Content management is a broad discipline. I find it useful to distinguish between content that helps enhance the customer experience or content critical to a business process. Roughly, whether the content is external facing or internally oriented. In practice, web content management (WCM), digital asset management (DAM), marketing automation, content marketing are some of the key external-facing content systems. Enterprise document management, records management, cloud storage systems, and search are the key internal-facing content systems. Note this is not a water-tight categorization scheme but a reflection of how the content ecosystem has evolved over the last two decades.

In the world of content management, the quest has always been to infuse intelligence around content and extract more value for it. However, for many organizations, content counted more in the cost column, rather than on the asset side. But thanks to recent advances in machine learning, we now are at an inflection point in content management. More specifically, improvement in areas Natural Language Processing, Natural Language Understanding, Text Analysis, Image Recognition and Video Processing – all areas which traditionally have not been the strong point of Content Management technology. There is also a democratization of sorts happening in the AI field – in the sense that many of the new capabilities are available packaged as easy-to-use-APIs, which decreases your time-to-market.

Here are the top use cases machine learning use cases for content management:

Top 25 Artificial Intelligence Use Cases for Content Management. Source:

Web Content Management

  1. Micro-targeting and personalization of content during content delivery
  2. Content recommendations and dynamic content generation for website visitors
  3. A/B testing of alternative versions of content items and automatic serving of successful variants to increase customer engagement and conversions.

Digital Asset Management

  1. Automated tagging and generation of metadata for images and videos
  2. Generate labels that describe images and identify text occurring on images
  3. Identify key moments/events of interest in videos
  4. Search within videos for specific content (e.g. people, scenery, background)
  5. Automated conversion of text-to-speech and use the transcripts for video search 

Content Marketing

  1. Recognize the tone and sentiment of articles to ensure messaging consistency and alignment to brand positioning
  2. Automated topic generation helps identify which topics are overrepresented or whether any desired topics are missing from your content corpus.
  3. Analytics of content performance to provide insights for future editorial decisions 

Marketing Automation

  1. Real-time promotions and customized offers based on better customer segmentation
  2. Virtual Assistants and Chat Bots to make the customer journeys more interactive
  3. Enable omnichannel customer journeys thru integration of online and offline customer touch points.
  4. Analytics and attribution models of different marketing channels and campaigns to help prioritize marketing budgets.

Enterprise Document Management

  1. Generate automatic summaries of documents
  2. Automatic text analysis for better categorization and classification of documents
  3. Intelligent Capture helps identify and index documents while scanning them
  4. Transform documents into smart artifacts by identifying the underlying content (e.g. dollar value of a contract, customer type or industry and trigger desired workflows accordingly)

Cloud Storage

  1. Enhanced security through identification of sensitive content using automatic content classification (e.g. confidential documents, personally identifiable information) and prevent their inadvertent exposure
  2. Better data loss prevention by raising alerts when improper or suspicious content access patterns are detected.

Enterprise Search

  1. Topic clustering and content categorization to enhance relevance
  2. Support Natural Language Queries in search
  3. Allow access to content repositories via voice interfaces and commands
  4. Visual Search of Image Assets

This is only a partial list but you’d have noticed that while some use cases are new, some are improvements on existing use cases. What becomes clear though is that AI can be applied to both external and internal content use cases and across all areas of content management. Machine learning bolsters the traditional weak areas of content management and the coming together of AI and Content Management potentially very transformative.