Results from our “Using AI in techcomm” 2025 survey

In April 2025, Cherryleaf conducted its third annual survey into the use of AI and ChatGPT in technical communications. Below is a summary of our findings.

Executive summary

This year, we had 103 responses from technical communication professionals. The overall findings reveal a field actively engaging with, yet still cautious about, Artificial Intelligence.

• Cautious adoption
  • While familiarity is high, deep, integrated use is still developing. Many are in an experimental phase or use AI for specific, limited tasks.
• Self-reliance in learning
  • The vast majority are teaching themselves how to use AI, indicating a gap in formal training opportunities or a preference for self-directed learning in this fast-evolving space.
• Productivity v. quality trade-off
  • AI is seen as a tool for efficiency (drafting, summarising, rephrasing), but concerns about accuracy, hallucinations, and the need for significant human editing are prevalent.
• Ethical and security concerns are significant
  • Worries about copyright, data privacy, and the “black box” nature of some AI models are common.
• Job security is a top concern
  • The potential for AI to devalue or replace technical communicators is a recurring theme, often linked to management’s perception of AI capabilities.
• Desire for guidance
  • There’s a clear demand for best practices, strategies for effective AI use, and understanding how to optimise content for AI interaction.
• Organisational lag
  • Many organisations are still formulating their AI strategies, including whether and how to best implement AI systems that use their documentation.
• The “Human-in-the-Loop” is crucial
  • Most users recognise that AI is currently an assistant, not a replacement, and that human expertise, critical thinking, and editing are essential.
• Autonomous AI – The next frontier
  • Familiarity with autonomous AI agents is much lower, suggesting this is an area for future exploration and development in the field.

About the respondents

The respondents this year were very similar to previous years. This means we can examine the trends over time.

“Technical Writer”, and variations of this job title, were the main people who completed the survey. There were also a few documentation managers, students and freelancers.

Most work in the software sector (including Cloud, SaaS, mainframe, development, security, accounting, market research, etc.)

Country of residence (Top 5):

1. USA 32%
2. UK 17%
3. Canada 10%
4. India 8%
5. Australia 6%

Other countries included: Germany, Poland, Israel, Switzerland, Greece, Belgium, New Zealand, France, Denmark, Romania, Norway, Hungary, Netherlands, and Ukraine.

Organisation size:

• Small business (including “It’s tiny”): 33%
• Medium sized: 26%
• Large: 23%
• Extra large: 17%

Familiarity with AI

We asked how familiar are you with AI and ChatGPT and how it can be used in technical communication?

Most respondents have moderate familiarity with AI, but few consider themselves experts.

There has been a significant change between 2025 and 2024, as more people become familiar with AI.

We also asked, have you had any training in how to use AI?

• 75% learned AI tools independently
• 25% received formal training

This is a slight change from 2024, where 79% learned AI tools independently and 21% received formal training.

This indicates a gap in workplace upskilling programmes, and a need for more structured training, such as the AI training course that Cherryleaf offers.

AI use in technical communication

We asked, do you use AI (such as ChatGPT) in your role as a technical communicator?

55% use AI on a regular or semi-regular basis, while 15% avoid it entirely, often due to ethical concerns or employer restrictions.

Compared to the survey responses from 2024, there has been a noticeable drop in the number of people not using AI at all, and in the number of people using it a lot.

How AI is being used by technical communicators

Respondents who use AI employ it for a diverse range of tasks:

• Content generation and drafting
  • Creating first drafts of topics, guides, release notes, blog posts
  • Generating outlines
  • Scaffolding content
  • Getting started on a “blank page”
• Editing and rephrasing
  • Rewording complex sentences, UI text
  • Improving clarity, tone, consistency
  • Summarising long texts (Slack threads, meeting notes, articles)
  • Checking for style guide adherence (though some note hallucinations here)
  • Simplifying verbose English, aiming for Simplified Technical English
  • Grammar and spell checking
• Research and information gathering
  • Researching new technologies, features, or complex topics
  • Asking questions about code or specifications
  • Summarising technical content for quick learning (AWS docs)
  • As an “interactive Wikipedia”
• Coding and technical tasks
  • Generating code examples (Python, shell scripts).
  • Building regex expressions
  • Assisting with HTML, CSS, JavaScript (for Docusaurus sites, customising authoring tool output)
  • Formatting (Markdown tables)
  • Automating documentation tasks (scripting in PowerShell)
• Process and workflow improvement
  • Analysing specifications to create conceptual topics
  • Brainstorming and generating ideas
  • Comparing content to templates and reorganising
  • Tagging articles
  • Creating UI naming conventions
  • Generating personas
  • Translation (as a first draft)

