A (4:25)

And it's a super, super interesting piece. And I had sort of a sardonic thought which is that I don't think, I don't think my grandchildren are going to say they're like podcasters, children, you know, that either of ours are. I hope they're, I hope they're not, whether the miners children are good or bad. But what are, Anne Marie, what are some of the implications of that? Like, what are we not seeing here and what does it mean as a society for us when we move from jobs for life? Or, you know, my work is my identity to, I mean, it feels like the economy and you know, tell me if you're seeing the same thing in the uk but it's not even that, it's that it's shorter stints at organization. It feels like it's more part time, it's more piecemeal work. Like, what are you seeing and how is that impacting people?

B (5:13)

So what we're therefore seeing, like you're saying, is there's maybe less, slightly less of a sense of identity in work, or there's a mismatch where folks have been looking for that, have assumed that they'll have had that and it's no longer being supported by the way that the industry is being transformed. So, you know, the, the, I don't know. As an example, we can say if we look at the legal practice, for example, the way that folks have kind of moved through the ranks, a lot of those kind of entry level roles are transforming, are morphing. And so therefore the way that we're evaluating folks to then be future leaders, you know, the, the legal industry is having to rethink elements of how that's done as we bring in the new forms of technology. I think the other thing that we are seeing, and we're a big proponent is kind of where we started actually as an institute was our good work charter. We're definitely looking at how do we ensure that folks are intentional in the way that they're deploying technology in the workplaces to ensure that people still have good work, that does promote dignity, that does promote learning, that allows them to have all these things that I think we might have had as part and parcel of jobs and the way that work was built in previous generations. But yes, if we remove this sense of identity, remove the way that things are working now, then actually we've got to be mindful of I'm deploying this technology in a way that allows folks to continue to learn. And so then that becomes the way that we set work up and that we instill that we're still going to have good work for folks to be doing rather than them maybe being at the mercy of technical and Kind of robotic overlords.

A (6:50)

Right. Well, and it seems like we're kind of backing into something you discussed earlier, which is that there's the worker lens on this, but there's also the company lens and, and the societal lens. And you described it as sort of a sorting out period. But it feels, at least from where I'm sitting, it feels like we're increasingly at odds with some of these mandates as we sort that out. And that what's best for the company doesn't necessarily feel like it's best for the worker or best for society. And there's this, you know, kind of this triplicate tension there. What does that look like from your lens? And, you know, can you tell me a little bit more about what the institute and you are exploring?

B (7:26)

Yeah, I think it's a funny one. You say it's at odds. I think it's something that, because we've never had to explicitly say that's how we're developing work. Well, that's what we're creating work to do. I think, folks, it ends up feeling like it jars a little bit to say, what do you mean? Good work means that this person is learning or that we're ensuring that there's dignity or that they've got access to kind of what it is that they need to do a good work was just that that person got paid well and that was good enough. And so I think we're definitely seeing elements of that jar. But I think what we're seeing more though is a lot of companies, a lot of industries are trying to make sense of how they make the most of this fourth industrial revolution. What is the transformative? Everyone's saying it's going to be disruptive. We're here talking about digital disruption. What is that actually going to look like? And what we're seeing is that this is allowing folks to have a better way to approach this transformation in a way that they can feel at peace, maybe with the idea of legacy. I talked to a lot of audiences actually about this notion of legacy, that the decisions you're making today aren't just just about the next quarter or the next annual round, or the next strategic kind of three to five years. But actually these, a lot of these decisions we're making now have this kind of 50 years down the line. Think of the person sat in your seat and the decisions you're making today, the norms that you're codifying, the intentions that you have for the industry, for the job, for the role, for the nature of the products and services that you're developing. If when you think about it kind of from a long term perspective, then actually it helps for folks to say, yeah, what part of this allows people to continue to learn? What part of this allows folks to have dignity? And so we're finding that organizations are thankful actually to have a framework through which to think about this that isn't purely about profits and more money. I think even more so. There's so much rhetoric that we have in this hype cycle that ends up being quite dystopian and a bit Black Mirror or Wally or you know, insert other kind of dystopian end here of things are going to be terrible because we're just going to follow along with the technology and we're all going to end up killing ourselves by accident or whatever else it might be. And I think some of these things end up being checks and balances that folks can then feel a little bit better as yes, I've used this audit, I've used this assessment, I've used this tool from the Institute, I've read this, this paper. I've understood some of the ideas that are floated here and the warning signs that we have. And so actually any step that I make in the right direction, any set that I make in this direction is going to be in the right direction and is going to, you know, prove a good one in the long term. So that that's what we're beginning to see with people genuinely wanting to work out these details and whether that's unions, whether that's regulators, whether that's academics, whether that's in, you know, we, we work with so many different organizations and types of stakeholders that we're able to have this kind of holistic view of what good looks like in lots of different places and spaces.

A (10:22)

If you work in it, Infotech Research Group is a name you need to know no matter what your needs are. Infotech has you covered. AI strategy, covered, disaster recovery, covered, vendor negotiation, covered. Infotech supports you with the best practice research and a team of analysts standing by ready to help you tackle your toughest challenges. Check it out at the link below and don't forget to like and subscribe. I want to clarify a little bit because we're talking about this notion of the fourth Industrial Revolution. We're talking about all this change and the need to do right. And by the way, I love the, I love the framework around legacy for a lot of reasons that I want to get into in a minute. But just before we go there, when we talk about this fourth industrial revolution. Like, is this 90% AI? Is it 10% AI? Like, aside from the AI piece, like, what are some of the other big drivers in your mind of what are shifting for work?

