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Digital Transformation & AI for Humans
Welcome to 'Digital Transformation & AI for Humans' with Emi.
In this podcast, we delve into how technology intersects with leadership, innovation, and most importantly, the human spirit.
Each episode features visionary leaders from different countries who understand that at the heart of success is the human touch—nurturing a winning mindset, fostering emotional intelligence, soft skills, and building resilient teams.
Subscribe and stay tuned for more episodes.
Visit https://digitaltransformation4humans.com/ for more information.
Digital Transformation & AI for Humans
Innovation & AI: Proven Strategies & Amazon Best Practices to Transform Ideas into Game-Changing Opportunities
In this episode we dive into the Innovation & Artificial Intelligence and uncover Proven Strategies & Amazon Best Practices to Transform Ideas into Game-Changing Opportunities with Amir Elion, an AI adoption and strategy advisor, Generative AI expert, innovation teacher, author, speaker, and CEO of Think Big Leaders from Israel, now based in Stockholm. Amir brings a wealth of experience from both startups and global corporations, along with valuable insights and inspiration from his former senior leadership role in Amazon’s (AWS) global innovation program.
Amir shares powerful growth insights from his journey at Amazon and innovative startups, offering valuable strategies that drive customer-centric product-led growth and innovation.
🔑 Key Topics 👇
- Insights from an ex-Amazon leader on AI-driven innovation
- Proven strategies for turning ideas into game-changing opportunities
- Overcoming barriers to innovation: lessons from Amazon & beyond
- Best practices for fostering a culture of continuous innovation
- How to integrate AI for scalable and effective execution
- Common misconceptions about AI in business innovation
- Measuring success: Key metrics for AI-driven innovation
- Future AI trends shaping the next decade of business growth
Connect with Amir Elion on LinkedIn
Learn more about Think Big Leaders: https://www.thinkbigleaders.com/
💯 If you enjoyed this episode, don't forget to subscribe, rate, and share it!
About the host, Emi Olausson Fourounjieva
With over 20 years in IT, digital transformation, business growth & leadership, Emi specializes in turning challenges into opportunities for business expansion and personal well-being.
Her contributions have shaped success stories across the corporations and individuals, from driving digital growth, managing resources and leading teams in big companies to empowering leaders to unlock their inner power and succeed in this era of transformation.
📚 Get your AI Leadership Compass: Unlocking Business Growth & Innovation 🧭 The Definitive Guide for Leaders & Business Owners to Adapt & Thrive in the Age of AI & Digital Transformation: https://www.amazon.com/dp/B0DNBJ92RP
📆 Book a free Strategy Call with Emi
🔗 Connect with Emi Olausson Fourounjieva on LinkedIn
🌏 Learn more: https://digitaltransformation4humans.com/
📧 Subscribe to the newsletter on LinkedIn: Transformation for Leaders
🔔 Subscribe and stay tuned for more episodes
Hello and welcome to Digital Transformation and AI for Humans with your host, amy. In this podcast, we delve into how technology intersects with leadership, innovation and, most importantly, the human spirit. Each episode features visionary leaders who understand that at the heart of success is the human touch nurturing a winning mindset, fostering emotional intelligence and building resilient teams. In today's episode, we explore how to think big about innovation, people, technology and impact, together with my fantastic guest, amir Elion, from Israel. Living in Stockholm, amir is AI adoption and strategy advisor, generative AI expert, innovation teacher, author and speaker. He is also ex-Amazon innovation program lead and business owner CEO of Think Big Leaders. Let's dive into the topic of innovation and AI and explore proven strategies and Amazon best practices to help transform ideas into game-changing opportunities. Hello Amir, it's such a pleasure to have you. How are you?
Speaker 2:I'm very well, I'm very happy to be with you here, Amir, and I think it's amazing what you're doing connecting digital transformation, AI and humans and I think when we first met, it was very great to see someone who kind of connects with me as well on the emotional level as well as the rational level, so maybe we can bring those two things together in this conversation.
Speaker 1:Thank you so much for your kind words. It warms up my heart and I can say exactly the same. I've got the same impression about you and that's why, actually, we are having this conversation today. It is a very important topic and let's create this magic together. Let's start the conversation and transform not just our technologies but our ways of thinking and leading. If you are interested in connecting or collaborating, you can find more information in the description, or collaborating, you can find more information in the description. Subscribe and stay tuned for more episodes. Amir, to start with, could you please share a few words about yourself, your journey?
