Join us as we dive into the evolving world of organisational development (OD) in the age of AI. In this episode, our hosts - Sarah Fraser, Tim Haynes and Markus Edgar Hormeß - reflect on a recent hands-on workshop where OD practitioners experimented with teaming alongside AI.
We unpack the challenges and opportunities of integrating AI into the workplace, from building trust and navigating power dynamics to fostering intentional experimentation and learning. Discover how creating “playgrounds” for safe experimentation can break down barriers, encourage human connection, and unlock new ways of working.
Whether you’re an OD professional, a leader, or simply curious about the intersection of people and technology, this episode offers practical insights and thought-provoking reflections on the future of teaming with AI.
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Sarah: Hello and welcome to Mayvin's Research Hub podcast with me, Sarah Fraser. So we've been experimenting carry on with our experiments on AI and organizational life, and earlier this [00:01:00]month, Tim Marcus and I hosted what we called an AI jam, which was all about teaming with ai. It wasn't about the technology so much as what happens when you put it right in the middle of human relationships.
So in the middle of the, of a team, in the middle of conversations and potentially disrupting, changing the dynamics in the room and affecting trust too. We asked what does it mean when, when AI isn't just a tool you use on your own, but a real participant in the room with you. What followed was interesting, sometimes messy, surprising, sometimes uncomfortable, but a really good day experimenting and playing around with AI in person, in teams, and it was exactly what's needed.
If we are serious about understanding how organizations will change in the years ahead, this podcast is our attempt to make sense of that day of it, and to explore what it tells us about teaming. [00:02:00]Leadership and the role of AI in organizational life. Enjoy.
Okay.
Sarah: So we did something quite cool this week, and this is our chance to make sense of that. Share a bit and, see what's next. We, ran a workshop on teaming with ai. To think about what does the world of OD organisation development change look like when organisations are, becoming, you know, they're integrating AI into the way they work, it changes the teams, it changes the way we're working.
It's happening, it's changing the demands of leadership. We brought a load of brilliant od. Practitioners together in a room, in person to do some experimenting, and see, see what the implications might be and see what the OD community might do with ai.
So [00:03:00] that's the quick overview. Marcus, do you wanna say a little bit about what happened on the day?
Markus: Yeah, so basically, teaming with ai, it's an initiative that has been running for a couple of years we run experiments to make that change tangible and experiential.
You can talk a lot about ai, but in the end you need to get, friends with the tools and you need to try stuff, specifically if you're collaborating in a team together with AI partners. How does a dynamic change? How do you create, if now suddenly the AI creates and you are shifting more towards curation, that that hits for, for some folks, that hits close to their identity of who they are.
Mm-hmm. And how does it change our conversations and decision making? And so what, what we did is basically dropped them into a room. We gave them, a bit of introduction about the bigger picture of teaming with ai. It's not just about technology, it's also about the people and [00:04:00] how they collaborate, and what implication that has, not just for, the moment in the workshop, but also beyond that workshop
the structure of the team might look like in the future. So after a bit of introduction, we actually tackled a challenge. People could choose what they wanna work on, in terms of the future of, OD and at the intersection with ai. We asked them to ideate using AI tools in teams of three or four.
Having a partner in crime ideation, and reflecting on that,
Sarah: which some was, yeah, that was even, that was sort of quite challenging, but confronting, isn't it? Like Right. I dunno, I don't, I dunno about AI yet. Like how do, how do I even get into this?
Markus: That that's true. And, and there's, uh, what we observe is, many people know about ai use it every day and other people don't. And how do you balance that in a team? And the, the good thing is that we found, um, is that AI is an interesting technology because it's so much intertwined with the expertise that you have.
[00:05:00] You cannot just use AI by just knowing the technology. You need to be an, an expert or at least proficient enough about the problems you're trying to solve about the, the, the field that you're in. Like od in our case. And so you need both. And then it becomes a sweet spot.
So even the people who didn't know a lot about technology took a lot out of it because they could see other people using it, I thought that was quite brilliant. So basically we asked them to interact with different versions we call that , many brains one bot where multiple people sit in front of one AI that helps them understand what comes out of that chat. So we all can read that. We, we all read the output, we discuss it so we have a shared understanding , what's being prompted and what, what's the output. 'cause the other mode of working is. One bot, one brain where we all split up and do our thing with ai.
