Forrester RPA+AI最新趋势报告 | 首席分析师Craig解读后疫情时代，RPA如何打造以人为本的企业--RPA中国 | RPA全球生态 | 数字化劳动力 | RPA新闻 | 推动中国RPA生态发展

7月11日，Forrester副总裁、首席分析师Craig Le Clair受邀出席2020世界人工智能大会云端峰会RPA+AI分论坛，分享RPA在全球的发展现状。

Craig带来了RPA最新趋势报告《后疫情生产力时代，智能自动化打造以人为本的企业》，并为众多云端观众解读了自动化的关键趋势。他的分享为后疫情时代，企业智能自动化相关建设指明了思路。

01.

3 Key Trends in Intelligent Automation to move forward postpandemics

后疫情时代智能自动化的三个主要趋势

Human & Machine Cooperation

人机协同

As machines become moreintelligent, they replace more and more manual processes, this is generallyreferred to as the human in the loop issue. Benefit from AI technology, peoplemove towards the less deterministic type of processes, where we use machinelearning and other related AI technologies to make decisions in the process.This really alters a lot of things around the process that humans used to do.

随着机器智能水平的不断提高，它们取代了越来越多的人工流程，这通常被称为人机协作-“Human-Machine Cooperation”。得益于人工智能技术，人们逐渐向规则更复杂、确定性更低的流程自动化领域迈进。在这个过程中，我们使用机器学习等人工智能技术来进行数据处理、辅助决策。这极大改变了人类很多传统的工作场景。

Intelligent Document Extraction

智能文本提取

It's using machinelearning, which is a subset of AI, to go into documents, forms, emails, what'sgenerally referred to as unstructured information. Based on that, you have aclean set of data that's high quality, then you can apply analytics. For example,you can look at errors that might have occurred in someone filling out of form,you can check transactions that may not be correct based on analyzing the data,and you can dig customer sentiment in the text.

文本挖掘属于机器学习的范畴，也是人工智能的一个子集。所处理的文本通常是文档、表格、票据、电子邮件等非结构化的信息。基于智能文本抽取技术，可以获取更高质量的数据，用于后续的数据分析工作。例如，查看用户填写表单时可能出现的错误，检查可能不正确的交易，挖掘文本中的客户情感。

Although the core ofthis area is still natural language processing, what’s interesting now is usingmachine learning to train a model to understand the meaning behind the text.There are also a lot of advancements in computer vision and spatialunderstanding. So you can understand the forms and images that are on adocument to be able to get a better context. So it's much more of aninsights-driven value now where in the past it's been more about just takingcost out of the process.

虽然这一领域的核心仍然是自然语言处理，但用机器学习的训练模型，来理解文本背后的含义，也是近期热门的领域。随着计算机视觉、文本分类等方面技术的逐渐成熟。机器可以理解文档上的表单和图像，以及上下文的逻辑。在AI技术的赋能下，智能自动化蕴含了具有洞察力的价值。而不仅是过去的降低运营成本。

Automation Strike Teams

自动化突击队

It is very important fora company to take a broader view of intelligent automation. There are a numberof reasons for this.

One is that some ofthese automation introduces some new issues that need to be considered from agovernance standpoint. Second, robots are using human credentials and areoperating on very trusted and secure application areas. So you need to haveguard rails or control around the use of those credentials. Also, Intelligentautomation has involved a number of software technologies. You need teamsinternally to explain the use of the different automation technologies to thebusiness than to apply it in the right way.

对于企业的自动化建设，用更广阔的视野进行整体规划十分重要。这里有多方面的原因：

一是一些自动化场景，引入了需要从组织治理角度考虑的新问题。其次，机器人通常会使用员工的密码凭证，进行一些生产系统的日常操作。因此，需要关注密码凭证的安全管控。此外，智能自动化涉及许多技术的应用，需要团队在企业内部解释不同自动化技术的特点，以便用正确的方式应用和落地。

02.

