2018-06-29
每个人都在谈论人工智能（AI），但是很多公司不知从何处着手。在最近的调查和简报中，安永会计师事务所（EY）指出了这方面的挑战和机遇。
人工智能正在渗透不同的行业，逐渐重塑全球竞争格局。然而，大多数企业领导仍然不知道机器智能会如何影响他们的业务。
安永最近发表了一份简报，重点介绍了人工智能的现状。我们采访了安永全球创新人工智能团队负责人奈杰尔 达菲（Nigel Duffy），他与安永全球创新技术团队负责人兼全球首席分析官克里斯 马泽伊（Chris Mazzei）共同撰写了该简报。
该简报很好地勾勒出了人工智能的现状：“大多数企业并没有利用人工智能的潜力，他们还处于人工智能之旅的初期。阻止企业使用人工智能的原因似乎应该是缺乏人才，但实际上是对人工智能能够带来什么缺乏了解——尤其是那些大型企业管理层。”
人们通常很难想象新技术对企业的影响。当然，人工智能并不新鲜。根据最近的研究和发展，人工智能的使用进入了一个拐点，更多的企业已经将人工智能用于生产过程或者处于试验阶段。
有人说人工智能正处于一个拐点，即处于指数“曲棍球棒”[译者注：曲棍球棒效应是指在某一个固定的周期（月、季或年），前期销量很低，到期末销量会有一个突发性的增长，而且在连续的周期中，这种现象会周而复始，其需求曲线的形状类似于曲棍球棒，因此在供应链管理中被称为曲棍球杆（hockey-stick）现象]增长的初始阶段。如果这种说法是真的，那么后来者可能会远远落后于竞争对手，这是因为他们没有在第一时间思考和学习人工智能将会如何影响自己的公司。
根据达菲的说法，有些困惑源于人工智能是一套广泛的技术，而非一种单一的能力。考虑到这一领域的复杂性（机器学习、计算机视觉、自然语言处理、深度学习、神经网络等），企业领导并不清楚人工智能会如何影响业务。
此外，对于人工智能的宣传失之偏颇。当新技术出现时，推广者和媒体往往把重点放在机遇上，而忽视潜在的挑战。这些偏颇的观点导致很多人认为人工智能技术是万能的“银弹”，而实际上“银弹”是不存在的。要想真正理解一项技术的潜力和局限性，企业需要亲身体验，包括体验成功和失败。
“不言而喻，人工智能具有彻底改变业务的潜力。最近我在中国《财富》全球论坛上就此话题发表了演讲，全球500强公司的高管到主席的所有与会人员共同讨论了人工智能的转型潜力。”达菲说，“大家普遍认为，人工智能具有转型潜力，但是也带来了挑战：企业需要付出精力和投资来制定实现这一潜力的战略和愿景。”
许多企业正处于人工智能采用的早期阶段，所以他们还未投入足够的时间和资金。为了缩小这个差距，企业领导应开始积累经验，开发初始用例或概念验证。达菲建议投资于全面战略，并就如何改变公司或部门制定愿景。
人工智能是数字化转型谜题的一部分。在数字化转型中，技术只是一部分。最有效的策略侧重于解决业务问题。
达菲表示：“我认为，人工智能对以业务为先、价值为导向的企业的影响最大。采用人工智能的最佳方法是专注于为企业增加价值，而不仅是提高成本效率。企业必须从战略角度思考人工智能，并问自己，通过更智能地使用人工智能可以提供多大的价值。”
当然，也有一些与技术相关的障碍。正如技术早期采用阶段的典型情况，最初的工具倾向于针对能够使用这些技术的狭窄技术用户群。然而，随着技术的成熟，易于使用的工具将会出现，去除了一些复杂性。通常这些工具的目标是“专家用户”。
最后，大众也可以轻松使用这些工具，如企业中的分析仪表盘等。人工智能已经内置于多种设备和软件中，实现了对用户的透明。例如，即使不是人工智能专家，也可以使用智能音箱Amazon Echo。
达菲说：“人工智能将在每个行业中创造赢家和输家。人工智能已经存在，可以提供重大的价值，你真得愿意在采用方面慢于竞争对手吗？”
达菲说，企业领导和组织应该熟悉人工智能技术，因为这样可以更容易地确定人工智能可以有效解决问题的领域。
企业领导和技术人员需要协作。安永在内部就是这样做的，以融合文化并打破传统的思维方式。
在简报中，达菲和马泽伊说：“许多早期的项目投资回报率（ROI）低，影响有限。”那么，企业应该在什么时候投资人工智能呢？一方面，早期的采用者获得了旁观者无法获得的洞察力和经验。另一方面，后来者可以使用更成熟的工具并从别人的错误中学习。
“早期采用并不一定具有较低的投资回报率，早期采用者往往专注于技术而不是业务问题——这会导致投资回报率低。”达菲说，“但是，早期采用确实会获得宝贵的经验。”
早期的技术主导型项目也可能具有较低的投资回报率，因为它们既侧重价值也侧重学习经验。企业不应局限于技术主导型项目，而是应该根据自己的企业价值来确定项目，并由企业利益相关方牵头。
达菲表示：“由于人工智能具有转型潜力，如果你观望而你的竞争者立即采取行动，那么你将处于劣势。人工智能将会创造赢家和输家，而且速度会越来越快。大多数人还处于人工智能之旅的早期阶段，相对于潜力而言实际投资可能还很小。在一开始进行较小的投资是明智的决定。”
对人工智能的巨大兴趣创造了就业机会，同时也导致了对其能力的夸大。达菲说，获得合适的人才是非常重要的。
“在这个领域，需要特别注意邓宁-克鲁格效应（译者注：Dunning-Kruger effect，是一种认知偏差现象，指的是能力欠缺的人在自己欠考虑的决定的基础上得出错误结论，但是无法正确认识到自身的不足，辨别错误行为）。”达菲说，“这个领域发展得如此之快，以至于有很多人可以解决技术问题，但是他们缺乏深厚的经验。”
安永对200名高级人工智能专家行了调查，其中56％表示缺乏人才是业务运营最大的障碍。如果企业没有专业人才，就会面临三大风险。如果企业领导提前思考，积极处理，就能够很容易地预防这些风险。
第一个风险是测试。与其他技术相比，人工智能测试更容易出现错误。因为有许多微妙的统计问题，要做到正确，需要一定的精密性。据达菲说，人工智能测试需要人才深厚的专业知识。
第二个风险是机器学习会放大偏见。41%的受访者表示，现有AI人才的性别多元化会影响机器偏见。研究人员需要特别注意偏见，特别是种族、性别或其他文化偏见。为了主动避免这些偏见，企业需要确保从不同的人才库中招聘AI人才。
第三个风险是AI工具自动制定决策。达菲说，需要部署一个精密的监控系统，以确保迅速捕捉异常情况。
缺乏AI经验的企业领导可能会纳闷，到底该怎么提出正确的问题。
“其中一些问题是关于逐渐积累经验的，这反映了我的观点——早期采用者的优势。你提问，得到答案，产生结果，然后基于此提出更好的问题。”达菲说，“尽早采取行动，企业领导就可以获得经验，确定人工智能如何对他们的业务产生最有意义的影响。”
1/1/2018
07:00 AM