B (11:20)

It shifts and it differs. So AI, it's not 80, 80%, not 90%. I'd say it's probably more 60% AI. I think quantum is definitely on the horizon. We've had big data for quite a while. We do a lot of kind of physical manufacturing kind of pieces as well. So for us, it's rather than focusing specifically on. On the technology across the kind of wider set of research that we're doing, we've gone quite broad and I think we've had to do that because of the longitudinal nature of what we're trying to establish or set up. I think if we got caught up in AI, we'd end up restricting really the impact we're able to have, but also the quality of what we're doing. Because I think with AI, actually there's quite a lot of things. I mean, when we started, there was also a notion of what is AI and what's not AI. A lot of people were taking statistical analysis and calling it, packaging it as AI and kind of getting more funding and getting more eyeballs on it based on that. So I think we kind of wanted to rise a little bit above the hype and not be kind of AI specific. Quantum is one that. Something I was reading yesterday. I'm still kind of surprised. Maybe not. I mean, I'm more surprised than I'm not that Quantum isn't featuring so much in conversations now as much as AI is. This research is going on. There's supposedly updates and progress that's being made on that front. And it is going to be incredibly transformative once this goes live and once we really do have the kind of the. We've worked out all the elements of what needs to happen for it to be kind of stable and at large. So I think for us, yeah, as an institute, we're wanting to be broader and look at kind of all manner of different technologies in the here and now.

A (13:08)

Sure. And you know, you mentioned AI hype, which I think for anyone even tangentially tied to this space is just impossible to get away from these days. And so I'm curious, from your perspective, Annemarie, what are the things you're hearing in this sort of hype sphere that from your perspective and what you've seen on the ground, like what's BS versus what are the pieces of it and the, you know, implications and implementations of it that you really think are going to be transformative and disruptive.

B (13:36)

What I think is really funny about this is that so much of the BS is on the potential, right? And I get to work at the Institute and in other work I do, I get to work with all manner of, I guess work with governments, right? I get to work with NGOs, I get to work with industries, kind of various different ones. And what I find funny, slash frustrating, is that the, the use cases, the proven use cases, where we've said, this is something that folks able to say, we're going to set the AI to do it and we're going to look away. We're not still trying to figure it out, we're not still trying to make it happen. We're not still trying to realize, have a kind of real, have benefit, realize roi realized it's kind of fraud cases, it's customer service, and then it's not much else. So I think a lot of folks are trying to use it for creative ends. A lot of folks are trying to use it for tagging kind of en masse. But maybe that's another one that I've seen. But again, the tagging as a use case kind of doesn't stand on its own. It's kind of then part of a wider process, maybe fraud, but fraud is one, customer service is the other. And then folks tend to go quiet. I've seen at all levels on what else we're really getting the AI to do. And so a lot of it, it's not that it's completely bs, it's just that this is so. It's still so theoretical or it's still so early on that no one's been able to genuinely realize that benefit, draw it to the ground and say from that, because we've done it faster, because we've done it with less resource or fewer resources, we no longer pay for this. Or we, you know, the speed at which we're able to serve our customers is now this. And that's been realized on their end. I think for me, it's quite frustrating, actually, not getting. Not that folks are struggling, I guess, to identify that and stick to it and have it repeat again, unless it's customer service or fraud detection.

A (15:31)

It's really interesting. And I'm, you know, I've seen something similar with a lot of the organizations we work with over here. But I'm curious when you see that and when you see that it's, you know, one to two things that companies or organizations, NGOs governments are getting value out of what's typically your advice for them. Is it, you know, you're not looking hard enough, go back out there? Is it, you know, go all in on AI, Is it, okay, this makes sense for now. Let's wait until we find more promising use of the technology. What do you typically tell them?

B (16:00)

So my, my advice is, I mean, not. It's not as negative as you're not looking hard, hard enough. I think my advice is there are a lot of exciting, sexy things that folks, the big, big tech thinks are the problems. And so go look for the unexciting things. One of my favorite ones recently has been potholes. The AI of potholes. Who's thinking about that, talking about that? I know the big folks, there's not a huge amount of money supposedly in potholes, but actually, if you're able to sit down and think about it, and the folks working in that space really could focus on what is the data that we have, what's the logic that we have around this, what are the learnings that we have? And we can look at the AI of potholes. It's almost like that kind of, what's the unsexy stuff? And then how do we start to experiment with that? And you have to do those experiments and you have to make those mistakes in order for you to then have that kind of first move as an advantage. And so that's what I end up talking to folks about, is there will be things internally that are almost hidden problems that you have to then go and work and experiment on and you want to make quality mistakes is the other notion I'm talking to audiences a lot about. I give the example of there was a time before Cloud when we'd have to save files as we went along, right? And most folks who are old, most folks you know of a certain age will remember you'd have to control S or command S as you went. And God forbid you forgot to do that or your file was corrupted, then you'd have to rewrite your document entirely from scratch, right? And you'd kind of do that and you'd call it final. And then I'd know you'd use the loo, the toilet, you'd come back, you'd realize there was like a heading at the end that you changed. So you change that and you'd call it final, final. And then maybe your mum would call you and while you're on the phone to her, how much something she says triggers your mind. And then you call this final, final. Final. And most of us would then be like, brilliant, we're done. You'd send final, final off. And then realize, no, no, no, final, final, final is what you should have sent, right? And I know we're reaching back into our memory banks. Then the next time you sit down to write a document, not only are you control essing compulsively the whole way through, but now you've called it final. I don't know, 5th of January, 2026 maybe that's when you're listening to this podcast, right? And then you're like, okay, cool, so now I know which final it was, I'm going to take it off the date. Or if you're particularly sophisticated, you start versioning and you have version and you don't even use the word final. And I talked to folks with this example that there's the journey you've been on, which means that now you don't use the word final. You've got a fairly sophisticated versioning system that you're following along with, but actually you wouldn't have gotten there if the file wasn't corrupted the first time. And I talk about now when we're using AI in the same kind of way that there's going to be so many different elements that you want to try and use it on. And then you remember, no, actually AI doesn't understand humans. So what's the non human element of what we're doing that I can train this on for me to then get the results that I want to have or, you know, all those kinds of things. And so I think there's a high quality mistakes that folks need to be making. But as you do that, you're going to make that. You're going to have that quality of learning that others don't have. And that's going to be your competitive advantage in making the best use of AI. And so that's where I want to end up talking to folks about.

