Are We Really Transforming? Why Better Isn't Always Different - with Terry Young

Speaker A: Well, hello and uh, welcome to the latest episode of the Transformation Leaders, uh, podcast. I'm uh, delighted today to be joined by Terry Young, who's joining me all the way from New York City. So welcome Terry. It's great to have you, um, on the podcast. Do you want to just tell us a little bit about yourself and your background and what the things that you've got involved in and are involved in?

Speaker B: Yeah, absolutely. So, uh, you know, I've been working in the innovation and transformation space space for a couple of decades, um, from McKinsey management, uh, consulting, um, as well as Madison Avenue. I ran a firm called Sparks and honey for 12 years. I founded that company and we really, uh, really put our finger on the pulse of cultural change. Like what's happening out in the world and how does that translate into new business models, so forth and so on. But for the last two years I've been working on a brand new discipline that we call Possibility Sciences. And we've been building the ins and outs of that, uh, discipline. And um, it is really a reinvention of the way we do innovation and transformation, which is very on point for our chat today.

Speaker A: No, great. And I know you've got a book coming out in November, I believe, so. Possibility Sciences, A discipline for Turning Bold Ideas into reality. I'm sure we'll cover that off a little bit more in, in due courses as we go through the, through the show. Um, I always start these podcasts off with one question. It's the only standard question that we have. Uh, but it, I think it frames the contest for context for the rest of the discussion. And it's how you define transformation.

Speaker B: Mhm. Love starting there. It's perfect. So, um, Possibility Sciences, as I mentioned, is a new discipline which is rethinking the way we do innovation and transformation, really from the ground up. And so what I would say is we think of transformation through the lens of systems and how we rebuild the systems that dictate the kind of transformation that we actually produce. Um, every system can be rebuilt and rethought and we anchor all of our transformational activity around moonshots. And what we're trying to do is build systems that allow us to pursue more and realize more of those moonshots. And many people ask me, okay, but what do you mean with systems? And so I'll just double click there for a second. I mean, a system can be an infrastructure, system, healthcare system, hiring system. Innovation itself is a system. And what we want to look at is zooming out so we See the people, the technology, the incentives, the, the capital structures that impact those systems and acknowledge that all of that can be rebuilt and reshaped. And when you do, it opens up brand new transformational pathways.

Speaker A: Yeah, um, I'm glad you um, went a little bit deeper on the system side because you know that was going to be my follow up question. How do you define systems? Because people's view, as soon as you say systems, a lot of people will go to technology. But you're looking at things in a much broader way, including processes, ways of working, communication, culture, all of that which is the system of the organization, isn't it ultimately?

Speaker B: Exactly. Yeah. We take a much wider view on systems and many times we don't zoom out far enough to think about the system. We think about the invention or the innovation or, or we think about one individual aspect of the system. But they all are connected. And this idea of a connected system that has leverage points within it and that you need to find those leverage points is really what we're building uh, Possibility Sciences around. And I would also say that like many times, and you probably see this in the podcast and the conversations, um, in many organizations we mask transformation. We say transformation, but it's really optimization. Right. And I see many times people use the word transformation, but they're going in and at its worst optimizing their core. Um, maybe at its best when it's optimization, you're optimizing at the edge. But what we're talking about today is neither of those. We're talking about how you look at that system holistically and begin to get back to real moonshot thinking. That could be breakthroughs, that could be category defining, you know, industry shifting.

Speaker A: In some of the material that you've sent me, um, ahead of, ahead of this session, um, uh, there's one article which was around Possibility Gap. Do you want to just explain a little bit more about that? Because I think that feels like, and sounds like, you know, there's a link into what you've just been talking about there around that movie shot aspect.

Speaker B: Yeah, so there's uh, a, ah, belief within Possibility Sciences that we do not have a shortage of ideas. We have, we have amazing ideas and we've all, you know, we have them individually, we have them at the organizational level. We're um, putting them in our notebooks. We're going to incredible brainstorms. We're reading these amazing solutions to some of our most complex problems. So the issue from our, my view doesn't lie in the ideas themselves. It is in the bridges that allow us to get those ideas out into the real world. And so we may be able to come up with great, uh, solutions to healthcare, uh, great solutions to our infrastructure, educational challenges. But can we uh, get them to a point where they create impact? And that's what this is about. How do we build the systems, the railways, the scaffolding that's required to get those amazing ideas, um, actually out in the real world so that they stick?

Speaker A: And what does that look

Speaker B: like, all that impossibility gap? Yeah.

