Finding Brave Epis 332- How to Stay Brave, Valuable and Visible in the Age of AI, with Raman Rai

Hello everyone. This is Kathy Caprino and welcome to my podcast, Finding Brave. I've created the show for everyone who longs to create something bold and brave in their life, to rise up, speak up, and stand up for who they are and to reach their highest and biggest visions. Each week I'll be speaking with inspiring guests from all walks of business, leadership, entertainment, the creative arts, and the entrepreneurial world. And they'll be sharing their intimate stories of finding brave, and offer their best strategies for building your most rewarding, joyful, and meaningful life, business, and career. Hello everybody. Welcome to another episode of Finding Brave. I am Kathy Caprino. How are you? This is coming to you, I believe, in early July. We're not there yet. We're in June and oh boy, it's hot where, where we are. But that's what summer is typically, so we're rolling with it. But thank you again for being here, and I'm so, so excited to have our amazing, inspiring guest, Raman Rai. And I can't say it properly, so Raman, you're gonna, you're gonna help us and say it, say it for us so we know we can hear it.

Amazing. Yeah, thanks, Kathy, and great to meet everyone. My name is Raman Rai. That's how you say it.

There we go. Bless you. Thank you for being here, and I know you're in the middle of tech week and it's craziness with the president visiting and the Knicks game and the heat and the— wow. So thank you for taking the time to join me. Thank you so much. We are talking about— I don't think there's a more important topic truly right this minute— how to stay brave, valuable, and visible in the age of AI. Wow. And let me tell you all about Raman. Oh, not getting it right. I'm going to just call you R. How about that? I hate it when I butcher a name. Uh, Raman is an award-winning AI product and deployment leader who helps organizations move AI from strategy and experimentation to real adoption. Formerly at PwC and Google, she helps scale one of the UK's largest enterprise GenAI programs, reaching over 200,000 people globally in partnership with OpenAI, Microsoft, and Harvey. Oh wow. She works hands-on with Fortune 100 leadership teams mid-market organizations, and early-stage founders to move AI from strategy into real-world deployment. Her work spans AI strategy, agent and workflow prototyping, leadership enablement, adoption design, and enterprise go-to-market. She focuses on the human side, and you're going to hear that in spades, of AI adoption, helping leaders build trust, teams build confidence, and organizations turn experimentation into meaningful change while ensuring AI creates access and opportunity for people historically left out of major technology shifts. Brilliant. There is so much more that you've accomplished, but read the bio in our show notes. It's all there. Thank you again for taking the time. And let us, let us launch in, you know, You all know my guests fill out a prep sheet before we meet, and so often there's nuggets and really important gems. And Raman mentioned 3 key points that we're going to talk about, but I want to kind of frame it. Number 1, you are not as behind on AI as you think. Number 2, AI adoption is emotional before it is technical. And boy, am I seeing that and hearing from leaders and employees and managers who are in the process of being told how it needs to unfold and what that brings up for everyone. And number 3, the future of work is still being shaped and you have a role in it. I love it. All right, now could we begin, 'cause this is like a 7-part series, it could be. Could we begin with what do individuals, who are at organizations and they are being told what is happening and how it's happening and how it should be embraced, et cetera, what do they need to understand that maybe their own organizations are not sharing with them? What do they need to know, Ramin?

That's a fantastic question, Kathy Caprino. And it's really interesting trying to think about where do we really begin with this. I think everywhere we look, left, right, center, We're hearing about AI, we're hearing that we need to use it all the time. And then there's this fear that if you're not using AI, you're going to lose your job. So I think what's really important to first understand is no one really knows the answer of where we're going as, as the future of work and as a business. And I think what it means to be a leader in this world is to try and make sense of the unknown. So if you do have managers and bosses and, you know, they're telling you something but it doesn't seem to feel right, it's because they also don't know, um, in terms of where we're going in the direction. And something that I tell people in and out who are employees in these companies is that the way you're feeling is okay and it's natural, right? Like, if you're being bombarded by AI updates every single day with other ongoing global geopolitics that's also happening, it's going to make you feel really uncomfortable. And I think having that lack of knowledge of AI and that certainty and that constant change is going to make us feel really, really uncomfortable. So I think there is this one part of the element that I don't think we're saying enough, and it's, it's okay. Like, it's okay in terms of the way that you're feeling. But then the second element is, okay, so where do we go from here? Like, how do we manage that feeling? And something that I would encourage that we're hearing across organizations and in the world is to really try and get a better sense of what's really happening on the ground. So, you know, if you are working in a company or if you're also laid off, one of the great ways is to really understand, like, what is AI and what is this thing that everyone's talking about. And, and one thing I really just want to specify is it might feel like everyone is using AI, but in fact it's only 16% of the world, you know.

