As the market matures, AI in content management systems will improve the searchability and organization of enterprise content.
Metadata, or tagging, is arguably one of the most powerful features in a CMS; it’s also an area that has the most room for AI capabilities. Tagging enables content managers to attribute meaning and context to content, especially for nontext content, such as images and video.
Tagging enables organizations to support and manage content within the CMS and integrate content located in other systems and applications. It also enables content to easily surface in a relevant, meaningful manner, whether from a simple search query or a structured interface, such as a shopping site or business application.
AI in content management
As CMSes mature, tagging mechanisms have evolved from purely manual approaches to automated techniques, such as optical character recognition, which recognizes written or image-based content, such as a scanned document.
Machine learning can take that a step further by automatically recognizing tags from the semantics of content. Some digital asset management systems come equipped with AI tools, such as Amazon Rekognition Auto Tagging and Google Auto Tagging, that can automatically find the context of uploaded images.
Machine learning can support more complicated content as well, such as legal contracts or a doctor’s handwritten medical notes.
Use cases for AI in content management
Machine learning and natural language processing are two branches of AI that play a role in content management. AI in content management will crop up in several ways:
- analyze all forms of content, including images, video and audio, to improve findability;
- aid search accuracy and targeting, and narrow search results through better understanding of the searcher intent;
- automate content generation to support and potentially replace content authors;
- chunk and summarize content to deliver just the right amount based on device form factor and communications bandwidth;
- enable additional interfaces to content sources, such as voice, chatbot, gesture and visual search;
- improve analytics to better assess relevancy and value of content and to infer trends and sentiment from social media;
- learn patterns of user behaviors and relevance to target content; and
- preserve data security and protection through automatic classification of content, such as personally identifiable information, as well as commercial, military and governmental sensitive information.
AI capabilities for CMS
CMS vendors will include AI capabilities within their tools in a few ways. CMS vendors can acquire partners with AI specialization, provide interfaces to partners’ tool sets or integrate with third-party add-ons. Some CMS vendors already include AI capabilities:
- Box: Box Skills Kit enables users to interface with AI partner tools that provide document classification, speech-to-text conversion and image recognition.
- IBM Watson Content Hub: IBM Watson can propose tags based on content.
- M-Files: Smart subjects provide tag suggestions based on document content.
- Microsoft SharePoint and Office 365: SharePoint and Office 365 tag images based on object recognition and geolocation data.
- OpenText: Magellan’s AI capabilities include speech and text analytics from contextual hypothesis and meaning deduction.
- WordPress: Plugins provide several capabilities, including spam comment detection, content curation and virtual chat.