Emma Möller, Shaping a Brighter AI Future by Leading with Human-Centered Curiosity - Innova.Buzz 689

Speaker A: Foreign.

Speaker B: Welcome to the innovabuzz podcast, where our job is to help you build visibility, professional credibility and connection with your ideal

Speaker C: client by putting the human at the

Speaker B: center of innovative marketing so you can build and strengthen an engaging, enduring relationship with your ideal clients. I'm Jurgen Strauss from Inova Biz and I'm honoured that you're here with me. Uh, if you haven't yet joined our wonderful Flywheel Nation community, go to flywheelnation.com and join in the podcast conversations. Do subscribe to the show and also leave a review because it helps others find us. Let's get into today's masterclass on this and InovaBuzz podcast.

Speaker C: Hi, I'm your host, Jurgen Strauss from innovabiz and I'm really excited to welcome to the innovabuzz podcast today. Coming to you from Lisbon in Portugal, Emma G. Muller, who's an AI strategist, a former diplomat, helping leaders understand and navigate the complex systems of AI to prevent the pitfalls of people not recognizing what's going on around them and being stuck in their thinking, which we were just having a brief conversation about before we started recording. Welcome to the InovaBuzz podcast, Emma. It's a real privilege to have you as my guest.

Speaker A: Thank you so much for having me.

Speaker C: Now, I think we've got a lot to unpack today. Uh, you. You, um, run. And you founded and run Lumiere.

Speaker A: Lumiere. So Lumi. For light and era for the future. So it means bright future. Lumiera.

Speaker C: Excellent. Well, that was going to be one of my questions. Where did the name come from? Wonderful. Um, well, before we get started, though, a quick note for our listeners, particularly if you're first or new listener to the NovaBuzz podcast, we like to have practical conversations with wonderful guests like Emma and dig into what makes them tick, how they kind of approach their work, um, at a deeper level, and how they overcome challenges and also, um, how they might tackle things like AI coming down the track at us, and how can we integrate that into our work without losing our humanity? So what I like to do is start with the big picture, dig into stories, then, uh, hopefully we can uncover some strategy and actionable ideas that you can take away and perhaps put into action as a result. And at the end, we wrap up with our, uh, signature buzz round, which are three rapid fire questions I ask every guest. So as a listener, you're here with me, with Emma, and we're all learning together. Eva, I'm really looking forward to this conversation today. I really like your logical and principled approach to preventing the misuse of technology. And I think it's so vital right now with all the changes, ah, that are happening and the developments. Um, it does seem to stem from a very clear sense of purpose. And perhaps you can share with us what the impact is you're aiming to make in the world today.

Speaker A: Yes, thank you. Um, it does stem from a much deeper sense of purpose. Um, I grew up in a small village with 60 people in the north of Sweden, which means that I was very connected to nature and very connected to animals. We had a little lake right in front of the house, and, uh, my main neighbors was the animals in the forest, essentially. And when I started, I moved to Paris when I was quite young, and when I started learning about the world and about the way that we are framing the world through knowledge, I just realized that there is a lot of things that were missing out on the deep human experience that I was fortunate to have, uh, growing up. And m. I felt like that was not properly captured by technology and frameworks because I'm also a lawyer. So I studied a lot to try to understand how the systems of society is matching our reality and how the systems that we build is also creating the reality that we're in through policy and other types of decisions. So that's basically where this purpose comes from. And it doesn't really matter what I've worked on. It's always a matter of understanding systems in one way or another. So I'm very much a systems thinker, as they call it. And I'm also quite, um, stubborn in wanting to make sure that we adapt these systems to the type of reality that we want to live in and experience. So that is the reason as to why I do this work that I do.

Speaker C: Fascinating. Well, it's fascinating that you talk about systems and people being disconnected from human experiences. Like you had growing up with nature, surrounded by nature and interacting with animals. And it's always a surprise to me. I, um, always reflect on, like, an interaction with an animal. And we have, um, wild animals here in Australia. We've got lots of wild birds around that actually interact. They're not afraid to kind of interact with people at some level. Even if it's like magpies attacking us on the bicycle or some of them are actually very friendly and they'll come into the garden and they'll ask for water in dry spells. They actually know how to get your attention and how to communicate that they're thirsty. Um, and I always reflect on that and think if we're open to those experiences and see the signs. We can kind of communicate with not just other people, but other living forms and then learn from them. Um, how do you see, um, once you get into, like, that ecosystem, perhaps, that you talked about when you started studying law and digging into the systems and policies, how do you see that being disconnected from those experiences?