Tell me more about that, 16%. Does that mean if we say we're in the US here, of course, do you mean, do you mean worldwide or do you mean in looking at enterprises now? I'm interested. I don't know if you have this stat, but how many companies are trying some way to bring AI to the workforce? Do we know that? Do we know that?

Yes. Yeah, it's a really great question. So essentially that 60% of the world is taken in in terms of the entirety of the world. And, you know, there is definitely that lead that is happening in the West, specifically by the US and certain markets in Europe. And then we're seeing pop-ups of it in Latin America and Africa and India and in other regions. But the group is very, very small. And in fact, in that group, it might not even be some of the organizations that we're thinking about. They're actually left behind as well in that journey, which is why now seeing such a surge of these companies that are trying to understand how to use AI and are trying to upskill their employees, or are trying to partner up with the likes of AI companies like Anthropic or Google or OpenAI in order to get people to use it. So that noise that we're hearing and that we're exposed to is very disproportionate to the usage, and secondly, the adoption. So what you might see a lot, Kathy, is there are companies that are trying to use AI, but it's not actually working. Only 9% of U.S. enterprise companies have been able to use AI effectively.

9?

9. And that was in 2025, so 9.7%, and that's up from 3.7% in 2023. So we're talking very, very small numbers. So I think there's a lot of, as what we're talking about, lots of discussion and lots of understanding of like What is AI? What are the tools? How do I use it in my day job? But when we actually think about if it's being adopted and used daily and effectively, it's very low.

I have so many questions. Okay, let's not exhaust you as they come flying out, but I'd love to ask this. Doing the work I do with people, I'm seeing— there's probably more nuance than this, but basically two, and I didn't prep you on this and didn't even talk to you about it before we pressed record, but here we go. I'm seeing two different approaches from companies and, you know, some are massively large companies that these people, my clients, are working in. Some are very small and everything in between. But I'm seeing two approaches and I'm really interested in your perspective. One, and this is based on, you know, a small sample of my clients, The organization has created a committee of representatives within each division, and they meet very regularly. And the idea is to get the team members invested in, you know what you do best. Let's try these tools that we have, and we can't roll out Claude to everyone. It's not cost-effective right now, but let's do some testing. And there are people who are the representative of that department, and their job is to kind of evangelize it a little, or, you know, talk about, look what I just did, and I think you could do that. So it's from the ground up where people are looking at what is most time-consuming, what is most, you know, probably easily replicable. And it seemed to be in those situations, it seems to be going well. There are some people that don't want any part of it. And are dragging their feet and it's not what they want to be. You know, it takes time to do all this. But overall, it seems like because it's led by a C-suite member or a senior person and there's excitement, it's going beautifully. Another approach is we're bringing in a consultant and the consultant has worked with this industry. These top organizations in this industry and they understand where the efficiencies will be. So they use the outside consultant to advise. What do you— and you are an outside consultant, so this is a hard question, but which do you think works better? And maybe it's a beautiful hybrid, but I'd love to hear because I'm really curious.

That's really interesting. So I've played on both parts. So I, before I was an independent consultant, I worked at PwC and And 3 years ago, we ran one of the largest GenAI programs with OpenAI and Microsoft, where we helped upskill 200,000 people at PwC. And the way we did that was an element of both.

So elements of what?