Speaker 2:your background and why you chose to work with this exciting topic of AI and innovation. Yeah, so, you know, reflecting on my career, I would say there's several things you can identify that are kind of red threads throughout that career. I've always been curious about innovation. So I've, you know, I've had maybe a hundred books about innovation at home and I've read them and I've also used many of them for theologies and always curious about, you know, doing innovation better, making it in a systematic, repetitive way. And even, you know, when I had roles which were not innovation, I always tried to bring that into the role. So I led some innovation initiatives internally and so on. The other aspect of maybe my career is around technology, right, because I've always been curious about technology in trying to play with things by myself and build all sorts of things and, you know, learn new technologies as they come out. So that's kind of my curiosity around technology. And the third is around, you could maybe say, people and business. So those kind of pillars about innovation, technology and people in business have been throughout my career. And you know, it's no matter if I was like in a small startup and I led the product there. I always brought these kind of pillars into my role and then, when I was also in, say, training roles in motorola and teva pharmaceuticals, I always tried to innovate and and look at how we can solve things with technology right and and with new ways of doing it, but still listening to people. So this is why I really love the theme of your podcast, because it connects very much with many things I've done in my career.
Speaker 2:Maybe you know, jumping to the more recent years, so I moved here to the Nordic, to Stockholm, almost four years ago. I led the innovation program for Amazon Web Services and I shared how I think, how Amazon thinks about innovation with Nordic customers and learning from them as well about their great businesses and their great way of doing things. And then, a few months ago last year, I decided it was time to try it out on my own and I'm still trying to help people, leaders, companies with, I would say, with the things that I mentioned that I take with me throughout my career with innovation, with technology and AI and with people in business. Specifically with AI, I was doing things with AI, with customers before, all kinds of projects and building great stuff for their customers. Like many others, when ChatGPT came out.
Speaker 2:I was very curious about new intelligence that we can play with, and then I tried to think can I use it to the things that I just mentioned? You know, the innovation that I'm doing, the thinking about technology. And then I used ChatGPT at the time to create my own innovation buddy, if you will, or innovation co-pilot, and I taught it all those methods from those hundred books that I learned and I had it help me the way that I do it with customers or with groups or with teams. So that is when I got really curious about AI and since then I keep exploring it. I keep trying things out, discovering new things and share them, you know, on social media, with customers, in workshops, in, you know, when I write stuff. So that's my way of learning and sharing what I learn with others.
Speaker 1:I know that what you are doing is so valuable and it's truly amazing to see how many initiatives you are supporting in such a powerful way. And, by the way, it was a great serendipity to meet you at that event oriented on building AI agents and seeing all the other leaders from big corporations working with AI, searching for the new applications and the ways of creating those success stories in using artificial intelligence. But coming back to your journey at Amazon, can you share a little bit more about it and how your role there shaped your approach to innovation and artificial intelligence?
Speaker 2:to innovation and artificial intelligence, yeah, so I would say it kind of made me have more clarity on principles or things that I never articulated the way that Amazon articulates them in a very clear way. So the first one is customer obsession. This is kind of Amazon's mission to be customer obsessed, to be the world's most customer-centric company, and you can be cynical about it or you can be critical about it, but I think it's a really true intention and for me it's always been like that. But I never put it in so many words. So I think that putting it in words right and Amazon does it in a very powerful way for its leadership principles. And then it's not just words on the wall or the training that you do on the first week of the job, it's really there in every interaction and every internal meeting and every time that you do things with customers. So there's now, I think, 16 leadership principles.
Speaker 2:Customer obsession is one of them. There's others that I really sympathized with and felt they expressed many of the things that I did along my career. So you know, learn and be curious is one of them and I just mentioned right, I love learning, I love trying things myself. So learn and be curious is another one. And ownership. You know, taking ownership of things and being responsible for making sure things are great when they're out there. There's other think big, right, I named my company based on this kind of borrowed with pride from this principle Think big about the future and about people and about what we can do. So this is one thing that I take from Amazon.