Speaker A: Um, to start with, I would say that the reason as to why we are building certain systems is that we need to sort of generalize and categorize and make sure to find a system that works reasonably well. It's not perfect. Right. Um, and I think that that whole approach to wanting to make society, for example, or technology work for most people, that means that you are generalizing and that you're not looking to each individual experience. So that is for sure, one thing that is just part of, of building systems as a whole. Um, but then I would also say that we have. Depending on, uh, the types of decisions that we make and depending on whether you're looking at policy or whether you're looking at law or whether you're looking at systems in technology, such as artificial intelligence, we have a lot of biases about what's right and wrong. And the people that are building these systems and they're making decisions around these systems, they have their own experiences that they are building and basing their decisions on. And that automatically means, for someone like me, for example, but I am also not. My experience is not the only valuable experience, of course, but it's an example of an experience that is maybe not the average experience. And it's also not, uh, an experience that a decision maker or lawmaker that sits in Stockholm would have closest to them in their environment. So I think that the generalizations that we make and the different factors that we take into consideration when we are making decisions and when we are designing systems, um, as well as our own personal biases that comes into it that does create a disconnect in so many ways. And then we have the economic factors as well as in what are the things that we value and that we are incentivized to do, and what is it that we're not incentivized to do and value? Um, in the economy today.

Speaker C: Yeah, there's certainly a lot to unpacked there and a lot to dig into. I mean, the economic divide is really a big one. And I sort of really can't see that, um, being bridged in our lifetime very quickly. And yet it's so obvious to me that unless we bridge that there's Going to be a whole lot of fairly serious problems for humanity. And, um, if we, if we're able to bridge that, we can overcome a lot of those and we can change a huge amount of things for the better. But then there's the, um, education stuff. One of the things that I'm curious about, and I keep asking myself, um, certainly at a personal level, if I'm using AI a lot, which I do, am I diminishing my ability to think or am I diminishing my ability to think critically? Um, and how do we, how do we guard against that? And then also thinking of kind of the generation that's coming through school now and getting to the point where they're about to start a career or a life journey, um, in the adult world, what are the skills that they should be building or looking to build as kind of AI takes over a lot of, a lot of jobs that we've had in the last 20 or 30 years.

Speaker A: Yeah. So, um, to start with, looking at AI taking over jobs is maybe something that I'm, I'm not sure that it will. I'm sure that it will reshape, uh, the workforce and that it will have us focus on other skills and other tasks. And I think that that is happening much earlier than we see AI completely replacing jobs. Uh, in the short term. I think in the long term we're going to see, um, quite big change, but we don't know what that change is going to be yet. And it's also up to us to look at the change that we want to create. Right. Also through the ways that we're using this technology. Um, so there are some things that you mentioned that I also spend my days thinking about in many different ways when it comes to how do we use this technology ourselves. So if we're talking about generative AI and if we're talking about the app layer of large language models such as Claude or ChatGPT, um, it's quite easy to notice that you can get intellectually lazy very, very fast if you don't pay attention. Um, and of course that will have a huge impact on us as individuals. And it also shapes our thinking. And I read a report the other day, seeing that we've already started, um, shaping our language as well around the use of AI, because some words, like for example, delve is used frequently in these large language models due to the, uh, locations, the geographic locations where they've been trained. Um, and that is now a word that we're using a lot more. So I think to answer Your question? Um, there are a lot of things that we can do to uh, um, just maintain, um, our human ways of reflecting and thinking and uh, being curious is one of them. That's something that I always encourage because I also want to say, and I know that this is the same for you as well, I think AI is an amazing technology just because I'm skeptical about the different ways that we can use it and the potential negative impact that it could have on society. Um, I'm also seeing the positive impact that it could have and how it helps us in our day to day if we use it in the right way. Um, so the question is, how do we use it in the right way and what is it that we should avoid doing? And I think the main thing is staying curious. Don't take the answers from large language models as the truth. Um, know that these models are sycophantic, which means that they are sort of set up to agree with us. Which is why we very often see, yes, you're totally right about that. Whatever we're writing or, um, other ways of agreeing with us or the fact that they are so apologetic when we spot something that they are doing wrong. I don't know if you've noticed this as well, I can see.

