Data, Decisions, And Digital Transformation With AI In 2026 | AI Strategy Webinar | Simplilearn

Here I see some introductions that are starting to come in. Um Okay, I think we have Riti from Pune. I'm not sure if I'm getting your name right. Do let us know your name as well. Hi Sailesh, thank you for joining us from London. Hi Anand, thank you for joining us from London again. Richard from London. And someone else from India, but I don't have your name. Okay. Hi Rami, you're joining us from Liverpool. Here's someone Muhammad from Chicago. Hi Gopal from India. Great. Amazing. We are also live on LinkedIn and YouTube. So once again, a very very warm welcome to all of us watching us there. Do let us know in the comments your name and where you're joining us from. We just want everyone to be as active and as engaging as possible because we have like a great session planned with amazing speakers. So please do stay tuned. Um right. So before we begin, we have a couple of very quick ground rules that we request everyone to follow. All the questions that you have, please use the Q&A box to post any questions that you have. Please don't use the chat box because as you can see, um already there there will be a lot of pings coming in. So we want to ensure that we're not missing up any important question or you know, observation from you. So please do use the Q&A box effectively. If you want our expert to answer something directly, please do post your question in the Q&A box. We will be doing a Q&A segment towards the end of the webinar. So we will try to address as many queries as possible. And if you would like a recording of the session or this presentation, please do stay tuned till the end. We will be emailing you the materials for all those who stay with us through the end of the session. Um right. So give me just a moment. I will be Quickly, um So if you guys can let me know, are you joining Simplilearn webinar for the first time or have you been with us before? We'd love to know where everyone stands. So just let us know in the chat if it's your first time, you can just type first time and send in the message. Okay, there's someone who is joining you again. Thank you Riti for um being part of the webinar again. Okay, there's someone here for the second time, someone here for the first time. Great. Okay. Um so allow me to quickly introduce Simplilearn. This will not take more than a minute. At Simplilearn, the core focus is on helping professionals stay relevant in an environment where skills and expectations are evolving so quickly. We work with millions of learners globally across industries. And increasingly, you know, what we're seeing is that the biggest challenge is not access to information, but actually knowing what to do with it. This is especially true in the context of AI. Many professionals today are exposed to so many AI tools, dashboards, and outputs, but translating that into decisions, into strategy, and organizational change is where the real complexity lies. And this is exactly the gap that sessions like this and Simplilearn aim to address. So to address this gap effectively, collaboration becomes important. So Simplilearn partners with leading universities and institutions globally to bring not just content, but structured thinking and research-driven perspectives into learning. What we're really, you know, proud of is um the outcomes that we've been able to showcase. Our learners report an average salary hike of 50% and they've rated us 4.8 on 5 overall. This is like, you know, a direct and powerful outcome from people who've gone through structured and credentialed upskilling in high-demand fields exactly like what we're also going to be covering today. So with that, we'll move straight to the session. So yes, if we look at the broader landscape, you know, a few patterns are quite clear. AI adoption is accelerating rapidly across industries. Organizations are investing significantly in data infrastructure, machine learning systems, and automation. And at the same time, expectations from leaders are also changing. These there is an increasing expectation that decisions will be data-informed, that strategies will incorporate AI, and that, you know, risks associated with technology will be understood and managed. However, alongside this whole progress is also less visible trend. Despite more data and more advanced tools, many leaders report lower confidence in decision-making. The volume of information is increased, but clarity has not, you know, kept its pace. So this creates an interesting tension, one that many professionals in this audience may, you know, likely be experiencing as well. So the question is how to engage with it in a way that leads to better outcomes. Um so to explore that question and a lot more, we will, you know, be covering a lot of topics in today's session. We will begin by looking at, you know, what the AI era is actually demanding from leaders beyond technical understanding. We will then explore how these capabilities can be developed and what distinguishes effective AI leadership from surface-level familiarity. Finally, we will take a closer look at a specific program designed to build these capabilities in a structured way. We will also, you know, leave time at the end for your questions, which I encourage you to start sharing as we go along. Um and yes, so before we move into the first segment, let me introduce our guest speakers. Um it is my absolute pleasure to introduce Professor Daniel Armanios. Professor Armanios is the BT Professor and Chair of Major Program Management at Said Business School, University of Oxford. Um his work brings together engineering, organizational systems, and leadership, particularly in the context of how complex technologies are adopted and governed within organizations. Professor, it is an absolute pleasure to have you here today. Thank you for joining us. Um is there anything you'd like to add on to that introduction? You can also say your hi to um a lot of the participants that we have here. No, nothing there. This is great. Looking forward to the conversation. Okay, and