Transcript
I'm Amy Horowitz, I lead solution sales here for North America. If you've ever been lost in the chaos of data and AI, you've come to the right place. We'll be conversing with industry experts who are here to shed light on the challenges that have rocked these arenas. We're here to bring clarity to the chaos, myth-busting the confusing parts and providing insights and guidance for complex data problems by delivering trusted data for analytics and AI. Welcome to the data and AI life podcast. I'm your host, Amy Horowitz. Today I'm so excited to be joined by Steve Brown, an AI futurist, innovation advisor and keynote speaker. I'm going to turn it over to Steve to introduce himself. Steve, hello and welcome on this beautiful day. Tell us a little bit about yourself. Welcome to the podcast. Thank you. Hello, Amy. It's nice to be here. A little bit about me. I'm mostly British, as you can probably hear from my dulcet British tones. I've lived in the US for almost 30 years, had a good head of hair when I started here and I have a career in high tech. So I worked for Intel for many years in a wide variety of roles. I've been an independent futurist and keynote speaker for over a decade. And more recently, I got to go back to London and recharge my accent working for Google DeepMind. Google DeepMind is Google's AI research labs in the heart of London. Now I help companies to understand what's happening with this AI revolution that we're all swept up in and give them practical steps on how do they navigate it? How do they make sure they're doing the right things to stay relevant, to survive and then thrive in that world? So that's me. Let's jump right in. You know, I hear about AI at the water cooler. We hear about it in boardrooms, everywhere we go, even at the dinner table. People are talking about AI. It's changing people's lives and what they think about. Let's talk about transformation and the strategy behind what you do and what you teach, if you will. Many companies still believe they can wait and see. They're a little cautious about what's happening before really investing in AI. Why is this mindset in your mind, Steve, no longer viable? What's the smarter way to approach this transformation with AI now? Yeah, the time for wait and see is over. There were certain people who went early and they had some failed AI deployments as a result of that. So now is the time and there's a carrot and a stick here. The stick is your competitors are going to invest in AI and that is going to make them more capable. It's going to amplify the people that they have and amplify the impact they can have and help them reduce costs. They are going to out-compete you if you don't. This is kind of a no-choice scenario. On the plus side, the opportunity here is enormous. Instead of perhaps thinking about, hey, we could grow by 12% next year or 20% or if you're ambitious, 30%. If you were to make AI the core engine of your business, and we can talk about what that means, your opportunity is to start thinking about growing the company 2x or 5x or 10x next year and increasing your impact. The time to move is now because the technology has matured its capabilities to the point where it absolutely makes sense to jump on board now if you haven't done so already. So you talked about the first cohort that went early, that were early adopters. If you think about it in terms of a bell curve, let's talk about the ones on the left that were really first to start this journey. So can you talk a little bit about customers or prospects out there or listeners to avoid some missteps when they started this early? Can you tell me about some of the biggest strategic missteps you've seen organizations begin their AI journey and how can leaders avoid these missteps? I think the first one is they didn't have their data house in order. It's kind of a garbage in, garbage out with AI. If you give it bad data or you don't connect it properly to your data and to existing legacy systems that house corporate data, it's just not going to work well, as simple as that. So that's the big one, is people don't get their data house in order. If you have data scientists, data engineers, give them all a raise, you don't want them to leave you. You need them. They are a critical part of your organization. So getting the data house in order, number one, treating AI like just any other technology, big fail. This is not just a simple tool, there's a lot more that you can do with AI. And the third one is just failing to recognize this is a huge change management exercise for leadership. You have to communicate, communicate, communicate. You need to bring people in upfront. I had one client and they had an early agentic AI solution that they rolled out and they rolled it out to their employees and their employees threatened to quit and said, I'm not using AI. Why did that happen? Because they hadn't involved their employees at all in the conversation. I said, so did you ask people what they wanted and how it was going to change their workflows? Oh no, we just built it and rolled it out. Mega fail. So it's a communication and inclusion exercise to bring everybody along because you're trying to turn your employees from potential saboteurs into supporters. And so that's a big one as well. That's right. You know, our tagline at Informatica, which I love, is everyone's ready for AI except for data. And I think what you just described, it is your number one thing that you've seen. I will also just comment on the change management piece. I have never seen a hype cycle like I've seen with AI. I'm not going to tell you how old I am, but you can guess that I've seen some pretty big hype cycles when it comes to technology. And this one, I think you hit spot on. We have customers and prospects that are panicked because they realize a change management piece has been left behind. So I'm glad to hear you say that. I want all of our listeners out there to really take this into consideration. Global change management, change management is critical. It's not just a tool. This is a way of life. So thank you for bringing that up. And it requires a different