I would say the way that it— no, of course, elements of both of the approaches that you mentioned. So the way that I see that it could work well is, so we are seeing a top-down approach where a C-suite executive member you know, believes that they need to use AI across the organization as a way to, you know, get ahead of the market and, you know, get ahead of the game. Now, I think that's absolutely okay, and I think that's a really clear strategic decision to make. The challenges is how do you take a 100,000+ organization on that journey? And one of the ways is, is what you mentioned, it's how do we identify champions within the business who already are advocates of AI or certain AI tools, and how can we use them as the role models of the entire company to get that going? And I think that first step works as well. But what needs to happen underneath that is we need to understand with AI, where do we actually use it in, in the business? So it's not just a matter of, oh, hi Kathy, you and I work together and I've got this exciting cool tool called ChatGPT and I want you to use it. That's not going to be useful. I think when we think about teaching, it's about what would, what would improve Kathy's day? What does Kathy do day to day in her workflow where AI could be used as part of that workflow to make her be more productive or be more efficient? Or, you know, she doesn't have to do some of the repetitive manual tasks that she actually doesn't really find that fun doing anyway, but AI could do that instead. And That is more of the conversations that we need to be having, and that is what we're seeing now more on the ground. So the way that would work is businesses can either, either find people within their company or they can bring in independent consultants. But step one is really identifying those high-value use cases. So where in the business can we identify maybe 3 to 5 top use cases that is actually going to bring a return on investment and is a place where AI can be used. What we're seeing in the past 3 years is most companies are just kind of spending a lot of money, so spending so much money in their AI budget. They're trying to get their employees to use AI across so many different pilots, across hundreds of thousands of ideas, and it's not bringing in any return of investment whatsoever. What we're trying to do more in 2026 is focusing on those high-valued ones that are tied directly to a business metric, whether that's revenue generated, risk avoided, or cost reduced. And that is more of the angle that we're going down. And just to add, with what I mean by a high-value use case, it's something that solves a problem people feel every day, and even better, it connects to a business goal rather than it just being a convenience, and it's something that you can measure. So a classic one that I see a lot in companies is anything that revolves around drafting lots of emails, documents, you know, anything that's like repetitive writing, you can now use AI to do it for you. So a great one is Kathy Caprino is, you know, using AI to record this podcast, right? So neither you or I need to make any notes. AI can come in, it can be that note-taker for us. And brilliant, at the end of this podcast, not only will we have a recording, but we'll also have a transcript. It's in areas like that where we take our day-to-day flow and we're thinking about where we can implement AI so that it feels seamless, people want to use it, and it's actually improving the productivity of how people work.

Love it. And you make it so clear and so calm. One further question I'd love to ask. I am an avid user of ChatGPT and Claude, and Even, not even I, I'm so not an expert, but if someone else tells me, you know, you can take that very complicated Excel spreadsheet with, you know, the 20 tabs and you can do, you know, what you're needing to do and see the statistical difference between these two categories, you know, which would take me, I used to be a market researcher, it would take me hours. Now I go bink and it's done.

Done.

I don't know that I would have realized that was a capability. So one thing I see, and I'd love your thoughts, if we're giving it to people and say, look at where you're spending the most time, look at what's repetitive, look at where there isn't a lot of nuance, look at— or maybe there is— but, but I think the downside of that is we don't know what we don't know. We don't understand that it can do things that never in a million years would we have thought it could. Do you see that as a problem when, when we're asking employees to try to find ways to roll it out? Do you see that as a problem, that if, if you really don't understand the, the incredible ability of AI, you're not going to see the opportunities? I think— what do you think?

That's a really good question. I think it's difficult when things are all happening at once. If we take history, and again, I haven't been around for centuries, so I don't know, um, but when we take a look at every kind of technological transformation, it's usually a bunch of really cool, smart, talented people come up with this new form of technology, whether it's the internet, mobile apps, social media, and it will create a natural engagement, a natural, you know, catalyst of people wanting to use it. It generates hype. Then companies are like, oh, have you seen this exciting new trend? This is something that we should invest in and look into. And then only then, and the kind of approach we see there is more from a bottom-top, where people within companies have, you know, learned about social media or learned about the internet or come across an exciting tool. They then use it in their personal lives. They then use it in their day job. And then they tell their bosses, their managers, their C-suite, hey, this is something we should invest in.

Right.

