The Hidden Costs of Managing Technical Documentation in Word, Google Docs, and Confluence

You can’t blame technical documentation teams for using authoring tools like Microsoft Word, Google Docs, or Confluence to create technical documentation. After all, they are well-known, easy to use, and cheap (in some cases free). However, what looks efficient today can quickly turn into a bottleneck as documentation scales across products, regions, and audiences.

We’re going to look at why relying on tools like MS Word, Google Docs, or Confluence feels easy now, but can lead to significant long-term challenges in terms of efficiency, scalability, and content quality. We’ll also explain why structured authoring tools are the better option now and in the long term.

Unstructured content refers to any content that is locked inside entire documents or pages, without metadata or modularity. It looks organized on the surface, but behind the scenes, it’s just text and formatting, making reuse and automation impossible, and using AI at scale problematic.

Unstructured authoring is the creation of content as single documents or wiki pages. You may have heard the term “blob content” in content management before. This is what unstructured content is – a single, linear document. It combines formatting and style with content, resulting in a specific publishing format with limited underlying metadata, semantic tagging, or modularity.

Examples of unstructured content tools include Microsoft Word or Google Docs (text files) and Confluence (wiki pages). Both of these types of authoring tools allow a writer to write their content in any manner they want. While these documents do typically have a structured format, it’s for visual formatting (headings, bullet points, tables); they don’t carry deeper meaning for reuse, automation, or AI..

Unstructured authoring tools appear efficient because any team member can jump in and start writing, but they lack essential capabilities for technical documentation, such as content reuse and multi-channel publishing.

We talk about the difference between structured and unstructured content all the time. Check out these links to go deep:

1. Structured Authoring: What It Is and Why It’s Important
2. Making the Business Case for Intelligent Enterprise Content

We’ve explained what unstructured authoring is, but before we go further, let’s define structured authoring.

Structured authoring is the opposite of writing blob content. Instead of writing one big document, content is broken into modular, reusable topics (like procedures, concepts, or references). Each topic follows a consistent structure (e.g., headings, metadata, taxonomy), and the style or formatting is separated from the content itself. 

A component content management system (CCMS) is a structured authoring tool that allows documentation teams to: 

• Reuse content topics across products, regions, and formats
• Improve compliance through versioning and metadata
• Single-source publishing to multiple formats and channels, including PDF, HTML, portals, chats, knowledge bases, and more. 
• Support improved synchronous collaboration on the same publication, including writing, editing, and subject matter expert review. 

Let’s take a look at each tool to better understand its capabilities and limitations for writing technical documentation.

We’re going to group Word and Google Docs because they are the same type of unstructured authoring tool (just made by different companies).

Word and Google Docs are word processing software used for writing and reading content. Both are available as free or paid cloud-based tools, and Word is also available as a desktop application. Creating content in these tools is straightforward; you simply create a new document and start writing using a WYSIWYG (What You See Is What You Get) editor interface.

As you write, you apply formatting styles like headings, tables, page breaks, and bulleted lists. You can add images and insert other content such as drawings, charts, and esignatures (each tool may provide different options for additional content types). Documentation authors can spend up to 60% of their time formatting their document, and the final version can appear highly visual and well-written, yet still be a single format.

While simultaneous editing is possible online, there’s no way to lock sections or prevent conflicting edits. You can view a document’s history to see who made changes, but you cannot revert specific changes; only the full version is available. Also, reverting changes runs the risk of overwriting changes made by other writers. If you are using the desktop version of MS Word, review workflows are external. Track changes and comments occur in a returned file, which must then be reconciled manually.

What if you want to use the content you have written in other ways? What if you want to publish parts of the content as a set of FAQs, as HTML-based knowledge base articles in your support portal, or as HTML pages on your Resource site? Beyond saving as Word or PDF, other formats (HTML, Markdown, ePub) are often afterthoughts, with limited control and inconsistent output, especially when using Word Online.