Everyone's talking about AI, but many companies have trouble getting their efforts off the ground. In a recent survey and brief, EY pinpoints the challenges and opportunities.
AI is seeping into different industries, slowly remolding the global competitive landscape. However, most business leaders still don't know how machine intelligence will impact their businesses.
EY recently published a brief, which focuses the current state of AI. We interviewed Nigel Duffy, EY Global Innovation AI leader who co-authored the document with Chris Mazzei, EY Global Innovation Technologies Leader and Global Chief Analytics Officer.
The brief frames the current state of AI well: "Most organizations aren't exploiting the potential of AI; they are just at the beginnings of their AI journeys. What should be holding companies back is a lack of talent, but it's actually a lack of understanding of what's possible – particularly at the top of large enterprises."
Addressing the C-Suite disconnect
It's often hard to imagine the impact new technologies will have on a business. Granted, AI is not new; however, due to recent research and developments, it's finally at a point where more organizations are either using it in production or experimenting with it.
Some say AI is at an inflection point, namely, at the beginning stages of exponential "hockey stick" growth. If that's true, the latecomers may find themselves blind-sided by competitors, simply because they didn't think about and learn first-hand how AI would affect their own companies.
According to Duffy, part of the confusion stems from the fact that AI is a broad set of technologies as opposed to a single, coherent capability. Given the complexity of the landscape (machine learning, computer vision, natural language processing, deep learning, neural networks, etc.), it's not surprising that business leaders don't have a clear understanding of how it will transform their businesses.
Also, the hype about AI is skewed. When new technologies hit the scene, evangelists and the media tend to focus on the opportunities and disregard the potential challenges. These skewed views fuel silver-bullet belief systems when silver bullets do not actually exist. It takes hands-on experience, including successes and failures, to truly understand the potential and limitations of a technology as applied to a specific business.
"It goes without saying that AI has the potential to completely transform business. I recently spoke on a panel about this topic at Fortune Global Forum in China, and everyone there, from prime ministers to chairmen of Fortune 500 firms, discussed the transformational potential of AI," said Duffy. "There is a broad understanding that it is going to be transformational, but the challenge is that it requires work and investment to develop the strategy [and] vision to realize that potential."
