, and performance management.

[00:14:58] So just in those two areas, where should we consider what guardrails we need to have in place so we can use these amazing tools but we're not taking the humanity out of it? I have a sort of like a rule of three Cs. And for these three Cs that I'm going to give, if these are involved, don't use AI for it. And so one is crisis.

[00:15:24] If there's a crisis, don't use AI to communicate about the crisis. Like if someone's having something, like if an employee is having a crisis in their life, go and speak to them. Talk to them. Have a conversation with them. You know, if someone is, you know, dealing with illness or an early retirement or, you know, like just all sorts of issues.

[00:15:50] If it's a crisis to anyone involved, be human about it. Do not use AI for it. Do not, you know, do not let your email agent craft a response. Like, no. Two, if it's a commitment in the sense that, you know, a lot of the tools that are out there can really help us match people to roles and things like that.

[00:16:18] But when you're making a commitment to someone, so a hiring commitment or a job change within the organization, also be very human about it. So be human about commitments. And then be human about conflict. So, you know, if someone is getting pipped, do not send an AI-generated email for a pip, you know.

[00:16:41] So if there's a conflict between two employees, you know, on any level, do not use AI for it. So the three Cs, crisis, commitment, or conflict. Do not use AI. And for the rest of it, you know, there's different ways to do it. And, you know, responsible AI guardrails, et cetera. I love that. And your employees will know because your email will have a bunch of em dashes in them. Yeah.

[00:17:10] Well, also, people are getting really, you know, people are starting to get attuned to what it looks like because it's a very flat voice. Yeah. It's very, very, very flat. And so, you know, certainly I think people won't be able to tell if anything that they see is generated or any text necessarily is generated.

[00:17:33] But what they will be able to tell, you know, whether AI was used or not was whether or not a real human being was on the other side. Like, is your soul in that or not? Is your essence in that? Is your humanity in that or not? People will be able to tell that. Yeah. Well, Nicole, this was a great conversation. Thanks for chatting with me. Thanks for having me.

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