Atlassian Confluence is team collaboration and knowledge management software. Writing in Confluence is similar to writing in Word or Google Docs. You create a page and write content using a WYSIWYG editor, including text, images, headings at multiple levels, code, tables, and Figma files. Pages can be versioned and organized into a hierarchy, but they exist as a single flat-file format.

As it’s a collaboration tool, Confluence is good at enabling collaboration. Up to 11 writers can work on a single wiki page at a time, and changes are saved and synced automatically. However, managing large-scale content sets is difficult, and while there is some basic content reuse, it’s not comparable to content reuse in structured authoring tools.

Confluence is both a content creation and content delivery application, which means the primary publishing channel is Confluence itself. To make content available via public links for external customers, you must have a paid version. You can publish Confluence wiki pages in other formats, such as HTML, Word, and PDF, but these are still single-page content formats.

Now that we’ve seen the individual limitations of Word, Google Docs, and Confluence, let’s look at the bigger issue they all share: scalability.

MS Word, Google Docs, and Confluence all share the same key challenge: they are document-first authoring tools, not content-first. This key challenge is why scaling your documentation with unstructured authoring tools is so difficult.

If you are managing a small set of technical documents with a small team, unstructured authoring tools may be fine. But as your documentation grows, as you add more publishing channels, and as you support more languages, scaling that documentation gets hard really fast.

Publishing documentation across channels requires making copies of each document for each channel. Now you have to deal with duplicated content and find a way to keep track of each version and where it’s published. If you want to change some of the content in those documents, you have to copy and paste your changes manually. Manual work typically leads to errors and inconsistencies.

What if you are creating technical documentation for different audiences, such as end users, administrators, and developers? There is a lot of the same content across these documents, so you would want to reuse as much as you can to prevent duplication of effort and maintain changes. With tools like Word and Confluence, this is not possible. You will have to create separate versions and copy/paste content between them. Maintaining changes to the content again requires going to each version and making the changes. As your documentation grows, keeping track of all the versions (and versions of the versions) becomes unmanageable.

Now, let’s add in translations. If you are managing documentation for a global audience, with Word and Google Docs, you have to maintain separate copies of the documentation for each language. Each time the content changes, you have to send the whole document for translation. Since your documents are formatted, this process takes longer to translate the content and ensure the design is properly maintained.

Confluence is a little different. Although it does not offer out-of-the-box support for automated language translations of page content, it does have several partners offering translation capabilities through add-ons. It includes additional add-ons that provide browser-based translation leveraging services like Google Translate. Also, if you have Atlassian Intelligence (Atlassian’s AI offering), you can translate sections of content or an entire page and paste the output into language macros or new pages. However, in almost all cases, there is manual work to track and manage translations.

Getting cross-organizational buy-in for a modern technical documentation authoring and publishing system doesn’t have to be complicated. When you tailor the message to each stakeholder, including the Team Lead, Department Lead, Head of Product, CFO, and the C-Suite, you replace concerns about resources, effort, and costs with clear, measurable upside. Whether it’s a CCMS and CDP, or a CCMS alone, you can migrate in sprints, build reusable content, and publish richer, searchable documentation exactly when you need it. 

And remember, when the numbers show that great documentation wins customers, approving your migration becomes the obvious business decision. Start with a small pilot, track the gains, and watch the buy-in follow.

Watch the complete webinar on-demand to hear more from Josh and Fabrice. 

When your technical documentation is in an unstructured content format, a search engine indexes the entire document and returns the entire document when someone conducts a search. This makes it hard for users to find the exact information they need. Instead, they may have to scroll through pages of information (even with a detailed table of contents).

Structured content allows fine-grained retrieval, which makes it perfect for modern capabilities such as chatbots and AI. It’s also easier to leverage your content in FAQs, snippets, and summaries.

In regulated industries, proving what was published when, and to whom, is almost impossible with Word and Google Docs. Even Confluence doesn’t provide the detailed audit trails needed to show when changes were made.

Structured authoring tools provide clear audit trails, with full version history, including tracking translated versions, and where content is reused across documentation. This information is critical in highly regulated industries where even the smallest change can have a huge impact.

