在业务中实施AI - 挑战和决议

经过Harry Keir Hughes,Isaac LaBauve November 2019 | POV | 13 min read |通过电子邮件发送这篇文章|下载

Large enterprises need to implement AI solutions quickly and efficiently, but many don’t have the right people, process and technology. Firms can accelerate progress to AI maturity through use of a robust workbench, an operating model built on a data science-led pyramid of skills, and pre-built solutions when data complexity and talent scarcity are too big a challenge.

介绍

人工智能有可能引起商业革命。最好使用它的公司可以重新发明自己和他们的服务，破坏竞争并抓住市场份额。

但这很难做。

There’s a lot of noise and hype around the technology, a cloak that veils the challenge of actually implementing enterprise AI. Only 8% of firms engage in core AI practices that support widespread adoption.¹

通常缺乏一个好的业务案例，运行复杂算法的处理能力不足，AI术语使高管保持黑暗，并且数据科学家的供应不足。许多Laggard公司没有工作文化可以与Google和Facebook等泰坦队竞争，也没有数据来创建复杂的AI模型。许多人转向提供者和平台以使他们的旅程迈进，但这也需要对正确的用例应用程序进行认识，并且通常会导致“出于AI的缘故”。

With the challenges, however, are resolutions that, if carried out effectively, improve efficiencies, create innovative operating models, and — for those brave enough — discover new revenue streams.

What is enterprise AI today?

AI正在运行亚马逊数据中心，并在几分钟内阅读数千份法律合同。它正在发现癌性肿瘤，并预测客户何时会感到无聊并离开网站。它可以比其他方法更好地确定欺诈性信用卡行为。它可以赋予那些正确使用它的人的极端竞争优势。研究表明，AI可以将企业利润提高38个百分点，同时到2035年为公司提供14万亿美元的额外额外价值。²

AI将利润增加38个百分点

AI的形式多种形式，包括理解自然语言的图像识别和能力。这些算法处理数据，了解其上下文，然后根据智能采取行动。

AI can create meaningful solutions to business problems. Live Enterprise, an Infosys initiative, uses its ‘Nia’ AI capability to optimize employee and partner requests and resolutions. The platform guides users to take actions that save time, freeing them to do more challenging work.

飞轮旋转,高管必须把a strong business case in place and decide which use cases to pursue. They must then understand the implications of this road map on people, process and technology — the challenges they will face, and possible resolutions they can turn to.

企业面临的AI挑战

Challenge No. 1: The need for lots of clean data

Google，Amazon，Facebook和Apple（统称为GAFA）擅长于AI。他们有数据 - 大量用于处理处理的数据。Google可以在每24小时进行每24小时进行的35亿次搜索中培训AI算法，而同一时期发布到Facebook的6万亿个赞”是潜在的金矿。³Gafa并没有打算成为其Genesis的AI公司，但是知识树提供了非常有利可图的水果，这太诱人了，不要咬人。

IBM云和认知软件高级副总裁Arvind Krishna指出，收集和清理这些数据是AI项目的80％，而较小的公司可能需要一年多的时间才能完成此过程。⁴Many lose faith and run out of patience.

挑战2：复杂算法

存在用于处理数据，得出见解并最终对该智能采取行动的算法。

零售用例提供了思考的食物。给定的采购订单被送入系统。AI需要发现实际上是否是采购订单以及其数量，价格，数据和其他变量。订单编号可以与供应商一起提供产品三角，提供有关是否订购和交付之间是否有任何差异的见解。如果在六个月内发现过多的差异，这种见解可能会导致供应商黑名单。

This bouquet of algorithms needs to function as a unit and scale up easily to work on enterprise systems. They will vary among industries and will be dependent on data type (a complex document of text and images is very different from a text document of five lines).

Challenge No. 3: Complex data types

Many algorithms exist for processing complex data types such as video and audio. Their accuracy is often questionable, however, and there is the further challenge of scaling the solutions.