posture as a leader. Leaders are used to being, you know, the sages, the experts. They've been around the block. They've seen, they know where the bodies are buried, and they're able to lead based on their experience. We're going into a brave new world where leaders don't have all the answers. And so they have to take on this stance of being much more sort of philosopher explorers. They're willing to think through a problem with their teams in partnership with their teams and say, I don't have all the answers, but we're going to figure this out and we're going to go there together. Let's talk and switch gears about agents. We have to talk about agents. I feel like every podcast I do, somebody's talking about AI. Obligatory. Yeah. Yeah. Right. Let's talk. Let's jump into that. So you've worked a lot on cutting edge applications of AI agents and emerging hardware. As you often say, and I love this quote, I'm going to steal it here. Your next hire works for electrons, not dollars. Tell me how you think leaders rethink the workforce strategy in the next two to three years as AI agents, LLMs, and robotics begin to automate these tasks and really reshape human roles. I think you talked about the leader. What about everybody else? Yeah. I think what we're seeing is a shift in what work is. We're moving from doing the work to designing the work and then overseeing the work of agents and robots. Jensen Huang was asked about what his future workforce plans at NVIDIA, and this is back in, I'm going to say like October, 2024. And he said, you know, we've got about 30,000 employees, which is what they had back then. They're now in the mid forties. And I expect over time to grow that to about 50,000 employees supported by a hundred million AI assistants across every group. So it shows you it's a different way about thinking about workforce planning. How are you going to expand and grow your impact by bringing in digital employees to work alongside your human employees and to use that to amplify the humans that you have? It's not a substitution thing. It's how do I increase my impact? I love that. I think that's so great. You know, we always hear, oh, my job's going away, or I don't want to tell you how to do that because an agent's going to come in and do it. I love what you said. So I think human in a loop is still very positive, right? I mean, you see that as where we're going. Yeah. And we get to oversee the work. We all become managers, whatever level in an organization you're at, however junior you are, you become a manager of machines. Right. Yeah. That's such a change in how we go to market today. Let's talk about human plus machine collaboration. You just hit on this a little bit, but I want to dig in if I can. So you spend a lot of time at Intel and I know you did a ton of advisory work with companies like BCG. I have to ask you, what have you learned about what it takes to get the human machine partnership right? So everything starts with people. Everything starts with people. I learned this when I was working as a futurist in Intel labs. My boss was a cultural anthropologist, Dr. Genevieve Bell, and she drilled that into me. So you always start from the perspective of people. One of the things I talk about in my new book, which I'm sure we'll chat about towards the end of this conversation, is that when you're thinking about use cases for AI in your organization, it starts with the role or the person. Or if it's external and you're providing new services, it starts with the customer. You have to think about the person, and then how do you use AI to help that person? Actually there's a process that I describe in my book, and there's a tool that I give people called the AI Innovation Canvas that does exactly that and helps you think through if you have a workflow, how do you intelligently divide that between the human worker and the machine? And then what are the handoffs? Because you're going to hand off between these two things, and what's the interface for that? What's the modality? Is it speech? Is it text on a screen? What is the information? What's the data that has to pass back and forth? So you always have to be thinking about the human and designing these workflows around the people, and then integrating agents, and perhaps robots as well. There's a physical component, intelligently to make it all flow smoothly. So I'm going to ask you this, and if you're able to share this, if you had to look back at everything you've done, which company or use case really recently surprised you and how quickly they scaled their AI solution the right way? And most importantly, what was different about how they did it versus others, and how did they treat their data? I'm going to pick an example that I was not involved in, because it's such a good one, and it's Starbucks. Starbucks has this wonderful app that if you're a Starbucks addict like I am, then you use this app regularly, and it's very convenient, right? You can order ahead. It's ready for you when you get there. You get points. It's great. It's easy to pay. Yes, all of that is true, but it's also Starbucks' way of gathering data on all of their customers. So they get to know you, so they can serve you better. They built an amazing AI solution called DeepBrew, and DeepBrew not only takes in all of that data from the apps of how customers behave and what they want, it also is connected to every store. It knows the inventory levels in every store. It knows how busy that store is in real time, and they use all of that data to then make fully customized offers. I'm talking about personalized, hyper-personalized to the individual. The offer that you might get, Amy, will be different than the offer I get, and it's presented in a different way. Right down to the color of the button to say, yes, I'll have that chai tea latte, whatever it is, might be blue, sorry, green for me or gold for you, because they know that you're going to respond a little bit more. When they first rolled that out, they saw in a limited supply, they saw 100% uplift in response to offers. Once they fine-tuned it, expanded it to their entire customer base, 