As a global community, we find ourselves navigating uncharted waters, where we have had to adapt and find new ways to interact with each other exclusively through digital channels. It would seem that we have made a seismic and permanent shift towards embracing the power of digital communication and virtualization. With all these changes, how do marketers evolve? How do brands stay in touch with their customers and build strong relationships, in spite of the physical separation that has become a part of our collective realities?

作为一个全球社区，我们发现自己正在未知领域中航行，我们不得不适应并找到仅通过数字渠道相互互动的新方法。 似乎我们已经朝着拥抱数字通信和虚拟化功能的方向迈出了永久性的转变。 通过所有这些变化，营销人员将如何发展？ 尽管物理上的分离已成为我们集体现实的一部分，但品牌如何与客户保持联系并建立牢固的关系？

Adobe recognizes content is the lifeblood of the digital experience. Understanding content and how customers’ interact with it is the key to finding better answers to the quintessential questions marketers grapple with: the “who”, “what”, “when”, “where”, “how”, and “why” of engaging with their customers. In this digital-first world, these questions are more important and relevant than ever before.

Adobe认识到内容是数字体验的生命线。 了解内容以及客户如何与之互动是找到营销人员所解决的典型问题的更好答案的关键：“谁”，“什么”，“何时”，“在哪里”，“如何”和“为什么”与客户互动。 在这个数字第一世界中，这些问题比以往任何时候都更加重要和相关。

Content intelligence may be the answer. It’s a key capability that customers can employ to transform how they work with content. It allows them to form a deep understanding of their customers’ preferences and by doing so, personalize how they interact with each and every one of them, resulting in stronger relationships.

内容智能可能是答案。 这是客户可以用来改变其内容处理方式的一项关键功能。 它使他们能够深刻理解客户的喜好，并以此个性化他们与每个客户的互动方式，从而建立更牢固的关系。

In this article, we outline how we are bringing the power of Adobe’s content intelligence natively into our Digital Experience products, specifically Adobe Experience Manager (AEM), with the creation of a robust extensibility framework which allows the application of AI precisely where it’s needed in the asset workflows. Done right, AI can be transformational — but the key is to get the packaging and features of AI spot-on so that it can best augment the effectiveness of our marketer customers.

在本文中，我们概述了如何利用Adobe的强大功能 在我们的数字体验产品 (特别是Adobe Experience Manager(AEM))中原生引入了内容智能 ，并创建了一个强大的可扩展性框架，该框架允许在资产工作流中准确地将AI应用到需要的地方。 做对了，人工智能可以带来变革-但是关键是要充分了解AI的包装和功能，以便可以最大程度地提高我们的营销客户的效率。

什么是内容智能？ (What is Content Intelligence?)

Content intelligence is a set of Artificial Intelligence (AI) microservices built to understand what aspects of a digital experience resonate with a customer and how the use of those insights could be used to deliver meaningful personalized experiences. For example, we are able to tell you if it was the color, celebrity, hero image or copy that resonated the most with your audience. Adobe’s AI services are built to scale up to enterprise workloads, which means the ability to handle these insights at throughputs of millions of assets and an even higher magnitude of customer interactions.

内容智能是一组人工智能(AI)微服务，旨在理解数字体验的哪些方面与客户产生共鸣，以及如何利用这些见解来提供有意义的个性化体验。 例如，我们能够告诉您是颜色，名人，英雄形象还是复制品引起了您的观众最大的共鸣。 Adobe的AI服务旨在扩展到企业工作负载，这意味着能够以数百万个资产的吞吐率以及甚至更大的客户交互量来处理这些见解。

The outcomes including an ROI that content intelligence delivers to customers are:

结果包括内容智能为客户提供的ROI：

• Marketer Productivity: Automate content-related tasks with high accuracy, marketers and site creators are free to scale up their operations, slash delivery times, and focus more on the strategic aspects of their customer engagements.

