吴恩达-AI-Transformation-Playbook.pdf

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2021-02-22

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1 Andrew Ng

AI Transformation

Playbook

How to lead your company into the AI era

AI (Articial 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 from $500M to $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

LANDING AI

https://www.mckinsey.com/featured-insights/articial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-

on-the-world-economy

Andrew Ng

1. Execute pilot projects to gain

momentum

It is more important for your rst 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

ywheel spinning so that your AI team can gain momentum.

Suggested characteristics for the rst 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 dened and measurable objective that creates business value.

When I was leading the Google Brain team, there was signicant 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 rst 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.

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