Every one of us comes back with a [00:06:00] different picture, with different ideas, possibly all of us being overwhelmed, and then we take a lot of time having to align and make sense of what we just brought back. So these different modes of working, uh, were some of the core of our experiments and , this allows people to be more intentional about their use of ai. And it, again, it cuts back to that it's a lift experience that we then in the workshop actually could reflect on what does it mean? And I, I found it inter interesting. I think one of the participants said, the AI acted almost like two or three participants, and this is something that we see in our research as well.
If you're facilitating groups, make the group smaller if AI is part of it, because otherwise it becomes too complex to handle because AI introduces so much content and complexity that is kind of worth these two or three people, not, not in a sense of that their contribution is equal, but it's, in, in, in the sense of facilitation , of [00:07:00] the conversation that we need to have.
Tim: And I, I thought that was fascinating when the groups were starting to really talk about , do you see AI in that group setting as an additional teammate? You know, is it an augmented human that's sitting with you, having the conversation, helping inform the conversation, or do you view it really as a tool, as a machine that's sitting there on the table and actually some of those mm-hmm. Human characteristics and the human experience of sitting literally with a PC in amongst the, the group to get that prompting done together using the technology. I think there was some really, helpful insight for all of us and learning around, well, how we feel about the use of the technology in that human setting.
So that was a real insight for me.
Sarah: It was clearly disruptive. It was about, how do you get the best from it, even though it is a slightly disruptive force in a team setting? Because we, we know that, you know, AI coming into the way we work in [00:08:00] organisations is going to affect like the, the very human side of trust how our decisions get made, how dynamics, like how do we, you know, what privilege do we give? Well, what, whose voice do we privilege? Noticing in the teams, did they privilege the AI voice or say, okay, that's interesting but not quite what we wanted and let's build on that. Or let's take the, just the little bit what of what we wanted.
Mm-hmm. I really love some of the reflections and the challenge around that. I'm thinking like, how do we. How do we decide what the contract is here between our AI teammates and tell it what the rules of the game are here. Um, but we need to make that explicit, not implicit. Yes. So lots of relational conversations, in the face of actually making sense of how to use the tech.
I love that. That's what was. Great about the day and the conversations and some of the reflections that you, you really drove at the end, Tim . People are coming into this to make sense of how the tech's gonna work in, our world of work. .
Very [00:09:00] much about the human side of things , teaming , relationships, power dynamics, all of that stuff that we work with. And we definitely brought it into the OD world.
Tim: We did and beyond, right? 'cause I think then we started to explore the ethics around this, notions of inclusivity, which voices are really getting heard.
I think the conversation went deep. But what I also loved about your process, Marcus, was, using design thinking in the way that we worked, actually challenged frankly, some of the OD orthodoxies and methods. So, you know, as an OD professional myself, I love conversation and deep conversation, getting to the core of what's really going on.
But you encouraged us to really, I think. Fast fail, fast, learn, experiment through learning and learn by experimenting. These notions challenge me around, Hmm. That doesn't work in group processes as well. Actually in this context, the rapid prototyping of solutions, [00:10:00] these ideas and concepts I thought were super helpful.
Mm-hmm.
Markus: Yeah. Th thank you. But this is something that is, I don't think it's unique to the OD world. This happens . To any professional I work with, this topic is so hard that everyone has an opinion about it. The problem is the are is this opinion that I have rooted in the lived experience with the systems that we're, that we're using right now.
Their capabilities, their limitations, and. Um, basically also the, the way it's then being used in an organisation for better or for worse. And, um, you know, because any of the new technology can be used for good, that can be used for bad stuff, and we need to. Continually have, of course, try something, then have an informed conversation about what just happened.
And this is what we are trying to actually push with that format. AI teaming with OD like, uh. Things where we, instead of just talking about stuff, [00:11:00] we build something and then we try it, and then we actually talk about it and have the conversation that is informed by, that experience that we just had.
Mm-hmm. It's a lot about, intentional experimentation and building playgrounds, uh, playgrounds in a sense of a safe space where we can try things and it doesn't immediately create a big risk beyond that experimentation.
