Artificial Intelligence

The Role of AI in Digital Content Management

In the Digital Content Management lifecycle — from content composition to content disposition — there are many areas where Artificial Intelligence (AI) and Machine Learning (ML) have the potential to support and speed up content enrichment activities.

Let’s take a detailed look at each stage of Digital Content Management and explore how these technologies can come into play. Below is a high-level look at a typical content lifecycle.

  1. Content Composition / Digitization :
    • Content composition (CC) consists of using tools like Photoshop or custom-built modules that use an API or document composition engine to create the final form of a document. Education and financial services are two industries that can benefit greatly from intelligent content composition.

      In CC, especially in content rationalization, organizations can leverage AI and ML to obtain information on any duplicate composition of the form layout, as well as provide better suggestions on content fragments.
    • Content digitization (CD) consists of converting hard copies into soft forms, such as Microsoft Office format, PDF or various rich content, audio or video files like BMP, JPEG, AVI or MP3/4. Financial services, high tech, education and government services are heavy users of CD and stand to reap the rewards of a more intelligent approach.

      Using AI and ML in CD helps accelerate the digitization process by providing indexing suggestions, aiding classifications by invoking a “best fit” algorithm, and helping manage the information extraction workflow to create a 20-30% time savings for admin users.
  2. Content Classification / Enrichment: Content without metadata is of no use, making this phase of the lifecycle critical to providing meaning to your content. The moment content is loaded into a repository, AI and ML algorithms can crawl the content, identify the content, then automatically tag and set the associated metadata. If required, they can also create and preserve the appropriate taxonomy.
  3. Content Searching: In industries like healthcare, manufacturing, media and entertainment, content is widely distributed via emails, attachments and file systems. With a vast amount of unstructured and rich content stored in databases, finding the right content can be tedious and time consuming, affecting the overall speed of response and deliverables.

    AI and ML helps users find the right information using various natural language processing (NLP) methodologies. These techniques provide multi-language support, ranking and relevance, highlight keywords and contextual based search, and display content in a holistic manner.
  4. Content Disposition or Archiving: It is important for organizations to have a proper and planned approach to the disposition (deletion) and archiving content, to ensure your infrastructure and assets are optimized to enable prompt maintenance and upgrade.

    Using AI and ML helps organizations find relevant data and move little-used or outdated data for disposition or archiving. This can be accomplished with an algorithm designed to meet your organization’s specific needs.