Speaker 2:You need to articulate your principles and you need to sit down and think. So if you're building your own culture right now, I would urge you to think about those principles. What are those principles that drive you every day? Write them down, but then don't stop there. Keep talking about them, keep using them every day to say, you know, invoke them in discussions, right? I think now we need to think big. Or I think now we actually need bias for action. Because this customer is in trouble, we need to act quickly, even if it's a bit of a risk, right? Or, in other cases, maybe we need to take some time and dive deep, which is another leadership principle, right? So it's not that those leadership principles, you have to hold them, all of them, all of the time. They need to be a basis for your way of thinking. So of the time, they need to be a basis for your way of thinking. So make sure what are those principles that drive you? Make them flexible so you can invoke them when you need them, and then let people use them and encourage them and teach them and demonstrate how to use them in a smart way and have continuous discussions on them.
Speaker 2:So that's one thing that I really learned very strongly at Amazon. I think he did it very well the power of culture, how to express it and how to foster it within teams. And I think the other thing, which is often said but much harder to do, is see failure as a learning opportunity, embrace it and even encourage it, because I actually and, by the way, talking about Amazon, I encourage people to read the annual Jeff Bezos letter to shareholders, because it captures a lot of Amazon's culture and a lot of the things that I'm kind of mentioning here shortly are explained there and demonstrated. But in one of those letters he talks about failure, and actually, when I was speaking with customers and when I was trying to explain this, I would suggest that, instead of calling it failure, call it an experiment. We did an experiment, we learned something from it, the assumptions that we came into that experiment proved to be wrong and now from that experiment we can learn for the next experiment.
Speaker 2:For me life is a continuous experiment and some of them succeed and some of them are learning opportunities. There are never failures right, so they're either a success or even successes are learning opportunities, but there are always learning opportunities. So I hope that was kind of captured. Some of the things that I see are the strongest learnings for me from being at Amazon five years.
Speaker 1:Thanks for sharing. It definitely captures a lot from your experience at Amazon and I agree. Actually, we learn so much more from those lessons which weren't the most successful experiences in our life, and they help us become so much better in many ways in order to create the next common success stories on the next level. So I love what you just shared with us. Let's take it a little bit deeper. What were some of the best practices you worked with, or maybe even implemented, at amazon and other companies you've been working with to foster a culture of continuous innovation and creativity?
Speaker 2:yeah, so here I actually like to to think of it. You know people, right. Your, your podcast is all about humans, right, and about what drives people, and and here I actually use a framework which is not from Amazon, I actually have been using it for many years and you know Daniel Pink talks about you know, motivation and drive. You know, if you haven't read his book Drive, highly recommend it. And he talks about and this is kind of science-based and definitely things that I've seen working myself, also in practice. There's three things that drive humans it's autonomy, it's mastery and it's purpose. Right, and again I'm quoting, I'm building on his shoulders and other people's other great experts in this.
Speaker 2:So give people autonomy to make decisions and decide how they do things. Maybe you need to talk about what you should do and you know the goals and things like that, but then how they do things. Maybe you need to talk about what you should do and the goals and things like that, but then how to do it, which approach to use, which tools to pick right and when to do what. Give them the autonomy and the confidence and the security to do so. It comes back to what we talked about failure and feeling safe to try things out. It comes back to what we talked about, you know, failure and feeling safe to try things out. So autonomy is one thing. Mastery is allow them to reach mastery. So, obviously, skills and training, and, you know, coaching and mentoring, whatever they need to grow, but then let them express this mastery, give them opportunities to show what they've built, because then you'll have super fast innovation, because otherwise they'll be. You know, I just need to do the minimum. I just need to, you know, maybe reach my goals, my KPIs, and that's it. But if they feel like masters and have place to grow and express that mastery, your organization, your products, your services will be the masterful ones, right, and then purpose is, of course, connecting it to a higher goal, starting from the team level but then growing, of course, for the entire organization or society. You know, why are we doing all this things? You know and talk about this. Don't take it for granted. It's not enough that it's in your slogan, it's not enough that it's kind of once a year, talked about, this should be. Why are we doing this? You should always come back to this question.
