利用AI优势：人工智能革命的实战指南

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"The AI Advantage: How to Put the Artificial Intelligence Revolution to Work" 是一本由Thomas H. Davenport撰写，由MIT Press出版的书籍，属于"Management on the Cutting Edge"系列。该书探讨了如何在商业环境中有效地利用人工智能（AI）革命带来的机遇。本书的核心主题是人工智能在现代商业中的应用和优势。作者Thomas H. Davenport是一位知名的管理学思想家，他深入研究了AI如何改变决策制定、流程优化、以及企业的竞争优势。书中可能涵盖了以下几个关键知识点： 1. **人工智能定义与分类**：AI的基本概念，包括机器学习、深度学习、自然语言处理、计算机视觉等不同分支，以及它们在不同业务场景中的应用。 2. **数据驱动决策**：AI的优势在于处理大量数据并从中提取有价值的信息，帮助管理层做出更明智的决策。这可能涉及数据收集、清洗、分析和解读的过程。 3. **流程自动化**：AI如何通过自动化任务和工作流程来提高效率，减少人为错误，例如在客户服务、供应链管理和生产流程中的应用。 4. **智能产品和服务创新**：AI如何推动产品和服务的创新，如个性化推荐系统、智能助手和预测性维护等。 5. **企业战略与AI整合**：讨论如何将AI纳入企业的总体战略，包括投资、合作、人才招聘和培养等方面的考虑。 6. **伦理与社会影响**：AI的快速发展带来了伦理问题和社会影响，如隐私保护、就业结构变化和决策透明度等，这些都是企业在利用AI时需要考虑的。 7. **案例研究**：书中可能包含多个实际企业成功应用AI的例子，展示AI技术如何转化为实际的商业价值。 8. **挑战与风险**：AI实施面临的挑战，包括技术成熟度、数据安全、法规遵从性以及组织变革管理等。 9. **未来趋势**：对未来AI发展的预测，以及企业如何为未来的AI变革做好准备。 10. **参考文献与索引**：提供进一步阅读和研究的资源，方便读者深入探索相关主题。这本书对于希望理解和利用AI优势的企业领导者、管理者以及对AI感兴趣的读者来说，是一本宝贵的指南，它提供了理论与实践相结合的视角，帮助读者理解并有效利用这场人工智能革命。

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Artificial Intelligence Comes of Age—Slowly 5

on average, the average is now once in fifty-five years, and replenish-

ment trips have been reduced by over 10 percent.

In human resources, DBS is predicting churn of its salespeople. Using

factors like timing of holidays and medical leaves taken, and even how

quickly employees answer emails — factors identified with machine

learning models — it can now predict with 85 percent accuracy whether

someone will leave within three months.

The bank is also using AI to detect fraud in trading, for algorithm-

based lending models, for chatbots in customer service, and several other

uses. AI plays a particularly large role in DBS’s “digibank” in India — a

digital-only bank with 90 percent fewer people than a traditional bank.

Throughout the entire bank, AI-based customer interactions are reduc-

ing the volume of calls made to the call center by 15 percent.

Gledhill commented on the change in AI focus for DBS:

The initial robo-advisor was our most ambitious project, and it didn’t go as

planned because we were expecting something way ahead of our time. But we

learned from that first project and didn’t back away from AI at all. We are “pick-

ing low hanging fruit” by using AI to optimize business processes across the

bank and are seeing huge amounts of success with it. These projects are less

ambitious individually, but are transformative in their aggregate as they contrib-

ute to lowering operating expenses, increasing employee productivity, reducing

error rates, and increasing speed-to-market. Our primary goal is not to reduce

headcount, but to create a much better experience for the customer, and go

beyond transactional banking to relationship banking. We are looking to grow

our revenue and our business while maintaining a reasonable cost-income ratio.

The most aggressive users of artificial intelligence — both in their

products and their internal processes — have been tech companies like

Amazon .com. That fast-growing firm claims that it has been “investing

deeply” in AI for over twenty years — almost its entire history.