Speaker C: Oh, absolutely, yeah, yeah. It says thank you for pointing, thank you for calling me out or something like that.

Speaker A: Exactly. Always so, so cheerful. And I think to me it's a matter of literacy. Which is also why we have this newsletter at Lumiera where we are writing so much about the way that technology works for decision makers, but also the way that it connects with our reality, how it impacts our mental states as well. Um, because the more knowledgeable we are about how this technology works as such, for example, that the models are sycophantic so that they are built to agree with us. Which means that maybe the better use of it is to encourage, um, and prompt these models to disagree with us, to have us think a step further, to ask us questions and to find blind spots that we haven't thought about yet. I think that that is an excellent way of using large language models. And then if we go a step deeper as well, there are a lot of automations and a lot of different ways that you can redesign your processes to focus on the work that you actually want to be doing and where you want to create and where you want to think and where you care about that part of your job as being the creative part or the intellectual part. And then you can leave the administration syncing Your calendars, with your email and the other systems that you're using, you can leave that to AI. That is also an excellent use of this technology.

Speaker C: Yeah, yeah, there's, there's lots of great points there. And I think, I mean I like to, particularly if I'm thinking through a strategy or something and using AI for that, I like to test it and say okay, I want you to argue that what I've just told you is a really stupid idea and I want you to tell me why it's a stupid idea and I want you to point out where the flaws are uh, in the idea. And then, and then it'll come back and it'll give me generally somewhere between three and five points and I say, I ask it, are there any more? Until it kind of starts repeating itself and I know, okay, it's kind of reached its limit and then I'll tell it. Now I want you to tell me how I can proactively minimize the risk of these things happening in my original strategy or how can I adapt my original strategy so that these things don't happen. And I find in that way, um, whilst I still get agreement all the way through and um, it, it will um, switch because I've, I've asked it, you know, criticize the idea um, and given it that permission. So that's, that's a really useful one but one of the ones that blew me away and I'm playing a little bit more with this but um, when were talking pre recording, I've just come back from a four week trip to Japan and I had this idea of doing a hike there but wanted to see a whole lot of other things as well and thought okay, putting together itinerary and then trying to figure out the rail system and the logistics of going from one place to the next and is it better to go from uh, Tokyo to Kyoto and then halfway back to see Mount Fuji or is it better to do it the other way and all that. And I thought it must be an easy way to do this through AI. And I ended up building a digital um, AI tour guide that had even had a name, so that was Yuki. Um, and Yuki put together the itinerary and I said well okay, how long will I need to get from there to there and what's the best way? So I gave me all of that. So I'd really saved a heap of um, searching on Google and then I just confirmed the facts and made sure I checked timetables with in Google, um, maps to make sure that the timetable information was actually accurate. And there was one or two instances where it wasn't accurate, but it was, it, it got me close enough to the information that I was able to get, extract the right information from Google Maps. But the other thing it did, because I built this in, I thought, well, I'm sort of fascinated by Japanese history, particularly the Edo, uh, period. And I told it this and I gave it some books that I'd read. And I also told it that, hey, I'm a keen photographer, although I knew that it remembered that from all my profiles. And I said I want to learn, I want this experience to be more about, um, not just seeing things, but also learning how it fits into the context of the history and why it's important and how it impacts culture today. And I want to know where are the good photo opportunities and what time of day. And so I got uh, this amazing kind of set of information for each location saying what happened here historically, why that was an important event, who were the people that um, were key figures in, in the history of this location and how did they fit into the big picture of Japan? I ah, had all of that information even before I got there. It just enriched the whole experience.