A (19:13)

I love that approach. And by the way, I'm shuckling as well. I've like very getting PTSD about final underscore, final. The one, the one that really gave me like philosophical pause was I once had someone sent me finalfinal new and like, to me that naming convention tells a story of like, oh my God, what have we done? Like, we thought we were done and we've gone back to the drawing board. But all of this, all of this. And the reason I laugh is, you know, as I listen to you talking about finding your own mistakes and looking in the unsexy parts. It feels like it's very cultural within an organization, the ability to do this successfully. What does a culture look like in an organization that you find is going to be successful with this versus unsuccessful?

B (20:03)

I mean, it's a big point and it's maybe something that again, it's not an overt kind of end of messaging of kind of, you need the right culture in order for these things to do well. I think it is those cultures that are open to those mistakes and are open to that iterative approach, which I think if we're working on the Agile framework work and you're particularly technical, you will understand what that means. If you build in that you're going to have a retrospective right as you're going, that means that you're saying we are going to have that we're going to have to take the opportunity to reflect back on because there will be things for us to reflect on, there will be things that have gone wrong or things for us to learn on, or things for us to measure how that has gone and then have a higher quality iteration. So I think the organizations that do that are the ones that do well. I think the other ones are the organizations that culturally value difference as they're going through these processes. So the other example I have, and anytime anyone gives me a mic at a tech conference is my favorite example to give is that of periods. Periods, menopause, pregnancy periods. Right? These things that we don't talk about often enough are taboo. Everyone's afraid of them and runs away from them. But so many of us have to deal with these anyway on a regular basis. Every couple of years, the health tech industry discovers the period for the very first time. Right? So five years ago, I won't name who it is because it's been recorded. If you read my book, she's in control. I have the example in there. There was a big health tech company that discovered the period for the very first time and allowed users to track 10 days of their period as a new feature on this fitness tracker, which some folks listening will think, oh my goodness, this is why. Switching off, switch it off. The woman's talking periods again. Others are thinking, 10 days of a period, what's the significance? And then you have to ask yourself the question, is it that at least health tech companies, or this one in particular, that had the 10 day period that they'd never met anyone in their team who'd ever met anyone who'd ever had a period? Or was it that they had a couple of people there who might have known something about it, but what they were contributing or what they had to say wasn't valued in those spaces. And if you're literally working on a period tracker in this company and you can't hear that from them, what else have you been literally working on that you've not been able to hear from those people? And so actually the right kind of cultures allows those nuances and those differences to come to the fore. And it's not just about periods, it's not just about gender. We all manner of all things. I mean, another one I love and love to reference is Legally blonde, the great 2001 film where, spoiler alert, she does really well in a real law case based on her in depth knowledge of perms. And again, these things feel so tangential. But actually, is your culture allowing different people to show up in the different ways and the different experiences that they have? Because again, a lot of these spaces, it's a very narrow set of life experiences and so a very narrow set of solutions that we end up proposing to problems. Whereas actually something a lot broader allows you to make the best use of the AI because you're not focusing on the same doom, gloom and tiny set of use cases that everyone else has already done to death.

A (23:06)

I love the examples and who knows if listeners are like, ah, close, close, close when they hear the period story. But I like it and I think it's a really, really, I feel like it's a really important point because culturally it's not just, it's not just an impact within the four walls, you know, physically or metaphorically of your organization. It impacts your customers. And, you know, unless your customers all look exactly like your, you know, your CEO or whoever your, you know, founder is, it's, it's important to have those, it's important to have those voices right? And how can you, how can you build that into the organization itself? So is this something you help organizations get better at? I mean, one of the adages about culture is that culture is notoriously difficult to change. Do you work with organizations on cultural change and what does that look like typically?

B (24:00)

In some ways I do with the Institute for Future of Work is definitely something that we are doing with the research that we do as a lever. I think for me as an organization, I run stemet. So we work on the kind of attraction of different types of folks into the industry. But then ad hoc, there'll be particular partners and particular pieces of work that we put out to help folks think about what does that culture change look like? And you're right. The culture is the average of everybody's actions. And so it is about how do we empower everybody to evolve, I guess. And again, like I said, use a legacy lens. Perhaps on actually 50 years down the line, would I be proud of this decision that I've made and the impact that it will have had? And so that's something I end up working with, folks. But it's a really tough one. It's a tough one to do. It's a tough one to transform, I think even more so as a classically trained computer scientist. I know it's definitely something that wasn't there and hasn't been a core part of what we think knowledge is and kind of is value across the technology scene. So it's definitely also something we're looking to see. How do we change the systems and the structures to make sure it's part of the curriculum? That's part of, you know, the way people learn about computer science and learn about a lot of these things that they understand. Also the implications that it's not just about deterministic, not just about the numbers. You know, life is so much more complicated than the maths. And so how do we reflect that in the way that we build and that we do and that we deploy?