AI and

machine learning technologies are at the core of Amazon’s successful

voice recognition products, Echo/Alexa. And Amazon has some highly

visible and potentially transformational applications of AI in its busi-

ness model, including its delivery by drones project Prime Air, and fully

automated checkout at Amazon Go convenience stores.

These projects face significant technical, behavioral, and regulatory

challenges, and are not fully launched. However, Amazon seems likely to

6 Chapter 1

at least partially accomplish some of these moon shots (I recently visited

the Amazon Go store in Seattle, and the automated checkout system

seems to work quite well — although the store still has several employees).

Few companies would seem to have more capabilities than Amazon to

pull them off. The company has many AI algorithms — both open source

and proprietary — that it offers to customers, and uses itself, on Amazon

Web Services. No one seems to know exactly how many data scientists

work at the company, but at the moment 505 job openings can be found

in that category at Amazon. The company’s own recruiting website lists

171 open jobs in artificial intelligence. Both numbers are far more than

most companies employ or could even imagine hiring.

If anyone could

create ambitious, complex, and highly visible cognitive technologies for

internal and external use, it would seem to be this organization.

However, in his 2017 letter to shareholders of Amazon .com, Jeff Bezos

argued that the primary impact of AI (or machine learning in particular)

at the company would be important but invisible:

But much of what we do with machine learning happens beneath the surface.

Machine learning drives our algorithms for demand forecasting, product search

ranking, product and deals recommendations, merchandising placements,

fraud detection, translations, and much more. Though less visible, much of

the impact of machine learning will be of this type — quietly but meaningfully

improving core operations.

Get Cognitive Slow

Despite several decades of “AI winters” and “AI springs” where research

into AI fell out of favor and was then revitalized, AI is all the rage today.

Large organizations like M.D. Anderson, DBS, and Amazon are embrac-

ing it. Tech startups in Silicon Valley and elsewhere, and professional

services firms all see AI as the next — or even the current — big thing.

Venture capital firms are pouring money into the field. Media attention

and hype is higher than ever for the technology.

I certainly agree that some of this excitement is warranted. When

AI technology is up to the task, and when the problem isn’t exceed-

ingly difficult and complex — like curing cancer, capturing all of a large

Artificial Intelligence Comes of Age—Slowly 7

bank’s knowledge about investing, or managing a fleet of drones without

human pilots — it can succeed and yield significant benefits. But artificial

intelligence isn’t going to transform the work of organizations — or the

lives of individuals — as fast as many people seem to expect. It will be

one of the many technologies that comply with Amara’s Law (named

after the scientist and futurist Roy Amara):

We tend to overestimate the effect of a technology in the short run and under-

estimate the effect in the long run.

In the short run, AI will provide evolutionary benefits; in the long

run, it is likely to be revolutionary.

AI does offer a lot of business value, but much of that value isn’t terri-

bly sexy or visible. Products and processes will be made somewhat better

and easier to use. Decisions will be better informed. We’ll continue — and

perhaps even accelerate a bit — the amazing progress that we’ve seen

over the last couple of decades in data and analytics. But as all of the

early adopters have discovered, it’s still difficult to create systems that

think and communicate like humans — even in narrow domains. This

is particularly true with AI technologies that are relatively new and

complex, as IBM Watson was when M.D. Anderson and DBS tried to

apply it.

While there are clearly many sources of potential business value

from AI, there is a valid question of just how visible AI and its benefits

will be in organizations. The technology could lead to dramatic trans-

formation in a series of moon shots, or it could be important but largely

invisible. I think the latter future is somewhat more likely. Remember

that real moon shots were preceded by a number of less ambitious space

flights. Back in 2014, the technology prognosticator Kevin Kelly wrote

that the AI he foresees is more like a kind of “cheap, reliable, industrial-

grade digital smartness running behind everything, and almost invis-

ible except when it blinks off.” Kelly compared AI to electricity, arguing

that, “There is almost nothing we can think of that cannot be made

new, different, or interesting by infusing it with some extra IQ. … Like

all utilities, AI will be supremely boring, even as it transforms the Inter-

net, the global economy, and civilization.”

