百度ai人流量统计
Gartner predicts the business value created by artificial intelligence will reach $3.9 trillion by 2022. AI technology is finding its way into every corner of the modern-day enterprise, and company leaders across all industries are realizing its benefits. In fact, a higher proportion of executives believe that AI will be more of a “game changer” than any other emerging technology today — including cloud, IoT, mobile, blockchain, and more.
Gartner预测，到2022年，人工智能创造的商业价值将达到3.9万亿美元 。人工智能技术正在进入现代企业的每个角落，各个行业的公司领导者都在意识到其优势。 实际上，更高比例的高管认为 ，人工智能将比当今任何其他新兴技术(包括云，物联网，移动，区块链等)更能成为“游戏规则改变者”。
From streamlined operations to improved customer experiences, AI offers tremendous business opportunities. Here’s a look at the current state of AI in the enterprise and where it’s headed.
从简化的运营到改善的客户体验，人工智能提供了巨大的商机。 这是企业中AI的当前状态以及前进的方向。
Over the past three years, the proportion of companies with AI initiatives has grown from one in 25 to one in three, according to Gartner. The adoption of AI in the enterprise is happening at an unprecedented speed — possibly one of the fastest in technology history.
根据Gartner的研究 ，在过去三年中，采用AI计划的公司所占比例已从25分之一增加到三分之一。 在企业中采用AI的速度前所未有，可能是技术史上最快的速度之一。
Today, there’s no shortage of AI-driven software suppliers and plug-and-play AI services. Nearly every global technology vendor, including Amazon, IBM, Google, and Microsoft, is also offering AI products or services. Companies now have a variety of AI solutions from which to choose.
如今，不乏AI驱动的软件供应商和即插即用的AI服务。 几乎每个全球技术供应商，包括亚马逊，IBM，谷歌和微软，都在提供AI产品或服务。 公司现在有多种AI解决方案可供选择。
A number of AI applications have emerged to help improve business strategies and streamline operations. As a result, many companies are turning to AI-based applications for a competitive advantage. According to Gartner, one in 10 companies now use 10 or more AI applications, and revenue from AI-based applications is expected to reach $31.2 billion by 2025.
已经出现了许多AI应用程序，以帮助改善业务战略和简化运营。 结果，许多公司正在转向基于AI的应用程序以获得竞争优势。 根据Gartner的研究 ，现在有十分之一的公司使用10个或更多的AI应用程序，到2025年，基于AI的应用程序的收入预计将达到312亿美元 。
The most popular enterprise AI application use cases include:
最受欢迎的企业AI应用程序用例包括：
• Chatbots (26%)
•聊天机器人(26％)
• Process automation solutions (26%)
•过程自动化解决方案(26％)
• Fraud analysis (21%)
•欺诈分析(21％)
And although not as prevalent, companies are increasingly using AI applications for consumer or market segmentation, computer-assisted diagnostics, call center virtual assistants, sentiment analysis or opinion mining, facial detection and recognition, HR processes, and much more.
尽管不那么普遍，但公司越来越多地将AI应用程序用于消费者或市场细分，计算机辅助诊断，呼叫中心虚拟助手，情感分析或观点挖掘，面部检测和识别，HR流程等等。
According to Deloitte, 82% of businesses have gained a financial return on their AI investments. What’s more, businesses are achieving a positive ROI from AI across a variety of industries. From manufacturing to media and entertainment, AI can help improve operations and deliver higher quality customer service.
根据Deloitte的说法， 82％的企业通过AI投资获得了财务回报。 更重要的是，各行各业的AI都为企业带来了可观的ROI。 从制造业到媒体和娱乐，人工智能可以帮助改善运营并提供更高质量的客户服务。
For example, Netflix discovered that customers who search for something to watch for more than 90 seconds give up and leave the platform. The company used AI to improve search results and found that they could reduce frustration and customer churn, resulting in $1 billion in savings per year for the company from potential lost revenue.
例如， Netflix发现，搜索内容超过90秒的客户放弃了该平台。 该公司使用AI改善了搜索结果，发现它们可以减少挫败感和客户流失，从潜在的收入损失中为公司每年节省10亿美元 。
McKinsey & Company surveyed 2,135 senior enterprise executives and 23% based in North America indicated that they have embedded machine learning in a least one business function.
麦肯锡公司(McKinsey＆Company)对2135名高级企业高管进行了调查，其中23％的北美人表示，他们已将机器学习嵌入到至少一项业务功能中。
North America is a global leader when it comes to enterprise machine learning. For comparison, the study showed that only 19% of Chinese and 21% of European senior enterprise executives had successfully integrated machine learning in business functions.
在企业机器学习方面，北美是全球领导者。 作为比较，该研究表明，只有19％的中国人和21％的欧洲高级企业高管成功地将机器学习集成到了业务功能中。
Business leaders around the world are doing more to understand AI and how it can be used strategically. In the last 12 months, between half and two-thirds of leaders said they’ve improved their understanding of AI to a greater extent, according to MIT Sloan Management Review.
