AI (Artificial Intelligence) technology is now poised to transform every industry, just as electricity did 100 years ago. Between now and 2030, it will create an estimated $13 trillion of GDP growth. While it has already created tremendous value in leading technology companies such as Google, Baidu, Microsoft and Facebook, much of the additional waves of value creation will go beyond the software sector.

This AI Transformation Playbook draws on insights gleaned from leading the Google Brain team and the Baidu AI Group, which played leading roles in transforming both Google and Baidu into great AI companies. It is possible for any enterprise to follow this Playbook and become a strong AI company, though these recommendations are tailored primarily for larger enterprises with a market cap/valuation from 500B.

These are the steps I recommend for transforming your enterprise with AI, which I will explain in this playbook:

1. Execute pilot projects to gain momentum

2. Build an in-house AI team

3. Provide broad AI training

4. Develop an AI strategy

5. Develop internal and external communications

6. Execute pilot projects to gain momentum

It is more important for your first few AI projects to succeed rather than be the most valuable AI projects. They should be meaningful enough so that the initial successes will help your company gain familiarity with AI and also convince others in the company to invest in further AI projects; they should not be so small that others would consider it trivial. The important thing is to get the flywheel spinning so that your AI team can gain momentum.

Suggested characteristics for the first few AI projects:

• It should ideally be possible for a new or external AI team (which may not have deep domain knowledge about your business) to partner with your internal teams (which have deep domain knowledge) and build AI solutions that start showing traction within 6-12 months
• The project should be technically feasible. Too many companies are still starting projects that are impossible using today’s AI technology; having trusted AI engineers do due diligence on a project before kickoff will increase your conviction in its feasibility.
• Have a clearly defined and measurable objective that creates business value.

When I was leading the Google Brain team, there was significant skepticism within Google (and more broadly, around the world) of deep learning technology. To help the team gain momentum, I chose the Google Speech team as my first internal customer, and we worked closely with them to make Google Speech recognition much more accurate. Speech recognition is a meaningful project within Google, but not the most important one—for example, it is less important to the company bottom line than applying AI to web search or advertising. But by making the Speech team more successful using deep learning, other teams started to gain faith in us, which enabled the Google Brain team to gain momentum.

Once other teams started to see the success of Google Speech working with Google Brain, we were able to acquire more internal customers. The team’s second major internal customer was Google Maps, which used deep learning to improve the quality of map data. With two successes, I started conversations with the advertising team. Building up momentum gradually led to more and more successful AI projects. This process is a repeatable model that you can use in your company.

1. Build an in-house AI team

While outsourced partners with deep technical AI expertise can help you gain that initial momentum faster, in the long term it will be more efficient to execute some projects with an in-house AI team. Further, you will want to keep some projects within the company to build a more unique competitive advantage.

It is important to have buy-in from the C-suite to build this internal team. During the rise of the internet, hiring a CIO was a turning point for many companies to have a cohesive strategy for using the internet. In contrast, the companies that ran many independent experiments—ranging from digital marketing to data science experiments to new website launches—failed to leverage internet capabilities if these small pilot projects did not manage to scale to transform the rest of the company.

In the AI era, a key moment for many companies will again be the formation of a centralized AI team that can help the whole company. This AI team could sit under the CTO, CIO, or CDO (Chief Data Officer or Chief Digital Officer) function if they have the right skillset. It could also be led by a dedicated CAIO (Chief AI Officer). The key responsibilities of the AI unit are:

• Build up an AI capability to support the whole company.
• Execute an initial sequence of cross-functional projects to support different divisions/business units with AI projects. After completing the initial projects, set up repeated processes to continuously deliver a sequence of valuable AI projects.
• Develop consistent standards for recruiting and retention.
• Develop company-wide platforms that are useful to multiple divisions/business units and are unlikely to be developed by an individual division. For example, consider working with the CTO/CIO/CDO to develop unified data warehousing standards.

Many companies are organized with multiple business units reporting to the CEO. With a new AI unit, you’ll be able to matrix in AI talent to the different divisions to drive cross-functional projects.