Speaker A: Yeah, because then you also have something to build on. Right? Ah, you have all of this base information. And I think something really good about the way that you approached it as one looking at the admin that you don't necessarily want to do, as you said, like doing a lot of Google searches and checking what trains are going where at what time, um, and then also prompting it to go deep into a specific resource. This book that you mentioned, I think that that is amazing as well. And traveling in general is a great example of this part of what do we give to technology and what is, what do we maintain as being a human experience? Because at some point you also have to draw the line between, for example, getting lost a little bit. Is it great? Sometimes I think throughout my travels and as you mentioned, I've been working as a diplomat and I've also lived in Australia at some point. And some of the best adventures that I've had in my life and some of the best decisions that I've made as well is when I make mistakes or when unexpected factors, uh, show up that I was not able to take into consideration to start with or when we get lost. So that is also something that I, I think is a really, really good thing to consider as we are using AI and that goes for both our businesses, but also when we travel that we should allow for ourselves to get a little bit lost and not get too reliant on AI, but still recognize that it can help us build this foundation as it did for you, in terms of just having this capacity and planning the logistics so that we can spend time on being curious instead and having the experience of being in Japan and seeing all the things.

Speaker C: Yeah, I think, I think that's an important point. And certainly there was, I think there was a few instances in the planning stage where it kind of said, I thought, hang on, you filled this day to capacity. I don't think we can actually do all of that. And I said, and also I need some space to just let things happen, whatever happens. And so yeah, I think that's an important point. Um, and the other thing, learning from our ah, mistakes because that's part of the human experience, isn't it? If we can kind of test things out and push the boundaries. Um, and that's also information that ultimately gets fed back into the eyes at some point because people write reports or publish blogs or record YouTube videos, whatever they do.

Speaker A: Exactly, yeah. I think this thing of um, making mistakes and learning from our mistakes, that's also ultimately where the best strategies come from, where you iterate. Uh, it's ultimately where we get the best experiences but also the best uh, innovations really um, comes from failing forward or from um, also just making simple mistakes where you had different intentions and you're open minded enough to see that what you just created might not be perfect for the purpose that you intended, but maybe it functions for a different purpose. So this is something that I encourage people to do, knowing that AI is algorithms and knowing that we also have plenty of algorithms not just in these large language models and the app layers of the large language models. Uh, by that I mean ChatGPT and Claude and Gemini and Grok, but also that we have that when we listen to different music streaming services where they automatically choose the songs for us when we use Google Maps to navigate when we are driving. So also just being aware of the fact that when you give Google Maps, for example a task of taking you from point A to point B, you're also delegating the decision making around which route you're going to take to AI. And sometimes it's very good. And I think that you would recognize this if you drive in a neighborhood that you're very familiar with. For example, there are shortcuts that the AI or that the apps are not going to recognize. There is music when you get uh, already uh, made playlist. There are some songs in the albums that the AI is not going to automatically choose, but it might be the best songs. So create that space, uh, always, like, use AI for sure. Super good for logistics, but also create that space to get a little bit lost or to discover other things or to fail and to innovate. That's what I would say.

Speaker C: Excellent, Excellent. Um, well, you talked about algorithms and how they're shaping our culture, and I think we're seeing a lot of that in social media world as well. Um, which is a whole nother conversation. One of the things you, um, mentioned there as well is curiosity. And I know you see that as a muscle for escaping these bubbles that the algorithm, so the algorithm will, if it's Google Maps, will decide, here's the route you should be traveling, or if it's, um, yeah, if it's AI putting together, uh, a travel itinerary, here's what you need to be seeing. I think, um, taking control of that is probably one key step. Talk to us a little bit about building that curiosity at the same time that we're using these tools.

Speaker A: Yeah. So, uh, I believe that the best way of doing building curiosity comes from talking with other people, uh, spending time in nature, or also just putting down our phones or putting down our screens and seeing things for what they actually are. And also not maybe being so fast always to, um, take up our phones to Google for the exact right answer. And so allowing for imagination, I believe that reading is massively important. And that is a skill that we should all hold on to. So in general, just also listening through, if you're listening to an album, listen to the full album. Not don't just listen to your favorite song. Because if you go deeper into anything, regardless of what it is, but if it has been created by another human being or if it is created by nature as well, um, we are going to expand our reality. And that is what is crucial for using these AI tools because they are essentially just a mirror of our own awareness. So it will only reach as far as our own awareness goes. And if we don't expand that awareness through reading, creating in different ways, so writing or painting or, um, cooking or whatever it is, and uh, spend time in nature, spend time with other human beings, then we are not going to expand our awareness. Which means that the potential of these AI tools basically fall flat as well. And the same thing goes for all types of decision making around technology in organizations. It's that you need to be aware of your information landscape. You need to sort of go beyond what you already know. Because otherwise if you just make decisions based on what you are currently aware of, you probably have a lot of blind spots that is going to have you make strategically flawed decisions.

