Medical Writing Meets AI-Powered Document Authoring: What the Occupational Data Say About Efficiency and Oversight

Generative AI is rapidly making its mark in professional writing, but a critical question remains: is it actually doing the work, or simply helping us do it better? Recent research from Microsoft provides one of the clearest answers yet. By analyzing 200,000 anonymized Bing Copilot conversations, the authors measured where AI is assisting vs. where it is performing tasks outright—and the findings have direct implications for AI-powered document authoring in medical writing. 

Why the Distinction Matters in Medical Writing 

The study introduces two essential concepts: 

• User Goal – The task the human is trying to accomplish (e.g., drafting the discussion section of a Clinical Study Report). 

• AI Action – The task the AI actually performs in the conversation (e.g., providing a structured summary of results, explaining statistical terms). 

  
  

These are not always the same. In fact, in 40% of conversations, the AI’s actions were entirely different from the user’s stated goal. 

In medical writing, this distinction is more than academic—it’s about compliance, quality, and efficiency. If your user goal is “complete an ICF for a Phase III oncology trial,” an AI action that only provides a bullet-point outline is helpful but still leaves significant work for the writer to interpret, verify, and finalize. 

Insights into AI-Powered Document Authoring: What the Research Tells Us

Most Common User Goals 

The top three user goals identified were: 

1. Information Gathering – e.g., background research, summarizing evidence. 

2. Writing – creating original content such as summaries, narratives, or reports. 

3. Communicating Information – preparing outputs to share with others. 

   
   

For medical writers, this aligns directly with literature review, regulatory section drafting, and plain language content development. 

Most Common AI Actions 

On the other hand, the AI’s most frequent actions included: 

• Providing Information 

• Teaching or Advising 

• Editing and Formatting Content 

  
  

This paints a picture of AI as a coach, explainer, or proofreader—valuable roles, but not the same as full document authorship. 

Implications for Medical Writing Workflows 

Where AI-Powered Document Authoring Excels 

The study’s satisfaction and completion rate metrics show that AI performs best in tasks such as: 

• Drafting structured text in predefined templates. 

• Synthesizing large volumes of trial or literature data into concise summaries. 

• Editing for grammar, tone, and style compliance. 

  
  

For example, a medical writing automation platform could rapidly generate consistent baseline sections for multiple CSRs, freeing writers to focus on interpretation and nuance. 

Where Human Oversight Remains Critical 

Even high-performing AI actions require expert validation in: 

• Interpreting clinical significance and regulatory implications. 

• Making inclusion/exclusion judgments on supporting evidence. 

• Ensuring patient safety statements are accurate, balanced, and compliant. 

  
  

The Automation vs. Augmentation Question 

The researchers note that AI is far more often augmenting work than automating it—particularly in complex knowledge work. That’s not surprising in a regulated context, where the risks of over-automation are high. 

This means that, for now, the biggest gains in medical writing automation platforms come from augmentative use: accelerating evidence gathering, structuring sections, and improving clarity, rather than replacing human authorship entirely. 

Closing the Gap Between AI Actions and User Goals 

One subtle but important takeaway: the gap between what users want AI to do and what AI actually

delivers is often not a capability problem—it’s a design and integration problem. 

In the study, many AI actions fell short of the user goal because the tool wasn’t tailored for the user’s specific domain or workflow. For medical writing, this reinforces the case for SME-built, fit-for-purpose AI systems that integrate with existing infrastructure (e.g., document management systems, structured content authoring tools). Such systems can: 

• Understand the regulatory context of the request. 

• Pull structured data from validated sources. 

• Generate outputs aligned with submission templates and style guides. 

  
  

This alignment is what moves AI from “assistant” to “co-author” in a controlled, compliant way. 

Steps for Medical Writing Teams to Assess AI’s Role 

1. Map Your Tasks – Identify high-frequency, high-effort writing activities. 

2. Classify AI Use – Determine whether AI is acting as an author or assistant for each task. 

3. Pilot and Measure – Track satisfaction, completion, and scope using metrics similar to the study’s. 

4. Refine Governance – Define SOPs specifying where and how AI can contribute to each document type. 

Final Thoughts 

The Working with AI study suggests that people are largely using AI for what they’ve discovered it’s already good at—summarizing, explaining, and drafting within clear boundaries—rather than pushing it toward speculative future capabilities. 

For medical writers, the opportunity lies not in replacing authors, but in building AI-powered document authoring workflows that blend human expertise with systems designed specifically for regulatory, scientific, and editorial needs. When AI actions align more closely with user goals, efficiency, quality, and compliance all move in the right direction.