In the last video, you learned how to execute pilot projects to gain momentum for the in-house AI team and provide broad AI training. But if you want your business to not just gain momentum in the short-term using AI, but in the long term be a very valuable and maybe even defensible business. What can you do? Let's talk about AI strategy as well as perhaps important for some companies, internal and external communications relative to AI. To recap, this is the five-step AI transformation playbook, and in this video, we'll dive more deeply into these final two steps. Step four of the AI transmission playbook is to develop an AI strategy, which, I hope for you may mean to leverage AI to create an advantage specific to your industry sector. One unusual part of this playbook is that developing the AI strategy is step four not step one. When I shared this with many CEOs consistent request of please feedback ago was, can you please put the strategy as step one? Because I want to figure out what is my company strategy, then I want to find the resources, and then I'll execute on the strategy. But I've found that companies that tried to define the strategy as step one, before getting your feet wet, before trying out AI, knowing what a feasibility AI project. Companies like that tend to end up with sometimes very academic strategies that are sometimes not true to life. So, for example, I've seen some CEOs copy and paste newspaper headlines into this strategy. We read that data is important, he say, "My strategy is to focus on collecting a lot of data, but for your company, that data may or may not be valuable, and may or may not be a good strategy for your company. So, I tend to recommend to companies to start the other steps first, execute the pilot projects. Start building a little bit of a team. Start providing some training, so that only after you understand AI and understand how it may apply to your business, that you then formulate your strategy. I think this will work much better for your company than if you tried to formulate an AI strategy, before your company including specifically, the executive team has some slightly deeper understanding of what AI can and cannot do for your industry sector. In addition, you might consider designing a strategy that is aligned with the virtuous cycle of AI. Let me illustrate that with an example from web search. One of the reasons that web search is a very defensible business, meaning is very difficult for new entrants to compete with the incumbents with the existing large web search engines, is this: If a company has a better product, maybe a slightly better product, then that web search engine can acquire more users. Having more users means that you collect more data because you get to observe what different users click on when they search for different terms, and that data can be fed into an AI engine to produce an even better product. So, this means that the company with somewhat better product, ends up with even more users, ends up with even more data, and does an even better product with this link being created by modern AI technology. It makes it very difficult for a new entrant to break into this self-reinforcing positive feedback loop, called the virtuous cycle of AI. Fortunately though, this virtuous cycle of AI can be used by smaller teams entering new verticals as well. So, I think today is very difficult to build a new web search engine to compete with Google, or Baidu, or Bing, or Yandex. But if you are entering a new vertical, a new application area where there isn't a entrenched incumbent, then you might really develop a strategy that lets you be the one to take advantage of this virtuous cycle. Let me illustrate with an example. There is a company called Blue River that was acquired by John Deere for over US$300 million, and Blue River makes agricultural technology using AI. So, what they did was build these machines that would be towed behind a tractor, in big agricultural fields. This machine would take pictures of crops and figure out which is a crop and which is a weed, and use precision AI to kill off just the weeds, but not the crop. So, I knew some of the founders of Blue River while they were Stanford students are taking my class. So, to get the project started, they actually just use strap in his and sweat, they use their personal cameras and went out to a bunch of farms, and took a lot of pictures of crops in these agricultural fields. So, they started to collect pictures of heads of cabbage and weeds around the cabbage. Once they had enough data, starts off with a small data set, they could train a basic product. The first product, frankly wasn't that great. It was trained on a small data set, but it worked well enough to start to convince some farmers, some users to start to use their product, to tow this machine behind the tractor, in order to start killing weeds for the farmers. Once this thing was running around the farms through the process of taking pictures of heads of cabbage and killing off weeds, they naturally acquired more and more data. Over the next few years, what they did was they were able to enter this positive feedback loop, where having more data allows you to have a better product. Having a better product allows you to convince more farmers to use it. Having farmers use it allows you to collect more data. Over several years, entering a virtuous cycle [inaudible], can allow you to collect a huge data asset that then makes your business quite defensible. In fact, at the time of acquisition, I'm pretty sure that they had a much bigger data asset of pictures of heads of cabbage lying on a field than even the large tech companies had, and does actually makes the business relatively defensible from even the large tech companies that have another web search data, but do not have nearly as many pictures as this company does of heads of cabbage lying in the agricultural fields. One more piece of advice. A lot of people think that some of the large tech companies are great at AI, and I think that's true. Some of the largest tech companies are very good at AI, but this doesn't mean you need to or should try to compete with these large tech companies on AI in general because lot of AI needs to be specialized or verticalized for your industry sector. So, for most companies to be in your best interest to build AI specialized for your industry, and to do great work in AI for your application areas, rather than try to compete or feel like you need to compete left and right with the large tech companies on AI over the place which just isn't true for most companies. Other elements of an AI strategy. We are going to live in an