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New job descriptions and new team organizations will emerge. The way I now organize the work of my teams in roles like a Machine Learning Engineer, Data Engineer, Data Scientist, and AI Product Manager is different than the pre-AI era. A good AI leader will be able to advise you on setting up the right processes.There is currently a war for AI talent, and unfortunately most companies will have a hard time hiring a Stanford AI PhD student (or perhaps even a Stanford AI undergrad). Since the talent war is largely zero-sum in the short term, working with a recruiting partner that can help you build an AI team will give you a non-trivial advantage. However, providing training to your existing team can also be a good way to create a lot of new talent in-house.

1. Provide broad AI training

No company today has enough in-house AI talent. While the media reports of high AI salaries are over-hyped (the numbers quoted in press tend to be outliers), AI talent is hard to find. Fortunately, with the rise of digital content, including MOOCs (massive open online courses) such as Coursera, ebooks, and YouTube videos, it is more cost effective than ever to train up large numbers of employees in new skills such as AI. The smart CLO (Chief Learning Officer) knows that their job is to *curate, rather than create content, *and then to establish processes to ensure employees complete the learning experiences.

Ten years ago, employee training meant hiring consultants to come to your office to give lectures. But this was inefficient, and the ROI was unclear. In contrast, digital content is much more affordable and also gives employees a more personalized experience. If you do have the budget to hire consultants, the in-person content should complement the online content. (This is called the “flipped classroom” pedagogy. I have found that, when implemented correctly, this results in faster learning and a more enjoyable learning experience. For example, at Stanford University, my on-campus deep learning class is taught using this form of pedagogy.) Hiring a few AI experts to deliver some in-person content can also help motivate your employees to learn these AI techniques.

AI will transform many different jobs. You should give everyone the knowledge they will need to adapt to their new roles in the AI era. Consulting with an expert will allow you to develop a customized curriculum for your team. However, a notional education plan may look like this:

• **Executives and senior business leaders: (⩾4 hours training) **
  Goal: Enable executives to understand what AI can do for your enterprise, begin developing AI strategy, make appropriate resource allocation decisions, and collaborate smoothly with an AI team that is supporting valuable AI projects. Curriculum:
  • Basic business understanding of AI including basic technology, data, and what AI can and cannot do.
  • Understanding of AI’s impact on corporate strategy.
  • Case studies on AI applications to adjacent industries or to your specific industry.
• Leaders of divisions carrying out AI projects: (⩾12 hours training)
  Goal: Division leaders should be able to set direction for AI projects, allocate resources, monitor and track progress, and make corrections as needed to ensure successful project delivery. Curriculum:
  • Basic business understanding of AI including basic technology, data, and what AI can and cannot do.
  • Basic technical understanding of AI, including major classes of algorithms and their requirements.
  • Basic understanding of the workflow and processes of AI projects, roles and responsibilities in AI teams, and management of AI team.
• **AI engineer trainees: (⩾100 hours training) **
  Goal: Newly trained AI engineers should be able to gather data, train AI models, and deliver specific AI projects.Curriculum:
  • Deep technical understanding of machine learning and deep learning; basic understanding of other AI tools.
  • Understanding of available (open-source and other 3rd party) tools for building AI and data systems.
  • Ability to implement AI teams’ workflow and processes.
  • Additionally: Ongoing education to keep up-to-date with evolving AI technology

1. Develop an AI strategy

An AI strategy will guide your company toward creating value while also building defensible moats. Once teams start to see the success of the initial AI projects and form a deeper understanding of AI, you will be able to identify the places where AI can create the most value and focus resources on those areas.

Some executives will think that developing an AI strategy should be the first step. In my experience, most companies will not be able to develop a thoughtful AI strategy until it has had some basic experience with AI, which partial progress in steps 1-3 will give you.

The way you build defensible moats is also evolving with AI. Here are some approaches to consider:

Build several difficult AI assets that are broadly aligned with a coherent strategy: AI is enabling companies to build unique competitive advantages in new ways. Michael Porter’s seminal writings on business strategy show that one way to start a defensible business is to build several difficult assets that are broadly aligned with a coherent strategy. It thus becomes difficult for a competitor to replicate all of these assets simultaneously.

Leverage AI to create an advantage specific to your industry sector: Rather than trying to compete “generally” in AI with leading tech companies such as Google, I recommend instead becoming a leading AI company in your industry sector, where developing unique AI capabilities will allow you to gain a competitive advantage. How AI affects your company’s strategy will be industry- and situation-specific.

