Modern enterprises produce, receive, and manage a very large number of documents, such as textual documents, spreadsheets, images, and other types of multimedia content. These documents come from different sources (e.g., databases, enterprise applications, e-mails) and in diverse formats (e.g., Excel documents, PDF documents, various image formats). In most cases, they include unstructured information, which comprises, however, several pieces of useful knowledge. As such they must be treated as enterprise assets and used in proper ways that improve the efficiency of business processes and boost managerial decision making. Likewise, documents and other content items must be accessible by the right people at the right time.
Enterprise content management (ECM) systems address these challenges. They provide the means for managing the lifecycle of content items end-to-end i.e. they deal with their creation, approval, and distribution. ECM Systems centralize access to content by means of a secure repository. They also facilitate the streamlining of document access with other business processes, as a means of increasing productivity and reducing content processing costs. Furthermore, they facilitate knowledge workers to discover the documents they need and to extract enterprise knowledge from them.
Once upon a time enterprise content management processes relied on legacy databases. In recent years, the advent of cloud computing and BigData technologies has revolutionized ECM. Specifically, cloud computing facilitates on-demand access to the back-end services needed for managing and processing content. At the same time, BigData technologies facilitate the management of structured, unstructured, and semi-structured documents at scale. Cloud computing and BigData technologies have therefore given a significant boost to the capacity, performance, and scalability of ECM solutions, which are nowadays capable of managing the ever-growing volumes of enterprise content items. Furthermore, these technologies have also empowered the development and deployment of Artificial Intelligence (AI) solutions, which are set to disrupt ECM in the years to come.
AI Technologies for ECM
AI technologies provide a host of exciting possibilities for improving the automation, speed, intelligence, and usability of ECM solutions. Prominent examples of such technologies include:
- Automated Image Tagging and Classification: In recent years deep learning systems for image processing have greatly improved both in terms of performance and in terms of precision. This provides a sound foundation for the automated processing of large volumes of images to enable functionalities like classification, indexing, and content-based search. The latter can greatly boost the multimedia content management functionalities of modern ECM systems.
- Natural Language Processing (NLP) and Text Analytics: NLP functionalities are increasingly used for processing content automatically, including for example written documents, texts in web sites, and social media posts. This processing facilitates the automatic understanding of the theme and the context of a document and obviates the need for engaging humans in the document review process. As a prominent example, NLP and text analytics technologies can infer the main topics dealt within a lengthy document, as a means of automatically extracting keywords, checking grammar, and identifying potential privacy and data protection issues. Overall, NLP automates the task of processing and analysing unstructured documents, which saves significant time and reduces content processing costs.
- AI-based Document Translation: Modern AI tools enable the automatic translation of documents in multiple languages and at a very high quality. Furthermore, there are tools that go beyond the translation of words and phrases, to conveying complex semantics and idioms. Translation technologies are therefore very important for ECM applications that deal with multiple languages. This is for example the case with several content management systems in the European Union (EU), where multiple languages are officially spoken in the different member states.
- Voice and Speech Control Platforms: Nowadays AI applications like Apple’s Siri and Amazon’s Alexa are revolutionizing the user interfaces of enterprise applications. ECM will be no exception. Voice platforms will enable ECM system users to activate document creation, indexing and management features based on convenient speech interfaces.
- Robotic Process Automation (RPA): Software robotics systems such as RPAs provide the means for automating complex workflows. This is very handy for ECM systems, which involve complex document processing pipelines. The latter trigger specific actions (e.g., sending alerts and notifications, printing certain documents, sending e-mails) as part of the handling of enterprise content items. Future ECM systems will deploy several RPA processes to achieve more with fewer human resources.
- Prescriptive Analytics and Recommender Systems: These are Machine Learning systems that enable the development of powerful recommendation engines. In the context of ECM systems, such engines can be used to provide actionable recommendations regarding the structuring and organization of content collections (e.g., recommendations on how different documents within a collection relate to each other).
A List of Indicative AI-based Use Cases for ECM Systems
With the above listed AI technologies at hand, ECM systems are gradually enhanced with the following AI functionalities:
- Advanced and Intelligent Search Functionalities: AI provides novel ways for searching content, beyond conventional search i.e. using keywords and prominent metadata of the document (e.g., file name, creation data, format, size etc.). For example, content-based search and similarity-based search on images is provided, along with credible document similarity matching features.
- Content Extraction and Inference: AI facilitates the automated analysis of content items (e.g. textual documents) towards understanding their theme and main concepts. The latter are used for automatically tagging and classifying the documents, including both textual and multimedia documents.
- Document Analysis and Summarization: Text Analytics and NLP intelligent functionalities are used to analyse the content of the document and to summarize its contents. This enables users to access short and comprehensive summaries of key documents, without a need for a human reader to summarize them.
- Linking of Related Content Items: In many cases, relevant documents remain fragmented and reside unrelated in the database of an ECM platform. AI helps alleviating these document “silos” through identifying relevant documents and linking them based on their commonalities (e.g., themes, concepts, terms).
- Workflow Optimization: Future ECM systems will be also able to construct different RPA processes and evaluate their performance. By benchmarking the performance of different RPA’s for a given task, it will be possible to identify optimized workflows that save time and costs.
- Intelligent Content Generation: AI will facilitate content creators to produce richer and more comprehensive documents. In this direction, AI analytics will be employed as the document is created. These analytics will identify the topic, the theme, and the main concept of the document. The latter will be accordingly used to provide recommendations to the authors about how to best complete the document (e.g., by adding relevant images or links to external sources).
- Ergonomic Interfaces: AI will improve the interfaces used to access ECM functionalities. In this direction, voice-enabled commands could be employed, along with intelligent ergonomic visualizations of information about the topic and content of the document at hand.
The integration of advanced AI functionalities into ECM systems is coming soon in a theatre near you. It is destined to elevate enterprise productivity and to provide new opportunities for innovating in the way documents are created, accessed, and analyzed. Modern enterprises must therefore keep an eye on AI functionalities and their gradual integration in ECM products. This will enable them to get the most out of the emerging AI-based ECM platforms.
