Using AI to adapt technical documentation for a non-technical business audience

Picture this: You've just finished a technically perfect document—precise terminology, detailed specifications, and complex frameworks are all in place. Then your business stakeholders respond: "I don't understand what you're trying to say." Sound familiar? As documentarians, we've all faced the challenge of a technically accurate document that's unusable for its intended audience.

Instead of spending hours rewriting from scratch, I've found a better way: using AI to bridge the gap between technical accuracy and business readability. Using my experience transforming Indiana Consumer Data Protection Act (ICDPA) documentation from technical to business-friendly, I'll show you how.

Understanding the needs of your non-technical audience

When my ICDPA compliance documentation proved to be too technical for business stakeholders, I developed a systematic approach to identify and address their pain points:

1. I shared the written business stakeholder feedback with my AI assistant for summarization, which helped me gain insight into which technical details were causing confusion.
2. I used these insights to craft targeted follow-up questions for stakeholders, then analyzed their responses with AI to identify patterns and common needs.
3. Through this combined analysis, I identified what business readers needed:
   1. Context that they recognized (i.e. comparisons to similar state laws)
   2. Clear explanation of who needs to comply
   3. Practical steps for implementation

This gave me a clear roadmap for transforming the technical content into business-friendly documentation.

Using AI to identify reusable content

Rather than starting over from scratch, I leveraged the reusable content identified by AI and strategically transformed it. Working from high-level elements down to more specific details, I followed this approach to make changes while preserving valuable technical content:

1. I gave AI my original technical document to provide the context and the business needs identified earlier.
2. The AI identified foundational content that could remain unchanged—including the introduction, threshold numbers, and core compliance definitions.
3. It then highlighted which technical sections needed translation into business-friendly language while preserving their essential meaning.

This analysis helped me refocus the content on practical solutions. For example, my technical title "Master Indiana privacy law with better video metadata" evolved into this more business-friendly title: "ICDPA: Indiana's approach to video privacy."

To provide familiar context, I incorporated references to similar Virginia and Utah data privacy regulations from our business stakeholder resources.

Transforming technical content for business readers

I had my objective, the details of what I could reuse, and a business angle to rewrite the article. To implement these pieces, I found it best to start with high-level elements and work down from there. Here are the key content update decisions I made:

• Introduction: While keeping the same threshold numbers, I expanded the business context and terminology in my introduction. Instead of "The metadata framework below provides video managers a high-level overview of triggers for managing data under ICDPA," I rewrote the sentence to "Following Utah and Virginia's approach, Indiana created business-friendly privacy regulations. Here's what organizations need to know:"
• Structure: I replaced technical hierarchies (specified in the metadata framework) into more intuitive questions. For example, technical details like "Data Controller," "Video Subjects," and "Processors" became nested under headers like "Where does the ICDA law apply?" and "What rights do Indiana consumers have?"
• Definitions: I simplified technical terms while preserving accuracy. For example, I rewrote “Data Controller: An entity responsible for overseeing ICDPA compliance, risk assessments, and security measures. They determine the purpose and means of processing personal data…" I changed that text to: “….Data Controllers—entities responsible for overseeing compliance, conducting risk assessments, and implementing security measures—and Processors, who must follow the Data Controller's instructions in handling personal data.”
• Context: I added real-world references that business readers recognize. For example, I noted practical exclusions like riverboat casino facial recognition systems. This was not mentioned in the prior article.
• Action items: I transformed technical requirements into a clear checklist of steps organizations should take, emphasizing practical implementation over technical specifications. For example, I emphasized iteration and review and suggested to include decisions like the video creation purpose and usage justification.

Ensuring quality and accuracy

To maintain technical accuracy while improving readability, I collaborated with AI to review five aspects of quality and accuracy:

1. Clarity: We identified and simplified overcomplicated explanations while preserving meaning. For instance, the original lengthy description of Data Controllers and Processors became a concise explanation that maintained this key distinction: Controllers are primarily responsible for compliance measures, while Processors follow the Controller's directions.
2. Technical accuracy: I cross-checked AI suggestions against the original technical documentation to catch omissions. When AI missed critical information about Data Processors and thresholds, I restored these details to maintain compliance accuracy.
3. Style guidelines: AI helped identify inconsistencies with my business stakeholder's writing guidelines, such as header formatting requirements. Aligning with these standards ensured my guide integrated seamlessly into my client's documentation library.
4. Consistency: We established and maintained consistent terminology throughout the document, even where writing guidelines were flexible. For example, we standardized capitalization for terms like "Data Controller" and "Processor" to improve readability.
5. Readability: We conducted final checks for grammar and sentence structure—particularly in complex compliance descriptions—to ensure clear understanding for business readers.

While AI proved invaluable in this quality assurance process, my oversight remained crucial. Each AI suggestion required validation and sometimes a little research to ensure we weren't sacrificing accuracy for readability. This balanced approach helped create a document that was both technically sound and business-friendly.

Conclusion

Using AI as a bridge between technical and business audiences has transformed what used to be a time-consuming rewrite into a strategic adaptation process. Through this approach, we:

• Quickly identified and preserved valuable technical content
• Transformed complex language into business-friendly terms without losing accuracy
• Maintained compliance requirements while improving readability
• Reduced revision cycles through AI-assisted quality checks

Most importantly, this process turned what could have been a frustrating, time-consuming rewrite into a streamlined content transformation. Instead of starting from scratch, I used AI to preserve technical accuracy while enhancing business readability.

While AI won't replace your documentation expertise, it can help you bridge the gap between technical precision and business clarity. Think of AI as your collaborative partner that handles the initial heavy lifting of analysis and suggestions, while you maintain control over the writing process and business context.