yes, we also have Professor Seth Flaxman. He's an associate professor at the Department of Computer Science and a tutorial fellow of Jesus College at the University of Oxford. His work focuses on scalable methods for subtle I am so sorry that I might not be able to pronounce this. Statistics and machine learning with applications in public policy and social science. He has extensive experience in computational statistical modeling and investigates the regulation and societal impact of machine learning algorithms. Professor Seth, it's again like, you know, honor to have you here. Very excited for this conversation as well. Thank you for being here. Anything like that you'd like to add on? Just looking forward to hearing from you and talking more. Thanks so much. Okay. Great. Okay, so to begin with, let us focus on what we're calling, you know, the leadership imperative. For a long time, conversations around AI till now have focused on capabilities, what the technology can do, how it works, where it can be applied. But what we're increasingly realizing is that the more important question is what it requires from leaders. Because the presence of AI in an organization changes not just like the processes, but also decision-making, accountability, and risk. Um so this slide, you know, presents three questions that in many ways, you know, capture the essence of the challenge. They're simple on the surface, but like, you know, quite deep when you start to unpack them. So I would request everyone listening to think about these in the context of your organization. Um and I have a question for Professor Daniel. So how do you think, you know, the role of a senior leader has evolved in response to AI adoption over the past years? Yeah, I think I think with AI, there's a couple of things that need to be tweaked when we when we think about how to use AI relative to other digital evolutions. So the tendency of leaders is to outsource a lot of the technology strategy to the CTO, the technology function. And because of the level of ambiguity leaders face, they're going to have to increasingly be involved in training the algorithms themselves, being involved with the experimentation. It's something we talk about quite extensively in the in the in our online class that Seth and I are doing. So there's a lot more need for experimentation. There's a lot more need also to fact-check or to discern or interpret outputs coming from junior employees. So what's happening now is a lot of junior employees are using these algorithms to develop all sorts of things. And because they're so human-like in the output, it's much harder to discern issues. So it's requiring a huge amount of time on leaders to actually now do a lot of detection and fact-checking and purification and sanitization of the outputs to make sure they have a lot of their relevance. So, experimenting up the what I see the best leaders do is experimenting with AI upfront allows for these issues later around validation to be much easier because they're helping their junior employees and others understand what it is they're trying to look for and bringing their professional acumen. So, I think those are the kind of things The last point I'll make is there's a lot of promise as well because we do know from prior studies that actually this is a counterintuitive finding, but more data builds your intuition which allows you to make faster decisions. So, if you do that upfront investment in understanding how to interpret AI models, how to develop things more experimentally with much more direct usage as opposed to just outsourcing and delegating, I think you're going to get a lot more benefits in the long run. Thank you for that. Um, so moving on to the next slide. Um, so this idea of you know a confidence gap that we saw data points on a couple of slides ago is particularly important. So, many leaders today are required to make decisions based on systems they may not fully understand or data they cannot fully integrate. So, this is a structural challenge created by the pace of technological change. So, Professor Seth, my question here is what does effective leadership look like in the AI era? Like how can leaders build confidence in decision-making without becoming overly dependent on AI systems as well? Okay, I'm going to start this one, but if my colleague Daniel wants to come in, by all means, he knows a lot about this as well. So, I think that the AI illusion that's written down here, deploying AI tools is not the same as developing AI judgment, is a very important one. We don't know all of your backgrounds, but I am sure you are all operating in industries where everyone's talking about AI. AI is a really exciting thing. And yet no one has the full picture, and I think we can't lose sight of that. Always there are known unknowns, and I think that usually we have some reasonable handle on the known unknowns, although that becomes a little fuzzy with AI's confidence. And there's unknown unknowns, and that's where danger, risk, of course also opportunity lies. AI will always give you an answer. It will never say, "I don't know what I don't know." But you as the human, as a leader, should own that, should take that on. And so, I think it's really important that you don't lose sight of your own humanity while appreciating all of the new opportunities that AI presents. I think that perfectly sets context for the next slide here. Many professionals today are becoming familiar with AI concepts. However, familiarity does not automatically translate into leadership capability. So, leading with AI requires a different set of skills, perhaps particularly around judgment, prioritization, and communication. So, Professor Daniel, what are some distinct skills or capabilities that differentiates differentiates someone who knows AI systems and tools and someone who can lead with it? Yeah, I think the what's happening now, and this touches on what Seth just mentioned, is a recognition that AI is not one thing, and a very clear recognition that they understand enough about the models to know what kind of data it's useful for and