挑战4：需要加快处理的需求

复杂的算法,巨大的数据集,苏ch as those found in Amazon Alexa, can take days to train. According to AI consultant Andrew Burgess, any improvement in processor speed will ensure that AI systems remain useful while using up-to-date models.⁵Cloud computing is a boon here; AI-as-a-service uses specialized hardware that carries out processing on demand — a sure-fire catalyst for the democratization of AI.

挑战5：实时处理

实时或“流” - 洞察力的处理和生成在不破坏交易系统的情况下具有挑战性。

“Most of the algorithms today are built on offline insights,” says Dr. Raghavan, chief data scientist at Infosys. “The algorithms are scored against a model built on historical data. A new area of research that many experts are looking at is ‘How do we build models that are valid for data that was generated in the last half hour?’”

Challenge No. 6: Multiple providers with varying strengths

The sheer variety of AI platforms and vendors can be daunting for any business executive to contend with. There are tech giants such as GAFA that have cloud-based platforms to build models, understand granular data and deploy models into production. Then there are companies like RapidMiner, DataRobot and Dataiku that have built their own frameworks, with features evolving every six months or so. Finally, there are startups exploring just one aspect of the entire ecosystem.

A large enterprise such as a big bank or consumer goods manufacturer, will run proofs of concept to decide the functional areas in which to run these solutions, or whether to use an enterprise wide solution at all. For this, firms must employ in-house talent to understand vendor nuances, the algorithms in use, ease of implementation, data sources, ease of training and so on.

“Evaluating platforms is not easy work. You must test them out, understand their core production and capability, and determine whether they can be used in day-to-day operations,” says Dr. Raghavan.

第7号挑战：知识共享和代码重复使用的困难

In larger firms, different divisions may develop their own algorithms, leading to duplication if code is not shared between teams. These inefficiencies increase costs and ultimately lead to inconsistency across the enterprise. This can even affect how regulators view a firm’s use of AI, particularly if it seems that there is no single methodology or framework in place.

Challenge No. 8: Dependence on niche, expert talent

已经在AI路径上移动的公司与仍在评估技术的公司之间通常存在很大的人才差距。那些没有动力的人通常由于缺乏专用资源而放弃。⁶原因之一是AI从业人员的数量（全球300,000）与职位发布数量（数百万）之间的猛mm柱。最好的候选人是DeepMind，Google，Airbnb和Intel等公司的，以及其他人吹捧令人兴奋的用例和巨大的货币奖励。⁷

The 300,000 AI practitioners worldwide can choose from millions of AI job postings

更重要的是找到具有特定于行业知识的人才。如果发现，这些工人将是项目完成的必不可少的，直到完成为止。

Challenge No. 9: Developing the products

Once AI algorithms are in place, the business needs to commercialize the opportunity through the creation of products and services. This requires significant input from product designers, business analysts and business development teams. Those involved in the service design need to pay particular attention to the interfaces and user experience, and for many designers, AI is an area where they will not have expertise.

Embedding complex algorithms into existing operations and automation is also important. This calls for creating production grade AI applications that will work in legacy systems. A large telco such as AT&T might be dealing with 130 off-the-shelf software products, with data coming from 60% of those systems, which in turn may come from 10,000 databases — not an easy task for any production shop.

Challenge No. 10: Stakeholder involvement

顾问兼作家安德鲁·伯吉斯（Andrew Burgess）说：“在大多数公司中，将业务和IT利益相关者围绕桌子是一个重大挑战。”⁸

So too is managing expectations of business executives once they do collaborate with IT. For many, AI is seen as a magic pill that will reduce the costs of operations while improving production quality.