Speaker C: Yeah, I love the um, idea of the landscape. You need to be looking beyond your own landscape, particularly in the corporate world. And we talked earlier before we started recording about my experience um, with the birth of digital photography which has kind of left a scar for life on me I think. But it's, it parallels a little bit what we're seeing today. Um, and I wonder how you see that in terms of AI adoption in um, companies whether they're smaller or larger organizations and how um, whether there's on the one hand we don't want to have anything to do with it, ignore it, um, which is probably flawed because competitors are going to be using it for their advantage and perhaps gaining an edge or on the other hand what we've been talking about a lot is kind of abdicating a lot of the decision making to AI and letting the algorithms decide the strategy or the um, actions other than importing that human experience. So yeah, how do you see that whole landscape?

Speaker A: Yeah, so um, AI transformation or supporting companies with AI strategy and AI adoption is the main thing that Lumira does. And um, what we are seeing, and it's very much uh, comparable to your experience as well around the photo lab and when photography went digital is that the ones the companies and the leaders are doing it well, um, they are recognizing that the development of this technology is squiggly so we don't fully know where it's going. Um, they are recognizing that AI is not a solution as such. So what we tend to do is to focus on what is the vision of your business or your organization and what are the actual problems that you need to solve uh, on a day to day basis. And again AI is a general technology just like the Internet. So um, having a firm stance on whether you're for or against AI is not necessarily where you're going to find the best potential. Here is more approaching it in a, in a bit of a more nuanced way and actually looking at, and recognizing that some people might have a quite polarized stance against it. A lot of people are scared of change. So um, also recognizing that some people might fear this technology and taking that into consideration. So focusing on the problems that you're looking to solve, focusing on what is a pain in your day to day and knowing that AI might be able to help you with that regardless of whether it's machine learning or Natural language processing or large language models or in generative AI. And then the biggest part of it is actually management and leadership and making sure that you get the people in your company to work with you and to adopt it as well and that you help each other because that is how you get the perspectives needed, um, to actually adopt it in a good and a healthy way.

Speaker C: So it's a, it's a lot about integrating it into the teamwork and hopefully that, that culture is really strong and that human interaction level is really important.

Speaker A: Yeah. Because your teams, your teams are also the ones that knows their problems the most. So they are going to be able to find these bottlenecks and they're going to be able to innovate and find solutions that you yourself are not able to think about. So what we tend to do is that we look at the real business problems and we set up an AI project portfolio where we look at what is the type of technology that you need to solve these problems. And then also we create a system for change and to support uh, people in the organization to deal with this change in a good way.

Speaker C: M. So it's not just about. You don't just provide a technological solution, you provide the people support around that and um, almost addressing the culture as well.

Speaker A: Exactly. It has to be part of every strategy. I would say, um, otherwise uh, you're not going to. Just because you have good technology doesn't necessarily mean that people use it. Right.

Speaker C: Yeah. Well it strikes me that kind of a lot of what you've talked about is about um, a theme of using AI but having sort of a level of protection in place. Whether that's protection for our own curiosity, protection for the integrity of the information, um, or protection of the decision making process in terms of um, as the human we have the final say or the final. Do we have the decision, not just the decision, um, rights, but the responsibility to make the decision, even if it's well yeah, I've got the AI said all this, but I'm going to decide that um, not this. And um, yeah, it sort of sounds like there's um, a whole system behind that, uh, protection system. If you, if you had to boil that down, what do you think is kind of the core human quality that encapsulates all that?

Speaker A: Oh, we have so many core human qualities. I think almost approaching it from a different perspective, more of recognizing again, like what are the main pain points and what are the things that we think is boring and what are the things that we don't want to waste Our time on um, the rest, which is a very, very big part of it, is human and it should remain human. And uh, it's something that we can think about in so many different ways. So there's not just one general answer, I would say, but a lot comes from collaboration, from teamwork, from um, also deeply understanding a problem that you've been working on for a long time and knowing how time is changing as well. Um, having that perspective is super, super important. Um, and I don't think that that is something that we should leave to AI. But then again, as you mentioned also data privacy is so important and different ways that we can think about governance and the principles of responsible AI, which is transparency and explainability of these systems and a bunch of other things as well. Um, essentially the ethics of it all, um, so many, many factors to take into consideration, not just one thing being the essence of being a human being, uh, because we're not predictable, um, people. And that is the way it should be, I think. Um, but for sure, I think we're quite good at finding a balance when we're building strategies, so being quite technology forward and innovative and redesigning different processes and making life easier for the people that are working with it as well.