what data it's not. So, very basic example, we're all using large language models, generative AI more broadly in the umbrella. Those are particularly useful for sequences of text, right? How to craft better emails, how to deal with essays, and the like. If you're trying to give it a math problem, it's getting better and it's evolving, but it's trying to read math like text. So, there's all sorts of very well-known mistakes that it does with giving very basic algebra or calculations because it doesn't understand how to think of math symbolically. There's other other models that do that. For example, if you want to use images, then you use different kinds of convolutional neural networks. So, I think the the things that really stand out are those who are really careful about what data works for what models and are asking the right questions and in prompting the right kinds of problems as opposed to just saying, "Give me this." and getting results. So, it's those who can really walk through how how do they know when the models are struggling with something, what kind of data is appropriate, and I think there's ways to do that by just starting with first principles. What are the right questions to ask to which models? And if that can be conveyed, that's important. Now, I think people are hoping they can algorithmically see that in CVs and such. I think you can see aspects of it, but a lot of that's going to have to come through an interview. I think there's going to be more interviews now saying, "Here's a problem. I want you to walk me through, guide me through a set of tools you use, show me the prompts." And walking through maybe 10, 15 minutes as part of the interview, "Walk me through how you use these. Are you asking the right problems? Do you understand what it is we do as an organization and how do you translate that knowledge into the right prompts and the right aspects." And so, I think the second thing will be those with a lot of translational skills. Those who can take either a professional industry and then translate it to data terminology or vice versa, data scientists who know how to ask the right kind of professional questions outside of that context. So, to summarize, clear understanding of assumptions of data to the model, and then secondly, translational skills that can communicate across these now boundaries that are no longer siloed. Good. Perfect. Thank you for that. So, we have like we have spent some time understanding the challenge, the growing gap between AI capability and leadership readiness. So, the next question is how do you actually build that gap? Because for most professionals, you know, it's not about going back to school full-time or becoming technical experts. It's about building the ability to operate more effectively in in in environments where AI is already present, right? So, in this segment we are going to focus on how structured learning from Oxford online programs can help move from awareness to capability. Right. So, these are you know, in this slide you will see some areas where professionals already have partial exposure, but not necessarily a structured way to bring them together. Like you know, have from having data to making better decisions, from knowing about AI to exercising judgment or judgment with it. And so, this is where the Oxford online programs aim to create a more coherent approach. My question here for Professor Seth, how are like you know, learners guided from understanding the concepts to like you know, making better decisions or leading an organization or enterprise with with AI? Okay, thanks so much. So, this is critical to what we're what we've put together, what we what we hope you'll you'll join us in. So, we've designed the program around decision-making, thinking about decision-making in an organization. It's not just, of course we try to give you this as well, conceptual understanding of different AI, machine learning tools, but around how they fit into the strategy for an organization and how leaders should think about them. And that is developed through reflection, right? It's not we can just tell you how to do it. You need to develop those skills in an interactive way. So, we have a big emphasis on applying the concepts that we're talking about, that you all are dealing with day-to-day, but giving you a chance to reflect on them in real organizational context to develop your judgment through scenarios, through guiding you through applications, focusing on bridging strategy and executing that in the settings that you care about. So, you're [clears throat] going to bring to it your own expertise, your own experience, and we're going to be challenging you to think at the next level reflectively about how you can turn that into real leadership. And we really again want to emphasize that we're encouraging you to reflect on your own organizational challenges. This isn't at a high level, this is not theoretical, this is what you care about, and then inspiring you to use the framework that we're developing to make that work for you. That's exciting. Okay. So, at this stage I think like you know, it will be great to also understand how the Oxford online programs are positioned differently from many other offerings in this space. As everyone would know, there's no shortage of AI courses and certifications and content available today. However, not all of them are designed for the same purpose and not all of them are at like you know, same standard. So, Professor Daniel, if you can comment here, it would be great. From your perspective, what is like you know, the most meaningful differences between what are some most meaningful differences between Oxford online programs and more conventional AI or executive education programs? Yeah, I think I think it can be summarized in two points. One is that most of the work the um AI kind of executive education, even other curriculum, are focused starting and centering on the algorithm. What is the algorithms do, the different forms of algorithms, the assumptions they make, how you can use them with agents, not, etc. And that's very important. And we