AI and Structured Content: Enhancing Content Generation and Personalization

Structured content is information or content that is methodically arranged and formatted, making it easier to organize by both humans and machines. This structure enables the content to be used, reused, and repurposed across multiple platforms and channels. For example, consider a blog article: in a structured format, the title, author, published date, content blocks (such as text, images, quotes), and tags are all distinctly identifiable, making it easier for the content to be displayed correctly on a webpage, an RSS feed, or a mobile app.

Metadata and semantic markers play a crucial role in structured content:

• Metadata is the data about data. It provides context about the content such as the creation date, author, keywords, etc.
• Semantic markers provide meaning to the content. They tag and categorize the content, making it easily identifiable and searchable.

For example, consider a news website with thousands of articles. Metadata lets the system know who authored each article, when it was published, and what topics it covers. Semantic markers ensure each piece is categorized correctly under sections like “politics,” “technology,” “sports,” etc., facilitating navigation for users and effective content management for administrators.

A content management system (CMS) plays a crucial role in managing structured content by providing a centralized location where all content is stored, organized, and edited. This leads to several benefits that streamline content generation, management, and distribution:

• Consistency: A CMS can offer an ideal interface for displaying metadata and organizing your content. This can help you keep track of your content library and improve consistency.
• Easy updates: A CMS provides straightforward tools for finding and updating content.
• Scalability: A CMS facilitates the easy addition of new content while maintaining the same structure. As your content grows, the CMS can handle the increased volume without compromising the organization or presentation.
• Multi-platform delivery: A CMS can include options (for example, running in headless mode) that let you deliver your content to multiple platforms (websites, mobile apps, IoT devices, etc.) with ease, improving reach and user experience.
• SEO optimization: Many CMS platforms contain tools for SEO optimization that function well in combination with the metadata and tags of structured content.
• Improved collaboration: A CMS enables multiple team members to work on content simultaneously, promoting efficient workflow and collaboration.

Of these points, multi-platform delivery arguably makes the most use of structured content. Unlike traditional CMSs, where the content is tied to a specific presentation layer (for example, a web page), in a decoupled CMS (as well as in a headless CMS), the content is kept separate from the presentation layer. This means that structured content, if well organized, in a decoupled CMS can be pushed to any output — be it a website, mobile app, IoT device, or even an AI application — without needing modification.

Consider a product description on an e-commerce platform. In a decoupled CMS, the structured content elements — such as product name, price, description, and images — are stored separately from how they are displayed. This separation allows the same content to be pushed seamlessly to a website, a mobile app, or even read out by a voice assistant, like Alexa or Google Assistant, enhancing consistency and user experience across various platforms.
 

Not all CMSs are created equal. Picking one that effectively manages structured content — like Sulu — can significantly enhance your content management process.

Artificial intelligence, particularly machine learning models, thrives on structured data. In the context of content, AI algorithms can use structured content to analyze, interpret, and derive meaningful insights. The structure provides a clear, organized format that AI can use to identify patterns, relationships, and context.

For example, consider an AI-powered content recommendation engine on a media website. The system can analyze structured data—like user profiles, article metadata (topics, tags, authors), and user behavior (clicks, shares, likes) — to understand user preferences and deliver personalized content recommendations.

With structured content in place, AI can automate several tasks that traditionally require manual effort, thus improving efficiency and scalability. These include:

• Categorization: Categorization is a method of grouping content into broad, predefined categories or sections. This is typically a hierarchical system where content is placed into large “buckets” that represent the main topics or themes. For instance, on a news website, articles might be categorized under “Politics,” “Sports,” “Technology,” etc. Based on the context, AI can categorize content into these predefined categories.
• Content tagging: Tagging is a more flexible and granular way to associate content with specific topics or attributes than categorization. Tags are typically non-hierarchical and can span across categories. They can reflect specific details about the content that may not be captured by the category alone. For example, an article on a news website might be categorized under “Technology,” but could be tagged with specific terms like “Artificial Intelligence,” “Cybersecurity,” “Cloud Computing,” etc. This allows users to find all content related to these specific topics, regardless of their primary category. An AI algorithm can scan a blog post to identify key topics and automatically tag the content accordingly.
• Distribution: AI can optimize content distribution based on structured data. For example, an email marketing system can use customer data to send personalized content at the optimal time for each user.
• Content generation: AI can generate new content based on structured inputs. For instance, a weather forecast bot can use structured weather data to generate a comprehensive forecast report.

Structured content also plays a vital role in the successful implementation of AI-powered applications like chatbots. The organized format allows chatbots to understand user inquiries, match them with relevant information, and provide accurate responses. Take a customer service chatbot for an e-commerce website. If a customer asks, “What is the status of my order?” the chatbot can reference structured data — like customer ID, order ID, and shipping status — to provide an accurate update. Without structured content, the chatbot would struggle to understand the context and provide a satisfactory response.

In essence, structured content sets the stage for AI to perform at its best, driving efficiency, personalization, and enhanced user experiences. The growing role of AI-based tools makes structured content more important than ever.

The integration of AI and structured content is reshaping content management, enhancing personalization, and automating processes. As AI evolves, its ability to understand and leverage structured content becomes more significant, leading to more efficient operations and improved customer experiences.

To capitalize on these trends, it’s more essential than ever to have the ability to manage structured content. As we’ve seen in this article, a flexible, scalable CMS is the perfect solution.

Sulu is a CMS designed to handle structured content in headless mode and — thanks to its open-source nature — can easily be integrated with AI technologies, providing a versatile platform to meet the digital challenges of the future.

Let’s embrace the future of content management together.