A (25:20)

Right. And you mentioned being classically trained in computer science. And I know some of the work that you do is, as you said, with Stemettes and beyond, with women in traditionally marginalized groups in computer science, in some of these organizations, trying to get more of them into the field. So, I mean, again, this is probably too big a question to bite off in one go, but what's your sense about. About why women have been historically underrepresented in tech? And I mean, does it matter? And what do we do about it?

B (25:54)

Oh, I mean, there's. Yeah, we definitely. I don't know how much we have. How much time we have on this, I think. So women have been historically marginalized from the tech scene. Lots of different elements of our kind of heritage in the tech scene mean that we've taken some of this unhelpful elitism, maybe from. From maths. You know, we came as a field out of mathematics, academic math, mathematics, and kind of take a look at what's going on over in that scene. I think there's also this kind of myth of the kind of the lone genius and people that are born with this knowledge. And, you know, if you're born with this knowledge and born with this. These things, then you look a certain way because that's you know, all part of the. The kind of The. The adjacency we have to you being what I sometimes call dead and white and male with a beard. And, you know, you must be all of those. Those four things, dead included, for you to be someone that can be revered. Right. And so we're all aiming to be that. That dead genius who, you know, created something that's now kind of transformed the whole world, which I think is a high bar, actually, to say you must be dead in order for you to kind of contribute to. Technically. I think that's really. We don't grapple with that often enough, but I think there's lots of geopolitical notions. Ma Hicks has wrote. Written a great book, Programmed Inequality. There's lots, actually, the folks can kind of read to delve into why this is something that's happened again and again. And I think, if I'm gonna be completely honest, up until this point, it was almost okay because so much of what we were doing technically was very niche and for a certain set of people, you know, in a certain space. And we kind of just made it because it would be cool. Wouldn't it be cool to have a website that rated whether people were hot or not? And we kind of just built that. And I don't think any of us really grappled with the fact that you build that website and then all of a sudden you're moving elections and you're transforming democracy as we know it. And so I think it matters because if we're building things for the world, these women are part of that world. And so why wouldn't we have their experiences reflected there in what we're doing? So we can sell more if we really want to. But also, I feel that we end up creating more problems than we're solving in the way that we deploy technology at the moment, because we don't have different types of voices taken into account in what we're creating. And ultimately, I. I am a technologist because of the altruistic strain I have in me. You know, if I can create this thing, if I understand how it works the logically, and then I can recreate it, and then I can solve that problem again and again without me needing to be there. And so I think for me, it's only fair to the technology, right, that we actually end up building things and we make the right kinds of decisions. What we're prioritizing and how we're building it and what we're creating and what life experiences that are reflecting it. And so that's why it's important to have women be a part of the. Of the puzzle, but also other groups. Right. You've got gender, you've got race. There's all manner of different folks that should be part of it, given this is now global tech that we're building at any given point. And there are so many problems to be solved in the world without us creating new ones.

A (28:55)

So when we talk about solving this problem and the how, right, like how we create a more equitable or democratized environment where everybody can contribute through these technologies and with these technologies and you don't have to look a certain way or see yourself compared to a certain, you know, reverential figure. How do we get there and what sort of work are you doing to get folks there? I mean, I love the idea of, you know, destroying this narrative of, you know, the dead white guy with the beard and, you know, if you don't look like Albert Einstein or whoever else, like, you can't possibly be successful in this. Is it as simple as, you know, just having more certainly role models that look like, you know, you know, look more diverse is important. What else is. Are kind of the key factors in your mind in terms of making this a viable path for more young people who wouldn't necessarily have seen themselves in these roles.

B (29:51)

So it's fun that you start with role models kind of in your question. I think it's ensuring that we have the right environment for those role models to exist. And so I think, yeah, yes, we need the stories to be told of the. Of the different types of people that are thriving in the industry that are creating, that have already made things. You know, whether it's gps, right. Whether it's, you know, the. Insert. Insert name of any of the things actually that women have created. Kevlar. Like, there's kind of a long list actually of women innovators who've done things already, who we haven't eulogized in Hollywood movies or whose stories we haven't told again and again who have died off. Right. And actually, we've not given them their flowers before. They've. Before they've gone. And so there's a whole load of stories. And, you know, I'd implore Hollywood or anyone that's listening that has that ability to tell those stories to go and look up those stories and try and tell lots of them. You know, there's quite a long list that we have where there's Stephanie Shirley, whether it's Gladys West, Katherine Johnson. We've now actually seen, but there's always more. We've had so many for Einstein and so many of the others that actually let's kind of fill those in. So I think one is to tell the story, but I think the other thing is to genuinely work on ensuring that different types of people can thrive in our spaces, that then it doesn't need to be again that loan role model, that kind of list of people that can be just something that hey, name a female journalist. People can, can do that the same way they can name a male journalist. Right. And so I think we need to have the same sorts of spaces. I think we also need to be able to hold folks accountable for bad behavior which, you know, when we see the use of NDAs across our industry, when we see the kind of, the norms we have as to, you know, how people get equity in particular companies, how people are held accountable for, for like I said, bad behavior kind of corporately versus others. I think there's a lot of kind of bad habits and a certain type of person that gets protected in these spaces, and that's academically as well as in industry entrepreneurs as well, who's allowed to fail fast and often and who's not allowed to fail fast and often. And so I think there are quite a lot of tweaks actually that we do need to make to ensure that different types of folks can survive. Another one would be, and you have this to a lesser degree in the US but it still does show up in the uk we have it that you make decisions on particular subjects you want to study at a higher level at 13. And those decisions follow you throughout the whole of your life, which, I mean, I don't know, I don't know if you're even eating the same food that you ate as a 13 year old, let alone your entire career. And you know, God forbid you made the wrong decision at 13, hair color, what you're eating, clothing wise, whatever, but still that we have an entire industry that no, no, no, you cannot possibly be an engine it here because you did not make that decision at 13. And so there's, there's nothing of value that you have to contribute here because you weren't, didn't enter onto that pathway. And again, it speaks to this idea of you have to have been born that genius because then at 13 you would have locked in those ideas. Whereas actually there are a lot of folks who have a lot of things to contribute that we're doing other things at 13 and we should value that actually in what we're creating and what we're doing, so I think it's, it's updating the culture so that we can have more of those role models as well as telling the story of the role models who have somehow made it through despite how unfriendly the industry can be.