Speaker A: What does that look like in reality then for an organization? As you say, as soon as you start talking to people around some of the issues, some of the challenges, some of the opportunities. With an organization you get lots of people with lots of ideas. Some of them will be small, some of them will be massive. Um, and that's that usual historically, that innovation mindset, wasn't it? It was like, well, we're just having ideas poured in. We get all the ideas and then we prioritize and we fail fast and we move forward with the ones that have got the, the biggest opportunity. But in lots of organizations I've worked in the past, they get inundated with ideas. They then find it very difficult to really focus upon just a small number and it, it sort of fizzles out. So in, in terms of the um, you know, the possibility sciences and the possibility gap, what, what does that process look like, um, when you sort of embedding that within an organization?

Speaker B: Yeah. So you can imagine in a discipline we have lots of different components that, that are uh, utilized in order to leverage possibility sciences. And you know, the first starts with mindset. But I want to hone in on three that I think are really valuable to what you said. Uh, one is a philosophy that we call zero waste innovation. And this is a challenge to the notion. And you've done tons of corporate, uh, innovation. A challenge to the notion that um, an innovation, uh, it's better to hoard the innovation than to release the innovation. And the concept here is that we need to uh, see every innovation as a building block to the next. That innovations, uh, can be interoperable, can be modular, and we can begin to build them together. And that's a very important distinction because to where you started. We do hackathons and demo days and innovation summits and, and the list goes long. And we are very generative. We generate a lot of imaginative possibilities. And if you talk to a CEO or executive, they're going to tell you very often we do those things and we never get them across the gap. Right. We never see them get out into the real world. And what happens is they disapp. They go into the innovation graveyard. They maybe are a really good idea. And we say, hey, let's this, we could patent this and let's protect it. So everything is built around this, um, idea of fear and protection. And ideas need to stand as solo ideas. We're challenging that notion with zero waste innovation and saying every idea has a possibility and we need to connect them together and build on them over time. Time, right. The second piece to that is when we have an idea, um, every idea gets mapped, something that we call the innovation genome. And the innovation genome takes that idea and clusters it with other ideas that look similar to it. So you see, innovation clusters connects it into a moonshot. So that now it's not my individual idea that I invented, but I see how this idea connects to a long horizon moonshot. And those moonshots are then connected in the systems that we're trying to redesign. Right. Just by doing those two things, by changing your philosophy to zero waste and having a mechanism to build things and organizing things into the genome. Now all of a sudden we can see a long horizon view, um, over time for an individual innovation, which then opens the opportunity to stack innovations and to look at how they compound over time.

Speaker A: Yeah,

Speaker B: those are a couple, couple of components.

Speaker A: No, no. Great. And is that typically within an organization? Um, or you know, just that genome concept? Um, I'm thinking that, that with, within, within your organization, you'll be able to see these ideas from different organizations that could theoretically come together and create really good joint ventures or collaborations and stuff like that. Is that part of the process or is it just contained within the.

Speaker B: Yeah, so we, we, uh, the long term vision is we build something called the possibility Commons. And the possibility commons is a place where you deposit and withdraw innovations. And um, you could have a possibility commons that is specific to your organization. So you could be Ford Motors, you could be Virgin Atlantic and whatever. I mean, and you, and you build it, uh, with guardrails around it. But we also want to look at all types of innovations at the horizontal. And the reason we want to do that is exactly what you said. We want to begin to look at combinatorial innovation where we find collisions of innovations. We want to take things that you wouldn't typically put together and we want to collide them using AI. And AI, uh, can be very, very useful in this particular area because we may find reuses for something that we never imagined. We may Find secondary and tertiary ways of apply and innovation that we never expected. And when you use combinatorial innovation you're looking for novel but consequential ideas. And so that is one component. Once we have a uh, common set up and you have things organized into the genome, you can begin to look at the horizontal for those novel ideas that emerge from combinatorial innovation.

Speaker A: And have you got any sort of examples uh, that you can share at? Appreciate that innovation is by definition sometimes very early stage and you might not have developed it through, but is there any, any of the, anything that you can share that you've been working with organizations that you've done that way where one small idea has been merged with two or three others and it's created something completely different?

Speaker B: Yeah, I'm going to like zoom out and give an example. Actually I was listening to someone on uh, a, on another podcast and they use this example and I think it's, it's a good one because it doesn't give up anything. If you uh, were in an organization and we had fire and we're thinking about how do we heat food faster, right? And let's, let's rewind, you know, a few decades ago, um, the heat food faster. We continue to work around the edges of fire, but we came up with a microwave which is a completely different technology to heat food. And the, the way we did that is by finding connections and an interdisciplinary approach that is way beyond the area that we're focusing in on. And that's in essence the idea of combinatorial innovation that you've got to find territories. Maybe it's scientific, maybe it's molecular, maybe it's something in beauty. And you work in climate that you would never expect. But uh, AI now will allow us to collide those together and find these novel things. And you need that kind of approach if you want to find a radical, radically new way to do something that you've always done right. And you may be able to innovate yourself with fire for so long, but at a certain point you hit a ceiling.