What we're seeing today is the opposite, where it's the hype has, was created before people even knew what it was. We're seeing it being more of a top-down approach where C-suite executives really want their entire organizations to jump on the bandwagon of AI. And at the same time, you're losing your job. At the same time, there's living cost crisis. There's a lot of mixed emotions that are going on at the moment, which then causes people to want to be apprehensive and to stop and to question things of, do I really want to be using AI? Because right now I've got 10 other, other things that I'm worried about, which includes potentially me losing my job. And I think that's where the friction comes. And I think it's really hard to do something when it feels like it's being enforced upon you, and two, when you might not even be the most tech-savvy person or You might not even be interested in AI, but you're being told to use it. That is where there's a lot more resistance that we're seeing. A way to combat that, and what I'm seeing now is, I think there's a few things. One is I'd encourage everyone to use AI just from the basis of, if we look at technological transformations, when something comes, it will stay, and you will potentially use it at some point in your life. Whether it's now or whether it's 10 years down the line. The challenge is there's now more of an urgency to have to use it, which makes it a bit more difficult. So I'd always encourage people to try AI or ChatGPT, as you mentioned, Kathy, or any of the tools just in your own personal life. Doesn't even have to be at work, just so you can see the value of it and you can kind of learn it at your pace. Now, if you're listening to this, you're probably thinking, but why? Like, why should I do that? Why should I care? And I would say right now we're in the early adopters phase. So what, what's happening is we're seeing the emergence of these technology tools. That 16% of people that are using AI are called early adopters. They're people that before it actually becomes a norm in civilization, they've been using it before then.

Are we still in the early adoption phase? I feel like we plunked into it. I know.

I feel like we are probably in the early to mid where, as I mentioned, adoption rates are still very low. But how do we get to a point where it's normal? Kathy, how do we get to a point where we stop talking about AI and we just say, oh hey, like my agent has just— my agent is running my business, or yeah, I've got an agent that's set up the response to my emails. We'll get to a point where we don't even refer to AI. In the conversation. It will just be a norm in society.

Do you think that might be— I know you're not a magician, a wizard. What do you think?

I'm not, I'm not a magician. I would still say we're at least— I would, in my opinion, I'd say still say we're at least, you know, 5 to 10 years away. It's really hard to put numbers on things because things move so quickly. I still think on the adoption span, you know, this is still another 2 to 3 year game. And I think we'll learn as we go, you know, when we get those adoption rates higher in companies, are we keeping headcount or are we still reducing? That's a question we need to answer. Are companies actually making money or not? And I think there is also some data still out there that are people actually more productive with AI or not? Because it just seems like we have a lot more work to do. It's it's also really expensive, and we still need to understand the real value of it. So, answer your question, I still think we are in, we are in a hype, we are still in an AI bubble, but the bubble is similar to the dot-com crash where the internet will exist, but there will only be a few companies that will actually do well and stay. For example, the likes of the Googles of, you know, the early 2000s. It'll be very similar to to today. We just still don't know what that looks like. Wow.

So fascinating talking to you. So could you leave in terms of point number 1, all right, people, you're not as behind as you think. You know, we were talking before we pressed record. This is a time of, it seems like unprecedented change and our nervous systems weren't built to handle this. I don't think our brains were meant or designed to handle this. It's a lot. For people. And I think they're carrying fear and anxiety in ways they're— they've never even acknowledged or understood. But what would— what one encouraging thing could we leave people with? And what do you mean that you're not as behind as you think? What can people take from this conversation that will calm them a bit?

Yeah, I think that's really interesting. So I think first and foremost, I think it's good to identify where that anxiety and stress is coming from. So what I mean by that, what is the source that is creating a lot of that discomfort? And what I'm seeing across in my peers and in my community, that a lot of that is just generated by social media and generated by the internet. Um, we're living in a day and age where the algorithm is not only feeding you the things you want to see by what you're clicking, but it's now also being controlled by companies that want to show you certain things. Um, if we get a little bit spiritual, I always tell people to try and activate their third eye, like be that bug in the room and really just try to critically think and address some of the things that you're seeing and questioning, why am I seeing that? Like, an example is even, um, you know, being from London and being in New York I see a lot of AI adverts everywhere, even on the subway, and I question myself, why am I seeing that right now?

Why?