A (33:00)

So, I mean, I'll tell you off the bat that 13 year olds are not typically the target demographic of this podcast. But I am curious because I think, I think a lot of, I think a lot of listeners do potentially have their own 13 year old or own teenager in their house, which is, what do you tell young people these days about what the future looks like, what they should do, you know, how they should be thinking about their own future and their own legacy.

B (33:24)

So we at Stemet have ended up working this notion of stem steam, sorry, rather than stem, so including art and design in there. And I think what you do end up telling the 13 year olds is a lot of these things, we haven't figured them out. You know, as we started at the top of this conversation, we're at the beginning of this fourth industrial revolution. A lot of the answers, we don't have the adults. There's a lot of things the adults do not know. And so we end up talking to them about, okay, you know, what would be your ideal? What would you create, what would you prioritize? How would you see this? And we end up relating it as well, often to what they're already interested in. So, you know, not AI for the sake of AI, but how does that impact the football team that they support? Or how does that impact on the clothing brand that they, that they love to wear? Or how does that impact on the food that they like to choose? So we start with what they're interested in that kind of related the technology to that, but in quite a big way. I think this, this generation have grown up quite different to previous generations in that they've got to see the problems of the world up close in real time. And so I think the notion of what success looks like is quite different to them. And so we end up talking to them about how does technology, how might technology empower that version of success? How might technology be something that helps with climate change? How might technology be something that helps with fairness and equity and social justice? And so that's what we end up talking to 13 year olds about and leave the door open to say, hey, like study history, study languages, whatever it might be, but understand that technology is a tool that will have a role to play in whatever you end up doing. And so you can choose to study the technology, that's completely fine, or build your tech literacy. So then no matter what you do, you're able to apply the technology in a way that makes sense to you and solves the problem that you have. So those are the kinds of conversations, controversially or not, that we're having with them. It's not everybody learn to code, but it is definitely learn to have that critical thinking. Learn to collaborate, learn to create, because these are things that you'll still need to do no matter how many of our jobs the robots take away.

A (35:27)

I love that answer. And it preempted the next thing I wanted to talk about, which is you said critical thinking, collaborate, create. Are those. The question I wanted to ask you is for those coming back to the notion of the workers who are facing the reality of coexisting with more and more technology that can do things that could never do before. What are the critical skills for the roles of tomorrow and to be kind of a successful worker or leader in tomorrow? Is it those same three or how would you frame that out?

B (36:06)

Yeah, so it's definitely something. So we at the institute recently completed something called Episodes Review. So we did this with the Nobel laureate Cresperides, looking at kind of the transformation of work and jobs and how they're all being transformed at this time. One of the things we did was a huge analysis of job roles, kind of job openings across a big engine, and looking at over time, how the skills were, were transforming and changing across this kind of big data set. And what we saw that, yeah, where we've got AI and maybe data skills that are more prevalent across the different job descriptions that have gone out. This idea of communicating, of collaborating, of critical thinking, of creativity, these sorts of skills which some folks have called soft skills, I don't like that. Core skills, I think, is really what we're talking about if you want future proof skills, those notions of synthesizing information and connecting with it and understanding as well as communicating. I think those end up being skills that folks will need, you know, no matter what the technology is doing. And you can have them augmented by the technology, but those things can't be replaced by the technology. I mean, we've all seen what generative AI is spewing out now. We're calling it slopped. Right. It's the kind of overarching term. And so as a human being, yeah, the quality of slop that you would have put out manually is probably slightly different to what the AI generated slop is doing. Is creating. But critical thinking, I think is probably the biggest one, right? And, and it's possible that some of this is missing. And that's why a lot of those, for example, AI use cases just aren't hitting. Because folks aren't able to critically think and say if this technology is automating or if it's being able to do better, better data exploration or if it's generated, like critically thinking, which part of the processes that I'm doing at the moment or what part of the value that I'm providing to my clients or my customers at the moment requirements repetitive things that then can be automated or who has a lot of data that needs to be explored or is going to support me in making, you know, high quality decisions or whatever it might be. So I think critical thinking and saying, okay, just because the AI said this, should I believe it? Critical thinking to say when I give it this prompt, when I gave it that, it gave me this back. So actually what, what really is going on here? I think there's a lot of that that ends up going missing that will stand folks in good stead and allow them to explore and make better use of the technology. Because as much as we have AI as this hype machine, as this hype cycle, sorry. And as much as, you know, we can talk down on it, actually the AI is good at particular things. We just have to spend the time to say, where does that overlap with what my specific use cases and work is?