Speaker A: And is there any particular sectors or uh, industries that this is more relevant to or is it relevant to any organization in any sector?

Speaker B: Yeah, uh, it's possibility. Um, sciences is agnostic of sector because we're talking about principles, um, and ways of thinking and ways of operating. And so uh, you know we are uh, doing, working on tests around these principles uh, in corporate arena, government arena, uh, non profit, philanthropic. And so that's, that's broad in nature. I think there will be nuances in, in each of those sectors will um, apply possibility sciences and do things in slightly different ways because they have different metrics, different uh, outcomes on the other side. But the discipline itself I think cuts across those sectors

Speaker A: and I suspect a lot of it will come down to the mindset of the leaders within those organizations and whether or not they are prepared to invest not just the time but the money and the energy and um, release people to get involved in it. Uh, which is always, always a challenge in anything that's new, isn't it? You'll get your, hopefully you'll get your first mover and then you know there'll be a um, uh significant sort of following. Once you've got those first movers, do you feel that there is any particular size of organization that will be very prime for the, to get involved in those sort of first mover type of scenarios?

Speaker B: Yeah. So I think there's two components. One is I think um, you know like social sciences or any other discipline, you know possibility sciences is bringing you a complete stack of ways of operating right philosophy, uh, metrics, challenging a lot of the different things that we've done in innovation previously. The mindset is of equal importance and that's why we think of it as something we, we train people and we, we educate people. It's almost an awakening to this incrementalism that we're stuck in the innovation trap and, and how we it, we call those uh, individuals possibilians. And so possibilians are the individuals who um, uh, embrace the principles of possibility sciences. And what are some of those things? There's many aspects to it but one is that you are grounding what you do in a moonshot which means it's mission oriented. That you don't see innovation as a one off but you see it as a connected uh, over a long time horizon. We also believe that you are asking questions up front around ethics and the way things unfold. Uh, we uh, train possibilians to use simulations to look at possibility pathways early in the process before capital is deployed so that you can make some of those choices more visible. I mean no matter what we do, what innovation pathway we take, there's a trade off billions. Do a better job of evaluating those trade offs and figure out uh, do you zig or do you zag? So this idea of the possibility mindset is really important. So back to your question which is that the size of organization and type of leaders, I don't think the size is as important but the worldview of the leader is very important. It has to be someone who has the belief that yeah, we're doing way uh, uh, too little with uh, innovation and transformation. We are kind of stuck, you know, like we are a bit on the hamster wheel. We are not um, know too much of what I'm seeing produced is incremental. Too many of our ideas as you said, are never seeing their way to reality and we, we need a reset, we need a different way of doing this. That would be the, the first indicator of someone who's primed to embrace possibility Sciences. And then number two, a willingness to give their teams brand new skills. Right. And, and a brand new mindset. Uh, via the ethos of the possibility.

Speaker A: Yeah, I'm just thinking in relation to, you know, you touched on it there, that innovation is a constant. You know, any forward thinking organization needs to have that innovation mindset. I'm sort of very much um, a believer that actually uh, transformation is a capability, it's not a project. Um, and organizations need to continually evolving and develop that transformation mindset, that transformation capability within the organization. Um, I'm just wondering whether or not you see um, the um, possibility sciences element, uh, uh, around the innovation piece. Where does that fit in that cycle of transformation? I suspect. And your response will be actually we should be right at the start because we're going to then help to inform what that transformation could look like and bigger picture aspects. But sometimes that's quite difficult or maybe they've got a particular issue or a particular challenge that they're looking to resolve halfway through. So is there any. I've just answered my own question there by saying ideally you'd get an upfront but typically would you get involved midway through a transformation when they've got a particular issue or particular challenge? Is that your entry point? Typical entry point. Do you think