Speaker C: M. Excellent. Well, the idea of um, ethics and privacy, probably two entire episodes, if not more than that themselves. And I know, um, certainly in Europe, um, they've been at the forefront of Internet privacy, um, for a long time. And the GPDR laws in Europe are kind of the global standard now, even though they're not necessarily applied all around the world. So it would be interesting to find out what um, European legislators are, ah, doing to place guardrails for AI around privacy, around ethics. And even so there's still risks, I guess. I mean, we know that the Internet can be used for evil even despite all that. And so there's a risk there anyway.

Speaker A: Yeah, and you have a point there also because what we've learned from building the Internet, we did not build the Internet in a very good way, to be honest. Like there's lots of potential, um, both when it comes to Internet and social media as well, that we can learn now that we are adopting AI. So what are the things that we don't want to see again? Um, I mentioned before that we've had huge impact on mental health, especially when it comes to social media. That is something that I would like to avoid as more and more people are using AI as their companion in different ways. Um, in Europe there are a couple of regulations, so the GDPR is still super relevant, of course, because data, uh, is a big part of AI as well. And then we have the EU AI act, um, that went in force in the beginning of this year. So it's already in place and it's being rolled out successively through different phases, where we essentially categorize AI according to different types of risks and risk systems.

Speaker C: Fascinating. That's something that I'll have to learn more about and see how that impacts on, on people. But I'm sure you're all over that.

Speaker A: Yeah, I'll, uh, share some information with you. You have a newsletter on it, I believe.

Speaker C: Excellent. All right. Well, it's a, uh, fascinating conversation. I mean, we could go on for ages digging into all these aspects. There's so many kind of rabbit warrens to follow in the world of technology and AI. But I think it's a good point now to move on to the buzz, which is our innovation round that I mentioned earlier in the podcast. It's the same five questions I ask of every guest. And, um, the idea is, hopefully we'll uncover a key piece of knowledge that we haven't already uncovered. A value or an attitude that guides you. And short, punchy answers are fine, whatever comes to mind.

Speaker A: Perfect. Let's go.

Speaker C: Okay, so the first one's around innovation. So what's the most innovative way you've seen AI used to strengthen human relationships?

Speaker A: I would say that the best way of using it is in an organization where they have been using all the data that they have in the organization to break down the different silos. So you don't have the organization being, um, scattered or isolated from each other, but, uh, everyone actually understands each other's work. Um, and there's a whole technological layer to that that I won't go into. But essentially knowing that you have that opportunity and the ways that if people can learn from each other from different fields of knowledge, that creates a lot of innovation itself. So that is a big one. And then I love data visualizations of bird zones as well. So that allows for us to communicate and to understand nature a little bit better.

Speaker C: Okay, well, that one sounds fascinating. I was going to reflect on the first one, the silos. Um, so if I understand that right, it's kind of if I'm in the marketing department, and we always were at loggerheads with the sales department and vice versa in the corporate world, or both of us were at loggerheads with the production or the research department. So, uh, having AI internal systems that allowed us to. Well, what are the sales guys thinking? About this, or what are the production or the research guys? How do they see this and what are their challenges? Is that what you meant?

Speaker A: Exactly. So removing the friction that you have between certain teams. You mentioned a couple of classic ones here. Um, you quite often also have legal and HR or, um, R and D departments combined with legal as well, sometimes procurement, where a lot of different teams come in and they are supposed to be part of the process, depending on what you're buying. So, um, essentially, if you make that process easier of allowing for teams to understand each other, also because you might use different keywords sometimes for the same, uh, thing that you're talking about, there tends to be a lot of miscommunication in general, uh, between teams and between people that has different types of knowledge. So if you can break that down and help people understand each other a little bit better and understand each other's ways of working, um, that has huge potential.