Speaker 2:So you know, I've been in one customer which I worked with. It was a pharmaceutical company Not me as an employee, but I went as AWS and we did some innovation workshop. I love the fact they brought actually a patient to speak in their workshop and tell them how their products have changed their lives. So this is bringing the purpose of what and these were kind of data science, people and AI people right. So for them it's a bit they don't see those patients, they don't see the impact that they do in their work. You should bring them the opportunity to see the purpose of what they're doing. So pharmaceutical is maybe one story, but if you're doing things you know, other things you know. Find the purpose of whatever you're doing and make it vivid to people. So autonomy, mastery and purpose, I would say, are maybe one framework to look at things.
Speaker 1:I absolutely love it and you know this gives me exactly the same vibes of inspiration as when I'm listening to those TED Talks. So I have that feeling and I hope our listeners and viewers are feeling the same. It's really inspiring and truly valuable. Feeling the same. It's really inspiring and truly valuable. What are your recommendations on how to overcome common barriers to innovation, such as resistance to change or resource constraints? My podcast episode there is such an episode around resistance as a resource in change management and it is the most popular episode and I see that people really care about it and think about how to turn that resistance into an opportunity and into something what can fuel their growth. So what do you think about it?
Speaker 2:yeah. So know, what I think leaders need to understand or maybe they have it in the back of their mind but they never really thought about it is. This comes back to human psychology and you know, change is a big word, right, but it's basically about changing habits. Even you know, for myself, when I wanted to change, I wasn't happy with some things that I was doing personally about my own life, my health, my eating habits, whatever. And even if I really wanted to change it, it's very hard Because it's a habit. That's the way we are humans. And it's even more difficult when it's part of a team, or even harder when it's kind of an organizational habit. That is why change is hard and you need to help yourself and you need to help others with that change. And one of the ways to do it and again there's research about it and I'm not, you know, inventing the wheel here is you need to start by kind of artificially not artificial intelligence, but artificially creating kind of a starting to drive the wheel right. And the way to do it at scale is what, again, Amazon calls mechanisms, but you could call it processes or things like that in other areas. But basically because you understand that habits are hard to change or, in other words, intentions are hard to act upon. I get up in the morning, I want to eat better, I want to do more sports, or if, in the case of organization, we want to do things that are not just, you know, putting out fires for today, but also more long-term thinking, or whatever the the thing we want to change is, or we want to start to use ai, but you know it's hard because it's not in our daily habits. What you need to put in place is mechanisms, and those mechanisms force you to take on new habits, right, and you need to learn those new skills. And again, in the case of Amazon, they have multiple mechanisms. I'll just give you one example that maybe makes things clearer.
Speaker 2:When Amazon was looking into customer service, some people were complaining about products and again, customer service people would answer them. They would really want to help them and in some cases, it was problems about the product. As much as you're trying to do the best, there's always quality issues, and then they would report this problem, but because of every day, these would not be solved quickly enough or not be promoted to a high priority. So that was a problem. Now, how do you change it? If you just go and tell people, put this on high priority, it will not help. Maybe one day, maybe two days it will work, but it is not a mechanism. So Amazon built a mechanism which was called the Andon Cord, and this is kind of taking from the Toyota original Andon Cord for lean manufacturing that forces people to take care of this.
Speaker 2:So whenever there is a problem with a product, there's two things that happen. First of all, that product is automatically made unavailable to buy again. So this triggers everybody who needs to take care of this into motion. Right, the product manager can no longer ignore the fact that there's problems with their product. And of course, you know you can't do it at scale without some control. So the other part is automation, so that customer service representative, if they decide that this is a serious enough matter they've seen this happen again and again with this product they click a button.
Speaker 2:This also goes to what we talked about before right, Autonomy. They have the autonomy to make that decision. This is important enough. I don't want this to ever happen again to another customer. I'm going to take this product down. They don't need to call anybody, they don't need to email. That automatically triggers a chain of events that forces people to think differently and act differently, and with time this becomes a habit. This is okay to happen, because this keeps a continuous improvement. This is exactly what it's about. I don't know if this is a roundabout way to answer your question maybe but hopefully it gives some insights.
Speaker 1:Of course it's great as an answer. I'm thinking about those game changers. Could you tell us about a particular success story from your time at Amazon where an idea turned into a game changing opportunity?