Notable quotes:

• “I use AI to rephrase complex sentences when my brain is stuck.”
• “AI helps me generate release notes and check style compliance.”

Developing chatbots

We asked, does your organisation have an AI system, such as a chatbot, that uses information from your user documentation?

Compared to the survey responses from 2024, there has been a noticeable increase in the number of organisations creating chatbots:

Using agentic AI

We asked, how familiar are you with autonomous AI agents (which can act/”think” autonomously and automate tasks) and how they can be used in technical communication?

This is an emerging field, and unsurprisingly not many respondents were familiar with the technology.

Key questions, concerns, and perceived challenges

Respondents expressed a wide range of questions and concerns regarding AI in technical communication:

A. Job security and role evolution

• Will AI lead to redundancy or take writers’ jobs? (This was a frequent concern)
• How will the technical communicator’s role evolve to work effectively with AI?
• How can AI help improve work rather than lead to job losses?
• Fear of being “replaced by a tool”

B. Quality, accuracy, and ethics

• Hallucinations and accuracy – How to deal with AI making things up or providing incorrect information
• Ethical concerns
  • Plagiarism and copyright (models trained on potentially stolen content)
  • Data privacy and security (especially with proprietary information)
  • Environmental impact (energy and water consumption of data centres)
  • Transparency – should customers be informed if AI is used in content creation?
• Quality of output – AI-generated content often requires extensive editing; its “chatty tone” can be inappropriate
• Need for human oversight and critical evaluation of AI output

C. Implementation and best practices

• How to effectively integrate AI into existing workflows and tools
• What are the best practices for using AI in technical communication?
• How to write effective prompts
• How to train AI on specific style guides or large content sets (RAG, fine-tuning)
• How to make documentation “AI-ready” for consumption by AI systems
• What specific tools can enhance productivity?

D. Skill development and training

• How will up-and-coming writers develop skills if they rely too heavily on AI?
• What training is needed to stay relevant?
• How to evaluate AI-generated content effectively.

E. Organisational and strategic concerns

• Lack of AI policies in organisations
• Convincing management/executives that AI augments rather than replaces human writers
• Security of proprietary content when using external AI tools
• How to keep up with the rapid pace of AI development

F. Specific technical questions

• Using AI for IA and search experience
• Automating tasks like drafting short descriptions, making message libraries consistent, rewriting legacy text
• Connecting disparate tools (Confluence, Flare, code repositories)
• Using AI as a linter for automated style guide verification
• Automating documentation drafts based on application interface analysis
• AI’s ability to find instances needing change in large repositories based on updates

Potential outlook

The survey suggests that technical communication is at a crossroads. AI tools are becoming increasingly integrated into workflows, offering potential for increased efficiency and new capabilities. However, the path forward requires addressing significant concerns about job roles, ethics, and quality.

The field will likely see:

• A growing need for “AI literacy” among technical communicators.
• Development of new skills focused on prompt engineering, AI output evaluation, and managing AI-assisted content workflows.
• A shift in focus for some, perhaps towards content strategy for AI, curating knowledge bases for AI consumption, or developing specialised AI tools for documentation.
• Increased demand for ethical guidelines and organisational policies around AI use.
• Continued debate and exploration of how to balance AI’s capabilities with the value of human expertise and judgment in technical communication.

It shows a profession grappling with transformative technology, with both excitement and apprehension that comes with such change.

More information

Using Generative AI in technical writing (training course)