AI power world and the right strategy can help your company navigate these changes much more effectively. You should also consider creating a data strategy. Leading AI companies are very good at strategic data acquisition. For example, some of the large consumer-facing AI companies will launch services, like a free email service, or a free photo-sharing service, or many other free services that do not monetize, but allows them to collect data in all sorts of ways that lets them learn more about you, so they can serve you more rather than add, and thereby monetize their data in a way that is quite different than direct monetization about that product. The way you acquire data is very different depending on your industry vertical, but I have been involved in what feels like these multi-year chess games, where other corporate competitors and I are playing multi-year games to see who can acquire the most strategic data assets. You might also consider building a unified data warehouse. If you have 50 different data warehouses under the control of 50 different vice presidents, then is almost impossible for an AI engineer or for a piece of AI software to pull together all of this data in order to connect the dots. For example, if the data warehouse for manufacturing is in a totally different place than the data warehouse for customer complaints, then how can an AI engineer pull together this data to figure out, whether the things that might happen in manufacturing, that causes you to ship a faulty cell phone, that causes a customer to complain two months later. So, a lot of leading AI companies have put a lot of upfront effort into pulling the data into a single data warehouse because this increases the odds that an engineer or a piece of software, can connect the dots and spot the patterns between how a elevated temperature in manufacturing today may result in a faulty device that leads to a customer complaint two months in the future, thus letting you go back to improve your manufacturing processes. There are many examples of this in multiple industries. You can also use AI to create network effects and platform advantages. In industries with winner take all dynamics, AI can be a huge accelerator. For example, take the ride-sharing or the ride-hailing business. Today, companies like Uber, and Lyfts, and Ola, and DiDi, and Grab seemed like they have relatively defensible businesses because they are platforms that connect drivers with passengers, and is quite difficult for a new entrant to accumulate both a large rider audience and a large passenger audience at the same time. Social media platforms like Twitter and Facebook are also very defensible because they are very strong network effects where having a lot of people on one platform makes that platform more attractive to other people. So, it's very difficult for a new entrant to break in. If you are working in a business with these types of winner take all dynamics or winner take most dynamics, then if AI can be used to help you we're growing faster. For example, with a celebrating user acquisition, then that can pass translates into a much bigger chance that your company will be the one to succeed in this business vertical. Strategy is very comfy and industry and situation-specific. So, it's hard to give strategy advisers completely general to every single company. But I hope that these principles give you a framework for thinking about what might be some key elements of an AI strategy for your company. Now, AI can also fit into more traditional strategy frameworks. For example, Michael Porter, many years ago have written about low cost and high-value strategies. If your company has a low-cost strategy, then perhaps AI can be used to reduce costs for your business or, if your company has a high-value strategy to deliver really, really valuable products with a higher cost, then you might use AI to focus on increasing the value of your products. So, AI capabilities can also help argument existing elements of a broader corporate strategy. Lastly, as you're building these valuable and defensible businesses, I hope that you also build only businesses that make people better off. AI is a superpower. This is a very powerful thing that you can do to build a great AI company, and so I hope that whatever you do, you do this only in ways that make humanity better off. The final step of the AI transmission playbook is to develop internal and external communications. AI can change a company and its products, and its important to communicate appropriately with the relevant stakeholders about this. For example, this may include investor relations to make sure that your investors can value your company appropriately as an AI company. Investor relations may also includes government relations. For example, AI is entering health care, which is a highly regulated industry because government has a legitimate need to protect patients, and so for AI to affect these highly regulated industries, I think is important for companies to communicate with government, and to work collaboratively with them in public-private partnerships to make sure that AI solutions bring people the benefits it can, while also making sure that governments can protect consumers and protect patients. So, this would be true for health care or be true for self-driving cars, it would be true for finance and many other AI industry verticals. If your products change, then consumer or user education will be important. AI talent is very scarce into this world and so, if you are able to showcase some of your initial successes that could really help with talent and recruiting. Finally, internal communications is also important if you're making a shift in your company, then many people internally may have worries, some legitimate and some less rational about AI and internal communications, so reassure people where appropriate can only be helpful. With these five steps, I hope it gives you a vision for how you might be the hope a company become good at AI. If you're interested in reading the detailed AI transmission playbook, you can also download it from this landing AI website. I hope you enjoyed these two videos on the AI transmission playbook. I've seen companies become much more value and much more effective by embracing and become good at AI, and I hope these ideas they hope you take a first step toward helping your company good at AI. Having said that, I have also seen many common pitfalls, the companies run into when trying to implement AI across the enterprise. Let's take a look at some of these common pitfalls in the next video so that hopefully, you can avoid them. Lets go on to the next video.