Design strategies aligned with the “Virtuous circle of AI” positive-feedback loop: In many industries, we will see data accumulation leading to a defensible business:

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For example, leading web search engines such as Google, Baidu, Bing and Yandex have a huge data asset showing them what links a user clicks on after different search queries. This data helps the companies build a more accurate search engine product (A), which in turn helps them acquire more users (B), which in turn results in their having even more user data (C). This positive feedback loop is hard for competitors to break into.

Data is a key asset for AI systems. Thus, many great AI companies also have a sophisticated **data strategy. **Key elements of your data strategy may include:

Strategic data acquisition**: **Useful AI systems can be built with anywhere from 100 data points (“small data”) to 100,000,000 data points (“big data”). But having more data almost never hurts. AI teams are using very sophisticated, multi-year strategies to acquire data, and specific data acquisition strategies are industry- and situation-specific. For example, Google and Baidu both have numerous free products that do not monetize but allow them to acquire data that can be monetized elsewhere.

Unified data warehouses: If you have 50 different databases siloed under the control of 50 different VPs or divisions, it will be nearly impossible for an engineer or for AI software to get access to this data and “connect the dots.” Instead, consider centralizing your data into one or at most a small number of data warehouses.

Recognize what data is valuable, and what is not: It is not true that having many terabytes of data automatically means an AI team will be able to create value from that data. Expecting an AI team to magically create value from a large dataset is a formula that comes with a high chance of failure, and I have tragically seen CEOs over-invest in collecting low-value data, or even acquire a company for its data only to realize the target company’s many terabytes of data is not useful. Avoid this mistake by bringing an AI team in early during your process of data acquisition, and let them help you prioritize what types of data to acquire and save.

**Create network effect and platform advantages: **Finally, AI can also be used to build more traditional moats. For example, platforms with network effects are highly defensible businesses. They often have a natural “winner takes all” dynamic that forces companies to either grow fast or die. If AI allows you to acquire users faster than your competitors, it could be leveraged into building a moat that is defensible through platform dynamics. More broadly, you can also use AI as a key component of low cost strategy, high value, or other business strategies.

1. Develop internal and external communications

AI will affect your business significantly. To the extent that it affects your key stakeholders, you should run a communications program to ensure alignment. Here is what you should consider for each audience:

Investor Relations: Leading AI companies such as Google and Baidu are now much more valuable companies in part because of their AI capabilities and the impact that AI has on their bottom lines. Explaining a clear value creation thesis for AI in your company, describing your growing AI capabilities, and finally having a thoughtful AI strategy, will help investors value your company appropriately.

Government Relations: Companies in highly regulated industries (self-driving cars, healthcare) face unique challenges to stay compliant. Developing a credible, compelling AI story that explains the value and benefits your project can bring to an industry or society, is an important step in building trust and goodwill. This should be coupled with direct communication and ongoing dialogue with regulators as you rollout your project.

Customer/User Education: AI will likely bring significant benefits to your customers, so make sure the appropriate marketing and product roadmap messages are disseminated.

**Talent/Recruitment: **Because of the scarcity of AI talent, strong employer branding will have a significant effect on your ability to attract and retain such talent. AI engineers want to work on exciting and meaningful projects. A modest effort to showcase your initial successes can go a long way.

Internal Communications: Because AI today is still poorly understood and Artificial General Intelligence specifically has been over-hyped, there is fear, uncertainty and doubt. Many employees are also concerned about their jobs being automated by AI, though this varies widely by culture (for example, this fear appears much more in the US than in Japan). Clear internal communications both to explain AI and to address such employees’ concerns will reduce any internal reluctance to adopt AI.

A historical note, important for your success

Understanding how the internet transformed industries is useful for navigating the rise of AI. There is a mistake that many businesses made navigating the rise of the internet that I hope you will avoid as you navigate the rise of AI.
We learned in the internet era that:

Shopping Mall + Website ≠ internet company

Even if a shopping mall built a website and sold things on a website, that by itself did not turn the shopping mall into a true internet company. What defines a true internet company is: Have you organized your company to do the things that the internet lets you do really well?

For example, internet companies engage in pervasive A/B testing, in which we routinely launch two versions of a website and measure which works better. An internet company may even have hundreds of experiments running at the same time; this is very hard to do with a physical shopping mall. Internet companies can also ship a new product every week and thus learn much faster than a shopping mall that might update its design only once per quarter. Internet companies have unique job descriptions for roles such as product manager and software engineer, and those jobs have unique workflows and processes for how they work together.