There's a particular brand that you'll probably— if anyone gets on the L train, they know which one I'm exactly talking about. But the idea is, is these companies also are trying to win your attention. So the ball is actually in your court when it comes to using AI or not. Um, and the second thing I would just say is find ways to set boundaries on what you're allowing yourself to be exposed to. And a big one could be just, you know, turning off the phone, you know, trying to engage less with social media. And a big one that I say is just about being humans. You know, humans, we're creative people. I think society doesn't always allow us to, you know, to kind of foster and drill down to that creativity. But I would definitely say try and consume less. And create more. So when you've, you know, turned the dial down and you've set boundaries on what you're exposing yourself to and giving yourself time to rest and re-engage back with nature and the world and your friends and family, try and create ways of how you can learn AI on your own terms, in your own life, on your own workflows, without feeling like it's being, you know, being enforced by any— anybody. Because That's what I think will give people a real grounded sense of what AI actually is and what it does. And you'll be able to have your own raw experience of what it is. And like, if I was to, you know, pop a bubble, like, AI isn't as easy to use as many people make it out to be. Beyond the chat, it's very hard to set up an agent. It's very hard to set up a workflow. It's really hard to audit where you want to use it in your life. It's— these things are really difficult. I think those types of conversations are being blurred in, in the marketing and PR and noise of what we're seeing in AI. So I guess my 2, 3 takeaways would be: 1, to set boundaries on what you're exposed to. 2, consume less of what you're being exposed to, whether it's media or marketing, PR noise. And then 3, when you're at a point of rest, at your own pace and speed, figure out and learn how to use some of these tools and create. And the reason I say that is because AI will be part of our society. You will want to use it at some point, whether it's now or in the next 5 to 10 years. And I always think it's great to be ahead of the game before it feels like it's being— before you feel like it's a part of life and you already feel left behind even more.

It's so beautiful. I'll throw one more tip in and then two other things I want to touch on with you. Um, so I'm a writer, right? And I'm also— I'm doing a lot of posts and articles and outreach to clients. I'm finding, and this is— I hope this is helpful, people— I, when I'm struggling emotionally with something, like there's some hard message I have to deliver, some difficult message, I find it— chat and I thought amazing, and it just pulls me off the ledge and gets to the heart of what I really want to say without— it's brilliant. That said, I don't need to run every little darn thing that I'm doing by ChatGPT. I don't. But yet I'm finding that there's been such improvement in certain ways that it's almost now a very bad habit that I almost don't, you know, write anything without it editing. I don't let it write it scratch because it gets it so wrong, even though I've trained it for hundreds of hours. And I have my own digital tool, as you know. But, um, I would ask everyone, just what you're saying, stop a minute, breathe. This is an email that you've sent 2,000 times to 50,000 clients, 500 clients. Do we need ChatGPT to edit this? Do we need it? Because now it's taking twice as long. And so I think that in some ways it could breed insecurity because we see how much it can improve things. But that doesn't mean everything you're doing needs to be run by Chat or through Chat. Does that make sense? Do you think that's helpful for people?