And this can be if I go out and, do this in my. Spare time in a nonprofit or in a collaboration do with a university and it's kind of a research project. Or I do that with a client who's also experimenting and say, Hey, we do that for this one team. Let's run it for a month or two and see what happens before we roll it out.
So it's kind of, what are these, these little, spaces where we can try something and maximize learning. Not necessarily at high risk. One of the things we had a conversation about is, we see organisations being risk averse and that's a good thing unless it [00:12:00] blocks the learning that we need to do.
And so how can, you know, I call it sometimes, irresponsible caution. So what's responsible risk and what is irresponsible caution? Because if the caution actually for good reason, limits our experimentation and learning that we could do in safe spaces like our playgrounds.
Sarah: So if we were, if we were kind of, um. To name the benefits of what we did this week with, with that group. And that process of bringing people together to experiment. We kind of, we created that playground. By bringing people together and teaming with ai, it breaks down the barrier of like, I don't get it, I don't think I use it. I'm not really sure how to, like, break down the, the, the, um, anxiety or caution around using AI and what's appropriate, what's not. Like what are the rules here? So it breaks that down 'cause you're doing it together. It breaks down the assumption that it's all about the tech and it turns it into a collaborative thing, so it [00:13:00] like breaks that it, it sort of creates the paradox of, actually creating more human connection through using the tech, which is like, building the potential of what AI can do here.
Right. And um, and like exactly what you said about creating the playground, this is not about getting it right. Nobody knows what AI can do. It's a bit of a black box in terms of understanding how it works and stuff. So let's play with it, see what's possible, and solve some problems that we've got, do something useful
Markus: Yeah, I like your summary there. There is, um, I, I would like to, chime into the play with it part, play with it intentionally, this is, something that, experimenting to learn, that's fine. But a lot of organisations we find need first to learn to experiment. 'cause they hadn't had that pressure to experiment a lot in, in the past year. So when I work with small and medium sized companies, highly successful, they're, they're great, but they're sitting on one service or product they [00:14:00] created 20 years ago, and they didn't have to change a lot.
Now that new technology means they suddenly need to identify a, a new problem and so they, that they can solve, so they can add to that one thing, that one pillar. So that is quite risky to sit just one pillar and yeah. There, it's about learning how to experiment, how to explore, and do that while still maintaining your normal business.
And that's a, that's a skill before even, you know, going to, all the experimentation. So it's kind of these two layers. Yeah. That needs to be intentional. Hmm. Not just playful, it's play, but quite seriously.
Tim: Yes. And I think for me. Sarah, the, the sort of three takeaways if you, if you like for me, were around, this is very practical.
This is get rolling your sleeves up, working on real stuff in the room together, which makes it feel very tangible and real it's [00:15:00] applied, so we were working on real case studies problem statements around the use of AI in OD in organisations. So it was absolutely applicable to the work that we all do in organisations.
And this process is tailored to solving problems in organisations, which I think
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Tim: makes it really powerful. And it's also a very personal experience. I think that notion of, change needs to start with self. I am on a learning journey. We all are, when it comes to the use of AI in, in our OD practice.
And I think treating this as an opportunity, a safe space, to learn together, and change something about the way we each individually operate and practice od in organisations.
Sarah: Yeah. Nice. Okay. That's a great place to pause. Thank you for reflections.
That's been really helpful in organizing some of my thinking. Hopefully helpful to those listening. And yeah, come and talk to us if you wanna know more and, help us, uh, further with our experiments in this area. Great to talk to you both. Thanks so much.
Markus: Thank you.
Tim: Thank you.
Speaker 3: [00:17:00] I hope you enjoyed that conversation. , It is interesting that one really captures something essential. How easily a workshop focused on the tech, bringing people together to talk about AI got into deeper reflections on trust purpose and the critical human connection that makes work possible.
It's the work that we're doing with our clients, developing a people change approach to AI integration. If we can build a practice which engages people in live experiments, finding ways of asking new questions together. AI doesn't have to be the tech we've got all got to get to grips with, but instead could become the springboard for connections and creativity.
So if you'd like to find out more about how teams and organizations can work with ai. In ways that deepen connection rather than diminish it. We'd love to hear from you.
Speaker 4: You so much for listening to us today, and we hope to see you next time. Take care. [00:18:00] Bye-bye.