Deep learning, one of the fastest growing areas of AI, is showing parallels to the rise of the internet. Today, we find that:

Any typical company + Deep Learning technology ≠ AI company

For your company to become great at AI, you will have to organize your company to do the things that AI lets you do really well.

For your company to be great at AI, you must have:

• **Resources to systematically execute on multiple valuable AI projects: **AI companies have the outsourced and/or in-house technology and talent to systematically execute on multiple AI projects that deliver direct value to the business.
• **Sufficient understanding of AI: **There should be general understanding of AI, with appropriate processes in place to systematically identify and select valuable AI projects to work on.
• Strategic direction: The company’s strategy is broadly aligned to succeed in an AI-powered future.

Turning your great company into a great AI company is challenging but feasible with the support of great partners. My team at Landing AI is committed to helping partners with their AI transformations, and I will continue to share additional best practices.

An AI Transformation program may take 2-3 years, but you should expect to see initial concrete results within 6-12 months. By investing in an AI transformation, you will stay ahead of your competitors and leverage AI capabilities to significantly advance your company.

I welcome your feedback on this article at transformation@landing.ai.

Andrew Ng
Chairman and CEO, Landing AI

就像100年前的电力一样，人工智能技术现在也将逐一改变每个行业。从现在到2030年，它将创造大约13万亿美元的GDP增长。虽然AI已经在谷歌、百度、微软和Facebook等顶尖科技公司创造了巨大的价值，但今后的价值创造风潮将不再局限于软件领域。

这份《人工智能转型手册》，是根据我领导谷歌大脑和百度人工智能团队的经验收集汇总而来的，这两个团队在帮助谷歌和百度转型为伟大的人工智能公司的过程中，都扮演了至关重要的角色。

任何一家企业都有可能按照这本手册成为强大的人工智能公司，但这些建议主要是为市值在5亿至5000亿美元的大公司定制的。

我建议企业利用人工智能进行转型的过程中遵照以下步骤进行，我也会在本手册中对此进行解释：

1. 通过实施试点项目来蓄势

2. 组建内部人工智能团队

3. 提供广泛的人工智能培训

4. 制定人工智能战略

5. 开发内部和外部沟通机制

1. 通过实施试点项目来蓄势

部署前几个人工智能项目时，关键是要让项目取得成功，不能一味追求高价值项目。

这些项目必须具备充足的意义，这样一来，初期的成功就能帮助你的企业熟悉人工智能，还能说服公司内部的其他人也对人工智能项目展开进一步投资。

它们的规模不能太小，以免让其他人认为微不足道。关键是让飞轮不断旋转，好让你的人工智能团队获得足够的发展势头。

前几个人工智能项目应该具备以下特征：

• 应该为新组建的或外部人工智能团队（他们可能对你的企业所在的领域并不了解）和你的内部团队（他们非常了解你的领域）创造合作机会，并开发几套能在6到12个月内看到效果的解决方案。
• 该项目应该具备技术可行性。有太多的公司选择了不可能使用当今的人工智能技术完成的项目，因此应该让值得信赖的人工智能工程师对项目开展尽职调查，之后再启动项目，让你更加确信项目的可行性。
• 制定一个明确且可以量化的目标来创造商业价值。

当我领导谷歌大脑团队时，谷歌内部对深度学习技术充满怀疑（其实全世界都是如此）。为了帮助团队蓄势，我选择谷歌语音团队作为第一个客户，通过与他们的密切合作来大幅提高谷歌语音的识别率。

语音识别是谷歌内部的重要项目，但并不是最重要的，比如说，它对公司利润的贡献比不上网络搜索或广告业务。但通过深度学习技术让语音团队更加成功后，其他团队也开始信任我们，从而让谷歌大脑团队获得了发展势头。

一旦其他团队开始看到谷歌语音团队与谷歌大脑团队合作后取得的成功，我们就可以获得更多内部客户。我们的第二大内部客户是谷歌地图，他们使用深度学习来提升地图数据的品质。有了这两次成功经验，我开始与广告团队对话。