Speaker 2:Yeah, there are many, right, the good thing about that is like it's almost every day that that happens, but I, you know I'm trying to think of something, so I can mention one customer this is a public story so I can share it and one customer I worked with was kone, the finnish elevator and escalator manufacturer, and the idea. It took some time to build it. We kind of built a co-innovation program with KONE and AWS and I had the pleasure of working several times with different teams at KONE, with different business units, different products and sharing kind of working backwards, writing press releases which is Amazon's way of expressing the vision about what you want to do and frequently asked questions, documents, so basically having really thorough discussions on the vision and the details before you build anything. And through that kind of repeated work with Kone's teams, we were able to launch several products.
Speaker 2:One worth mentioning a digital twin of a metro station. Kone had several hundred thousand devices connected to the cloud with sensors and things like that, but if you add cameras and digital signage to it, basically you can help people flow through that station in a much better way through digital and AI. And Indithcate was also kind of digital twins as well and what I loved most is, after I've done this three, four times with Kone, I could almost feel like they were taking on that mechanism on themselves. They could write better PR FAQs than I could or at least from some Amazonians, right so it felt like I could really make an impact and we really connected well. So I really loved working with them and I was at an event where their head of innovation spoke of what they're continuing to do, and that was for me. It was a pleasure to see that. They took it with them, they took it forward and it's still, you know, making a big impact.
Speaker 1:So that was one very big story, which I'm happy about. That sounds so impressive. Thank you for sharing. What case strategies do you recommend for integrating AI into the innovation process to ensure ideas are effectively executed and scaled?
Speaker 2:You should think about different ways of using AI. One is your own processes. How can you boost your processes with AI? Because there's too much data out there, right, we get so much information. A lot of it is unstructured. We lose a lot of insights and anecdotes through that data, and we are forced because we have systems and we work in organizations right, and we need to communicate and we need to make decisions and allocate resources. There's still waste in those processes. There's things that we're not using in the most effective way.
Speaker 2:So one way would be how can I use AI to augment myself and other people on the team, other roles in that innovation and product process? Right, and you know, of course, you need to go into the details and I have a framework that I share with customers and we go through analyzing these processes to boost that productivity. So that's one way. The other way is how can you inject AI capabilities into your products so that you actually help your customers or your users become more effective or happier or have less friction in their experiences, or be happier and feel better about their day through the help of AI? So I'd say these are the two immediate paths that I would recommend looking into right two immediate paths that I would recommend looking into right. One is productivity, with the help of AI for your internal processes, and the second is value creation for customers in their journeys and experiences.
Speaker 1:Great approach. I totally agree with that. What are some common misconceptions leaders have about integrating AI into their innovation strategies and how do you think they can address them?
Speaker 2:Yeah.
Speaker 2:So I think I see three things. The first one is waiting. They're waiting. They're saying, okay, we're not ready, we don't have the resources, we don't have the time, the regulation is still unclear, right?
Speaker 2:I think that's one big mistake or misconception you can't wait, even if the end result will be we've looked into this, it doesn't work for us, or we shouldn't do this, don't wait, okay. And I think the other misconception and that definitely comes from gap of knowledge is fear. They fear ai. A lot of them right what it will do, what it will mean to to our products, to myself, to my role, to my team. So they kind of dismiss it, as I said, kind of either wait or decide not to do things without really looking into it.
Speaker 2:So one of the things that I'm trying to do is I'm trying to share very openly, you know, the good things about AI, the problems with AI, maybe some ways to think about it so that drive people to actually start and do things right and choose things and select what to do. And I think the third thing even if you start, you get over that fear. You're not waiting anymore. The third misconception I see, or mistake that I see, is not thinking big enough.
Speaker 2:There's a reason I called my company Think Big Leaders because I think leaders should think big, and AI is that every few decades we get this general purpose technology that changes or has the potential to change and influence, impact everything, and I think AI is such a case. This is an opportunity to think big about what impact you can have. And just worth mentioning together with some friends and Fernanda Torre, who is great, she leads a company called Next Agency invited me to co-facilitate a program which is called Sustainability Driven Innovation. I'm delivering the AI and digitalization module in that program, and what I encourage the participants of the program is think big about sustainability problems and how can you make big impact with the help of AI, because, in essence, in many cases, sustainability is a data problem. Or it starts or it can be viewed as a data problem, and there's many things you can solve if you look at the data and make the right choices and invest the resources and the changes in the places. Again, fear waiting and not thinking big enough.