世界各地的商业领袖正在做更多的工作来了解AI及其如何被战略性地使用。 根据《麻省理工学院斯隆管理评论》(MIT Sloan Management Review)的数据，在过去的12个月中 ，大约一半至三分之二的领导人表示，他们在很大程度上提高了对AI的理解。
Business leaders are increasingly aware of the disruption AI may cause and the challenges it will bring to their businesses. AI is predicted to shift how companies generate value, and the technology will require workers to develop new skill sets to work alongside it. Therefore, business leaders must be prepared for AI’s effects. Leaders that haven’t already introduced AI in their business functions should at least familiarize themselves with the technology’s possible implications on their specific business, workplace, and industry.
商业领袖越来越意识到AI可能造成的破坏以及它将给他们的业务带来的挑战。 预计人工智能将改变公司创造价值的方式，而这项技术将要求工人开发新技能以与之协同工作。 因此，业务领导者必须为AI的影响做好准备。 尚未在业务功能中引入AI的领导者应至少熟悉该技术对其特定业务，工作场所和行业的潜在影响。
The supply of AI talent simply isn’t keeping up with demand. Currently, there are two roles available for every one AI professional. The technology and financial services industries are absorbing 60% of available AI talent, resulting in limited access to AI talent in other sectors and causing an academic “brain drain.”
AI人才的供应根本跟不上需求。 当前，每位AI专业人员都有两个职位 。 技术和金融服务行业正在吸收60％的可用AI人才，从而导致其他领域对AI人才的访问受到限制，并导致学术“人才外流”。
The good news is that supply is increasing. According to MMC Ventures, machine learning is the top emerging field of employment in the United States. Data science boot camps, online courses, and the inclusion of AI in university computer science courses are all helping to close the AI talent gap. For now, investing in staff training programs and upskilling current employees can help organizations facing a talent shortage get the help they need.
好消息是供应正在增加。 根据MMC Ventures的说法， 机器学习是美国就业的新兴领域。 数据科学新兵训练营，在线课程以及将AI纳入大学计算机科学课程中的所有内容均有助于缩小AI人才缺口。 目前，投资于员工培训计划和提高现有员工的技能可以帮助面临人才短缺的组织获得所需的帮助。
Staff readiness remains a top concern among tech leaders who are taking AI and machine learning projects to production in their organizations. A recent Tech Pro Research poll found that most respondents feel their AI/ML projects will be more difficult than previous IT projects. Furthermore, 47% of respondents worried that their IT team lacks the necessary skills to implement and support these projects.
在将AI和机器学习项目带入其组织的技术领导者中，员工的就绪状态仍然是最重要的问题。 一项最新的Tech Pro Research调查发现，大多数受访者认为他们的AI / ML项目比以前的IT项目更加困难。 此外， 47％的受访者担心他们的IT团队缺乏实施和支持这些项目的必要技能。
AI and machine learning are emerging technologies, so it makes sense that so many business leaders feel uneasy about how they can implement AI and ML projects successfully. Between a skills gap and lack of tools necessary, businesses often fail to get machine learning models into production or face extremely long delays when doing so.
人工智能和机器学习是新兴技术，因此，许多企业领导者对于如何成功实施AI和ML项目感到不安，这是有道理的。 在技能鸿沟和缺乏必要工具之间，企业通常无法将机器学习模型投入生产或面临极长的延迟。
To combat any hesitations surrounding AI and ML, company leaders should consider smaller pilot projects and proofs of concept before full implementation. Data science platforms such as Quickpath can help with pilot projects as well as bridge the gap between the team who builds the AI or ML models and the deployment environment — including infrastructure and IT requirements. This not only allows businesses to test their solutions on a small scale but it can help teams feel more comfortable about deploying future AI and ML projects as well.
为了避免围绕AI和ML的犹豫，公司领导者应在全面实施之前考虑较小的试点项目和概念验证。 诸如Quickpath之类的数据科学平台可以帮助开展试点项目，并弥合构建AI或ML模型的团队与部署环境(包括基础架构和IT需求)之间的鸿沟。 这不仅允许企业小规模测试其解决方案，而且还可以使团队对部署未来的AI和ML项目也感到更自在。
Quickpath grants organizations a repeatable and consistent path to AI and ML production, helping leaders make more data-driven decisions and extract more business value from these emerging technologies.
Quickpath为组织提供了一条可重复且始终如一的AI和ML生产路径，可帮助领导者做出更多以数据为依据的决策，并从这些新兴技术中提取更多业务价值。
For more information about how Quickpath helps enterprises achieve their goals with AI, check out this overview of our platform or contact us for a free demo today.
有关Quickpath如何通过AI帮助企业实现目标的更多信息，请查看我们平台的概述或立即与我们联系以获取免费演示。
翻译自: https://medium.com/@alex.fly/learn-seven-surprising-stats-about-how-enterprises-are-actually-using-ai-today-c1c1ee5d2db5
百度ai人流量统计