逐渐积累的发展势头也让我们开发出越来越多成功的人工智能项目。你也可以在自己的公司中采用同样的模式。

2. 组建内部人工智能团队

如果外包合作伙伴拥有深厚的人工智能专业技术，可以帮你快速蓄势。尽管如此，从长期来看，用内部人工智能团队执行一些项目的效率还是更高。

另外，你肯定希望把一些项目保留在公司内部，以便获得更为独特的竞争优势。

想组建内部团队，必须要获得高级管理层的认可。在互联网崛起的过程中，招募一名CIO成为很多公司制定有凝聚力的互联网使用政策的转折点。

相比而言，有的公司开展了很多独立的尝试，包括数字营销、数据科学和新建网站。但如果这些小规模的试点项目无法通过扩大规模来给公司其他部门带来变革，那就无法充分利用互联网的能力。

在人工智能时代，很多公司的关键发展势头都需要通过组建集中化的人工智能团队来实现，因为这种团队可以对整个公司形成帮助。如果专业范围合适，这种人工智能团队可以归CTO、CIO或CDO（首席数字官）领导。也可以安排专门的CAIO（首席AI官）。

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人工智能部门的关键职责是：

• 组建一套人工智能技术来支持整个公司。
• 在初期开展的一系列跨职能项目，用人工智能项目支持不同的部门/业务。在完成初期项目后，确定一套可以重复的流程，以便继续交付一系列有价值的人工智能项目。
• 为招聘和留住员工开发一套一致的标准。
• 开发覆盖整个公司的平台，这个平台不仅对各个部门都有帮助，而且不太可能由单一部门开发出来。例如，可以考虑跟CTO、CIO、CDO合作开发统一的数据库标准。

很多公司都会通过多个业务部门分别向CEO汇报工作。组建新的人工智能部门后，便可通过矩阵模式将人工智能人才分配到不同的部门，从而推动跨职能项目。

新的工作说明和新的团队组织将会出现。我给团队成员安排的职位包括机器学习工程师、数据工程师、数据科学家和人工智能产品经理，这都跟人工智能蓬勃发展之前的时代大不相同。一位优秀的人工智能领导者可以给你提供相应的建议，帮助你确定合适的流程。

人工智能人才市场现在硝烟弥漫，可惜的是，多数企业都无法招到一名斯坦福大学的人工智能博士生（甚至连一名斯坦福大学的人工智能本科生都招不到）。毕竟短期来看，人才大战是一场零和游戏，与招聘企业合作组建人工智能团队或许可以给你带来不小的优势。

然而，为现有团队提供培训也可以在内部培养很多新的人才。

3. 提供广泛的人工智能培训

当今没有一家公司在内部拥有足够的人工智能人才。

虽然媒体报道人工智能人才的工资时有些夸大其词（媒体提到的数字往往是异常值），但人工智能人才的确很难找。

幸运的是，包括Coursera、ebooks和YouTube视频在内的各种数字内容渠道都提供了非常划算的方式，让很多员工可以接受人工智能等新技术的培训。聪明的首席学习官知道，他们的工作是收集内容而不是制作内容，然后确定一个流程来确保员工完成学习过程。

10年前，所谓员工培训就是要聘请一些专家来到办公室讲课。但现在这么做已经显得效率太过低下，而投资回报率也不够明确。相比而言，数字内容成本更低，而且给了员工更多的个性化体验。如果你真的有钱聘请专家，也应该用这种面对面的授课方式来为网络内容作补充。

这被称作“翻转课堂”教学法。我发现，如果方法得当，这便可以加快学习速度，令学习体验更加愉快。例如，我在斯坦福大学教授深度学习课的时候就使用这种教学法。

聘请几位人工智能专家当面授课，还有助于激发员工学习这些人工智能技术的热情。

人工智能将会改变各种各样的工作，你应该让所有人都掌握在人工智能时代适应新职责所需的知识。咨询一位专家，可以帮助你为自己的团队定制课程。你可以参考以下这种培训计划：

1) 高管和高级企业领导者：（≥4小时培训）

目标：让高管理解人工智能可以为企业做什么，开始制定人工智能战略，制定合适的资源分配决策，并与人工智能团队展开顺畅的合作，以支持有价值的人工智能项目。

课程：

• 基本了解人工智能的商业问题，包括基本技术、数据，以及人工智能能做什么和不能做什么。
• 理解人工智能对公司战略的影响
• 针对人工智能在关联行业的应用案例展开研究。