Speaker 1:Sounds so good. However, I think this technology is very different from all the previous revolutionary technologies we've seen through the history, because this technology is going to impact not only the world we're living in around us, but also us as human beings, on the level of who we are and how we navigate and how we think, and even the capabilities we have in our bodies, which is so groundbreaking, and it creates both amazing opportunities but also requires some attention around how we are going to develop those technologies. Did it happen to you that an AI initiative didn't go as planned? What lessons were learned and how did it influence future projects?
Speaker 2:Yeah, I mean, if I said no, then it would be ridiculous. One of the things you do need to realize that it is going to fail sometimes. That's the nature of the technology. It's not algorithmic, and because we don't have best practices yet, we are writing them as we're going right, and the technology keeps evolving so quickly, so it's very hard to keep track as well, right? So definitely I fail. I'll tell you the story in a minute, but I think also not just my failure. Be ready, for this. Failure is going to come. It's part of the game. So, and then of course, you need to do it in such a responsible way that that, whatever that failure is, you learn from it, it doesn't ruin people's trust and it doesn't cause negative impact on anybody. At least you minimize it. You have some guardrails around it.
Speaker 2:So the story is I was helping a customer build a chatbot with AI and then retrieval augmented generation. They had a lot of knowledge from their history about how to help customers learn that topic. I suggested one approach to solve it. First, because AI is very much related to data, I suggested that we restructure the data for that chatbot to retrieve and that kind of makes the answers more accurate right. One of the problems is, you know, hallucination, inaccuracy and maybe not finding the right answers. So I was trying to help AI find the right answers by structuring the data, and I followed some advice from some experts that I listened to and tried, and so we spent a lot of time rewriting a lot of unstructured material that they had and structuring it and then feeding it to the chatbot. But then what happened? Is it actually lost? Because of the in-depth translation from unstructured to structured data, we lost a lot of the information. So the answers were much poorer than knowledge that we started with.
Speaker 2:And then we pivoted after it took a few weeks, and then I did some tests. We did some tests with users and we weren't happy with the answers. So we pivoted and we restarted. We were smart enough. The customer was smart enough also to walk back with me and say, okay, we're going to throw aside all the things that we did with the data before. It was hard work, it took some time and so on, but it doesn't work. There's no point in continuing in that direction.
Speaker 2:And they were smart enough to realize and accept that. And we use the unstructured data as the basis for the solution, and it was so much better. We played around with the prompts, with the temperature, with a very different approach than let's structure the data first and let's help AI. We didn't need to help AI Technology. This is another thing you need to realize. Those capabilities are very different. It's not like a database. It's not like the regular search that we're used to. So you need to be ready to try new ways of doing things, because you're going to discover things that work in a way that you didn't expect. So I think an interesting story of how AI taught me not to overcomplicate things.
Speaker 1:Actually, it is a very relevant story. I loved it and it is applicable not only to the case you just shared, but as a strategy for the future, because now, with the reasoning capabilities of the latest models, we can see that the less guidelines you are giving can see that the less guidelines you are giving, the less you are limiting with your own prompts, your own approach and instructions, the better results you might get. So it is actually a completely new approach of working and collaborating with all those AI solutions where they can think and help you in a completely different way, on a new level, which is amazing. How do you measure success of AI-driven innovation initiatives and what metrics are most effective? This is a difficult question.
Speaker 2:Yeah. So there are the obvious ones that you know some people are talking about and worthwhile mentioning. You know, accuracy, you want to have accuracy, and it also depends on the task, because for some tasks, you want more creativity rather than accuracy. If it's like a creative writing task or an advertising image, maybe you want more creativity. But at least decide where are you on that axis between accuracy and creativity, right? How much freedom do you want to give to that AI? So you know, define that level of accuracy versus creativity that you want and see that you're getting close to what you wanted to achieve. So that's one thing. The other thing I would say, the other things that keep coming up, are level of control. Do we have the right amount of this depends also on how critical is the application that you're using. If it's something that's going to influence people's lives, maybe you want to give some more control to humans. If it's something maybe not as critical and the result maybe just a bit disappointing, then maybe it's okay to give away control with, you know, the benefit being, you know, speed, cost scalability, because we kind of delegate it to the machine and then cost is often forgotten, or I would say it doesn't show up until you scale. So measure it early and you can do things around cost. You can choose a different model you can play around with. How much data do you use right? Do you need real time? Do you need just every now and again and all kinds of things that you can do to get the cost down. And this is important when you scale. It depends again on the nature of the product. If you're going to just serve a few people, then maybe cost is okay. It's the value of each iteration. Each output is strong, so it's worthwhile the cost. But if it's small things but at scale, then cost matters. So these are kind of the key parameters or some of the parameters that are specific to AI that you need to think about.