The Pandemic Will Create A Surge In Digital Transformation

疫情给数字化转型带来的机遇

Despite trends inmobility, in social media, and the digital disruption of companies like theUbers of the world and the great Chinese companies that have come into thesharing market, the progress on digital transformation has been quite slow.Unfortunately, modernization is hard. Digital transformation is hard.

尽管在移动互联网、社交媒体等领域，Uber等全球公司，以及美团、滴滴等中国互联网企业，在数字化驱动业务模式创新等方面的势头表现良好。但传统企业数字化转型的进展相当缓慢。这让我们不得不面对一个现实：现代化是艰难的，数字化转型不易。

When we came to thispandemic point in January, February, March. Suddenly, we had to transformdigitally really fast. Everyone had to work from home. We had to conduct remotebusiness in new ways. Then we encountered some issues with our supply chainsand so forth. So under tremendous pressure, we had to innovate. The best out ofthe worst, with a surge in digital transformation, we've developed more digitalmuscles in the past two months than we've had in the last five years.

在2020年1月到3月的全球疫情高峰期，突然之间，企业快速进行数字化转型。每个人都不得不在家工作，企业必须以新的方式开展远程业务。随之而来的，是在供应链等领域衍生出的一系列问题。在巨大的压力下，企业不得不进行业务创新。不幸中的万幸，疫情也从侧面推动了一部分企业的数字化转型进程，部分企业在过去的两个月里构建了比过去五年更多的数字化能力。

That's what that spikeyou see in this graph. Now the challenge for companies is to take thistransformation that's occurred under stress and to see what should remain as wemove back to a more normal work environment. This will affect theinstitutionalize progress and transformation that we made in the past fewmonths.

上图中闪电标志描述的数字化转型激增的区域。现在，公司面临的挑战是如何在压力下进行这种转变，并在疫情结束恢复更正常工作环境时，继续保持数字化转型的势头。这将影响我们在过去几个月中取得的数字化转型成果。

03.

Changes in the Intelligent Automation Roadmap

后疫情时代的智能自动化路线图

Forester proposed apost-pandemic roadmap that gives you a way to prioritize your intelligentautomation projects. This is what we're seeing our clients do, who are thecompanies and governments that we interact with consistently. If you wereworking on a large AI project, transformational project, you might push thataside. That might drop into the losing momentum zone. Because this recessionthat we're going in is going to be, by most estimates, long and painful.

Forrester提出了后疫情时代智能自动化路线图，为企业提供了一种确定智能自动化项目优先级的方法。基于Forrester对所服务企业、政府客户的调研和沟通。一些正在进行中的，大型人工智能、数字化转型项目，进度会受到影响甚至停滞，会掉进左下象限的动量损失区。因为根据大多数人的估计，我们正在经历的这场疫情引发的经济衰退，将是漫长而痛苦的。

It's going to focus likeall previous recessions on cost reduction and cost take out. So in theacceleration zone to the upper right, RPA task automation becomes veryimportant because it has a very visible ROIfor cost reduction. What we havementioned above, text analytics, which allows you to extract a lot of hoursshuffling paper and dealing with forms and finding errors, sentiment, fraud,and other issues. Also, remote work is a practical technology that's availabletoday.

就像以前所有的经济衰退一样，企业将更专注降低成本和成本转移。因此，在象限右上方的加速区中，RPA自动化变得非常重要，因为它在降低成本方面具有非常明显的ROI。

上面提到的智能文本提取也在这个区域，它能帮助员工节省大量的时间，来整理文档、处理表单、发现错误、客户投诉、欺诈风险等问题。此外，一些视频会议、远程协作类的办公产品和工具，也在疫情期间发挥了巨大的作用。

No one was prepared forwhat we call a systemic shock, as the pandemic. The next systemic shock mightbe climate change. So there's a greater awareness of companies in the sort ofgovernance spectrum to be prepared for these kinds of systemic risks that mayoccur. So resiliency has become a top priority item for automation. And thismight mean diversity in your supply chain so that you can quickly get new bidsout. You have the ability to have a more agile approach to sourcing variousgoods and services.

