Rick: Cool, want to dig into that more, but before we do, for those of you who’ve never heard Peter’s podcast, Undiscovered Country, it’s actually right here in our feed, but I’m wondering if you could just give us a quick synopsis, Peter, of what you’re doing over there.
Peter: Oh, sure. Yeah, I’d be happy to. The Undiscovered Country is a reference to the future. We started with a lot of our clients that, in addition to getting smarter and more capable, were saying, “Hey, it seems like the future is coming a lot faster than it was in the past and bringing along with it a lot of uncertainty for the ride.”
So, you know, could you, BTS help us think through future back thinking, basically, not as a thing that you do at an offsite, but as a regular way of being in the world? And as we started doing this more and more with a lot of clients in an off sites and in classrooms, I started thinking, you know, wouldn’t it be interesting if we created a podcast where, one, you could explore all of these techniques, but two, you could have conversations with people who were actually doing them?
And hence the, uh, Undiscovered Country was born. And that’s pretty much what we’re doing over there. We’re bringing people in who spend most of their present living in possible futures or thinking about possible futures. And, uh, we invite them to come over and share what’s going on.
Rick: Well, perfect. Thank you for that, Peter. So, what I was hoping we could spend the majority of our time today is what I found so incredibly interesting when you and I had a chance to chat live not too long ago. And that is that space that you mentioned earlier around organizations moving from experimentation to impact. but At the high level, first, what does that mean? And second, what do you think is required for us to do that successfully?
Peter: Yeah, that’s a really good question. The way to think about it, and I’m sure most of your listeners would relate to this as a function of practical experience. But what we’re seeing is that organizations broadly are going through one of three different shifts. So, the first shift is from not using AI at all to exploring it, which is what I would call the initial foundational piece, and that’s just getting your hands on the systems and getting familiar with what they can and cannot do. And then going from there from awareness to experimentation. And, you know, the difference between the two is, as opposed to just getting comfortable with using them, and, the safety considerations that come along with it, starting now to use them to experiment and reimagining certain workflows or going after very specific use cases.
But the experimentation piece is, you know, not being sure whether or not AI could actually work for those use cases. And then, next phase that we’re seeing a lot of firms getting into now is, okay, we’ve done the experimentation. We know it actually works for certain use cases. So, how do we scale it?
So how do we drive it and, use it to get to some real impacts and results? So, that’s the way to think about it, from awareness to experimentation, from experimentation to impact, which is another way of saying operationalizing it at scale.
Rick: Cool. All right. And I, think so many of us are still kind of stumbling around in that space. I actually have my phone set up now, so I can just say, “Hey Wilson, I’m curious and start a new conversation and sometimes argument with ChatGPT, which is even funnier. But while that’s interesting and it’s nice to have somebody to help me think out loud because that’s what I do, that’s how I solve problems personally, I can’t necessarily point towards hard impact from that kind of work. It’s useful. Sometimes I think it makes me faster. Sometimes I wonder if it makes me slower. But I’ve kind of, put that in the category of experimentation.
when I think about it in my own world, then how do I begin to pivot towards understanding real impact and operationalizing AI into what my team and I do.
Peter: Yeah, it’s another great question. There’s, I would say, an maybe an obvious part of how you do this, and then a less obvious part. I think the obvious part is you do wanna start by identifying a use case that matters, you know, or a problem that matters this is what we do is business people most of the time, right? So it’s you want to take a task that has a lot of value at stake, which means it’s, you know, a thing I don’t do that often that really, really matters, or a thing that I do that maybe has a small impact, but I do many times during the course of the week.
I either way, it’s something that really drives value for the business one way or the other. So, you identify this use case that has high value at stake, and then you have an opportunity to look at it and say, all right, is there a pain point associated with this particular task that I can automate or mitigate?
Or conversely, is there something about this task that I absolutely love that makes me engaged or that’s a source of competitive differentiation for me and my team that I can augment? So, those are your two tasks. But the first part is, is of course, figuring out a task that has a high value at stake because otherwise you’re likely to use AI to automate or augment something that doesn’t really matter, in which case you shouldn’t be too surprised if week later you’re using it and you’re like, okay, that was, you know, that was clever, but it’s not really making a difference. So that’s the obvious bit. if you’re going to use AI to automate or augment something, make sure it’s something that matters.
Then maybe the less obvious bit is, as you start to use AI in this capacity, and in this case, by the way, we’re talking about generative AI specifically, there’s a difference between using generative AI simply as kind of search on steroids or very sophisticated kind of Microsoft Excel to optimize a task. And you want to shift from thinking about it that way to thinking about it as a virtual teammate.
And this anthropomorphism may sound kind of cute or clever, but it’s absolutely pivotal to getting the most you can out of these systems. And more importantly, to avoiding the basically the downsides of these systems and the easiest way, you know, I like to think about it is think of your AI as a very well-read but very inexperienced coworker that really wants to please you and to help you get work done. And something interesting starts to happen. You notice immediately if you do this anthropomorphism that you’re going to do things like be very clear on what you ask it to do, especially if you’re a good manager, right?
And maybe help your coworker understand, take the first few steps together and do things like check the quality of their work and engage in a conversation with them. I would say maybe for you, you want to start by being clear on, if I automated tasks or augmented them, would actually make a difference because they’re meaty tasks to begin with?
Rick: Yeah, for me, Peter, it definitely is easier to think of generative AI, even if it’s a fictional friend, but that coworker kind of thinking, I’m wondering if you could say a little bit more about what’s holding people back from that way of thinking and also how that potentially comes to life.