A (38:40)

When you talk to leaders then who are looking at the technology, they're ideally thinking critically. They're looking at the organization of the future, the workforce of the future. What's your best advice for them? And are you finding that they're typically, you know, thinking too small or thinking too abstractly? What are some of the biggest mistakes you're seeing?

B (39:01)

I think that they're thinking too short term. I think is the big mistake that I see from businesses at the moment. There's a lot of this is the problem I have here and now. And you're kind of dealing with the symptom. You're not dealing with the root cause, you're not dealing with the wider set of things. The kind of, the example I almost always give folks is so bias comes up and folks always end up asking me, oh, you know, what can we do to mitigate the bias in the AI that we're building? And I'm like, okay, cool, why don't we rewind? We can build an AI that helps predict, analyze whatever the best sandwich is for us. To create and to have on the menu at our downtown Toronto lunch space place, right? And we can say we can build and fine tune the AI to the nth degree, right? And say this is the best AI that's ever been known for anyone to build, to choose sandwich flavors. But there are a lot of people in Toronto who don't eat sandwiches for lunch. I said, so you have to think a little bit. But you know, like, you've prescribed this, you've set this problem, you've set this scope, you don't, you're not thinking wider, you're not thinking 10. Then this is why I say 50 years down the line when you do that, it allows folks to zoom out a little bit and actually not focus on the current pain point that they have or even the next pain point. But to say, let's go back to first principles and really think about how we can do this in a transformative way. And I find that a lot of business leaders are held by the neck, by shareholders, stakeholders, you know, whatever the shorter loop cycles that we're operating in. And so actually the folks that do the best are the ones that kind of can pull back and say, no, we're going to take a long term view on our very niche industry. And again, this is not about tech, this is about other places. So it's okay. In education, let's take a long term view. What is the point of education? Therefore? Are we going to use AI to help teachers in the here and now deal with admin in the current system, or are we going to use AI to completely transform the picture that we have of skills and knowledge that our learners have? And if you start to think about that, then you work on a whole other set of problems, you're working at a whole nother level and then you're able to kind of fundamentally transform the way we're looking at just the admin that they're having to do today, which is a tiny use case. And you know, it's ended up not actually necessarily giving the right kind of payoff for folks, but when you start to think about assessment, we start to, you know, and so I think it's that strategic thinking long term and then working your way back then allows folks to have the vision to have a little bit more motivation, allows them to maybe have a bit more room for the kind of accountability on learning from mistakes, as we mentioned already. And that's what we're seeing is working for folks when they're able to look beyond the problems of the here and now.

A (41:44)

I love that approach. And it makes, I mean, to me it makes complete sense. And I can, you know, you say it and it's like, yes, like I'm 100% bought in. One of the things I've seen is a lot of leaders seem to, and a lot of people, frankly, seem to have a convenient belief right now that we're in such a period of disruption that the long term is completely unknowable. It's just like, who, who knows? We can't possibly know it, therefore we can't plan for it. Therefore I don't have to look beyond the next three months because that's all that matters. When you look out over the horizon, do you give much credence to that or do you think that's just an excuse for, you know, not having to do the thoughtful work required to, to, to plan?

B (42:29)

I think it's fear. I mean, we're only human, right? And so I think you, if things you can't control, you fear. I think my biggest response to that, though, is that, you know, there's less, there's less fear if you are in the driving seat and if you make some decisions and if you lock some of these things down now and you do have the agency to do it because of the time that we're at in this cycle, there will come a time when maybe it is too late and too many of these decisions have been locked in and too many of these norms have been set, but that time isn't now. And so I do get folks excited. You know, I do like to, you know, there's so much you see in science fiction, right, that's ended up being real. I mean, Kit, Knight rider from the 1980s. I don't remember much of the 1980s, right? But David Hasselhoff played this guy called Michael. He talked to his car called Kit. And it was such a far off thing in the 1980s for him to talk to this car and the car, to hear him, understand him, then to successfully fight crime together and always get folks to think that we talk to our cars now in 2025, soon to be 2026, whatever, you're listening to this, there are people for real talking to their cars. There's no science fiction thing. They might not be fighting crime, maybe they're, you know, turning up the temperature or changing the track, right? But the car is understanding them. And so I get folks to think like, this doesn't have to be a big, major, huge shift that you invent the Internet in your organization, but there'll Be small tweaks based off desires, dreams, imaginations that you have. And so you better imagine. I mean, we're human beings. All of us have desires. All of us, you know, have things that we wish we could have instead. And maybe not all of them are the most worthy of desires that we want to have. But actually, across an organization, you'll have a lot of people that will have a lot of ideas on what could be next. And you could definitely make that. I mean, if we, if we don't, if we don't, you know, assume that's something we've got within our gift, then why do we bother getting up to work and getting up to create anyway? It's a thing to be human, to dream and to hope and to explore. So, yeah, I mean, that's another thing I get, folks, is, you know, write. If you're not a sci fi person, write your version of sci fi. So 200 years down the line, what would be. What would you. In 200 and, you know, 22, 25 or 26, whenever you're listening to this, what would they be doing? Just write that. What. What do you imagine? Because none of us know completely what's going on. But all of us can dream and can explore and can hope. But I understand the fear. It's very human to be afraid of the unknown.