do discuss that, but our starting point is different. Instead of starting with the algorithm, we start with you as individuals, stakeholders, and organizations around the algorithm. We're focused on the context in which the algorithm is placed. And that's how we center how we think through this. So, that's a very key point. Our our approach is much more kind of outside-in than inside-out. So, inside being the algorithm, outside being organization, we start with your context so that it makes sense why you're bringing it in. Because you may come away saying, "I thought that this problem could work with algorithms. Actually, it's not good for this, and that this is why." That is equally important to you is to know when not to use these things versus when to do them. When to worry about missing out versus the joy of missing out, right? So, you have to think about this. The second one is the way we think about the scaffolding. So, when I think of good micro-credential programs is that think of it like learning a language. You start with some basic building blocks, and as you build it, we can do more complicated things. So, the way this program starts and the way I I recommend even anyone using this is when I'm starting to learn how to use an algorithm for the first time. If I don't know how it is, it seems like a black box. Is to start with taking some data I know the answer to. Take, for example, public data that you're that is okay to be shared on an algorithm, let's say around a contract template, where you know that if I fit it into this template, I should get this answer, and see where the algorithms differentiate. Why is it not understanding this? Oh, it doesn't understand this context. Here's how I'm going to have to add to it. You start with things where you know the answer to it, so you can unpack the assumptions. Then we come in and say, "Well, what happens when you put that in a team context? What happens when you put that with your stakeholders?" And then you can progressively go through that, and hopefully from that, you start seeing different pathways for where the algorithms can help you in this way. But we start with very basic like, "Here's what these algorithms kind of do. Try some data yourself. Try some contexts to see whether it's giving you the answer expected, why or why not." And so, to summarize, much more organization, you as as the first starting point as opposed to the algorithm, and secondly, trying to build scaffolding that tries to build that progressively throughout the three the the three modules in the course. Okay. Great. Thank you for that overview. Um moving on to the next aspect to consider um is the institutional context behind uh these programs. Uh the Oxford online programs are anchored in Said Business School, which brings together the broader University of Oxford ecosystem, as well. Uh Professor Seth, uh what advantages does uh being part of the Oxford ecosystem bring to uh learners beyond the curriculum itself? Yeah, so I think the important thing to know about is what we call research-led teaching. So, the teaching that we do, all of the instruction that we do, comes from the research that we do. So, uh at the beginning um uh there was some jargon that was uh hard to pronounce, but I work on spatiotemporal statistics, spatial statistics, and time series. And this is something that I do with partners in a lot of different industries and indeed in the public sector, looking at things like forecasting, forecasting demand across fields like health care, across energy. All of those are for me applications and areas to develop new machine learning and AI methods. And so, when I'm teaching you about things like uncertainty, I'm teaching you from the research that I do, and that's true for everyone. That that's that's how Oxford works, and that's I think what makes the teaching that we've put together special because it comes out of the cutting-edge research that we do with our other hat on. Perfect. Thank you so much, Professor Seth. I just noticed that my um slide deck is not moving on to the next one. Just give me a moment. I'm going to stop the screen share, and I'll restart this so that we can move forward. Okay. Yes. Yeah, this was the slide that I was meant to share. Great. moving on. Um so, this slide, like you know, outlines exactly how the program is structured. Um you know, it starts from understanding and diagnosis moving into deployment frameworks, and then into um application. Um so, this reflects how real-world implementation also tends to work. Um Professor Daniel, I think you've covered this a little bit already, but it would be great if you can, like you know, further uh reiterate how these modules are structured um and uh you know, how they how they are built on each other to create a coherent learning journey. Yeah, sure. So, we start with kind of putting, as we said, the algorithm within the organizational team stakeholder context. And so, we're spending there trying to understand what are the different tensions and gaps and paradoxes, and we talk about some very particular organizational ones that are at their core. Once we set up the gaps, the strategic organizational challenges, we then start saying, "How can you systematically kind of experiment through those tools based on the kind of team you have, um the assumptions you're making from the task to what you want the goal to be?" And that's in the the Cypher model. And then finally, with um the guidance through Seth, you then try to actually prototype and build something that you can start actually start testing and deploying in work. Um so, I'm looking at some of the questions some people are asking about, "Will by the end you know how to differentiate all the hundreds of different tools?" I think that's obviously ongoing and evolving, but I think what you'll come away with is a set of categories by which to classify these tools and then see within that. I think it's easier in this world to classify, you know, the idea of large language models or what they call, you know, tokenizer sequence of text models versus convolutional neural