Peter: Yeah. I think that, people, ourselves included, can be forgiven for being a little confused about this particular technology shift. I mean the analog I like to draw is with the industrial revolution, and the analog goes something like this. If you think about the industrial revolution as simulating what we did with our physical bodies using machines, the AI revolution is simulating what we do with our brains using math. I mean, basically using, math on steroids, if you will, which is what most AI models are.
But the problem with this, of course, it’s a little tricky to get your head around, no pun intended, is, well, how do I think about a tool that basically does thinking for me, or at least is simulating cognitive thought?
So that’s why, you know, I think a lot of people are simultaneously worried about the systems and confused about how to use them. I find this anthropomorphism actually helps a little bit because everybody, I mean, there’s no one listening to this podcast who hasn’t worked with a coworker who is a PhD in one topic or another, and who wants to partner with you to get some work done. So, if you tap into that experience you have and do it with a little bit of imagination, you realize, oh, okay, lucky me that I have a PhD-level coworker available to help me think through the problems I have. And luckily, this particular coworker is available to me 24/7.
Doesn’t talk back and doesn’t pass judgment. So, you know, I can ask questions that I might be embarrassed to ask in any other context, freely because there’s no judgment with these systems, at least not yet.
It’s funny. I don’t want to do any advertising, but, um, you know, a little shameless plug for BTS. We have some programs where, you know, imagine you’re going through a training program and then suddenly you have to interact with an AI bot .
And for one client in particular, I’m not going to say who. It’s all about having difficult conversations. Right? And this one bot is really funny. It’s a high-performing salesperson who is feeling quite pleased with themselves for hitting their numbers but is just absolutely not collaborative and treats younger employees horribly. And you have a conversation with this thing and it’s so realistic, you know, as you’re talking and you’re saying, well, do you really think that’s a good idea? And the AI comes right back and says, well, what do you want? You want more sales or not? What do you think?
And I’m like, oh man, shame glaze coming from the, uh, the artificial intelligence.
Rick: Well, as a grizzled old sales leader, I can say I’ve probably had way too many of those conversations through the years. It seems like there’s always that one great seller that unfortunately leaves a path of destruction.
So I guess Peter, everybody talks about how quickly things are changing. It seems like there’s something new literally every day. I guess i’m curious, what surprised you most in the last few weeks, months as you’ve been doing this work?
Peter: I think the most surprising thing is just how clear it’s becoming that creating a world that will flourish from using AI is not a technology challenge, it’s actually a leadership challenge. And I guess that’s true for most things most of the time. But, you know, for a very long time, I think much of the focus and the fixation was on the technology, and it still is to some extent, right? if you follow the news, you know, the latest is, people are concerned or were concerned about the cost of these things.
And, you’d hear people say, like, wow, we’re going to spend hundreds of millions and billions to build up the infrastructure. And where’s the data going to come from? And where’s the energy going to come from? And then, you know, more recently, DeepSeek in China. I’m sure you’ve noticed the news and your listeners are aware of this as well, came out with a cheap model and so that seemed to suggest that things were going to get cheaper faster. But hidden beneath all of this, is these companies are getting better at building machines that are better and faster and, quite frankly, so intuitive to use, it’s becoming just really, really clear that moving forward, the problem is not going to be around the technology.
You know, the technology companies will take care of that and it’s all going to come down to a leadership challenge. And by a leadership challenge, I mean, in rather than just some phrase, specifically, some pivotal mindset shifts that organizations will need to undertake. And once they do, things could be amazing, but until they do, things are going to be quite difficult.
Rick: Cool. Can you say a little bit more about what some of those shifts are?
Peter: Yeah. I mean, I alluded to one of them. So, you know, a simple one is a shift from thinking of AI as a productivity tool to an interactive teammate. Right. Or, you know, a very well-read, but very naive teammate and things like moving from being a, gatekeeper of information to a steward of collective intelligence in the business. I think this is going to be a particular hard one as people start to realize that they’re not the smartest person in the room anymore. Because there are systems that are as smart, or at least as knowledgeable, right there, available to everyone 24/7.
And so, rather than look at that as kind of diminishing you or your sense of identity at work, be excited about that by thinking, okay, well now this is great. So just imagine the kind of partnerships that are capable. That’d be two examples. And you know, a third, actually, that’s one of my favorite, is shifting from trusting your intuition to testing your intuition.
And, you know, I think that was always an important skill for people to have most of the time. But now in a world where expertise is available 24/7 to everyone everywhere, it’s now really the case that you want to understand the difference between using data to prove you’re right and using data to discover what is right.
And again, doing this with your AI teammate, who’s right there to help you figure that out. It really is exciting that there’s something really remarkable to be discovered at the other side of the shifts, but you have to make them, and they’re going to make a lot of people uncomfortable, especially people who more or less identify themselves for being an expert at something and being the only expert at something, for example.
Rick: Wow, that last one especially gets me. I think that’s a great insight. so as always, our time is too short, my friend. For our audience, if you want to continue to learn along with Peter in the world of AI, I would encourage you to follow him on LinkedIn. He’s very prolific there. We’ll make sure that link is in the show notes. And we also encourage you to listen to Undiscovered Country. And with that, Peter, my friend, thank you so much for joining, and look forward to our next conversation.
Peter: Good seeing you, Rick. I look forward to it as well.
Rick: Thanks for joining me today. It’s always a pleasure to bring to you our fearless thinkers. If you’d like to stay up to date, please subscribe. bios for our guests and links to relevant content are always listed in the show notes. If you’d like to get in touch, please visit us at bts.com. And thanks so much for listening.