A (44:49)

We've talked. It makes sense, and it's something that I think has, you know, is being profoundly felt by a lot of people right now. We've talked at length, Anne Marie, about kind of the worker piece, about the organizational piece. We haven't talked a lot yet about kind of the society piece, which you said is sort of the third leg of this stool and in some ways is maybe the most difficult to nail down, but also the most powerful. What are some of the big, I guess, risks like change risks of society, as well as some of the benefits you see kind of emerging that maybe we're not taking seriously enough or not planning for well enough.

B (45:29)

So I look at this from the social justice lens. I think the biggest risks that we have at societal level is that we forget or we ignore or we are dull to or done to the social inequalities that we've already had thus far. It's a frustration that I have in quite a lot of the discussions that I have with people and with technologies in particular. As said just earlier, you know, life's more complicated than the maths. There are a lot of data sets that we just don't have. A lot of things that we just don't know about people and have never known that those in power haven't deemed important enough for us to have a very UK specific example. Before COVID when folks got married, they entered their names onto the marriage registrar marriage register. We have a central marriage register in the uk. Before COVID they asked you for your father's occupation only if you've got marriage. Since COVID you've entered your mother's and your father's occupation onto the marriage register. So if you're at the whole of eternity pre Covid, that's just information we do not have for people that got married. Whether there's something wrong with that or not, I'll let folks unpick. But actually when we then build new models and we take in data sets from lots of different places without asking the question or being blind to gaps that we've had thus far, that we're only going to repeat the issues and the problems that we've had already, but we're just now going to do it at scale millions of times a second. Whereas before, I dunno, in medieval England, that was just something that when someone went back through, they didn't have that information. And so I think there's so many things that we have societally of these are real people, these are real things happening in real spaces that people should really be doing something about. Another example, Invisible Women, another book that I always talk to folks about, about. And if you haven't had Caroline on, you should definitely have her because it's fascinating some of the things that she was able to unpick and kind of communicate in that book. Another example that I always love to share from that book is single parents. So again, this is a facet of life that actually, you know, single parents, people being widowed, whatever reason it is that you end up as a single parent, that's not a new notion. But whether that's a concept that we've had kind of reflected in law and spaces, whatever it is, no. Right. Caroline gives us an example of someone who's a director at a company and also a single parent. So the people who are directors at this company, they're all invited to this dinner, they have a look at where it is. They or their assistant book themselves into the hotel next door. Let's say it's $200 for the night for them to attend this dinner and they tick, accept, and that's good, that's done. Booked on the company card. The person who's a single parent doesn't need a hotel room, they need childcare for that night, let's say quite expensive, but whatever, $200 or less on the childcare for that night, and that person ticks accepting on the invite. And this will differ, I'm not sure, I guess in the us, I think it might be the same actually. In the UK, the hotel, the $200 on the hotel is an allowable business expense. In the UK, the $200 or less on the childcare is not an allowable business expense, but it's the same amount of money being spent by the same company for people at the same rank to attend the same event. Why does it matter? And what have we not built? And so I think this is the thing as a society is again, there's still all these little things to unpick of us doing better for different types of folks and like I said, solving problems faster rate than we're creating them and all the other ideas that people are creating and products that people are kind of pushing out to the market. And so I think societally, again, there's an opportunity. And with AI in particular, I think this is something I've been really excited about, is so many of these things people have had a hunch on and been like, yeah, that doesn't seem right, or that doesn't seem fair, or that's a gap. Whereas now AI is helping to show a lot of these biases that are in real life, not necessarily really for us to mitigate in the AI, but for us to mitigate in life, mitigating the way that we run our societies, mitigating the way that we are building our communities and our groups. And so I think, you know, that there's. It's an opportunity. There's an opportunity for all of us to do better, for everybody and for all of us to gain from it.

A (49:41)

I like the examples that you use precisely because they're so. The scenario is so obviously dumb, right? Like, the example isn't dumb. The scenario is dumb that how can we not just be better at this? And I think for all of us, you know, in some capacity, we've encountered this and we just say, like, why is this fair? Why does this make sense? Why am I being penalized for this? Or why is that person being penalized for this? There's certainly a lot of intractable problems, but to your point, I think there's a lot of problems that are not at all intractable. We just have to say that this does not make sense. Let's fix it, figure it out. Yeah, let's figure it out. Is that you know, a mindset thing. Because again, there's. I don't know, I feel like there's so much talk about like, oh, no, that's like, that's not my job. Or, you know, that that should be government. That's not me as like a business leader or in the government being like, that's the business leader. That's not me or government. Like, is this just a matter of all of us kind of pulling up our bootstraps and saying, let's try to make the world a better place, or what's kind of your best guidance for actually making some of this happen?

