In our previous post, we explored the first stage of AI maturity in technical content: individual experimentation. Now, let’s dive into the second stage: assisted automation. This stage represents a significant step forward in the integration of AI into technical writing workflows, moving from personal experimentation to more systematic, team-wide implementation.

Here are the four stages of AI maturity in technical content (and links to the other posts):

1. Individual Experimentation (Part 1)
2. Assisted automation for documentation (Part 2)
3. Orchestrated AI automation for documentation (Part 3)
4. Full automation for documentation (Part 4)

The focus of this post is on assisted automation.

Stage 2: Assisted Automation

In the assisted automation stage, AI systems are integrated into the content creation process to provide alerts, suggestions, and implement minor changes. This stage is characterized by a more structured approach to AI usage, often involving dedicated tools or platforms that work alongside human writers and developers. Let’s explore some key aspects and examples of this stage:

1. Automated Content Checks

At this stage, organizations implement automated systems that scan content for various issues before publication. These systems go beyond basic grammar and spell-checking, looking for:

• Consistency in terminology and style guide adherence
• Broken links or outdated references
• Readability scores and complexity levels
• Potential bias or non-inclusive language

For example, a team might use a tool like Vale or a custom-built linter that automatically checks documentation against the company’s style guide every time a change is committed to the repository.

2. SEO Optimization Suggestions

AI-powered SEO tools are integrated into the content workflow, providing writers with real-time suggestions for improving search engine visibility. These tools might:

• Suggest relevant keywords based on the content
• Recommend changes to meta descriptions and titles
• Highlight opportunities for internal linking

A practical application might involve a plugin for the team’s content management system that provides SEO recommendations as writers draft their content.

3. Content Structure and Organization Assistance

AI systems at this stage can analyze existing documentation and suggest improvements to its structure and organization. This might include:

• Identifying redundant or duplicate content
• Suggesting logical places for new content within the existing structure
• Recommending ways to break up long sections for improved readability

For instance, a documentation team might use an AI tool that analyzes their entire knowledge base and provides suggestions for reorganizing content to improve user navigation and findability.

4. Automated Tagging and Categorization

AI can assist in automatically tagging and categorizing content, making it easier to manage large documentation sets. This might involve:

• Suggesting relevant tags based on content analysis
• Automatically categorizing new documents within the existing taxonomy
• Identifying content that doesn’t fit well within the current categorization system

An example might be a system that automatically suggests tags for new API documentation based on the content and existing tag structure.

5. Writing Style and Tone Recommendations

AI systems can analyze content for consistency in writing style and tone across different authors and document types. They might:

• Suggest phrasing changes to maintain a consistent voice
• Flag instances where the tone doesn’t match the intended audience or purpose
• Provide recommendations for clarity and conciseness

For example, a team could use an AI writing assistant that’s been trained on their preferred style and tone, providing real-time suggestions as team members write.

Challenges and Considerations

While the assisted automation stage offers significant benefits, it also comes with its own set of challenges:

• Integration Complexity: Implementing these AI systems into existing workflows can be technically challenging and may require significant upfront investment.
• Change Management: Team members may need time and training to adapt to new AI-assisted workflows.
• Over-reliance on Suggestions: There’s a risk that writers might over-rely on AI suggestions, potentially stifling creativity or leading to homogenized content.
• Data Privacy and Security: As more content flows through AI systems, ensuring the privacy and security of potentially sensitive information becomes crucial.
• Maintaining Human Oversight: It’s essential to establish processes for human review and approval of AI-suggested changes.

Looking Ahead

As teams become more comfortable with assisted automation and refine their processes, they may begin to explore ways to give AI systems more autonomy in certain areas. This gradual increase in AI responsibility and capability sets the stage for the next level of AI maturity: orchestrated automation.

Conclusion

The assisted automation stage represents a significant advancement in the integration of AI into technical content creation. By providing writers and developers with AI-powered suggestions and assistance, organizations can improve content quality, consistency, and efficiency. However, it’s crucial to implement these systems thoughtfully, always keeping the balance between AI assistance and human expertise in mind.

As we continue to explore the potential of AI in technical communication, the assisted automation stage serves as a critical stepping stone, allowing teams to harness the power of AI while maintaining full control over their content strategy and output.