Absolutely. I was in Zurich a few weeks ago, and Zurich has a really deep, like, AI ecosystem of researchers and lots of people that have PhDs. And we were also having an intellectual discussion there around people are not taking notes anymore. Like, people don't write, people don't make notes anymore. And if you're not making notes, how are you learning? How is that reinforcement going in about what have you taken away from a meeting? What have you taken away from a session? What have you taken away from a conversation? And I think what's interesting is, Kathy Caprino, if we go back to school and if we were to write a history essay, and let's say we took a history book from the library to help us write the essay, or I remember at university you could find a lot of essays just online, so I could just copy somebody else's essay. We were told that that was plagiarism, or we were told we shouldn't do that because, one, we're not learning, or we're not creating our own opinion or our own perspective of something, and two, we're just copying somebody else's. What's happening here is we don't have the governance, we don't have that teacher or that boss to tell us no, because there is this element of we need to use it, but the guardrails and boundaries aren't set of how we should use it. So I'm absolutely with you, Kathy. I think, I think we should use our own original human thinking and, you know, critical thinking skills to come up with our own original thought of whether, of, you know, of how we write and how we come up with perspectives. And then use AI if you feel like, you know, Maybe you need it to review your essay from a grammatical error sense because you need it, you know, you need to format it, we need to review it in some ways, or perhaps you want to get another perspective that you haven't thought of. I always encourage people to do the work yourself, pen to paper, and then use AI as a reviewer. Don't allow it to take away your thinking. And I can definitely say, as someone who is an early adopter, 3 years ago, I was so overly reliant on AI that I just stopped thinking. But Kathy, I got to a point that when it would come to like thinking about what to eat for dinner, I would just ask ChatGPT like, what should I eat for dinner? What shall I wear to work? Where should I go on holiday? When actually those would usually have been things I would have thought of, or there would have been conversations I would have had with friends and family. So it's also showing how these tools are actually replacing how social conversations are taking place. And then when I noticed that over-reliance, which then led to a deeper insecurity in— I didn't really believe that what I have to think or say is important, or I, I don't know, I just, I think I struggled with trusting my own self-belief. And then I would say in the last 18 months I've switched, and I think a lot of that came from being around people like my friends and family that don't use AI, and they notice that behavior changing me. And they said, I think you need a break. But the thing is, Kathy, I would say that to any of the younger generation if they were using social media. And now we're seeing governments around the world bringing in bans for under-16-year-olds and using social media. And yet, Kathy, social media came in the early 2000s and, you know, the 2010s of the era, and only in 2026 are we having the conversation around constr— of how do we constrain ourselves from using these platforms. And I think it's the same with AI, but I'm with you. It's this balance of we're hearing a lot of, you must use it, but then we're also seeing, wait, we don't want it to replace exactly what we're doing. And I definitely would encourage people to look at other tech transformations that have come before us. Social media is the most recent one, and looking at some of the challenges that are coming out of that, not just from a human behavior psychological standpoint, but how it's impacted relationships in society and how we're now, you know, forming relationships. It's, it's changing, yeah, and sometimes it's not, not for the best either.

So valuable. I know you have to go soon. There's two quick points I'd love— because you're an expert in this, I really want to get at, because it's not talked a lot about, I believe— the bias that's inherent in how our tools are being programmed and what we must be aware of. I know you're an expert in this, so I'd love to hear a minute about that if I could, if you have time. But one other minute, um, could you just give us one big tip? If we're a leader of a small, big, medium-sized company, what would you say the biggest mistake you see that's very damaging in how these leaders are attempting to deploy AI that, that a leader who's listening can say, all right, I'm not going to do that, I've got to change course. What would you say the biggest error or mistake is?

Yeah, so I guess to tackle the first question around data, so you need data to be able to have AI success. So 91% of organizations say a reliable data foundation is important. Now, what is data? Data is anything that captures information about something. So it could be, um, anything around like documentations, dataset, you know, knowledge management, all of that jazz. Data is created by people, right? So when you're going to create an AI app or a product or a system, you're going to need data as a prerequisite, um, in order to, to deploy something. Now, if all that data is created by people, and as Kathy, you mentioned very earlier at the start this podcast, that as people were biased, then a lot of that bias is then implemented in datasets. So, um, I can give an example. I spent a year in Cape Town where I was working with early-stage founders across Africa who are building in the voice AI space. So a lot of the models we're seeing currently right now that are coming out of the US and China, they are trained on the English language. Now, if you're in Rwanda, and Rwanda has 4 languages, 4 main spoken languages, is, um, Kinyarwanda, Swahili, French, and English. Well, if you're an English-speaking person, then brilliant, you can use the likes of any of these models. If you speak Swahili or Kinyarwanda, it's a lot more difficult because these models don't actually understand the language at all. So there's people working on these African dialects that are spoken by tens of millions of people. Just to put in reference, By 2050, 1 in 4 people will be African.

By 2050?

2050, yes.

Say that again, it cut out 1 minute.

