Speaker 1: Are you drowning in data, but starving for insights? Engineers at this insurance company walk their CEO, Mona, through a highly secure data center, telling her that the data stored in here is 18 times more than what the Library of Congress holds. Yet, Mona is frustrated. Her direct reports tell her that they're not able to extract much information from this data to make day-to-day strategic decisions. All this expense to store and manage data seems useless. Digital companies of the future will be competing on a number of fronts – technology, business model, business strategy, capabilities, people, products, and data. Companies are collecting data by the truckload, and the rate of data growth is exponential. But we have a problem. New companies are exploiting it for business value. Suddenly companies are finding that their traditional way of doing business is not working. New technology startups are smashing existing business models and stealing customers away. Today, customers are demanding more and can get upset if you don't meet their expectations. They are comparing your company to the Amazons and Apples of the world. Financial stability is collapsing, replaced by speed and agility. The hard fact of life is that sometimes companies will not be able to change. What happened to Blockbuster and Borders? What will happen to yours? Things look good now, but how about the future? What if changing the way you operate is the wrong thing to do? If you decide to change, how will you propel your company forward to adapt to the new world? You report to MONA, and insurance has been a laggard on catching up with technology. You think you should use AI or artificial intelligence and machine learning to understand your customers' buying patterns better and offer them the right suggestions at the right time. AI machine learning will use the data to apply its models and algorithms, which in turn will generate great insights, which you can use to make great business decisions. With these, you can then understand what happened in the past and predict the future. If you create the framework to do this, you will be a star in your company. You also get bombarded by many vendors hawking their products and services. There are too many choices. Who can be your ideal partner in this journey? What should they bring to the table? It's all so confusing. So you decide to bring in one of the big consulting firms to help you figure out a strategy. Over a half a dozen consultants spend six weeks at your company working in a makeshift conference room. When you had the chance to peek in, you see that it looks like your teenager's room. Stuff strewn all over the floor, whiteboards filled with notes, drawings, arrows and boxes and circles of priority and a whole bunch of stuff. You figure they must be working hard. In the final week, there's a share out of their findings. You and your colleagues assemble in a big conference room, curious to hear the results. The slideshow is excruciating to sit through. Some are even getting bored. Most are checking their phones. What? You just spent millions of dollars and nobody's listening? But in spite of the drudgery, the bottom line seems to be this. The consultants find that your company is losing and will be lagging far behind your competition pretty soon. The only way to leap forward is to transform the organization. You're not sure what that means, but to the consultant company, it means hire more of us so we can study this further. They suggest process changes, new system implementations, cultural change management, reorganization and a few other things. You give them their next contract. Now, a whole floor is taken over. There are a lot of young faces and it looks more like a college campus. The energy level seems high, lights are on late at night. About six months into the one-year project, leaders want to take stock. The consultants have blown through 80% of your budget. The water cooler talks are not uplifting anymore. The mood is more somber. You don't see tangible change. Something is not going well. A group of your leaders happen to go to the AI conference in Las Vegas and they return with a common message. Machine learning is becoming a commodity. Anyone can do machine learning because companies are putting the algorithms in a black box and offering it as a service. What you have to provide is the right data. That data is uniquely yours. Suddenly you realize that you've been doing all this wrong. You think that you need to shift focus away from setting up a machine learning environment to bring more care and nurturing to the data that will give you the competitive advantage. It's all sunk cost, but you decide to bite the bullet and fire the consultants. It hasn't produced much. Instead, you decide to seek help for organizing your data. You've heard about the small firm in Chicago that might be able to help. With less than 20% of your budget remaining, you have nothing to lose. You decide to dive in. Two people from Scale AI, Lisa and Ram, walk in to survey the mess left behind, figuratively and literally. They begin to clean up and start to look at your data. You have lots of data, but it's just not usable, says Ram. Let's begin to fix that by starting with the business need and not by starting with a technology like AI, he says. You can't simply ask a general question like, what does my data tell me? Instead, you have to ask specific questions like, what factors affect my sales? Even then, you'll only get the answers that the data can give, even when you get the coolest machine learning techniques. When you create a hypothesis, data experts can start looking into the data to figure out the answers. So say your question is, how do we increase revenue? You'll have to start with why customers of the future would want to buy your insurance products in the first place. Here are some reasons. Maybe you offer high quality products backed by a strong financial position. Your products might integrate well with each other to offer total coverage for the customer. Your prices are not necessarily cheapest, but they offer great value. You have a great brand and your service is unbeatable. And you offer an ecosystem of partnerships with other companies for added customer value, like discounts on hotels and car rentals. If you had enough data, you could analyze that to figure out which factors matter most to your customers. Different customers may seek you for different reasons, but let's say the biggest factor for sales is through the friend's referral for millennial segments, and you're not even collecting that information. Then you have no way of knowing that from the data. Analytics can only work on data that you already have. That's why there's always a need for human expertise to think out of the box. So that's an important factor to consider in your strategy. On the other hand, don't collect everything. For example, if you ask your customers too many unnecessary questions, they may not buy your products in the first place because the experience deteriorates. You don't always have to get that data from the customer. You can get it from other sources. Your AI-based transformation strategy requires you to focus on the data, consider many other aspects as well, such as employee expertise, customer experience design, business capabilities, business processes, your technology, your culture, and so on. But you're going to do this from a top-down business perspective and an outside-in customer perspective. Finally, Ram and Lisa from Scale.ai have helped set the direction. The strategic partnership is off to a great start, and with the clarity and simplicity of the transformational message, you believe you're on the right path. If you enjoyed watching this video, please consider subscribing.