Speaker 2:But because this is a general purpose technology, because there's so many different ways to use it, I would say for each product, for each initiative, for each team, you need to define specific APIs. There are no one size fits all here. You will have to decide for this project what are the key KPIs, and they should always kind of be rooted in users, customers and business. How do these projects? Well, I can be amazed. Wow, what writing does it do? Blah, blah blah, but if it brings no value, if it doesn't help people, then it might be a curiosity for a few days, but then people will never come back to it. You know these applications that you have on your phone. You get excited, use it once, use it twice and then, after one year, oh what? Why did I download this one? You don't want to be there. You want to be influencing KPIs, things that people care about daily and every time that they use the thing.
Speaker 1:That makes so much sense and summarizes everything the leader should keep in mind. It's a great guideline and actually a playbook for those who are introducing AI initiatives on how to evaluate their results as well. Amir, looking ahead, what future applications of AI do you believe will be transformative for innovation in the next five to ten years? I understand it's impossible to know it for sure, but I want your opinion on it.
Speaker 2:I will never risk predicting 5 to 10 years with the speed of technology now, If anybody asks you if you need to make a plan now for 5 years about how we are going to do AI, I would push back. So I'm going to push back on the 5 to 10 years. I might risk the next two or three years a bit, but even that is a risk with the speed of things.
Speaker 1:Okay, let's renegotiate and take it two to three years from now. How do you see it? What is your vision?
Speaker 2:Yeah, so you know, coming back to the point that generative AI in general, AI agent in particular, coming back to the point that generative AI in general, ai agent in particular, means that we have now intelligence as a commodity. We can pay a few cents or even fractions of cents and we get intelligent results for at least some of the tasks that we are used to be done by humans, right? That's going to have far-reaching implications for products specifically and, as I said, it's going to impact the speed of innovation. Just to give a few examples, you can now move from an idea to a prototype in a few hours, if not minutes, and to maybe like a production-ready product in a few days or weeks. So you don't need a big team for it.
Speaker 2:I'm playing around with this all the time. You know I come up with all kinds of ideas too many and now I've started to build them. I'm not a programmer myself. I don't trust myself to build really anything, but now I have tools at my hand that can help me design, first of all, plan this right. So I talk to Claude, I talk to ChatGPT just help me push myself as well and get more structure about the idea and decide where to start and so on, and then I use other tools. Or again, claude is already there and we already have programming agents that are there to help us to build a prototype, and I play with it and I give it to some friends and I get feedback and I can do that in a few hours or a few days. That's what I meant.
Speaker 2:You know, talking about agile and speed, and the feedback loops are going to be so much faster and so much augmented by AI that we need to think differently about how we do things. That's one thing that I think will change tremendously. The speed will be 10 times, 100 times faster. We can very quickly decide, based on data and based on acceptance, what works, and then I think we're going to have opportunities and maybe that's a bit more towards the three years that you're kind of pushing.
Speaker 2:We're going to see some barriers breaking between maybe industries, between departments, a whole rethinking of the way we do things, because, because of this kind of capability is just general purpose technology, hopefully we're going to be able to connect material science with r&d and production and engineering with rolling things out to production and supply chains and logistics. So hopefully we're going to see some things and robotics we haven't even spoken about. You know robotics and things like that. Hopefully we will see more interdisciplinary things happening. We have multi-models now, multi-model AI capabilities, and that's going to grow and accelerate, and I think it's going to allow us to discover new ways of connecting disciplines and creating products that we've never thought were possible.
Speaker 1:It resonates so much with my own view on this topic and actually you know, I'm developing the AI Game Changers Club, which exactly has the vision of gathering under the same roof experts and leaders from all over the world, the multidisciplinary background, so that we can have that enriched conversation and apply each other's best practices and experience in order to move faster and become more creative and more powerful, more impactful in what we are doing and building and innovating.