8 Chapter 1

As at Amazon, there may be opportunities for capable organizations to

develop prominent and highly ambitious applications. But they are likely

to be few and far between, and the chances of them missing the moon are

relatively high. However, more prosaic opportunities are plenty valu-

able to make cognitive technologies worth pursuing.

There should be no doubt that AI is worth the attention of busi-

nesses, but they need to experiment with the technology and build

enough experience to use it effectively. Some early projects have already

failed or encountered substantial difficulties because neither the tech-

nologies nor the organizations using them were quite ready. Just as the

smartest investors “get rich slow,” companies will need to become more

cognitive slowly over time. The businesses and organizations that suc-

ceed with AI will be those that invest steadily, rise above the hype, make

a good match between their business problems and the capabilities of

AI, and take the long view. This book describes how companies can take

that approach.

However, I’ll also argue that it is dangerous to do nothing in this area,

or to move too slowly. I speak with large, established firms almost every

day that have realized the power of this technology. Later on I’ll men-

tion substantial majorities of surveyed managers in these established

firms who believe that cognitive technologies will transform both their

internal processes and their products and services. Those companies

and managers are likely to be your competitors, and it would be foolish

not to begin building AI capabilities today.

The other major threat, of course, is disruptive startups. As I’ve already

suggested, the most aggressive adopters of AI have been online “digital

native” firms like Google, Amazon, and Facebook. We’ve already seen

how these data-driven firms have entered a variety of industries and

challenged established leaders. Google, for example, has used its exten-

sive mapping data and its prowess at AI to become a strong competitor

in the autonomous vehicle industry.

We’re also likely to see numerous startups in particular industry

sectors that focus on AI as a core component of their business models.

Take, for example, the property and casualty (P&C) insurance indus-

try, with many established U.S. firms like State Farm, Allstate, Geico,

Artificial Intelligence Comes of Age—Slowly 9

Progressive, and others. Some of those firms, like Progressive, were

strong in traditional data analytics, but it’s not clear that any of them

has taken a very aggressive approach to incorporating AI into their

businesses.

But those established firms are being disrupted by startups like Lem-

onade, a New York–based startup that has put AI at the center of its

business. As its CEO and cofounder Daniel Schreiber wrote in a blog post:

In recent years the insurance industry has paid close attention to insurance-

tech-startups. They take note of how being digital transforms the user expe-

rience, appeals to younger consumers, and removes costs, while expediting

everything. That’s all true, but it is only Act 1. … While everyone is bedazzled by

the tech of Act 1, these delightful apps are generating mountains of data. These

will soon reach the billions of entries that machines go to town on, and that’s

when Act 2 will begin. … Act 1 showcases the power of technology to transform

any business by reducing costs, increasing speed, and delighting consumers.

But when Act 2 begins, we will see the power of AI to transform insurance

in a uniquely powerful way. It will go beyond thrilling customers and driving

efficiencies, to being able to quantify risk like never before. That day is nigh.

Of course, established firms in P&C insurance are responding to this

competitive threat. Sompo Holdings, a large insurance firm based in Japan

(where I am an advisor), is pursuing AI across multiple fronts (although

startups like Lemonade are currently less of a threat in Japan). It was an

early experimenter with IBM Watson’s intelligent agent application for

customer service. It is generating predictive models with automated

machine learning. It is using AI to extract key data from requests for busi-

ness insurance, and modeling weather data with machine learning. Som-

po’s CEO, Kengo Sakurada, and its chief digital officer, Koichi Narasaki, are

well aware of the power of AI to change their business and are determined

to explore the technology aggressively.

What Do We Mean by AI/Cognitive Technologies?

Broadly speaking, AI or cognitive technologies employ such capabilities —

previously possessed only by humans — as knowledge, insight, and per-

ception to solve narrowly defined (with the current state of technology)

tasks. The tasks are those that can usually be accomplished quickly by

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