2) 负责实施人工智能项目的部门领导（≥12小时培训）

目标：部门领导应该可以为人工智能项目确定方向、分配资源、监控和追踪进度，并按照需要进行修正，以确保项目成功交付。

课程：

• 基本了解人工智能的商业问题，包括基本技术、数据，以及人工智能能做什么和不能做什么。
• 基本了解人工智能的技术，包括主要算法种类及其要求。
• 基本了解人工智能项目的工作流程、人工智能团队的职责和人工智能团队的管理。

3) 人工智能工程师培训生（≥100小时培训）

目标：新培训的人工智能工程师应该可以收集数据、训练人工智能模型，还可以交付具体的人工智能项目。

课程：

• 对机器学习和深度学习有深入的技术理解；基本理解其他人工智能工具。
• 理解人工智能和数据系统开发工具的可用性（包括开源工具和第三方工具）。
• 能够落实人工智能团队工作流程。
• 另外，通过持续教育来学习最新的人工智能技术。

4. 制定人工智能战略

人工智能战略可以引导你的公司创造价值，同时也能形成护城河。

一旦团队开始看到初期人工智能项目取得成功，并对人工智能形成更加深刻的理解，你就可以确定人工智能最能创造价值的地方，并将资源集中投放到这些领域。

有的高管认为，应该从一开始就制定人工智能战略。根据我的经验，除非具备一些基本的人工智能经验，否则多数公司都无法制定经过深思熟虑的人工智能战略，而1-3条可以帮助你获得这种经验。

你开发护城河的方式也会随着人工智能技术的发展而进化。以下就是一些值得考虑的方法：

开发几个不同的人工智能资产，使之与连贯的战略广泛协调

人工智能将让企业可以通过新的方式获取独特的竞争优势。迈克尔·波特（Michael Potters）关于商业战略的开创性著作显示，打造防御性业务的一种方式是开发与一项连贯的战略广泛协调的多项不同资产。这样一来，竞争对手就很难同时复制你的所有资产。

利用人工智能针对你所在的行业创造优势

我认为，不应该试图与谷歌这种科技公司在“通用人工智能”领域展开竞争，而是应该努力成为你所在行业的顶尖人工智能公司，通过开发独特的人工智能技术来获得竞争优势。人工智能如何影响你的公司战略与具体的行业和环境有关。

设计符合“人工智能良性循环”的战略

我们在很多行业都会发现，数据不断积累之后，就会形成一项防御性业务。

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例如，谷歌、百度、必应和Yandex等领先的网络搜索引擎都拥有庞大的数据库，使之可以了解用户在搜索某个关键词之后更可能点击什么链接。这些数据可以帮助企业开发更加精准的搜索引擎，帮助其获取更多用户，进而获取更多用户数据。

这种正反馈是竞争对手很难实现的。

数据是人工智能系统的关键资产。因此，很多伟大的人工智能公司也拥有复杂的数据战略。你的数据战略可能应该包含以下关键元素：

• 战略性数据获取：有用的人工智能系统即可使用100个数据点（小数据），也可以使用1亿个数据点（大数据）。但数据几乎肯定是多多益善。各路人工智能团队都在使用多年的复杂战略获取数据，而具体的数据获取战略则是针对其所在行业和所处环境制定的。例如，谷歌和百度都拥有很多并未变现的免费产品，但他们却可以借此获取数据，然后通过其他渠道变现。
• 统一的数据库：如果你有50个不同的数据库，在50个不同的副总裁或部门负责人的领导下孤立运营，那么工程师或人工智能软件就几乎不可能获取这些数据，也就无法实现“连点成线”的效果。相反，应该考虑将数据集中到一个或至少也应该是位数不多的几个数据库中。
• 识别哪些数据有价值，哪些没有价值：单纯拥有庞大的数据并不必然表明人工智能团队可以从这些数据中获取价值。如果怀有这种想法，失败的概率就会大幅增加。我曾经见过有一些CEO投入过高的资金来收集低价值数据，甚至在收购了一家公司之后才发现目标企业的很多数据根本没有用。在数据获取流程中尽早引入人工智能团队，让其帮助你确定应该优先获取和保存哪些数据，便可避免这种错误。

创造网络效应和平台优势

最后，人工智能可以用于构建更加传统的护城河。例如，具备网络效应的平台是极具防御性的业务。它们往往天生具备“赢家通吃”的属性，迫使企业要么快速发展，否则就只能被淘汰。