Raghavan博士说：“许多高管没有意识到创建AI应用程序很复杂。”“通常，高管对生产的估计不切实际，希望在未来两到三周内准备好新系统。”

强大的“工作台”方法可用于规避许多此类挑战。公司还应在人员，流程和技术之间建立能力。预先建立的解决方案是那些挑战以确保顶尖人才或具有反感文化的人的前进道路。

强大的工作台

一个工作台将一个屋顶下的开发，部署和持续的维护汇集在一起。它为用户提供类似于工厂的过程，并在此过程中嵌入了自动化和DevOps。

“The AI workbench encapsulates the data layer for a data scientist,” says Dr. Raghavan. “All they need to worry about is where to pull the data from, giving them freedom to build and validate models across geographies where hundreds of scientists will be working on the same problem.”

This means that models can be standardized, and algorithms can be reused on a global scale. It also provides a single access point for validated models to be deployed into production. The workbench should consist of the following elements:

API驱动的发展

A good workbench offers Application Programming Interface-driven development, which forgoes dependence on ultra-niche talent. It also enables code reuse and knowledge sharing. Here, interactions with a program are standardized and essentially static. For example, IBM’s Watson (the heavily lauded AI capability) is just a series of APIs that each carry out a specific function, such as speech recognition or Q&A, which can be called by another program with the right access. APIs also democratize AI, since the value is no longer in the algorithm but in the amount of training that is carried out on a given dataset.

Containers

Containers — encapsulated applications with their own operating system and memory — mean that an AI application can be published as a service and can be maintained and monitored very easily. From a deployment perspective, containers ensure that downstream applications don’t worry about specific aspects of modules, libraries and functions or the AI application itself. Containers help with code reuse and sharing (like API calls) and reduce the number of complex algorithms in use.

Multicloud API enabled

即使在一个企业中，一个良好的工作台也必须具有支持多个云环境的灵活性，以促进创新和保留业务部门的自治（英国的财务部门可能会使用Microsoft Azure，而德国的采购则使用Amazon Web使用Amazon Web服务）。反过来，这解决了获得业务的挑战，以及桌子上的利益相关者迅速推动AI策略。

良好的AI工作台必须具有多云的灵活性，以促进创新和保留自主权

Guided experience

引导体验increas的“剧本”es momentum in any AI project during the design stage. The point is to showcase how the AI system will work once deployed, which can be used to attract data scientists to the project and ensure the right partner comes along for the journey. A retail banking chatbot can be simulated, for instance, offering guidance to key stakeholders on its look and feel, implications for back-end data sources and ultimately the user experience.

The guided experience is crucial for AI projects with complex algorithms and architectures. If solution architects overlook this part of the workbench, project development degenerates into “spaghetti,” with too many what-ifs occurring in design.

能力的积累

数据科学家主导的人才

Very few firms make the most of AI capabilities, which, with some thought, can be integrated through effective partnering and ready-made solutions. Most often this is due to a lack of in-house data science skills and AI expertise that prevents seeing the forest for the trees. Building up a pyramid of skills dedicated to AI-specific systems is crucial.

例如，数据科学家经典使用结构化数据。但是，随着文本，语音，图像和视频的扩散，组织也需要在这些特定领域建立功能。一旦创建了技能金字塔（银行可能需要文本数据的鉴赏家，而媒体公司将需要视频和图像分析专业知识），公司就会更有信心熟练的工人不会让他们通过一个项目使他们偏爱。他们还将发现，他们作为AI驱动组织的声誉将得到增强，这使他们有动力应对我们列出的其他10个挑战中的任何一个。

For firms that are not highly technical or those that are less mature, we advocate hiring experienced professionals who can evaluate external talent that will ultimately build up the pyramid.