Speaker C: Excellent. All right, um, next one. What's the best thing in your experience that you've done to integrate AI in a way that enhances that human connection?

Speaker A: I do love the fact that it's so easy to use, um, my voice these days when I'm working. So, uh, if I can, uh, dump my thoughts essentially, um, and use the voice mode of Claude, which is my favorite large, uh, language model. Also better data protection, which is a big one. Um, it helps me communicate in a more efficient way. And, uh, that also helps me reach true to people, um, in a much better way than I would be able to before. So, uh, it's a simple one, but it's something that I like a lot because it also gives me more time to do the things that does not require a screen. Right. So you can meet people in person faster and so on.

Speaker C: Yeah, yeah, it's. I think that's kind of. There's a couple of really important points there. I mean, the last one you made is, is a really big one. I think anytime we work with AI, if we save time to reinvest that time in a physical meeting with someone or a phone call, uh, direct conversation, that human experience, um, certainly is really helpful in terms of the human experience. Um, the other one, um, I'm a big fan of those voice apps too. I've got one on my phone now and on my PC. And being a podcaster, of course, recording my voice comes naturally.

Speaker A: Um, yeah, it is a more natural way of communicating. Right. Than, for example, writing. So, uh, there's something to it that I think is very special.

Speaker C: Yeah. And then the ability to Sorry, no.

Speaker A: Say, the ability to, uh.

Speaker C: The ability to kind of just spit out random thoughts and then have an AI turn it into something that has structure and makes a little bit of sense is, for me, really valuable.

Speaker A: Exactly. And the opposite of that is AI Slop. So when we have AI create unnecessary text or images for us, that does not have a connection to our reflections and our analysis, which is also becoming a much bigger thing now. Um, so I always also tried to stay aware of where does AI help me structure my thoughts and where does it just expand them, um, in a way that is unnecessary because then it's wasting not just my time, but also everyone else's time.

Speaker C: Yeah, that's right. All right, final question of the buzz round. What's the number one thing anyone can do to differentiate themselves in today's world of AI and particularly, how can they prioritize that authentic human connection?

Speaker A: Um, stay curious, read books. Um, connect with people that you don't know so well and learn about their perspectives and their ways of thinking.

Speaker C: Excellent. Yeah. Three really strong recommendations there. I love it.

Speaker A: Yeah.

Speaker B: All right.

Speaker C: All right. Well, thanks, Emma. This has been fabulous. Now, where can people find out more about Lumiera and about you and maybe even reach out and say thanks for what you've shared with us today?

Speaker A: Yes. So we have a website, Lumiera AI, which is the easiest way to find us. Um, we also have a newsletter that is called the Lumiera Loop. And, uh, I might be able to share some links with you so that you can also add it to this episode.

Speaker C: Excellent. That would be wonderful. And then we'll include those in the show notes. Um, so as we wrap things up, what's a specific action you think our listeners should take out of our conversation? Conversation today, something they can perhaps apply immediately.

Speaker A: Um, start. If you haven't already, start using AI Start thinking about the space that you can create to, uh, also remain curious and to make mistakes sometimes, because that's super important. And, um, go out, have a walk, enjoy nature, get some. Get some fresh air.

Speaker C: Yeah, wonderful. Those two. Two really strong points. So save some time on a little activity with AI and then take that time and go out for a walk, maybe even with somebody else. We have an in person conversation. Exactly M. Excellent. Well, thanks so much, Emma. Uh, I've really enjoyed our conversation today. All the best for the future. And please do stay in touch.

Speaker A: Thank you so much. And yes, we'll stay in touch for sure.

Speaker B: Thanks for listening. We'd love you to leave a review on this episode. It will help us to make the podcast better for you, Simply go to lovethepodcast.com inovabuzz and pick your preferred platform. Remember to visit Innovabiz co flywork and secure your M membership to the exclusion Exclusive Flywheel Nation community where you'll enjoy direct access to our incredible podcast guests, engaging meaningful conversations and participate in connection events designed to elevate your business journey. Don't miss out.

Speaker C: Join Flywheel Nation today.

Speaker B: Tune in again to the next episodes of the Innova Buzz podcast, where we've got yet more fantastic guests lined up. Until next time, I'm Jurgen Strauss from innovabiz. Remember, be awesome and keep innovating.