networks, etc. Those kind of classifications will help you kind of think about where to go into the future. But just to kind of and you'll see that through the course in terms of the um organizational other frameworks brought into it. So, the first is just characterizing the landscape strategically and organizationally in terms of the different tensions, how then to look at your particular team and so forth to develop a blueprint for how you're going to try to experiment and deploy that, and then the third one is you actually go and do some experiments. And we provide you some of the tools and scaffolding to to do that throughout the Okay. Perfect. Um we also see some questions that have come in. Uh please do keep Just a reminder for all of you watching us whether on social media or live in the webinar, please do drop your questions in the Q&A box, and we will try to get to them very soon in uh the Q&A segment towards the end of the webinar. Okay. Uh great. Moving on. Uh so, uh one of the key aspects of any program is how much, you know, it is how much of it is applied. Uh so, this slide outlines the types of projects participants can will work on. Uh the projects are designed to ensure that learners move beyond theoretical understanding into um you know, practical and tangible outputs. Uh Professor Seth, can you please elaborate on how the projects work as part of this program? What level of feedback and mentoring and faculty from faculties involved in the project work? Anything that you would like to add here? So, so unfortunately, I think your slides haven't gone again. So, it could be that you need to stop sharing, and I think actually I need to refer to someone else for this one. Uh I unfortunately am not totally up to speed on exactly how [clears throat] all of our feedback is going to work. So, if there's someone else in this call who knows the answer to this question, don't hesitate to jump in. Sorry. Okay. Give me a moment. I think I'm having some network issues at my end. Just a second, and we should be Okay. Uh great. Um so, okay. Uh these are basically the projects that we have as part of the program. Um there is an AI blueprint uh project, and then there is an implementation project, and then also a prototype uh project um that you know, will be um part of uh the program. Uh we can move on to the next slide, I believe, where you know, we can discuss the outcomes uh from uh the program. So, ultimately, you know, learners will evaluate the program based on what they're able to do differently after completing it. Uh so, this slide uh outlines all the outcomes that one can expect. Uh Professor Daniel, what are some um short-term or like immediate uh outcomes that learners can expect after completing this program? I mean, there's there's um kind of uh three things I think first. One is first, characterizing the kind of fundamental organizational strategic challenges that come in. The second one is through the Cypher framework, you're going to literally be able to take any take a task, not any, but a task you're thinking about with AI, and walk it through what are the assumptions I'm making on that task, What is the task trying to do? It's outcomes. What are the stakeholders and teams I need to have around it? And you can start thinking about what we call like an AI blueprint. What are the kinds of skills I need, the kind of team I need to be involved on that task? And then thirdly, you're going to come up with some prototypes that you'll build to starting start with. And I think as part of the course there'll be peer-to-peer interaction with regards to feedback, live some potentially some live sessions and others to help you do that. So essentially what you'll come away with is a general understanding and kind of strategic understanding of what's going on and a framework to do that with gaps and paradoxes. You'll then have a Cypher board that we say or Cypher framework you use to kind of look at taking the task and being able to populate where what's the task, the assumptions and their goals, the team stakeholder around it. The third one is build a prototypes. And then a fourth one which I didn't mention is a two by we help you develop a matrix to think about based on the task, what is desirable to be automated and what is feasible. And you start mapping this out as a way to facilitate how you discuss this with your team and also with the data scientists in your organization. Because there may be things you really want to get automated but can't be or the more the more problematic issue organizationally is things that you want automated but no one wants automated. Then you're going to be spending much of your time dealing with resistance to the algorithms as opposed to actually using them in ways people want. So we're going to give you these kind of artifacts and tools to use that and you will have some prototypes that you'll already started iterating or experimenting with I think would be some of the tangible things that come from this. Perfect. Thank you. And right, what we have is an important comparison. As mentioned earlier, there are many AI programs available but they're all they're not all designed for the same purpose. This slide sort of highlights the distinction between programs that focus on technical understanding and those that focus on organizational readiness and leadership. Professor Seth, I leave it to you if you can you know you can come in here or if you feel we have already covered this we can move on as well. But my question is Yes. My question is what is like the most critical gap in you know the most in in most AI programs available today? How does this you know organizing for AI program stand different according to you? So so two things that are different. So one is from my colleague Professor Amanios talking about the organizational context and making you think about deployment of AI and what that means for your organization rather than just jumping to here's this really cool shiny new AI model. And then two, what I bring in what we haven't talked about yet is prototyping, fast prototyping using AI to give you and your