如果人工智能让你获得比竞争对手更快的用户获取速度，那就可以借此建造护城河。更广泛来看，你还可以使用人工智能作为低成本、高价值的战略或其他商业战略的关键元素。

5. 开发内部和外部沟通机制

人工智能会对你的企业产生重大影响，也会对你的关键利益相关者产生重要影响，所以需要通过沟通机制来进行协调。以下是你应该针对各类受众考虑的内容：

投资者关系

谷歌和百度现在都变成了更有价值的公司，一定程度上源自他们的人工智能技术，以及这些技术对其利润的影响。如果能够清晰解释人工智能给公司创造价值的逻辑，阐述你的公司不断强大的人工智能技术，并最终制定深思熟虑的人工智能战略，便可帮助投资者给予你的公司合理的估值。

政府关系

如果企业身处监管严格的行业（例如无人驾驶汽车和医疗），那就要面临独特的挑战。讲述可靠而有吸引力的人工智能故事，以此解释你的项目蕴含的价值，以及可以为社会和行业带来的利益，成为赢得外界信任、提升自身商誉的重要步骤。此外，还应该在部署项目的过程中与监管者展开直接沟通和持续对话。

客户/用户教育

人工智能可以会给你的客户带来重大利益，所以从战略层面更新营销信息和宣布产品开发进度都很有帮助。

人才招募

由于人工智能人才很短缺，所以雇主的品牌强大与否，会对其吸引和挽留人才的能力造成影响。人工智能工程师都希望从事激动人心而且意义重大的项目。如果能向其展示你们取得的初步成功，那就大有裨益。

内部沟通

由于当今的人工智能并未被人充分理解，而通用人工智能也被过分夸大，所以会存在各种担忧、不确定性，甚至质疑。很多员工也担心自己的工作会被人工智能取代，尽管这种情况会因为文化差异而存在很大不同（例如，美国对此事的担忧程度远高于日本）。明确的内部沟通既可以解释人工智能，也可以打消员工的担忧，从而降低采用人工智能技术的阻力。

以史为鉴

回顾互联网给各行各业带来的变革，对于理解人工智能的崛起很有帮助。很多企业在互联网崛起过程中都犯过错误，我希望你能避免在此次人工智能浪潮中再犯同样的错误。

我们在互联网时代明白了一个道理：

商场+网站≠互联网公司

即使商场开发了自己的网站，而且通过网站出售商品，但它也并没有因此成为真正的互联网公司。一家公司究竟是不是互联网公司，关键定义在于：你是否对公司展开合适的组织调整，从而完成那些互联网让你如虎添翼的事情？

例如，A/B测试在互联网公司随处可见，你可以开发两个网站，然后找出效果更好的一个。一家互联网公司甚至可以同时开展几百项实验，这在实体商场是很难完成的。

互联网公司还可以每周推出一款新产品，因此比那些每个季度才更新一次设计的商场获得更快的学习速度。互联网公司还为产品经理和软件工程师等各种职位确定了独特的职位描述，这些职位都有独特的工作流程，方便其展开合作。

作为增长最快的人工智能技术，深度学习与互联网的崛起表现出很强的相似性。我们现在发现：

一家典型公司+深度学习技术≠人工智能公司

你的公司想要在人工智能领域表现一流，就必须展开合适的组织调整，从而完成那些人工智能让你如虎添翼的事情。

当今的每家大公司都或多或少使用人工智能技术。想要让你的公司真正在这一领域表现优秀，就必须：

• 拥有足够的资源，以便系统性地执行多个有价值的人工智能项目：人工智能公司拥有外包或内部技术和人才，可以系统性地执行多个人工智能项目，从而为企业创造直接价值。
• 充分理解人工智能：应该对人工智能形成普遍的理解，还要制定合适的流程，以便系统性地识别和选择有价值的人工智能项目。
• 制定战略方向：公司的战略需要与人工智能驱动的未来广泛契合。

要把你的公司从一家伟大的公司变成伟大的人工智能公司，是一件很有挑战的事情，但如果有一流的合作伙伴支持，仍然有可能实现。我们Landing AI团队就致力于帮助合作伙伴展开人工智能转型，我还将继续分享更多最佳实践措施。

一个人工智能转型项目大概要花费2至3年，但应该可以在6至12个月内初步看到切实的效果。通过人工智能转型，你就可以领先于竞争对手，还能利用人工智能技术推进公司发展。

吴恩达
Landing AI董事长兼CEO