Hello everyone and welcome to OPEX 26. My name is Chris and together with my colleague and co-host Pedro, we are so pleased to have you with us today. Over the next 90 minutes, we will focus on one core question that is top of mind for many leaders in operations right now. How do we move AI from promise to impact? You will hear from Ethan Mollick, who will give his perspectives on how AI actually changes work, decision making and the organizations we work in. After that, we will walk through a number of concrete cases from retail, industrial manufacturing and asset intensive operations, not theory, but real examples of what it takes to scale AI. But before we do that, let me hand over to Pedro to frame why this moment matters. Pedro Pérez, Ph.D.: Fantastic. Thank you, Chris. And welcome everybody. And thank you for joining us today. So what is different in AI and operations today than it was a few months ago? Why does it matter? And why are we talking about this? Well, behind me, you're going to see a graph. And in there, you're going to see every single model that was published within the last couple of months, or maybe a couple of years, and their performance improvement for the next model right after. What you're going to see and probably noticed is that each new model has a big jump in performance. And why should you in operations care about that? Well, there are several reasons, but one of the main ones is that every single leap that you have, there are more problems that you can solve in operations, whether that is in supply chain, in procurement, in manufacturing, in planning. Now, this opens a whole lack of possibilities for us to work with a completely different ways. Now, there's other reasons why this matters as well, Chris. And one of the main reasons for that is because every time that we've been through a technology cycle, there was a lot of companies that were basically allowed to sit and wait. The response was to do nothing and wait for the front runners to basically test the waters, create the playbooks, outsource that thinking to IT and get the solution when everything was ready. That reality is fundamentally different today. It's fundamentally different because if you wait, there are going to be the front runners that are actually trying things out and creating the right capabilities to take the next steps, learning from it, and take those things from pilots to real scale productions. So now what we see is not just one single cycle. We see a compound curve from the laggards that are being really, really left behind and the real front runners. And this distance is just completely increasing. Yeah, it's really, really interesting to see that this is where the implication becomes extremely concrete for leaders within operations. It is not a technology discussion. It's an operating model and leadership discussion. It affects how decisions are made, how the work gets organized, and how quickly we're able to turn insight into action. What we see in the leading organizations today is a very clear shift in the focus. AI suddenly is treated as part of the core operations. That means that ownership gets made very explicit. Value has an owner. Decisions have an owner. And accountability for turning insight into operational results is very clear. And importantly, organizations invest in their people. The planners, the engineers, and the leaders are all trained to work with AI insights and output to question it and to act upon it. That means that human knowledge still remains central, but it is augmented. And I think what a very interesting part of that, right, Chris, is that when all of this comes together, learning is becoming very institutionalized. Meaning that you know that a new break will come in a month or two or three, and then you basically need to catch up. That becomes a capability. That becomes something that the organizations and particularly in operations here, you need to cope over time. Yeah. And today what you see, if you fail to move ahead on this journey, you get stuck in what we often call pilot purgatory. That's the place where promising proof of concepts don't scale. Where the tools you have are disconnected from a platform and from real processes, and the energy of the technology moves a lot faster than the organization itself. The reason for this is really very simple. The technology moves so fast, but our way of viewing processes, organizations and structure means that they move a lot slower. And this gap is where a lot of AI transformation stall today. And unfortunately, impact fails to meet the expectations of the organization. Scaling AI is suddenly not about deploying the latest and greatest model because they come along all the time. It's about building the system around it, allowing for the organization to experiment. And when that system is in place, then a new model is no longer a transformation program. It's a drop in upgrade. And that's when impact to operational result becomes very tangible. And that's what we're going to be looking into today, right, Chris? But before we move on, we also have two simple tasks to you guys. The first one is that we really would like to interact with you. So when we have a poll, please answer it and let us get to see some of the stuff that you are thinking about or some of the options there. The second is be active on the chat. We're going to conduct a lot of interviews. And throughout those interviews, you have the opportunity to participate and ask the questions that you're sitting with. Absolutely. But enough from us. Let's get to the first part of the session today. We want to start off with a rather thought provoking piece from Ethan Mollick. He will hone in on how AI actually changes work organizations, but in practice. He is one of the voices that is shaping how leaders think about AI today and focusing on the very concrete efforts that sort of exist within the collaboration between humans and AI when you want to scale. And these perspectives are exactly where we want to begin the journey of this session. Without further ado, Ethan Mollick.