  营销人员的生产力 ：高精度自动执行与内容相关的任务，营销人员和网站创建者可以自由地扩大其业务规模，缩短交付时间，并更加关注与客户互动的战略方面。

• Content Velocity: Increase the speed at which content flows from creator to consumer by using AI to provide a robust and rich metadata system.

  内容速度 ：通过使用AI提供强大而丰富的元数据系统，提高内容从创建者流向消费者的速度。

• Customer Engagement: Deliver increased impactful content and experiences to the customer facilitates better engagement with the content, both in the short-term (in-session or return visits) and longer-term in terms of uplifts in brand loyalty.

  客户参与度 ：向客户提供更具影响力的内容和体验，可以在短期(会话访问或回访)和长期提升品牌忠诚度方面促进与内容的更好互动。

How can Content Intelligence be used to improve the experience?

内容智能如何用于改善体验？

We start out with a core hypothesis: could the deployment of content-intelligence AI microservices in the AEM environment and within workflows. These workflows are currently beloved by marketers and website creators. How can we allow them to be more effective in productivity? Could it help them deliver better value to their customers?

我们从一个核心假设开始：可以在AEM环境和工作流中部署内容智能AI微服务。 这些工作流程目前受到营销人员和网站创建者的喜爱。 我们如何才能让他们提高生产力呢？ 是否可以帮助他们为客户提供更好的价值？

Additionally, how can we develop an AI microservices integration framework with the flexibility to combine AI engines in workflows that are purpose-built for an enterprise's content management needs?

此外，我们如何开发具有灵活性的AI微服务集成框架，以将AI引擎结合到专门针对企业内容管理需求的工作流中？

The primary content intelligence-related use-cases where we see the largest transformational potential for our AI service is as follows:

我们看到与AI相关的主要内容相关用例如下：

• Content Tagging​: Automatically and accurately tag content (both internal and customer-facing) with relevant labels, topics, and concepts for the purposes of organization and delivery​. Accurate organization of content by way of tagging, classification, and categorization is a core capability that greatly enhances your ability to precisely access content and is fundamental to a data-driven approach to understanding KPIs.

  内容标记：使用组织，交付目的的相关标签，主题和概念，自动，准确地标记内容(内部和面向客户)。 通过标记，分类和分类来准确组织内容是一项核心功能，可以极大地增强您精确访问内容的能力，并且是理解KPI的数据驱动方法的基础。

• Content Reuse & Authoring​​: Assist content creators and users in the process of adapting existing content efficiently or even in the processing of authoring content for maximum impact. This is a prerequisite for efficient use and re-use of content.

  内容重用和创作：协助内容创作者和用户有效地调整现有内容，甚至在创作内容的过程中发挥最大的作用。 这是有效使用和重复使用内容的前提。

• Metadata Enhancement & Search​: Enhancing search and discoverability with AI-derived metadata can boost the productivity of your remote workforce. Search and Discoverability is fundamental to a highly-functioning organization, especially in the new paradigm in which corporations are decentralized into thousands of micro-offices all across the globe as employees work from home.

  元数据增强和搜索 搜索和可发现性对于功能强大的组织至关重要，尤其是在新的范式中，随着员工在家工作，公司被分散到全球成千上万的微型办公室。

• Visual Site Search & Content Similarity:​​ Adding visual search and content similarity for authors, content creators, and marketers, backed with rich metadata is a game-changer when dealing with text and imagery. This allows the use of multiple modes of search, increasing the synergy between textual content and imagery/media.

  视觉站点搜索和内容相似性：为作者，内容创建者和营销人员增加视觉搜索和内容相似性，并以丰富的元数据为后盾，在处理文本和图像时会改变游戏规则。 这允许使用多种搜索模式，从而增强了文本内容与图像/媒体之间的协同作用。

• Media Transform & Personalization​​: Allowing the adaptation of marketing content to cater to the exact preferences of a particular customer.