特定知识的剧本

在这里，开发了特定项目的文档和“ Wiki页面”，以详细介绍企业想要建立的用例的某些方面。例如，电信公司或零售公司可能会开发推荐引擎。引擎的“下一个最佳动作”将建立在剧本上，详细说明需要创建哪些特定组件以及需要包括哪些数据源。这降低了构建正确的代码的复杂性，并简化了业务与IT CXO之间的对话。

提供者特定的培训和实践经验

If an enterprise wants to build an Azure stack, an Azure developer is needed who knows the Azure data lake and is familiar with Azure machine learning, Azure Cortana and so on. This might seem obvious, but is often overlooked. So too is ensuring that workers have used a particular platform, in a particular style and in a particular industry. If predictive AI for a mining company is the use case, having someone familiar with the mining industry will ensure that they solve the right problems, since they talk the same language and understand the domain. Industry-specific experience also helps ease adoption. The relevant person will know exactly where to procure a platform or pre-built solution, and will know how to influence key stakeholders, while enabling easier adoption of AI into the overall system.

预先建立的解决方案

Access to AI software libraries speeds up development and deployment, while proof-of-concept (POC) solutions ensure that the product is built cheaply and quickly. Pre-trained models working on huge data sets help democratize AI and counter the talent challenge.

A pre-built box of algorithms

In-house development involves a lot of cost; identifying the right resources to create AI algorithms is expensive both in planning and development. Having a pre-built library is a much more convenient way of developing an algorithm that works for any given use case.

“If, for example, I’m doing clustering or time-series regression, having a library is always a blessing,” says Dr. Raghavan.

If artifacts are available at the library level, then a developer can custom build any application very quickly.

情感分析是一个预先建造的算法来解决问题的好例子。情感分析可以作为公司通信应用程序的一部分嵌入，也可以是产品开发应用程序的一部分。在这里，预构建的组件解析文本。分析中至少有10个不同的步骤，从语音检测开始，然后继续进行句子检测，句子结合并查看跨句子的相关性。这些步骤中的每个步骤都使用开源中可用的标准元素。

Raghavan博士说：“您可以作为情感分析的一部分将第一步到10线连接起来，并创建预先构建的API。”“任何想使用API的人都可能必须进行一些次要的自定义，但他们不会从头开始建造任何东西。”

因此，当涉及情感分析时，如果已经存在该预制层，则建立和部署解决方案变得容易得多。

POC解决方案

POC解决方案是用于构建和部署AI解决方案的自然敏捷周期的一部分，使公司能够对AI平台，潜在提供商进行重要了解，并确保该产品廉价，迅速地构建。

To build an AI solution, underlying challenges must first be understood. Very rarely is the first solution similar to the final solution. Different algorithms must be tested, along with different software vendors and software frameworks, and the solution must be tested in the user environment. The POC solution created builds confidence and, more importantly, ensures that the project runs on time and isn’t a failure. Once complete, the solution is scaled up.

AI-Off-the-the

Infosys建立了一种被称为文档理解的预训练的功能。

“When you upload a document, however complex, the pre-trained model is so sophisticated that it can derive questions and answers from the image and document by itself,” says Harinder Cour, a consultant at the Infosys Center for Emerging Technologies. “If you don’t like those questions or answers, you can ask your own questions and it will scan the entire document in real-time and give you a good answer nine times out of 10. In the future, insights will be derived from even more complex data types such as images and video.”

This pre-built solution effectively reduces the dependence on expert coders and solves the problem of processing complex data types in real-time — a truly disruptive technology if ever there was one.

The future enterprise

AI is guiding decisions on everything from bank loans to crop harvests. And it’s big business. Harvard Business Review estimates that AI will add $13 trillion to the global economy over the next decade.⁹However, as of this writing, most firms have only applied AI in a single business process. To improve efficiencies and identify new revenue opportunities, many challenges must be resolved, including scarcity of talent, risk-averse cultures, and an inability to imagine what the solution will look like on completion.

We advocate starting with a workbench that bridges the gulf between development and production. Building capabilities in-house must also be a priority, accelerating changes across people, process and technology. Finally, if these skills are hard to come by, pre-built solutions trained on big data can be employed to generate momentum toward AI maturity.

Before thinking about implementation, executives must put a strong business case in place, and decide which use cases to pursue. They must then understand the implications of this road map on their particular industry. Only then will they have clarity to make it back to shore and turn data into cash.