Say that again. Yeah, of course. By 2050, 1 in 4 people will be African, and tens of millions of those people are largely locked out of the current generation of AI tools. Because those tools are built on data that is not representative of them as a continent. So what's really important is if you are using AI is to just make sure you're using data that is representative of the work that you're doing, especially if you're building for multiple users across multiple countries. How do you ensure you're building something that, you know, fits a young Gen Z female in Utah versus a young male sitting in Nepal? You Right, how do we relate to both those people? So I think that taught me a lot around, you know, AI conversation, how it's so important that, you know, that you're building for a variety of languages and a variety of problems. So your bias will fit in based on what you know. The ways to combat that is to have a representative team, a diverse team of different people who can bring in their diverse thinking. And that's something I would highly encourage leaders to do as well. When you're investing in AI, what you're ultimately doing is you're investing in your people, you're investing in the people in that business. So the major mistake I see is leaders trying to bring in AI immediately without thinking about what do we need it for, what problem are we actually solving, and where do we want to go as a company. To answer those questions, you're essentially just doing a business strategy workshop with your company. And then 2 is mapping out how does our business operate, what are those workflows, do we think AI can fit in this? And then there's a 3-step approach that I will quickly allude to that I take, but that 3-stage journey is, you know, where is AI augmenting our workflow? So how is it improving the way we work already? The second is, how is AI removing or cutting or reducing those processes that we no longer need? And the third is, can AI actually create something completely new that we've never thought of doing before in our company? Um, and what does that look like? Most people sit in that first stage where it's improving the workflow. The second, I say 70% of ideas are in that first funnel. The second, you know, 20% is in that piece around removing, you know, processes that you may already have. And then that 10% or 5% is that very top, which is that blue sky thinking. So that's something I'd encourage leaders to do. And then the final piece is around, which I mentioned earlier, is let's invest in your existing people. Let's invest in what their motivations are, how they want to grow in their career. And then this piece around educating and upskilling them on AI as well. Um, and I think what's important is once you understand where you want to go as a company, and once you understand what your new processes or new workflows will look like, the people will then know where they fit into this new company. What's happening is we're upskilling people when we haven't changed the company. We're trying to upskill people to be AI fluent in a very traditional, non-tech savvy organization. And then that creates more structural problems.

Wow, that was so helpful. I really felt like I took an in-depth masterclass on like 3 different vantage points. Thank you so much for being here. Where— I could talk for hours with you, I've got to let you go. Where do we soak up all of what you have to offer? Where do we send people? What links would we send?

Amazing. Honestly, it's so interesting trying to tie in 7 years of Spritz in the AI space in this very short, sweet podcast. But if anyone wants to get help on whether how you deploy and adopt AI in your organization, you can reach out directly to me at ramonwai.com, um, and also very active on LinkedIn. And there's certain ways that you can work with me. One is we can have a conversation around how we can use AI in your company, what your strategy looks like, you know, where do we kind of get started and how do we do that change management. Adoption piece. And the second is I also work with early-stage startups who are building these AI products and need support from enterprises.

Thank you so much. Oh wow, my head's buzzing. There are even more questions now, which shows, you know, how rich this content was. And we do have to mention we met through a wonderful expert advisory platform that we're both on, Hubble. Social, so we'll link to both of our profiles there. I'm so grateful to have met you through, through that. Honestly, thank you for taking the time, and enjoy the rest of your time here in New York while it's so crazy, I know. And everyone, I know that I'm a broken record with this, but I say that we're, we're not just skimming the surface in these conversations on Finding Brave. We're going, I think, deep. So wherever you see this or hear this, please write us if you have a question like, "Wow, I didn't understand what that meant," or, "Sure, that sounds good, but how do I do that?" Please, you know, let's help you expand on what you're hearing by answering, and we'll do our best to address anything that you can throw at us about this really incredibly important and challenging topic. So I hope we've got so much out of it. Thank you again, Raman. Come back, come back soon, maybe in 3 to 5 years when our whole world is so different or sooner, I hope. All right, everyone.

Yeah, I would love to give a perspective then, but thank you so much for having me, Kathy Caprino. This has been so fun and it's so great to have a real conversation about a real thing happening in our world that, you know, you know, that isn't just, isn't just this thing that's going to go away. So it's always great to have a fresh perspective on how people are feeling about it.

Thank you so much. All right, everyone, hope you found this helpful. Love to hear, and wishing you a wonderful 2 Finding Brave weeks, and we will see you next time. Thank you. Thanks so much for joining us today, and please don't forget to check out findingbrave.org for more programs, resources, and tips, and tune in next time for your weekly dose of finding grave.