  媒体转换和个性化：允许调整营销内容以适应特定客户的确切偏好。

• Content-Aware Insights & Personalization​​: Applying the features and metadata associated with assets that form a part of the customer’s experience allows for the discovery of content-aware insights. This enables marketers to understand their customer’s affinities and deliver richer experiences based on customer behavior captured in user profiles.

  内容感知洞察和个性化：应用与构成客户体验一部分的资产相关的功能和元数据可以发现内容感知洞察。 这使营销人员能够了解其客户的亲和力，并根据用户个人资料中捕获的客户行为提供更丰富的体验。

我们的解决方案 (Our Solution)

Content intelligence brings together two technical innovations: AEM as a Cloud Service connected to Content and Commerce AI (beta). It is the first in a deep roadmap of content intelligence AI microservices.

内容智能结合了两项技术创新： AEM作为连接到内容和商务AI (测试版)的云服务。 这是内容智能 AI微服务深度路线图中的第一个。

There are four main classes of AI engines within the current beta release of Content and Commerce AI:

当前Beta版Content and Commerce AI中有四类主要的AI引擎：

• Keyword Extraction — Automatically extract salient keywords and tags from enterprise documents and content fragments​.

  关键字提取 -自动从企业文档和内容片段中提取重要的关键字和标签。

• Color Extraction — Automatically label and quantify the color composition of an image. ​

  色彩提取 —自动标记和量化图像的色彩组成。

• Visually Similar Content– Deliver visually similar product recommendations to customers, based on intuitive product features like shape, design, and color​.

  视觉相似的内容 –根据直观的产品功能(例如形状，设计和颜色)向客户提供视觉相似的产品推荐。

• Custom Classifiers — Automatically label an enterprise’s documents or images per a corporate taxonomy with custom AI models​.

  自定义分类器 -使用自定义AI模型根据公司分类自动标记企业的文档或图像。

在Adobe Experience Manager中使用Content and Commerce AI (Using Content and Commerce AI with Adobe Experience Manager)

Let’s explore how we could use AEM’s Cloud Service featuring Asset Compute Service to make seamless integration with Content and Commerce AI to help an online retailer of sports equipment serve their customers better and, in doing so, materially impact their KPIs

让我们探讨如何使用具有资产计算服务功能的AEM云服务与内容和商务AI进行无缝集成，以帮助运动器材的在线零售商更好地为其客户提供服务，从而对他们的KPI产生实质性影响

1. The online e-commerce brand ‘Venzia’ sells a range of sports apparel, from products for indoor athletes who love their exercise bikes, to gear for ski enthusiasts always looking for the perfect equipment for their next downhill run.在线电子商务品牌“ Venzia”出售各种运动服装，从为热爱运动自行车的室内运动员提供的产品，到为滑雪爱好者一直在寻找下一个坡道最佳装备的滑雪装备。
2. With Keyword Extraction, Venzia’s marketers were able to go beyond the basic category information they had, and extract product features like material, fit, and performance automatically from product descriptions and up-level those important facets to their customers.借助关键字提取功能，Venzia的营销人员能够超越他们所拥有的基本类别信息，并自动从产品说明中提取产品功能(例如材料，适合度和性能)，并将这些重要方面提升给客户。
3. With Custom Classifiers, they could understand whether their content was targeted towards Beginners, Hobbyists, or Highly-Technical customers.使用自定义分类器，他们可以了解其内容是针对初学者，业余爱好者还是高科技客户的。
4. With Color Extraction, they could accurately label all their product imagery with the right color labels, offloading their asset creation team from having to manually go to each product shot and add in the color. At the same time, the unique color weightage feature helps customers precisely find what they were looking for — for example, a “red jacket” which semantically meant they were looking for a garment that was more than 70% red.借助Color Extraction，他们可以使用正确的颜色标签准确标记其所有产品图像，从而减轻了资产创建团队的负担，不必手动转到每个产品照片并添加颜色。 同时，独特的颜色权重功能可帮助客户精确找到他们想要的东西，例如，“红色外套”在语义上意味着他们正在寻找红色超过70％的服装。
5. And finally, with Visually Similar Content, they could keep their customers engaged in the experience, by showing visually similar product imagery via a recommendation widget on their product description page.最后，借助视觉相似的内容，他们可以通过在产品描述页面上的推荐小部件显示视觉相似的产品图像，来保持客户的体验。