300% increase. Just by rolling out the solution, and they did it because they were thoughtful about the data and how to find patterns in that data that allowed them to give better offers to their customers, that one, delighted their customers, and two, increased their sales. Win, win, win. Amy Quinton So what I just heard you say is what I tell everyone, that is customer experience and beating your competitors, and I love to hear that. By the way, my order is a London Fog with sugar-free vanilla. So yes. Next time I see you, we'll go for tea. Done. Amy Quinton Okay. So you've worked across startups, you've worked across government agencies, you've worked very large organizations, your time at Intel. I got to ask you about leadership behaviors. What leadership behaviors and mindset do you believe really define success in the intelligence age, specifically for virtual transformation role? Tell me, if you had to write it down perfectly, what would it be? Don It's a mindset, and it's moving through different stages. And I've worked with lots of clients, I've worked with bigger clients since I left Intel than when I was there. I worked with Bank of America, and JP Morgan, and Nike, and Disney, and so on. What I see is that you think about it in three stages. I often see leaders and they say, hey, they're so proud. They've got a corporate site-wide Microsoft license for co-pilot, and we've done AI, look at the investment we've made. And I have to tell them, I'm sorry, you're 2% of the way there. That's a good start, but it's necessary, but insufficient. So there's this phase where, as a leadership team, you're trying to enable your existing team with tools. So you go through that phase of making sure that all of the tools that you use, the fancy tools, they turbocharge with AI, and I'm sure the stuff from Informatica and Salesforce are similar. Then you get to the next phase where you're planning how do you divide the work between your human employees and your digital employees, and we talked a bit about that already. So that's where you get to about the 50% mark. The other 50%, the remaining opportunity on the table, is to make yourself an AI-first company. What that means is re-imagining not just workflows, but your entire offering, and how you go to market, and how you generate value. So most companies, the engine that drives them is people. They are, the engine is people. What you're trying to get to as an AI-first company is to make AI the engine that drives the company forward. Now that does not mean you don't need people. The opportunity by making the engine AI, you then wrap your people around that to steer and direct that to be able to 10x or 100x your impact. That's what is the real goal for leadership, and that's something that leaders are only just starting to wake up to. So I help them with that process. So let me talk to you about AI governance for a second. So this is obviously a strong suit of Informatica. Everyone has agents, everybody's deploying agents. I think what we hear a lot of our customers and prospects talk about is, hey, I have an agent from my HR supplier, I have an agent from my ETL supplier, multiple agents. For us, and I'd love to hear your opinion about this, when GDPR came out as an example, a company might get fined if data sovereignty wasn't taken into consideration. With agents, what I'm seeing is, if you don't have the guardrails around those agents, and you're letting them do whatever they want, forget about the data being good or bad, we're now talking about potential reputational damage. Have you seen this happen before? I've not seen it yet, but it's about to happen soon. That's for sure. There's so much cavalier attitude out there. And you see it with OpenClaw, right? This new agent that you can load onto a Mac mini, OpenAI just snagged the guy who created that. It's full of security holes. So not only do you have to think about the security issues, but how do you put the guardrails around it? And to your point, how do you build governance to make sure that when you have agents, if something goes sideways, you have human in the loop to be able to spot there's something going wrong and fix it. So you need to think through all this in advance. Chapter five, it's all about that. I was just going to ask you, our listeners out there want to know more about you and about your journey. Where can we find out more? Tell us about your book. I'm really excited to hear about it. So my URL is easy, stevebrown.ai. Very simple. That's my website. That's how you find me. I blog there occasionally. You can also find me on LinkedIn and I can provide a link for that. My book, I wrote a book because I kept getting asked questions by business leaders who, business leaders are busy. They're busy running their companies and they don't have the time to do what they really need to do, which is to understand AI, the different flavors, how you can use it to solve business problems, how you think about agentic AI, what are the frameworks you had to put in place? How do you innovate? What are the gotchas? So I wrote a book for them. So this is specifically designed for business leaders. It's called the AI ultimatum. You can get it at Amazon, Barnes and Noble, any good online bookstore will sell you a copy. I think the last time I looked, the hardback version is the same price on Amazon as the paperback version. So get in there quick and grab the hardback before they figure out they shouldn't be discounting it by 20%. Amazing. Amazing. Steve, thank you so much for your time today. Such an amazing conversation. So happy to have you on my show. For our listeners out there, please don't forget to hit subscribe and like, download your podcast where you get all of your podcasts and we'll see you next time. Thank you very much and have a great day. Stay tuned for more illuminating discussions. Until we meet next time, keep harnessing the power of data and AI to bring transformative outcomes to your life and business. Make sure to click subscribe so you don't miss any future episodes and tell your friends about us too. On behalf of the team here at Informatica, thank you for listening.