Many organizations are in the early stages of AI adoption, so they have not yet invested sufficient time and money in the process. In order to bridge this gap, leaders need to start gaining experience now, developing initial use cases or proofs of concept. Duffy recommends investing in a big-picture strategy, and developing a vision for how this could transform a firm or sector.
AI is more than a technology
AI is a piece of the digital transformation puzzle. As with all things related to digital transformation, technology is only part of the picture. The most effective strategies focus on business problem-solving.
"I believe companies will have the most impact with a business-first, value-led approach," said Duffy. "The best way to approach AI is to focus on how to add value to a business beyond just cost efficiencies. Businesses must think now about AI from a strategic perspective and ask themselves how much more value they can deliver through more intelligent use of AI."
Of course, there are some barriers to adoption that are technology-related. As is typical in the early adoption stages of a technology, the initial tools tend to be targeted at a narrow, technical audience that is capable of using them. However, as the technology matures, easier-to-use tools follow and abstract the some of the complexity. Usually those tools are aimed at "power users."
Finally, becomes easy enough for the masses to take advantage of, such as analytics dashboards in the enterprise. Already, AI is built into and will be embedded in many kinds of devices and software, to the point where it is transparent to the user. For example, one does not have to be an AI expert to use Amazon Echo.
"AI will create winners and losers in every industry," said Duffy. "AI is here today and can provide significant value now. Can you really afford to be slower to adopt it than your competitors?"
Business leaders and organizations should get familiar with AI technology now, because it will make it easier to determine where AI can be used as an effective problem-solving solution, Duffy said.
Business leaders and technologists need to work together. EY does this internally to meld cultures and disrupt traditional ways of thinking.
Set reasonable ROI expectations
In the AI brief, Duffy and Mazzei say, "Many early projects will have low ROI and a limited impact." So, at what point, then, should businesses invest in AI? On one hand, the early adopters gain insight and experience that those sitting on the sidelines miss. On the other hand, those who are later to the game have the luxury of using more mature toolsets and learning from others' mistakes.
"Early adoption doesn’t necessarily have a low ROI. [To clarify,] the early adopters are often focused on the technology rather than the business problem – this can lead to low ROI," Duffy said. "However, [early adoption] does lead to invaluable learning."
Early technology-led projects may also have low ROI because they are (and should be) as much about learning as about value. Rather than limiting the scope to only technology-led projects; businesses should identify projects based on their business value and have them led by business stakeholders.
"Because of the transformational potential of AI, if you wait and your competitors don’t, you will be at a disadvantage. AI will differentiate between winners and losers, and the pace at which that is happening is only accelerating," said Duffy. "Most people are early in their AI journey and the actual investment can be small relative to the potential. It’s a smart decision to make a relatively small investment to start."
Overconfidence can be dangerous
The immense interest in AI is creating career opportunities and with it overstatements about qualifications. Duffy said it's important to get the right talent.
"The Dunning–Kruger Effect is of significant concern in this space, that is, people can be unskilled and unaware of it," said Duffy. "The field has grown so rapidly that there are many people who can solve technical problems, but they have a lack of deep experience."
EY conducted a survey of 200 senior AI professionals, 56% of which said that a lack of talent is the greatest barrier to implementation within business operations. If companies don't have competent AI professionals, they face three big risks that are easily preventable if business leaders think about them in advance and do something about them proactively.
The first is testing. It’s much easier to get AI testing wrong compared to other technologies. Getting it right requires a certain amount of sophistication, as there are many subtle statistical issues. According to Duffy, AI testing requires talent deep expertise, working with this type of technology.
[Maybe it's time for your organization to make a real investment and commitment to AI. Read more here.]
The second challenge is that machine learning can amplify bias, which was another one of the key takeaways from the recent EY AI survey. Forty-one percent of the survey participants said they see the gender diversity of existing AI talent influencing machine biases. Researchers need to be especially mindful of bias, specifically, racial, gender or other cultural biases. To proactively avoid those types of bias, organizations will need to ensure that they're hiring from a diverse talent pool when hiring AI talent.
Finally, AI tools are making automated decisions, quickly. A sophisticated monitoring system needs to be in place to ensure that anomalies are caught quickly, Duffy said.
How to ask the right questions
Business leaders who lack experience with AI may wonder how it's possible to know whether they're asking the right questions in the first place.
"Some of this is about building up experience over time, which reflects back to my point about how it’s better to be an early adopter. You start asking the questions, seeing the answers, and seeing the outcomes that lead to asking better questions,"' said Duffy. "By starting soon, leaders can get experience in determining how AI can have the most meaningful impact on their business."
附件：
《AI Has a Foothold in Business, Now for the Next Steps》--原文.pdf
《AI Has a Foothold in Business, Now for the Next Steps》--译文.pdf
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