In the longer term, with each click, search, page view, or ‘add to cart’ event, the product details gleaned using the AI services enhanced Venzia’s understanding of their customer’s characteristics and affinities. This is remarkably useful in delivering the best offers or marketing emails to those customers — for example, a subscription to an online class for yoga enthusiasts or a ski holiday package to the highly competent skier.

从长远来看，每次单击，搜索，页面浏览或“添加到购物车”事件时，使用AI服务收集的产品详细信息都会增强Venzia对客户特征和亲和力的理解。 这对于向这些客户提供最佳报价或营销电子邮件非常有用，例如，为瑜伽爱好者提供在线课程的订阅，或者向高级滑雪者提供滑雪度假套餐。

In broad terms, the Venzia use-cases described above resonate with our customers over and over again, in different situations where AI-backed metadata enrichment and feature extraction can greatly improve workflows in the creation and delivery of content to customers to interact with.

从广义上讲，上述Venzia用例在不同情况下一遍又一遍地引起我们的客户共鸣，在这种情况下，人工智能支持的元数据丰富和功能提取可以大大改善创建和交付内容以与客户进行交互的工作流程。

挑战性 (Challenges)

When we first started developing our AI microservices strategy we looked at the broader industry to try to understand the gaps in the different offerings. We realized that there were two major challenges that made it hard for companies to get any value from adopting AI:

当我们刚开始制定AI微服务策略时，我们研究了整个行业，以试图了解不同产品之间的差距。 我们意识到存在两个主要挑战，这使公司很难通过采用AI来获得任何价值：

1. Solutions were not developed for the marketer: A number of different solutions did not prioritize understanding the persona of their target consumer. Designing a product that is best suited for that customer persona is fundamental to maximize the value they capture.

   尚未为营销人员开发解决方案：许多不同的解决方案并没有优先考虑其目标消费者的性格。 设计最适合该客户角色的产品对于最大程度地获取他们的价值至关重要。

2. Solutions are not native to the customer experience: AI is best applied through seamless infusion into the product experience where the benefits of the technology are made available, but the technical details are hidden to the greatest extent possible.

   解决方案不是客户体验的本机：最好通过将产品无缝融合到可以获得技术优势的产品体验中来应用AI，但是最大程度地隐藏技术细节。

We took an in-depth look at the use-cases our customers work through with our products every day and the flow of content and metadata through the asset lifecycle. We then stepped back and designed our AI microservices to create outputs that explicitly augment those use-cases and workflows with richer and more accurate metadata. By doing so we have created AI microservices that produce outputs in the language our customers are used to making adoption easier.

我们深入研究了客户每天使用我们的产品处理的用例以及资产生命周期中内容和元数据的流向。 然后，我们退后一步，设计了AI微服务，以创建输出，从而通过更丰富，更准确的元数据显着增强这些用例和工作流。 通过这样做，我们创建了AI微服务，以客户习惯于使使用更容易的语言产生输出。

We manage this, on the technical side, by developing a bouquet of both pre-trained AI models, such as for Color Extraction and Keyword Extraction that can be readily deployed, and by providing the ability to train and deploy custom engines with limited amounts of data. The services are published on the Sensei AI Framework and are accessible through Adobe’s API gateway — Adobe I/O.

在技​​术方面，我们通过开发一整套既经过训练的AI模型(例如可轻松部署的颜色提取和关键字提取)，并提供训练和部署数量有限的自定义引擎的能力，来管理这一问题。数据。 这些服务在Sensei AI框架上发布，可以通过Adobe的API网关Adobe I / O进行访问 。

To surmount the second challenge, we wanted to infuse AI natively into the product workflows. We constructed an architecture that allows our customers to pull in AI engines at the appropriate places in their workflows, while largely abstracting away the technical details and allowing flexibility and scalability. In order for this to work, we developed the Asset Compute Service that allows developers to connect to AI engines seamlessly and access the vast array of AI capabilities in a uniform manner.

