10808_AI_Bussiness_School_EXT_FINAL_v3

[AI Business School] >> Norm: In talking to customers around the world, different industries, different sizes, different geographies, there's one common question that comes up, which was, "How do I start?" "Where do I start?" And looking at all that information, looking at all those customers of different sizes, we've synthesized this back into an AI maturity model, which is a framework to do the analysis and more deeply understand where you are as a customer today and how do you get to the next step? This model is a phenomenal tool really is an incredible tool for you to understand, as a company, your level of maturity, Where you should be successful, where you need to develop, what are the kinds of applications you should be developing? It talks about organizational model, it talks about governance, but the most important lesson for us is the four pillars that the model is built on, where only one of them is about technology and the other three are about strategy, organization and culture. It's about engagement from business leadership and really understanding how AI can actually help transform a business and either transform a process or in fact transform to new businesses of new types. It's a phenomenal tool to understand and benchmark where you are and what are the things you need to do next. How to build your organization, what roles that you need, what readiness you need, what are the characterizations of the applications that you're running at those different levels? And if you understand what level you're on, what are the next things that you have to do? And so we step back and we look at, well, what are the pillars of this model that we're trying to look at, and we identified four key pillars. One is about capability. That's really about the technical content. The technology, the capabilities of the engines, the IT architecture to support and operate it. But what you quickly see is that there are actually three other pillars about strategy, culture and organization, and when we see companies being successful, it's where they're excelling not only in the technical capability, but they're excelling in these other areas of strategy over all. Of organization, of talent management, of governance, of testing and learning, of agility, to actually take a customer-centered view of your data. A core element of this would be linkage to the business strategy. Can we actually take the underlying business hypothesis and tie that to the work that's being done? When we look at these four pillars, we can then build the core model around AI maturity, which starts at the bottom and which is foundational, which is customers either not using AI or questioning, "Can AI even help me?" Where they might have tried something and ended up at a point of disappointment. They typically will have a low level of digitization, a low level of data-driven decision making. They probably are running some BI, but it's much more around the reporting than it is deep analytics. When you move up to the next step, these are the folks who try. These are the folks who are attempting to do things. They probably built some bots already, they've learned about what a bot should do, what a bot shouldn't do. The notion that a bot actually has persona is a reflection of your brand, of your company, and it's not just a simple chit-chat, asking how to change your password or what's my balance. But parallel with that, we find that these are companies that are actually starting to digitize their process as well. The third tier, what we call aspirational, these are companies that are being successful. They've experimented with AI, and they're starting to build AI into the core of their processes; in fact, they probably are changing processes because of what they've they've able to do with AI. They also have a pretty deep data culture, but much more than data, they're starting to build knowledge of their customers, their services, their products, maybe even their employees, and really trying to understand this notion of moving from a data-driven business into a knowledge-driven business. And at the high end, it's just a small number of companies that are very mature in what they're doing. They have core capabilities around machine learning, around business understanding. They probably have a central organization that does strategy and governance and then within each one of their business units have a team of excellent engineers, data scientists, building solutions. But one of the observations that we've made is building that community from the central governance organization into the silos, that turns out to be the important step. It's not that you just have this distributed model, but you have this community. Because what you learn in one side is valuable to the other side. But more interestingly, what we start to see is data actually evolving and being developed in one silo can be incredibly useful to another organization. If it's between retail banking and commercial banking, or it's in a manufacturing organization between supply chain, the actual manufacturing, and defect detection. So being able to navigate across the data of those silos, that's when we start to see this degree of maturity that we really would expect. In sort of interesting ways. So taking this core model, we then went back and looked at some data that we'd got from 270 countries in Europe. And we looked at their behaviors against the model. We found this amazing distribution. What you'll find is about 30% of the companies sit at that lower tier, which is that struggling to understand how AI can help. The flip side of that is about 4% are in the super mature. And those tend to be banks or manufacturing companies of some kind, and in this clustering in the middle, of another two-thirds that actually is either approaching or aspirational. The question is, how do you move along? What are the things that you should be doing to take you from one tier to the next? How do you actually build the capability? What are the behaviors that we should see? What are the examples of systems that we're producing? And so what we're trying to do today is to actually develop tools to remove the friction at that lower end so that you actually can adopt SaaS-based solutions for customer care, for example, that allow companies to actually build a bot or actually get into a call center and to quickly be able to have an AI application up and running and use that application to augment human ingenuity to make people better. [Where do I start?] So one of the key observations that we see to answer this question of, "Where do I start?" is, who's engaged? And one of the things that we find is that in any company, if there is air cover, conversation and participation from senior leadership, it could be the CEO, it could be the sales leader, the marketing leader, the operations leader. When they're engaged, that's when we start to see greater success. They're engaged when we start to see a business hypothesis being proposed. In other words, How can AI help me understand internal? How can AI help me understand customer demographics or understand a new product offering? That starts with business-level engagement. [Start writing your AI manifesto today] Every company, no matter who you are, needs to develop an AI manifesto, which starts at your senior management and comes all the way through the businesses and technology. This manifesto is an articulation about what you believe about AI, what you will do with AI, how you will use the data, what you will do, and more importantly, what you won't do. And every company needs to have this framed in some way before they start moving up that curve too fast. That is one of the gateways, but more importantly, is actually to take the step to evaluate your maturity. Evaluate where you are and what are the next steps that you can take. This all comes back, in reality, to this basic definition of AI. Responsible AI to augment and amplify human ingenuity. AI is about making people better and making the systems that people use better, and to put humans at the center of AI. Thank you.