It’s not unusual for documentation teams to create one version of a document and continue to update it for minor changes, and then create a new version when there are significant changes required. Suppose a new product feature is only available for a certain edition or subscription level. In that case, the documentation team will need to create a copy of the documentation that includes that new feature. Now, there are multiple copies of the documentation that need to be tracked and updated.

Over time, some of these documents are no longer used or required, but they still exist in locations where customers can access them. The result is outdated, conflicting content that confuses and frustrates customers. Even Confluence Spaces, which are collections of documents, often get outdated, but are still living content that customers have access to.

Structured content, on the other hand, enforces central management and consistency of information. Its single-source nature means technical writers are always working on the most recent version of the content, and as that content is updated, the changes are published everywhere the content lives.

As companies rush to prepare their content for AI, they face challenges with unstructured content. While it is true that AI can ingest unstructured documentation, it is harder for AI to parse and repurpose it.

We’re not going into the details of how generative AI tools, such as large language models (LLMs), work, but there are important aspects to understand regarding how LLMs work (go deeper here). Unlike search engines that index large amounts of content and return results based on keywords, LLMs generate new content in response to a prompt that provides the best answer. These LLMs are trained on source content, such as your technical documentation, and the structure of that content can impact its reliability.

Unstructured content is less reliable than structured content because it requires more resources to process, lacks a consistent format for the LLM to understand, lacks metadata or taxonomy, and mixes content types that require special processing. LLMs also handle a limited amount of data at one time, so unstructured content often needs to be chunked. If it’s chunked through automated processing, key information may get separated, leading to less accurate processing.

Structured content improves the accuracy and reliability of LLM responses by providing consistency and context. Because structured authoring breaks content into self-contained topics with standardized formats and enriches that content with metadata and taxonomy, LLMs are better able to recognize patterns, relationships, and key entities.

In addition, structured content that follows defined formats like XML or JSON can provide more reliable output because it’s easier for an LLM to identify boundaries, categories, and relationships. It also integrates seamlessly with knowledge bases and domain-specific taxonomies, enhancing precision in industries such as healthcare, finance, and manufacturing. The result is faster learning, more consistent outputs, and greater reliability in applications like customer portals, self-service, and chatbots, where delivering accurate and context-aware information is critical.

One aspect we often overlook when managing documentation with unstructured authoring tools is the technical writer’s experience. When writing documentation, new writers spend a lot of time learning where documentation lives, how versioning is done, and which documents are the latest. Sometimes, it’s a game of hide and seek, trying to learn everywhere a topic is used and update it properly. Writers may also develop carpal tunnel syndrome with all the copy/pasting required to make changes across documents (that’s a bit of a joke, but has some truth to it).

Structured authoring tools reduce ramp-up because writers are working with a single tool and a single source of content. They don’t have to guess which version is the most recent, or update multiple versions or documents, because updating one updates them all. The biggest challenge for new team members is learning a new structured authoring tool, but the right tool will have a user experience that is intuitive and easy to learn.

According to a Salesforce study, 61% of customers prefer to use self-service tools to resolve simple issues. Self-service tools require clear, concise, and accurate content. Documentation teams do not have the luxury to spend a lot of time creating content over and over again. They require tools and processes that make their workflows fast and efficient.

Unstructured authoring tools have hidden costs, including wasted translation costs and time spent creating, finding, and updating multiple documents with the same information. Companies also need to consider the increase in support calls resulting from inaccurate documentation.

Structured authoring reduces and, in some cases, eliminates these costs, improves time-to-market of new and updated content, and drives increased customer satisfaction because the most up-to-date information is always available.

When documentation teams are trying to convince their executive leaders that a structured authoring tool is the best choice, they only need to share the costs associated with working with MS Word, Google Docs, or Confluence, and the benefits of structured authoring.

Word, Google Docs, and Confluence will always have a place for small teams or simple projects. However, if your documentation is extensive, multilingual, and central to the customer experience, unstructured authoring can create hidden costs that you can’t ignore.

Structured authoring is the only scalable path forward. The key is understanding your requirements and showing, through time and costs, why structured authoring and structured content are the best solution.