为了克服第二个挑战，我们希望将AI本身注入产品工作流程中。 我们构建了一个架构，该架构允许我们的客户在工作流程中的适当位置引入AI引擎，同时很大程度上抽象出技术细节并提供灵活性和可扩展性。 为了使它起作用，我们开发了资产计算服务 ，该服务使开发人员能够无缝连接到AI引擎，并以统一的方式访问各种AI功能。

Assets Microservices are running in the server-less Adobe I/O Runtime. Previous versions of Adobe Experience Manager have been able to process n/2 Workflows in parallel where n was the number of CPU cores available. With Asset Microservices, this is now completely offloaded and scales automatically. Processing Profiles can be used in Adobe Experience Manager to add custom workers (calling AI microservices from our library of ML/AI offerings) or asset processing through simple and flexible configurations.

资产微服务在无服务器的Adobe I / O运行时中运行。 以前的Adobe Experience Manager版本已经能够并行处理n / 2个工作流，其中n是可用的CPU核心数。 借助Asset Microservices，现在可以完全卸载并自动扩展。 可以在Adobe Experience Manager中使用处理配置文件来添加自定义工作程序(从我们的ML / AI产品库中调用AI微服务)或通过简单灵活的配置进行资产处理。

Furthermore, Assets Microservices have been designed and built with extensibility in mind. Adobe I/O Runtime allows running 3rd party code with full tenant isolation as part of the asset ingestion process for those cases where simple configuration is not sufficient and the execution of custom code is required. This enables a new spectrum of custom processing with the power of elastic scale.

此外，资产微服务的设计和构建考虑了可扩展性。 对于那些简单配置不够，需要执行自定义代码的情况， Adobe I / O Runtime允许在资产获取过程中以完全的租户隔离方式运行第三方代码。 这可以利用弹性标尺的功能实现定制处理的新范围。

发现 (Findings)

During the development of Content and Commerce AI we have been able to see the value of creating these AI microservices with the marketer in mind. We developed our product thinking of specific marketing use cases that augment their workflows and improve content velocity. During our pilot program, we worked with companies ranging from fashion retailers to news publishers and we were able to validate our core hypotheses. The use of content intelligence in the form of Content and Commerce AI Services, when used to enhance the metadata associated with our customer’s digital assets, allowed for increased worker productivity and boosted content velocity. We also saw a reduction in consulting spend through automation of asset processing and a sharp increase in customer engagement rates.

在内容和商业AI的开发过程中，我们已经意识到了在营销人员心目中创建这些AI微服务的价值。 我们针对特定的营销用例开发了产品思维，以扩大其工作流程并提高内容速度。 在我们的试点计划中，我们与时装零售商到新闻出版商等公司合作，并且能够验证我们的核心假设。 以内容和商务AI服务的形式使用内容智能，当用于增强与我们客户的数字资产相关的元数据时，可以提高工作人员的生产率并提高内容速度。 我们还看到通过资产处理的自动化减少了咨询支出，并大大提高了客户参与率。

By using the Asset Compute Service from AEM successfully we addressed huge friction. This allows us to successfully be able to deploy this AI microservices right into the asset creation and processing workflows of our customers and therefore making them native to the customer experience.

通过成功使用AEM的资产计算服务，我们解决了巨大的摩擦。 这使我们能够成功地将此AI微服务直接部署到客户的资产创建和处理工作流程中，从而使它们成为客户体验的本机。

We are running a Beta for AEM (Asset Compute Service) and Content and Commerce AI from August to January 2021. For additional information, please check out our technical documentation here.

从2021年8月到2021年1月，我们将针对AEM (资产计算服务)以及内容和商业AI运行Beta版。有关其他信息，请在此处查看我们的技术文档。

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