极少数人能够准备好，应对我们所说的系统性全球风险。下一个系统性全球风险可能是气候变暖。我们看到，更多的公司开始关注治理领域的问题，增强了风险管理意识，为可能发生的此类系统性风险做好准备。因此，系统弹性、业务韧性等可持续发展能力，已成为数字化转型中的重中之重。例如，提升供应链的多样性，以确保可用的材料采购，和商品交付能力。

04.

The Impact of IntelligentAutomation on Different Workers

智能自动化对不同类型工作者的影响

The degree of influenceof intelligent automation on different types of work is different. For example,cubicle workers are employees that may work in a contact center taking phonecalls or work in a back office doing finance and accounting. They are put inthe same category because the skills they have are very similar. So the effectof automation on their work will be similar.

智能自动化对不同类型工作者的影响程度是不同的。例如，呼叫中心的客服员工，企业后台从事财务工作的员工，由于他们拥有相似的工作模式，标准的工作流程，因此自动化对他们工作的影响也是相似的。

On the other hand, forthe knowledge workers who might be a legal strategist. They are makingconnections across a wide range of complex information and data. So automationmay not be used in this field for a long time. But we also saw some innovativescenes, such as digital assistance will help in the cognitive search in thehealth industry.

另一方面，对于法律从业人员等创造工作者，他们通常会处理复杂的信息，并在海量数据之间建立联系。由于工作的创造性水平、流程不固化等特殊性，自动化可能在很长一段时间内，都不会应用在这个领域。但我们也看到了一些创新的场景，比如数字员工助手辅助医疗行业的从业人员，进行认知搜索和知识发现。

RPA + AI most Affected Knowledge andAdministrative Workers

RPA+AI对特定职能的知识工作者和行政人员影响最深

Currently, the hottestapplication area which makes the real effect is in the operating field.Intelligent automation is very suitable for the function-specific knowledgeworkers, coordinators, administrative workers. This is the target area whereyou can do a lot of work with machines. A lot of automation technology, AItechnology is changing the traditional process in this area.

目前，RPA真正发挥作用的应用领域主要集中在运营领域。智能自动化非常适合于特定职能的知识工作者、协调员、行政工作者。这些流程标准、操作规范的场景是RPA主要的应用领域。很多自动化技术、AI技术正在改变企业的传统流程。

05.

Five Levels of Human & Machine Cooperation

人机协作的五个层次

There are differentlevels of human in the loop related to intelligent automation. Level five iswhere you're using the most advanced AI and the machine, such as a self-drivingcar. To the contrary, level zero is where human is doing everything.

在与智能自动化相关人机协作中，会根据技术复杂度和自动化模式的不同分为5个层次。第五层是应用最先进的人工智能技术实现机器的自主运动，比如自动驾驶汽车。相反，第零层是描述人类日常工作中没有自动化驱动的场景。

In the middle of them, we have automationtechnology developed to different stages. Level one is the area of workflow,where you're using software to design a process, generally moving from task totask, which is a deterministic pattern.

在这中间，随着自动化技术发展的不同阶段，又有进一步的细分。第一层是工作流领域，我们使用BPM软件来设计流程，连接不同的工作节点，处理一些确定性流程的自动化。

Level 2 Human Drives Machine Actions

第二层：人类驱动机器完成任务

Level two is what a lot of RPA is doing rightnow, where we have built some digital workers or digital assistance. And thehuman has some level of interaction with the robot. The human in a contactcenter can tell the robot to update all these addresses. So there's a level ofvery positive automation and productivity there.

第二层是很多RPA正在做的事情，企业已经构建了一些数字员工或数字助理。人类与机器人之间有某种程度的互动。比如在客服中心，人可以使用机器人批量更新客户的地址。以此来降低员工的信息系统负担，解放生产力，投入更有价值的工作。

Level 3 Human Completes Task with the Help ofMachine

第三层：人类在机器的帮助下完成任务

Level 3 is where aseries of AI technologies are combined with RPA to give people stronger dataprocessing capabilities. These AI components, such as NLP usually use machinelearning to provide a more flexible extraction of data. In the old days, youhad to know exactly based on a template where a particular field was and takethe data out perfectly. But now you can really understand the content in thedocument, in general with machine learning, where the data is, what it lookslike, and use the training of the data to be more and more precise on yourextraction.

