Sign in to continue learning
AI for Everyone
Beginner
6h54m
Topics
Deep Learning
Machine Learning
How can you help your company become good at AI? Based on my experience starting and leading the Google Brain team, as well as Baidu's AI group, which were respectively the leading forces for helping Google and Baidu become good at deep learning and AI, I've spent a lot of time thinking about what it takes to help a great company become a great AI company, and I wound up writing an AI transformation playbook to help other companies on this journey. In this video, I'd like to share with you the details of the AI transformation playbook so that you can better understand what it might take for your company to become good at AI. In case some of this seems like things that only CEOs need to know, I think that's not the case. It's useful for everyone in the company to understand what it might take for your work to impact not just a few projects, but maybe have a bigger impact on the company as a whole. Let's get started. Here are the five steps of the AI transformation playbook. We'll dive into greater detail in a little bit, but briefly, step one is for your company to execute pilot projects to gain momentum, to start to know what it feels like to work on AI projects. Step two is to build an in-house AI team. Step three is to provide broad AI training, not just to engineers, but to many levels within the company, including executives. Step four is to develop your AI strategy, and step five is to develop internal and external communications about your company and AI. The way your company will execute the steps may not be totally sequential, and so the different steps may overlap, but this numbering gives a maybe rough sense of the order in which I think you could do these steps. In this video, we will go in greater depth on the first three of these three steps, and the next video will cover steps four and five. Let's start with step one, executing pilot projects to gain momentum. If you want your company to gain momentum with AI, the most important consideration for the initial project or projects is for them to be successful, rather than necessarily be the most valuable. For example, when I was leading the Google Brain team, there was still a lot of skepticism at that time about deep learning, so my first internal customer was Google's speech recognition team. And speech recognition is nice to have, it's useful, but it's actually not the most important or valuable project for the company's bottom line. It's not as valuable as, for example, web search or online advertising. But by having my team make the Google speech team more successful, it started to fly in a real turn and help us get momentum, because then the peers, the other teams that are brothers and sisters in the speech team, started to see my team make the speech team more successful, and so they also started to gain faith in AI and wanted to work with us. So my second internal customer was the Google Maps team to improving the quality of the maps data using deep learning, and with the first and second successes, I then started other conversations with the online advertising team, for example. So when selecting your initial project, try to pick something that you think has a good chance of success, that can start the flywheel turning, even if it may not be the most valuable project that you could eventually do for the company. Because the goal of the first few projects is just to gain momentum, it's also nice if you can pick something that can show traction within six to 12 months, so you can start the flywheel turning quickly. Finally, your first one or two pilot projects can be either in-house or outsourced. If you do not yet have a large in-house AI team, it might be possible, it might even be advisable to outsource some or all of your first couple AI projects in order to get more expertise in-house and to let you start building that momentum faster. Now, beyond a certain point, you will need your own in-house AI team to execute a long-term sequence of maybe many dozens of AI projects. And so step two is to build an in-house AI team. A lot of companies are organized like this, where there's a CEO and multiple business units, which I'm going to abbreviate with BU, that reports up to the CEO. So what I recommend for most companies is to build a centralized AI team, and then to take the talent in the matrix organization and to matrix them into these different business units to support their work. Why a centralized AI team? Let's take an example. Maybe this unit is your gift card business unit, and the BU leader may be great at whatever he or she does. They may be great at the gift card business, but unless he or she is knowledgeable AI and knows how to build, retain, and manage an AI team, it may be very difficult for that business unit leader to hire and retain and appropriately manage their own AI talent. So in that case, I think your odds of success will be much higher if you find an AI team leader that can be responsible for consistent company-wide standards for recruiting, retention, And if you have a centralized AI team to give the team a community to talk to each other about how AI applies to your business vertical, it may be more efficient to take the AI talent in your centralized AI unit and matrix them into the gift card business unit so that your AI talent can work together with the gift card domain experts in order to develop interesting AI projects together. One other responsibility for the AI unit is to build company-wide platforms. If there are software platforms or other tools or data infrastructure that could be useful for the whole company, then a single business unit may not have the resources or the incentive to build these company-wide platforms or resources that can support the whole company. But a centralized AI team may be able to help build these company-wide tools or platforms that can help multiple business units. Finally, this new AI business unit can be under the CTO, the CIO, the Chief Data Officer or the Chief Digital Officer, or it could also be under a new Chief AI Officer. The CAIO, Chief AI Officer, is a role that I'm seeing more and more often in different companies, but if some other senior executive has the right skill set, they could also manage the AI unit. Finally, one last recommendation, which is that I think it is helpful if to get the AI unit started, the company or the CEO provides funding to build up the AI unit rather than require the AI unit to get funding from the business units. Eventually, after the initial investment and after the initial ramp-up, the AI unit will have to show its value that it's creating for the business units, but having CEO inject funding at the outset so they can get going will often help you get that initial momentum much faster. In addition to building an in-house AI team, I also recommend that you provide broad AI training. Now, for a company to become good at AI, it's not just that you need engineers to know AI, you need multiple people at multiple levels of the company to understand how AI interacts with their roles. For example, for executives and senior business leaders, I recommend that they learn what AI can do for your enterprise, that they learn the basics at least of AI strategy, and they learn enough about AI to make resource allocation decisions. So how much training should executives and senior business leaders receive? I think that numbers of hours of training is not a very good way to measure training, but with that caveat, I think you can deliver a lot of this training with maybe four hours or so of training. Leaders of divisions working on AI projects also need to know how to interact in their role with AI. I think these leaders would need to understand how to set project direction, so how to conduct technical and business diligence, how to make resource allocation decisions at the division level, as well as how to track and monitor progress of AI projects. So this type of training I think would take at least 12 hours, although again, number of hours is not a great metric for tracking how much you're learning. Finally, many companies are hiring AI talent from outside, but I would also not underestimate the importance and the impact of training up your existing engineering workforce with AI skills. For a software engineer to become proficient at AI does take a while, so plan for maybe at least 100 hours of training, but I'm seeing many companies provide training to help engineers learn to build and ship AI software, to gather and manage data, as well as help them become effective at executing on specific AI projects. The world today does not have nearly enough AI engineers, and so in-house training is a key part of many companies building up of their in-house AI capabilities. Finally, how do you get all this training done? Thanks to the rise of online digital content, ranging from of course online courses to also books and YouTube videos and blog posts, there is a lot of great content online about all of these subjects, and I think a good COO should work with experts to curate this type of content and motivate the teams to complete these learning activities rather than necessarily create content, which is a much more expensive thing to do. So with steps one to three of the AI Transformation Playbook, I hope that your company will be able to start to execute on initial projects, build a team, provide the training, and really start to get a lot of momentum going in terms of helping your company become more valuable or more effective using AI. Looking at the broader picture, AI also affects company strategy and how you align different stakeholders including investors, employees, customers with this transformation of the company. Let's go on to the next video to talk about AI strategy.