Speaker 2:So it is. I've read the idea of the club and everybody should join it.
Speaker 1:You are very welcome to join it, for sure. And just to wrap up this amazing conversation, I feel like keeping talking about it for another few days. It is so exciting and valuable. What advice would you give to leaders aiming to harness AI for driving innovation and creating new opportunities within their organizations?
Speaker 2:I would suggest, first of all, start with the right questions. Don't start with the technology. I mean, sometimes when I have a conversation with somebody, or a customer or an executive, they paint the picture for me, they say this is the situation and they go okay, how do we solve this with AI? And in some cases, well, I don't think it's an AI problem. So the question should not be how do we implement AI? Or the question should be what are we trying to solve or do people need this help? Is AI going to help us and if it is, in what respect? That will also guide you where do you want to use AI? Right? In some cases you still want to keep the control in the hands of the humans or not even introduce AI, because it doesn't make sense, it's not responsible, it's not cost-effective or it's not. There's no scale there, so there's no point in doing AI. In other cases, you know you should maybe automate everything to AI, delegate all the major decisions maybe only in rare cases to AI, because then you can achieve, you know you can reach more people. Think about education, the amazing things you can do in education with AI if you do it the right way. But in some cases it's kind of a mix, right. You have a human in the loop, they are involved. So ask the right questions Is it a good case to use AI In? Is it a good case to use AI? In what respect will we use AI and what will be the role of humans in that kind of combination? That's one big advice.
Speaker 2:And the other thing that came through from my previous answer is get people experimenting. Give them the safe environment, sandboxes, the hackathons, the experts that come and help, the workshops that help them get started, because then the ideas will start to flow, but also they will be more aware of the risk, the stupid thing that I did before right, or that didn't work. They will learn it through their own experiences, right. So you will have a better prepared workforce to address those opportunities, to make the right decisions, because they've tried things themselves. It's not enough to hear Amir talk about it or Emi give advice or come to the club. They need to try themselves right. So give them those opportunities to experiment. Don't just make it about writing songs or poems. Make it things that are related to the business. Bring real challenges. So if it works, it actually shows value quickly and you can quickly convert it or even scale it, or even if not, then at least they worked on something that is not, you know, just trivial.
Speaker 2:But even the last piece of advice is, even when you're doing just kind of this experimentation, and when you're even starting to ask the questions, think about scale early on. If this succeeds, how are we going to scale? Are we going all in on this? How are we going to communicate this to stakeholders, to customers? Do we have the right infrastructure? Do we have the right budget? Because all too often, I see a great pilot and a lot of companies are doing pilots now, right, they're doing pilots because they want to try, because there's a buzz, because the board is demanding or whatnot, but then they don't realize that to scale, it's another phase, right?
Speaker 2:So think about scale early on. How are you going to scale? Are you going to do this into your regular workflows and your regular R&D or regular product offerings, or is it going to be a separate product? Which teams are going to manage this and drive this forward? What kind of business model do you build around it? So, start thinking about this early on. Maybe write an Amazon-style press release about it. So it helps you think big very early. Be very strict or very clear about the vision, but be very flexible about the details. So have a very strong vision. Understand where do you want to get and why, and and what do you want to do, but how are you going to do it? That's going to change. That's going to be influenced by the things that you discover along the way. So that would be my advice.
Speaker 1:I absolutely love it. I just saw in front of me a picture, a visualization of everything you summed up as a swimming pool. And if you decided to learn how to swim, don't forget to add some water into your swimming pool. And then about scaling. Once there is a little bit of water and you already figured out how to do it, don't forget to scale up and add even more water. And then you, exactly. And then moving to the ocean.
Speaker 1:It is amazing. Amir, thank you so much for sharing all this with us today, so appreciated, and it's been a great pleasure to have this conversation with you.
Speaker 2:The pleasure was mine, thank you.
Speaker 1:Thank you for joining us on Digital Transformation and AI for Humans. I'm Amy and it was enriching to share this time with you. Remember, the core of any transformation lies in our human nature how we think, feel and connect with others. It is about enhancing our emotional intelligence, embracing a winning mindset and leading with empathy and insight. Subscribe and stay tuned for more episodes where we uncover the latest trends in digital business and explore the human side of technology and leadership. Until next time, keep nurturing your mind, fostering your connections and leading with heart.