Speaker B: so? Uh, there's uh, a couple layers. I mean at the highest level of possibility science as a discipline, you know, this is a way of thinking, it's redesigning a system. It's. And so it's not a project, it's, I'll use your words, I mean the transformation mindset, this is, this is a long term way of operating. Right. You can though and we are doing this um, with some of the tools that we've created. The genome, the possibility commons, the simulation pathways, uh, so the stackability of innovations. We can take a handful of innovations that someone is exploring and we can um, ingest those into what, what we, we're calling the possibility platform. In that possibility platform, it would take your innovations, uh, X organization, and stack them into the genome so you could see that long horizon view. It would, um, connect them to things across the possibility commons so that you can now do collisions, combinatorial innovation at the horizontal, which we talked about looking for novelty and consequential novelty. It would allow you to run those pathways, see those simulations, and then what comes out of it is a stacked innovation view. And so that isolated component of possibility sciences could be, uh, done on a set of innovations, could be done midway through a transformation project. The only thing I would say is you could do all that work and get to completely different places, and they could be sensational moonshots. And if the system is still designed for incrementalism, it will whittle those ideas down to something that fits into the way that organization can absorb it. Right. And so that's the real thing here. So we can come in at any point. You can apply things midway through. It won't always stick because organizations have a lot of habits that are designed. I mean, you've been in a meeting where legal gets a hold of it, brand guidelines gets a hold of it, uh, finance, ROI metrics, you know, what we have to do next quarter. And all those things shrink, shrink, shrink, shrink, until the amazing idea that was transformational is barely recognizable. Right?

Speaker A: No, absolutely. And, um, I'm having lots of conversations at the moment with organizations around AI readiness is what I'm talking about. AI readiness is lots of time and lots of energy and insignificant amounts of money had been invested in AI. But actually, the systems, the way that people work, the processes, the sort of culture has just not been changed. It's not been adapted to allow AI to actually deliver the value that's expected. And that's why I think it was McKinsey research, uh, just a couple of weeks ago saying, so, like, 86% of organizations are investing significantly in AI at the moment. Something like 20, only 26% are seeing a return on investment. Um, and I would guarantee that the vast majority of that is because they haven't done the things that you've just been talking about or changing the organization to allow that innovation, that AI, to come in and actually deliver those values. Interesting.

Speaker B: It's. It's, uh, like what I see often is that our first reflex with AI is definitely optimization. It's like, how do we go faster? How do we make something more efficient? And that is exactly what we're talking about. It's, you're taking the inherited system and you're improving, improving it at the edge and there, there's two things that are problematic about that. One, I think AI is a game changer if it's used, uh, to redesign. But you have to use it and build it from the ground up. Right. Number two, if you optimize and just focus on efficiency with AI, many times what you do is you eliminate some of those formative jobs and those formative. And we talk about as, oh, we don't need as many entry level people and a finance person. I think that that's a great thing. However, I think what we end up doing over time is capability hollowing that we take core capability that make people really good at what they do, that are very formative at the front part of your career and we eliminate those skills and down the road that's going to come back to haunt us because those are the formative skills that allow us to make those big leaps. And so I think there's a real hr, uh, discussion here around learning and development as we deploy um, AI at scale and we primarily use it for optimization. Not, not, not always only optimization, but in many, many cases we number one have the mindset that you're talking about that we have to train and number two, we have this issue of we're eliminating certain skills and we have to figure out how to train those as well. Like build that, that um, those capabilities so that you don't lose that over time.

Speaker A: Absolutely. And it brings two, two thoughts back into my mind. One is again, uh, conversations with multiple organizations around the optimization piece. It's a finite amount. You can only optimize to a certain level, so it's finite. So if you're looking at AI just to optimize or to take cost out, there's a finite benefit. Flip it on the other side and say how can we, how can we use AI to grow your business? That's infinite. Um, nine times out of ten it's, it's, it's, it's looking at the optimization or the cost out focus first. Um, but interestingly on the second, on the second point, um, we're seeing in the UK in particular, I don't know whether or not it's the same over in the States. I'm sure it is that, that realization that actually by, by stopping or reducing the number of new entry roles, it's all we're doing is running towards a cliff edge and we're getting there, we're getting there really, really quickly because there'll be a dearth of people that can then go into the mid, uh, the Sort of new management, middle management and there's also going to be this sort of dearth of creativity coming into the organization um, and some organ seeing that now and actually starting to change that. And what we're seeing over the last few months is where uh, a lot of graduate schemes last year, last couple of years have been reducing the numbers. Significant uh, additional intakes have been happening at the moment in the UK for recent graduates which is great because you know we've got a significant number of young people out of work at this moment in the uk.