team something to talk about. It's not for building something deployable. It's for your again strategy, team and organizational processes. Perfect. Thank you. Right. Moving on, so here we have we've mapped out like you know some skills and qualities that are useful to have to you know be part of the program. So you know as you look into this it is it is important to think of this less in terms of job titles and more in terms of qualities. So Professor Daniel, it'd be great if you can take us through you know the ideal qualities to have as a learner for this program. Do participants need a technical background to benefit the most from the program? This is like one of the most frequently asked question that we get from a lot of learners. Do they need to come from like a tech background to enroll in such a program? Yeah, I would say I would say our objective with this is to make you literate in using the AI but not to make you like a leading edge data scientist. That's not the point of this. The point of actually is to build your competency and your confidence to then know where to use AI or how to write ask the right questions. So you don't need massive amounts of technical background to run this. We try to give you as much scaffolding as possible and linkages so you don't need to have kind of huge technical background. Um and I'm seeing some of the question and answer from some about um three things recruiters look for in resumes, you know, roadmaps, et cetera. I think as I've mentioned before and to reiterate um is that being able to show that you understand the assumptions across different AI tools, which is what we're going to focus on in the class. Also um kind of understanding the translational skills you need between data scientists and your profession is another thing. And I think also in terms of roadmap and this is to deal with the technical grounding is we start by giving you this is why we intentionally start by giving the organizational lay and then within that showing you a primer on kind of AI, how to work it and how it doesn't work base and letting you kind of experiment with different organizational context to try that. My advice with starting this class is with any others, start using an algorithm with known data, a clear contract template you know how it should be filled out, a clear customer survey form, an email you know the template should be and see how does it align or not so you can get a sense of the assumptions. And once you understand the model assumptions better, the algorithm assumptions better, you'll be then able to build scaffolding that allows you to say well what if I did this complex thing versus that. So these are the kind of things that you'll come through. Um so the prerequisites are very minimal. The idea is to help you come in even with not knowing that much. Just you need to have that in a growth mindset. There may be things that you're confronted with that you don't necessarily understand. Don't worry about it. Don't stress. Just think about how do I take what I can from this and evolve and build it and we try to build the scaffolding in this way. So interest in a growth mindset, a desire to get literate in the technology. You don't have to be the most proficient ever to do it and that can help you kind of go through that in my in my view. Perfect. Thank you so much Professor. So in addition to this organizing for AI program, there are also other offerings designed to address different aspects of AI and leadership. This allows all the professionals here to choose based on their specific needs and areas of focus. So I'm just going to spend like just a you know a minute to take you all quickly through the other programs as well. Right. So first we have AI and business analytics. This is for leaders who you know who are looking to take more decisions based on data. The idea here is to connect business strategy with more hands-on AI application and to make you know evidence-based decision without like you know knowing a lot of technicalities or coding. This is a 12-week program. The mode is flexible online learning and the program investment fee is also mentioned here. The second one that we have is cyber resilient digital transformation. This is for leaders who are looking to protect and safeguard what they build. In this there will be a lot of explanation around digital disruption, emerging tech and also cyber risks through case studies and framework. Frameworks. So this is also the idea here is to build leadership skills to communicate technology risk. Again like I mentioned for the first program, this is also for 12 weeks and the mode of learning will be flexible online. Moving on, the third program that we want to quickly talk about is strategic analysis and decision making with AI. This is for leaders who are looking to sharpen their strategic thinking skills with artificial intelligence. So this program looks to integrate AI into strategic frameworks work with AI. So it enables people to work with AI as a thinking partner so that they can make more defensible decisions, faster decisions. This will also be a 12-week flexible online learning program and the program fee is mentioned here. Okay. Okay. So now that that is covered um we would also like to quickly brief everyone on the details of the admission and the way forward. We have discussed one program in focus and then three other programs as well. So for all of these programs we'll just quickly take you through the admission process. Right. So for those of you who are interested in exploring this further, in this slide we have outlined the process of admission. It is designed to be straightforward while ensuring that participants are aligned with the program's objectives. So step one is to submit your application. You can apply for the program by completing a simple online application form. Secondly, there will be a program fee stage like you have to pay the program fee to secure your seat and thirdly you can start learning once your cohort begins. I will move on to the benefits, exciting benefits of the program. First, completing the course will