B (50:46)

I think so. Ideally, that would be it, right? We'd all say, let's all do better for everybody and we all work together. There's a lot of it, though, that I see just ends up being norms. So we have so many unintended consequences from, I don't know, Uber existing has meant that the way that different folks, like the laws around gig gig work have transformed in so many different countries. Right. As a result of Uber existing. And so I feel like there's, there's. We haven't kind of, kind of scratched or used that lever as much as we can of, hey, these are norms that we could build in the technology that might just solve this problem. That means that then actually, you know, a woman bearing the title doctor doesn't get locked out of her gym locker every time she goes to the gym. You know, and there's so many, like, there's so, so many small bits that it's almost like, solve the small bits. And yeah, government and business eventually can solve the big, but the tiny, the small irritating pieces. We can definitely just say that's something we're not going to do on this platform or something we're not going to have going forward or something in our company we're going to set as a norm. That then means in our supply chain and in our customer chain, these are things that then in our ecosystem, our sphere of influence that we have, that then becomes part of the norm that we've upgraded and we've changed. And so I think, I think it's a combination of both. But if everybody took it upon themselves to understand their sphere of influence and then to implement things properly across there, then actually there'd be a lot of good things that would happen. I think the other thing would be potentially that, you know, if we go back to our point, if you have different folks valued and different folks to have access to make these technical decisions, then upstream would mean that we have better solutions for things. Which I think is the other thing that we, we end up missing out on. And we probably see it the most in health. Examples of the different things that come up from pharmaceuticals because of who had the most money, who they thought were the biggest market share, rather than the actual solving of a problem that we've had for ages, that endometriosis is not a brand new condition. And so someone should just have that as something they're going to work out and they're just going to do and solve in the small with the use of this AI. And it's why I get excited about things like AI for potholes. There are so many problems that actually the more that we have this accessible, the more that it's democratized and the more that folks understand they can just experiment, the more of new types of ideas and solutions we might get to see. I mean, a couple of years ago I had someone come to me who had conceptualized a new social media platform type that centered on the idea, not the person. And I remember thinking, yeah, like if you were in those rooms at the beginning when we were doing Burlington Boards and all these other forums, you wouldn't have centered it on the user or would have been another way that we cut that information, that we center on the thought, on the nugget of the idea. And how many lives could have been solved, how many, you know, all of these things that could have happened if we, we just had something slightly different. And there's nothing to say that if that person wasn't there at the beginning, that couldn't have been something, you know, cyber bullying in the way that we know it could have actually maybe just, we would have just missed that for just having something slightly different there at the beginning. And so it excites me. It excites me the capacity we have to solve problems by taking a slightly different approach to the way that we engage with technology.

A (53:56)

Well, and it excites me that it excites you. And that's something that is really great to see because not everybody I talk to is this excited by it because I think it's so easy to look out over the horizon and see so much change and so much uncertainty and there's obviously no shortage of things that aren't even going well today. And there's things that will be going better and things that will be going worse. But it seems like Net net you're optimistic about for now.

B (54:27)

Yes.

A (54:28)

Well, so tell me more.

B (54:30)

I'm optimistic for now. There will be a point I'm sure of no return where we didn't act as we should have in the time. And you know, we're much further along into this next revolution and you know, the norms have been set and the dies have been cast and you know, we've locked ourselves out, we've shot ourselves in the foot. At which point then I will run to a bunker or I'll run somewhere, I don't know, east coast of Kenya and just go and hide away and do maths on the beach forever. I'll retire to just do, just do solve math problems on the beach. But until then, I don't feel like it's too late yet.

A (55:05)

So now's the time to set the norms to influence some of these cultural changes and decisions and try and build the legacy that we want.

B (55:14)

Exactly. Now's the time to try to genuinely do better and be inclusive of folks to help us do better. I don't think it's too late for us to edit things, change things. And we see this even more. I see this so much at the work that we do at the Institute that folks are still trying to figure out these use cases, they're still trying to work these things out. So it's not set in stone yet. There's definitely a lot more disruption on the way. A lot of these kind of white collar industries that are kind of having to change how folks kind of enter. So there's a lot of disruption that's happening. I think it's when the dust settles on that, then might be slightly closer to me saying maybe it's too late.

A (55:56)

And I'll ask you the question that everybody hates, which is what's your kind of predicted timeline for when the dust settles on that? Is that 2027? Is it 2030?

B (56:07)

No, I don't think it's a time thing. I don't think it will be a time. As we've seen with this hype cycle, actually it's not just about. And we know this kind of classically, you know this about technology anyway, right? It's not in the presence of the technology, it's in the adoption of the technology. And so. And you can never really tell, I don't know, I don't know how many of us could have predicted that we'd be this deep into the AI hype cycle still here and now three years ago we would have been blockchain and bitcoin. I'm guessing maybe in five years the quantum volume will have come up ever so slightly, but I don't know that it's a particular time that when it's the technology that's relevant. I think now, again, as a classically trained computer scientist, it's less about the tech, it's more about the adoption and the kind of the socioeconomic scenario in which it's being deployed and it's being used. I don't think it's a time thing. I think there will be particular markers and particular norms and which tech superpower is using technology for. To what end? Sounds so mysterious, doesn't it? Well, it would be an internal feeling. She just started a website. Where is ami? I'm either on the beach or I'm not. I'm not on the beach or I'm in the bunker or I'm not. That'll be how we know the time is and the day, the day that I booked the ticket, the one way ticket to go and sit on the beach, then yeah, everyone can hunker down.

A (57:38)

Yeah, if you're on the beach, we're in big trouble.

B (57:41)

Exactly. Yeah. This is it. So just come and sit on the beach with me instead.

A (57:45)

There you go. It sounds. I can think of worse things. Hey Emery, I want to say a big thank you for coming on the show today. This has been really insightful, really interesting, lots of great stories and gives me lots to think about.

B (57:56)

Thanks for having me, Jeff. Good luck everybody.

A (58:00)

If you work in it, Infotech Research Group is a name you need to know no matter what your needs are. Infotech has you covered. AI strategy, covered. Disaster recovery, covered. Vendor negotiation, covered. Infotech supports you with the best practice research and a team of analysts standing by ready to help you tackle your toughest challenges. Check it out at the link below and don't forget to like and subscribe.