Structured Content Is the Only Sustainable Path to AI-Ready Documentation

To truly lead in this AI era, organisations must move beyond basic AI applications to embrace contextual intelligence – next-generation agentic AI built on unified, cross-functional knowledge. This approach draws from structured data, historical context, dynamic signals and the policies guiding customer interactions, enabling brands to build deeper, emotionally-intelligent relationships with customers at scale. Zendesk CX Trends 26

What Zendesk calls contextual intelligence is the same foundation that structured, technical documentation has been built on all along.

Zendesk is talking about AI for customer experience, but the same holds true for other AI-powered use cases. If AI needs unified, contextual knowledge, then structured content is the only viable foundation for modern AI and multi-channel publishing.

The days of writing technical documentation as static, chunky, downloadable Word documents or PDFs are over (although yes, some companies still want those PDFs, too). Unstructured content created with MS Word, Google Docs, Adobe InDesign, Confluence, and Zendesk Knowledge, cannot support the kind of agentic AI that vendors are promising. Companies need their documentation to be easily ingestible by large language models (LLMs), and PDFs and other static, unstructured content creation tools are not the answer.

Unstructured content mixes content and formatting, is not reusable, and doesn’t scale. If you’re managing multiple copies of a document to support different product versions or different audiences, then you run the risk of having out-of-sync versions of your documentation, inconsistent information across versions, and a lot of content duplication. These challenges have an impact on how your documentation is ingested by LLMs.

LLMs can ingest unstructured content, but when you choose this route, you face a number of challenges:

• AI systems work inefficiently. When information is messy or inconsistent, models require significantly more computational effort to interpret, often leading to uneven analysis and weaker data extraction. 
• Trouble understanding relationships. Without standardized formats, AI can’t reliably recognize how different pieces of information relate across documents (or even document sections).
• Ambiguity increases error rates. Unstructured content lacks metadata and often doesn’t follow predictable patterns or structures. This leads to inaccurate inferences or misinterpretations that only get worse as more content is processed.
• The chunking problem gets worse. Because large language models must break data into smaller sections to process it, inconsistent or disorganized content often gets split in ways that separate essential context, reducing accuracy and reliability.

With unstructured content, there are challenges dealing with all the different information formats, such as images, tables, and scanned documentation, that also don’t contain metadata. 

The key point here is that unstructured content does not provide the context AI needs to truly understand it. But structured content does.

Structured content separates content from formatting, uses topic-based authoring, can be reused across formats and channels, and scales. What has become a technical documentation best practice is also a foundation of AI. 

So, how does structured content improve AI?

Structured content allows AI to learn patterns more quickly and accurately. By breaking information into self-contained, consistently formatted topics, structured authoring gives models predictable signals using clear headings, standardized sections, and logical organization. Instead of wasting processing power trying to understand the layout, the AI can focus entirely on understanding meaning.

Taxonomy and metadata make context explicit. Controlled vocabularies, tags, and metadata clarify concepts, entities, and relationships, improving tasks such as categorization, topic modeling, and pattern recognition.

Relationships stay intact during processing. When LLMs break information into chunks, structured content naturally preserves hierarchy and context. Sections, subsections, and headings keep related ideas together, reducing the risk of misinterpretation.

Processing becomes far more efficient. Formats like XML or JSON provide models with a defined structure for parsing, making entities, attributes, and relationships easier to identify. This improves accuracy while reducing computational load.

Outputs become consistently reliable. Because structured content limits ambiguity, models generate more precise responses, which is critical for regulated industries like healthcare, finance, and legal, where accuracy is non-negotiable.

“All the things we do to make content better for humans – componentize it, structure it, clean up the grammar, tag it with metadata – make content better for machines as well.” Val Swisher

Structured content also integrates with external databases, ontologies, and RAG (Retrieval Augmented Generation) workflows. This enrichment strengthens the model’s knowledge base and provides users with more context-aware, accurate information.

Building on Zendesk’s vision of agentic AI, we need to understand that agentic systems do more than retrieve information. AI isn’t magical, but it also doesn’t simply index content and return keyword-based responses. AI has to understand the meaning of content, evaluate relationships among content topics, make decisions based on content rules (such as filtering and conditional text), and personalize outputs.

If you’re forcing AI to work with unstructured content like a Word or Google document, or an HTML knowledge base article, it will struggle to do these things.  

What you want instead is to build a unified knowledge framework that maps out how to create, manage, and store technical documentation. The framework includes a shared structure and language, taxonomies and metadata, consistent terminology, and content formats. 

To implement this framework, companies are turning to component content management systems (CCMS) that support structured authoring, reuse, metadata, relationships, and multi-channel publishing, the things that help AI understand content better. 

A CCMS provides the technical environment needed for the contextual intelligence that Zendesk talks about. From a documentation perspective, we can take Zendesk’s four pillars of contextual intelligence and break them down this way:

• Structured data: A CCMS provides topic-based authoring, metadata, and clear content models. 
• Historical context: A CCMS provides a single source of truth for all documentation, ensuring you have the historical context needed. It also provides version control and auditing to track changes to documentation over time. 
• Dynamic signals: With the right CCMS, you can define metadata, use variables, and apply dynamic filtering via profiles and conditional content to support the right person and context.
• Policies and governance: With a CCMS, you can apply review and approval workflows, controlled vocabularies, and reuse rules, all at the topic level.