earn you a prestigious certificate from the Said Business School at the University of Oxford. You'll also become part of the Oxford Said alumni network, joining a vibrant community of over 50,000 professionals from around the globe. Moreover, the curriculum is crafted by the Oxford faculty, ensuring you receive world-class education and insights. Finally, you'll have the chance to engage with a diverse cohort of experienced professionals. So, this is like a global classroom setting. Well, this will enrich your learning experience by offering diverse perspectives and industry insights. These benefits will advance your career, yes, but also, like, you know, enhance your professional network and personal growth as well. Professors, if there's anything that you would like to add in your, please, let me know, or else I'll move on to the next slide. Great, just give me a second. Here we have the program investment details. For learners from India, the program we're specifically talking about the Organizing for AI program. It is priced at 249,990 or 11,193 rupees per month if you want to pay in the installment options. For learners from the USA, the program is priced at $3,390 or $339 per month. And for learners from the UK, it is priced at 2,490 pounds or 249 pounds per month in the installment option. The cost is different for one of the programs that we discussed about, and all of the details can be explored in the in our page that I will be sharing just a minute. We have a question on how long the program is for. It is all the programs that we have talked about is for 12 weeks. So, just give me a second. Okay. I'm going to share in this chat box the link to access the Organizing for AI program. You can scan this QR code as well that you see on the screen. You'll be able to download the curriculum there, book a call with our advisor, and and learn a lot more about the program. So, with that, launching a poll to take your interest in enrolling in the program. All you have to do is click on yes, or if you need more time, then you can click on that option. If you're interested in any of the programs we spoke about today, you can click on yes in the poll that I launched right now. In case if you're not able to access the poll, which might be possible if you're joining us on your phone or you know, without the Zoom app, then in that case, you can scan the QR code or go on to the link that I shared and book a call with our expert here. Even if you are a little unsure, you want to know more details about the program, you need to talk to someone about this, you can click on yes. Our expert advisor will be getting in touch with you in the next 24 hours to discuss with you and guide you forward. Great. So, while we have that poll live, we can take up some questions now. I'm going to a a couple of Yeah, I can I can do some questions already. I've already answered some type, but I think they were important. Someone asked about whether we get into physical AI. We do to a degree. So, with Cypher, there's two key trends. One is called projection, and one is called haptic. The tension we get into there, and this is where Cypher's important is that there's a tension between what AI can digitally project, AI-enabled tools can digitally project like digital twins, mixed reality, and the like, versus what is actually manufactured on site. And so, there's a tension about how do you project physical into digital? Do you want it to look like exactly like it is on the ground so you can actually improve the way you do quality assurance and control, or do you want to help you think differently? And we get into this especially with cases around virtual reality-enabled kind of things. The other one is around robotics. It turns out still to this day one of the key issues is haptics. So, the problem is is that robotics still cannot get really, really haptic and tactile with very intricate hand movements. And so, if you don't place in the the AI-enabled robotics in the right part of the workflow, you can get really into some issues of bottlenecking and adding a lot of stress to the human in the loop. So, we talk about bookending your robotics AI-enabled robotics in your workflow in your manufacturing workflow versus putting in the middle, where it's going to put a lot of constraint on the humans in the in the loop. There's another really important question around AI and intellectual property. It's a very good question. If you're dealing with very sensitive information, there are algorithms that allow for enterprise kind of firewalled options like OpenAI and others. The problem though is is that in order to access the public version of these these these algorithms, is you need to approve you need to give consent, and not everyone really understands what they're consenting to. So, I've seen really kind of problematic issues where they separate the firewalled aspects versus the more things that are using public data. And people, you know, how many of you read the agree to terms and conditions on your on your websites? Well, in this case, when you don't always do that, you're allowing these algorithms to potentially use the data. So, my advice often, if you're dealing with a very, very sensitive data, is to do what they call on-premises. You you create your own closed-door kind of network. There are downloadable models from Hugging Face, for example, that you'll allow you to do it on just your data and do it locally, but the costs operational costs increase drastically. So, I'd only use that if this is really going to become core sensitive information, and also that you're going to be getting value from scale. But if you want to experiment, try with your public data first, and just play with the small things before you take this more sensitive data. It's just a couple of things I would think about, and and we touch a little bit on that in this course. I mean, I think those are two questions I could find from from here. And then another person asked about is this the only program in AI? No, there's obviously other ones. You're asking They were asking about C-level work. I think my view