Speaker B: Mhm. One of the other things that we think about, I mean you have optimization and moving, I like your growth is a good counterpoint to that. This capability hollowing and learning and development and the other is designing the systems that are hybrid intelligence. Because AI is good at getting us to the statistical center. It's good at um, at the knowns, you know it's using this vast resource of known information. But humans are kind of messy. We zig, we zag, we go to unique places, we're serendipitous, we're highly curious. Those are great traits. And we want the uh, machines and the humans to debate and we want humans to have the opportunity to expand the aperture of where the AI ah is being pointed. And I think this dance of, of um, and making things very hybrid. And that's very different than saying uh, a human in the loop. Because human in the loop could very easily be someone who knows nothing about what they're receiving, takes it at face value and passes it along as a checks and balance. That's not going to get us anywhere. We need a mechanism that forces, that encourages humans to ask unusual questions to get the AI to expand to uh, new possibility pathways. So that's the other area that we think a lot about as we're designing possibility sciences.

Speaker A: Interesting, interesting. So your book is coming out in November. I think

Speaker B: it is now going to come out December 1st. We were doing this really interesting uh, printing uh, holographic litho foil and it got a little behind. So we're now the, the book launch will be December 1st but it's right around the corner. So

Speaker A: within

Speaker B: two months by the book it's available on and Barnes and Noble and everywhere.

Speaker A: Oh brilliant. Okay so so can, can people pre order that,

Speaker B: pre order it on any of the retailers? Um, and, and yeah, hopefully everybody can have it in hand right in the beginning of December.

Speaker A: Super, super. Well I'll, I'll put a link in the show notes to um, Amazon then so that people can pre order it for you. Um, so we tend to sort of finish these sessions off with a real simple question. If you could leave the listeners with and the viewers with one core message, one key message. You want people to go away from this session sort of thinking and say, yeah, that could mean something to me. What would that question be? Or what would that point be?

Speaker B: Yeah, um, there's so many. But I'm going to boil it down to this, uh, competency isn't destiny, that just because we've gotten really great at doing something and all the incentives want us to get even better at it, that doesn't necessarily allow us many times to question everything that's around us. And what we inherit doesn't have to be the way it exists. You know, we, it can be redesigned, it can be rebuilt, but we have to be able to zoom out and tackle it from a different vantage point. And I think that's a, That's a personal journey. Right? I mean, as much as it is possibility, Sciences is this big system journey. But I think there's one inside of ourselves where we've been told that just getting better at something is the way to go. And I think sometimes that can also also put us on the hamster wheel.

Speaker A: Yeah, Yeah. I don't know whether this is relevant, actually, and, and, and hopefully you'll tell me if it's not. Uh, but as you were talking there, what was going in my mind, it was a video I saw, I don't know, six years ago, seven years ago, and it's on, it's on YouTube, uh, and it. Basically a video of Formula One. So it looks at, um, a Formula One car in 1950, 1950s, um, coming into a pit stop, and it counts down it's seven minutes to change wheels to something like 10, 15 years ago. And it was, you know, 20 seconds to now, which is, you know, less than three seconds typically, to change all four wheels. And it just demonstrates within a clip of less than a minute just how you can look at things in a completely different way and start to work through what that could mean to speed that process up or to completely change that process. So I don't know whether that is an example, historic example of what you're talking about, but it. When you were saying about that sort of, uh, um, the way you describe the sort of takeaway, that's the image that came into mind. And it's definitely one to look. I think

Speaker B: one of the things just on that we talk about is that possible, and I think you'll appreciate this Possibility is fragile at birth, right? And these incredible ideas, and the more breakthrough they are, the more fragile they are. And I think with possibility sciences we're talking about, are there systems that we can put in place to give more support and scaffolding to those fragile ideas so that we get them to reality faster? So can we shrink that amount of time by putting some of these in your formula? One example. So, but I think, I think that's, that's a fundamental, uh, component that it may not happen tomorrow, but do we. Are we patient enough? And can we see the long horizon view so that we ensure that it does happen, that it makes it into reality?

Speaker A: Great conversation. Thank you very much, Terry. Um, and, um, it's been, it's been a great conversation, so thank you.

Speaker B: I loved it. Thank you, Tony. We will do it again.

Speaker A: Thank you for joining this episode of the Transformation Leaders podcast. I hope you found today's conversation interesting and more importantly, that it's giving you something to think about and perhaps put into practice. If you've enjoyed the episode, please do subscribe. Give us a like and leave us a review. It really does help us reach more people who are passionate about making transformation work. And if you're involved in leading and delivering change in transformation, I'd love to invite you to join us at the Transformation Leaders Hub. We're building a global community where transformation leaders can connect, share experiences, challenge their thinking, and access the tools, the knowledge and support they need to succeed. You'll find out more@, uh, www.thetransformationleadershub.com. thanks again for joining me. I'm Tony Lockwood and I look forward to seeing you next time.