Multi-channel publishing and AI readiness aren’t separate outcomes. They share the same foundation.

Today, technical documentation needs to live everywhere: online help centers, in-app help, PDFs, customer portals and knowledge bases, chatbots, AI assistants and agents, and more. AI may be at the top of the conversation list, but it’s still one of many channels.

When you work with unstructured content, you are rewriting your documentation over and over for each channel. Or you’re cutting and pasting and dealing with manual updates. The process is time-consuming, error-prone, and can be expensive over time.

Structured content is the most efficient and effective approach for multi-channel publishing. Even if you are only publishing to one channel today, by following a structured content model, you are future-proofing your content for any new channels that come.

This includes multiple AI-related channels. Remember, AI cannot generate channel-specific responses (e.g., search, chatbot, assistant, agent) if the content isn’t inherently channel-agnostic and well-structured for reuse.

It’s worth taking a minute to talk about the importance of governance in the AI era. AI applications and agents amplify your content framework for better or worse. So, if your content is unstructured and messy, then your AI will be messy at scale. But if your content is well-governed, your AI will be much more reliable at scale and will be considered more trustworthy. 

What governance capabilities are important? An up-to-date content model that truly reflects your business is paramount. You also need clear roles and responsibilities for managing your content, along with content standards and style guides. There are more capabilities that you can read about here. 

In terms of AI-specific governance capabilities, the most important thing is providing the right documentation set for training the AI. If you train your AI on outdated, inaccurate, or inconsistent content, the responses provided will not be correct. 

In addition, because generative AI creates new content based on your documentation, you’ll want to train it on your brand and editorial style guides to ensure responses follow those standards. 

A CCMS provides a number of governance capabilities that ensure your content is AI-ready, including:

• A single repository for all technical content
• Review and approval workflows
• Versioning
• Auditing
• Taxonomy and metadata

If contextual intelligence and AI-driven customer support are becoming essential (and they are), then Zendesk is only part of the solution. 

Zendesk is a customer support platform that includes a knowledge base. But the knowledge base is not built on a structured content model. It’s designed for simple articles with limited structure, metadata, and taxonomy. Each article is a standalone document. There is some limited reuse through content blocks, but they don’t provide the true reuse that a CCMS like Paligo does.

Because the knowledge base is unstructured, AI systems will struggle to understand relationships, reuse information, or maintain context across versions, languages, or product lines.

Paligo CCMS can fill these gaps by providing content that is modular, structured, taxonomy-driven, and enriched with metadata. When content is clearly structured, AI can reliably interpret relationships, maintain context during retrieval, and produce more accurate answers in customer-facing experiences. 

Paligo also optimizes versioning, variant management, and multi-channel publishing, giving teams a single source of truth that, when integrated, feeds Zendesk clean, consistent, well-organized content. 

With this partnership, Paligo becomes the authoritative content engine behind Zendesk, while Zendesk becomes the delivery layer that incorporates AI assistants and agents.

AI has raised the stakes for technical documentation. What used to be “nice to have” things like consistency, structure, metadata, and reuse are now the backbone of every AI-driven experience. Multi-channel demands have multiplied, customer expectations have hardened, and Zendesk’s own message confirms what the industry has known for years: without structured content, modern documentation simply can’t keep up.

Unstructured articles and isolated knowledge bases won’t give AI the contextual intelligence it requires. Only structured, topic-based content provides the stability, clarity, and scalability needed for reliable AI performance across every channel, whether it’s human or machine.

The teams that embrace structured content now aren’t just preparing for AI; they’re embracing it. They’re building a knowledge foundation that grows with the business, supports every new channel, and strengthens customer trust over time.

We invite you to learn how you can bring together the strengths of Paligo CCMS and Zendesk to create the customer experiences your customers demand.

Unstructured content makes AI work much harder than it should. When information is inconsistent, mixed with formatting, or spread across multiple versions, AI systems waste processing power trying to interpret the structure instead of understanding the meaning. This leads to weaker accuracy, higher error rates, and broken relationships between concepts once the content is chunked into smaller sections. AI can still ingest unstructured docs, but you pay for it in inefficiency, inconsistency, and degraded output quality.

Structured content gives AI what it needs most: clarity, consistency, and context. Topic-based authoring, predictable structure, and rich metadata help AI models understand relationships, hierarchies, and intent. This reduces ambiguity, preserves meaning during chunking, and dramatically improves accuracy. Instead of deciphering messy layouts, the model can focus on interpreting information—making every downstream task faster, more precise, and more reliable.

A CCMS provides the unified knowledge framework AI depends on. It brings structure, metadata, versioning, relationships, and governance into one place. When mapped to Zendesk’s pillars of contextual intelligence, a CCMS delivers:

• Structured data through topic-based authoring and metadata
• Historical context through version control and a single source of truth
• Dynamic signals through profiling, variables, and conditional content
• Policies and governance through workflows, controlled vocabularies, and reuse rules
  Together, these capabilities give AI the stable, contextual foundation it needs to deliver accurate, personalized responses at scale.

Multi-channel publishing used to be a convenience. AI has turned it into a requirement. Structured content allows teams to create once and reuse everywhere—HTML, PDFs, portals, chatbots, assistants, and AI agents—without rewriting or duplicating information. Unstructured content can’t scale this way without constant manual effort. Because AI relies on channel-agnostic, consistent content, structured content becomes the only sustainable way to support today’s mix of human and AI-driven experiences.