for this class is that this is very conducive to those in the C-level who don't have time to get into the weeds of every aspect, but it'll give them really a compass and blueprint to decide when do they want to intervene and experiment with it in ways that are organizationally the most high-end impactful and relevant. So, I think this is very attuned for any level of leadership, whether it's managing a project all the way to C-level. And in fact, this work comes out of other learnings and practitioner-based research and other things we've done with several executive-level groups from major corporations. So, this is part of that that this definitely is conducive to that, and there's more of those kind of sorts Perfect. Thank you so much, Professor. I think you've answered pretty much all the questions that have come our way. One that I see one more that I see in the Q&A box is this. This is regarding AI tools and models. So, by the time you finish the course, there will be hundreds of additional tools and models. So, how do we learn to stay up to date? Seth, I think I answered one of these before, but I I would love your take on this if you if not to put you on the spot. No, no, it's a great question, and I think that what I'd push back is that yes, of course, you need to stay up to date, and and you all you're you're here, but you're doing that in your jobs. What we're trying to give you is a higher-level understanding of the questions you need to be asking as you're deciding how to spend your time trying to stay up to date. It's impossible, right? There's a new model every day, but the core underlying questions that you need to ask in an organizational context, the core underlying ways that you need to validate what you're doing to work together as a team, those aren't changing. New models mean new opportunities and new risks, and so it's more a matter, I think, of taking a step back. Of course, you want to go deep, and that's why you're here, and you're all curious, but we want to give you the tools to not to step off of the rat race for a minute and reflect. Yeah, precisely. I would just say that the key question that I think Seth is trying to get you to think and what we've tried to do is what kind of data do you think you're going to be frequently using? Because everyone is assuming AI is one thing. It's become so specialized now that there's entire disciplines of computer science and engineering that are focused on one particular algorithm. So, what I would spend time thinking about is what kind of data I am. If I'm using text data, then maybe I want to focus on tokenized models like large language models or others. Am I using visual data like gridded data? Then I might focus on the bucket of convolutional neural networks. We try to give you a compass to figure out the based on the right data. Is it network data? Is it longitudinal data? Which kind of bucket or category of algorithms to spend your time on? But you can't do that until you figure out what is it precisely the data I'm using. And then secondly, if I'm going to look at across two different data sources, then maybe I'm thinking about how I'm going to use agentic AI. And there's other things coming out with this as well. Um there was one more question about how do you see digital transformation impacting jobs in the next 5 to 10 years? I think um I would say it's um there's going to be two kind of moments. I think one is going to be those who can develop the algorithms and so forth, which is fine. But I think where you're really going to see how it's impacting jobs is there's going to be a lot more I think demand for translational skills. Those who understand enough about the algorithms that they can then apply it to their profession, whether it's nursing, engineering, psychology, um human resources, etc. The those who understand um the data science really really well, but the challenge with the data science terminology is often quite distance from professional language. So, if you're a data scientist that can really build a a proficiency of understanding how do I take this structured unstructured data and translate it into law, translate it into health. I think those skills are going to be massively in demand. I think also there's going to be a lot more time spent on how do you quickly detect problems as opposed to necessarily solving all of them because it's so human-like now. The issue is that you're going to be dealing with risks. The question is how quickly you can mitigate them because something will go wrong when you're dealing with such complexity. And so building heuristics, tools, the ability to validate quality assured output, I think it's going to become really really uh big. Great. Perfect. I think that has answered almost all of the questions. Uh I do see a couple of questions about uh the presentation and uh the link to replay this webinar. Um once again, let me tell uh that everyone who stayed till the end of the session will be getting a recording of the webinar as well as this uh deck on email within the next uh 24 hours. Um so, yes, I think uh that is about it. Um any closing remarks uh Professor any uh last um you know, line of advice for our learners before we close the session? Yeah, just growth mindset and I was excited to see that there's some high schoolers even on. This is really lovely to see. Um really just keep at it building your growth mindsets and and I think it may look really overwhelming. Just start with known data, start with this model step-by-step, build the scaffolding, but don't forget you in the loop. Don't just worry about the algorithm. Thanks so much for joining everyone. Yes. All right. Thank you so much everyone. Um for uh if anyone has any follow-up questions, please do reach out to us at uh [email protected]. and our advisors will be reaching out to all of you to um uh discuss further and especially to anyone who expressed interest in the program. Um uh Professor Seth and Professor Daniel, thank you so much for being here once again and sharing your uh insights and guidance to all our learners. Um it it was great hosting you. Thank you