第三层是一系列AI技术与RPA相结合，赋予人们更强的数据处理能力。一些AI组件，例如NLP，通常使用机器学习来提供更灵活的数据提取。在过去，我们必须根据模板准确地定位字段的位置，随后才能取出数据。但现在，机器学习可以理解文档中的内容，识别文字、数据在哪个区域，并利用训练机制，使机器的识别和提取越来越精确。

We can foresee whatlevels four and five do where the AI is making all the decisions. So you mayhave explained ability issues, transparency issues. No one knows how thedecision was made. You need a perfect algorithm. So that the car doesn't driveinto a stone wall. You need perfect data. So you're not doing a biasedassessment. Those issues in level four and five become less of a concern inlevels three and level two.

我们可以预见在第四层和第五层，人工智能将更多的参与决策工作。我们也会遇到一些智能自动化能力的黑盒问题，没有人知道决策是如何做出的。我们需要更完美的算法和智能技术，以保证无人驾驶汽车不会因错误识别引发的事故。第四层和第五层中的所面临问题，在第二层和第三级中，并不是那么引起关切。

06.

Hurdle to Scale for Enterprise-Level RPA

企业级RPA规模化拓展的阻力

Now, a lot of companies have made biginvestments in intelligent automation and enterprise-level RPA is alsogradually moving towards scale. But basically, about half of the companies haveless than 10 robots in production. That is not what we consider scale. You mayask why companies are struggling with this. There are a couple of reasons.

现在很多公司在智能自动化方面做了很大的投入，企业级的RPA也在逐步走向规模化。但整体上，大约一半的公司投入使用的机器人数量不到10台。这不是我们预期的大规模。其中的规模化拓展阻力涉及几个主要的原因：

One is that some of theearly robotic processes and automation solutions, these robots were a littletoo hard to maintain and were not supervised. Also, organizations arestruggling to find enough processes to automate. At first, it was easy to findprocesses that could be automated. But continuously discovering automationrequirements is not easy. So being able to discover processes has been anissue.

一是早期的一些机器人流程和自动化解决方案，存在产品能力的不足。这些机器人维护成本高，缺乏管控手段，不足以支持企业的规模化拓展。此外，组织面临着发掘自动化场景能力不足的问题。起初，很容易找到可以自动化的业务场景。但持续不断发现自动化需求并不容易。

So we suggestorganizations build automation strike teams, also understood as the center ofexcellence. The team consists of technical and business personnel. Thisintelligent automation experts have knowledge about automation technology andAI technology. The business personnel understands the business situation,digital requirements, and the human workforce. And they will work together todesign and build and maintain the digital workforce. So there's what we callfederated approach, where a lot of the design and development of the robots isactually in the business. And not in traditional technology management,centralized deep expertise, application development teams.

因此，我们建议组织建立自动化工作小组，或机器人卓越中心。团队由技术和业务人员组成。智能自动化技术专家具有自动化和AI领域的技术知识。各个部门的业务人员了解业务场景、数字化需求和人员安排。他们将共同设计、建立和维护数字劳动力解决方案。这就是我们所说的联邦工作方法，智能自动化不仅需要高水平的技术和产品，也需要了解业务场景、痛点需求的业务专家，在协力共建的模式下，才能确保自动化解决方案的有效价值产出。

申请下载《后疫情生产力时代，智能自动化打造以人为本的企业》报告地址：

https://www.encoo.com/whitepaper2019

特别声明：

文章来源：云扩科技

RPA中国推荐阅读，转载此文是出于传递更多信息之目的。如有来源标注错误或侵权，请联系更正或删除，谢谢。

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