Look around. It’s crystal clear that AI is changing the world of work, reshaping the way that we approach our craft. Technical writing is no exception.
Technical writing has been around for ages, but the way the job has always been done is evolving. It’s important to be able to evolve with it. AI might not take your job, but someone who can leverage AI well will.
In this article, we’ll explore how artificial intelligence is reshaping technical writing, what AI technical writing will look like in the future, and what you can do to set yourself up for success.
What Is Technical Writing?
Technical writing is the process of creating clear, concise, and accurate documentation that explains complex information about a product, service, or process.
Its main goal is to help users understand and use something effectively, typically in a professional or technical field.
Examples of technical writing include:
- User manuals.
- How-to guides.
- Instructional videos.
- Software documentation.
- API documentation.
- Reports.
- Proposals.
Because of the nature of technical writing, as a technical writer you’ll often collaborate with technical teams or subject matter experts (SMEs) for the product.
Technical writers often play the role of translator, taking complex technical details and making them understandable to understandable to a wider audience.
To be a great technical writer, you need be able to:
- Simplify complex technical information into easily understandable language.
- Ensure all information presented is factual and correct.
- Understand the target audience’s needs, technical level, and how they will use the information.
- Efficiently gather and synthesize information from various sources (subject matter experts, existing documentation, product features).
- Have a solid grasp of the subject matter they are writing about.
- Have excellent grammar, spelling, punctuation, and the ability to self-edit and proofread.
- Structure content logically and intuitively, often using outlines, headings, and formatting to improve readability.
If you’re interested in being a technical writer — or if you’re hoping to hire a technical writer for your support team — then these are the skills to focus on. While AI is changing how technical writing happens, these fundamental skills will still be valuable.
What Changes and Challenges Does AI Bring for Technical Writers?
In my experience working with both technical writers, content marketers, and other content-focused professionals, one of the common themes that has come up as a challenge has been:
“I love the core nature of my work, and I’m not sure if I’ll enjoy it if AI takes on the majority of the writing for me.”
This highlights one of the largest changes and challenges to technical writing (along with other fields):
- Functioning as a guide, rather than the one executing the task: In the age of AI, technical writers need to adapt to a more supervisory and editorial role, rather than solely focusing on content creation. They become curators and refiners of AI-generated content.
- Developing new skills: New skills, such as prompt engineering (the ability to craft effective prompts for AI tools) will now become crucial. This changes the type of writing and framing of content.
- Navigating the nuances of the human touch: Ensuring that AI-generated content retains a human-centric approach isn’t always easy. Learning to edit and revise AI content requires a different mindset than creating that content from scratch.
While these challenges will change the nature of work and the way that technical writers approach their craft, these new realities are also great opportunities for technical writers to continue learning and growing.
How Does This Role Evolve in These New Circumstances and Why Should Technical Writers Adopt AI?
With challenges to overcome, and new opportunities budding there are many ways to get ahead of the curve and make the most of the way that AI is reshaping technical writing.
Why Should Technical Writers Adopt AI?
Imagine this: Your engineers have built and developed a new functionality for your product. The code base is extensive, and you need to write customer enablement content for the new functionality.
How would you have approached this in the past?
You would:
- Deep dive into the functionality by meeting with the engineers and understanding the inner workings.
- Get hands on and get a feel for it, exploring all aspects of the functionality.
- Sift through the engineers documentation (hoping it’s all there).
- Understand the key use cases and problems it solves.
- Plan all of the content, architecture, and resource materials needed.
- Write the documentation.
- And more…
What would a new approach with AI look like?
You could:
Accelerate the data gathering process and your understanding
Instead of purely manual code review, AI tools can quickly scan the new functionality’s codebase, API documentation, and design specifications directly to identify key details and functionality.
AI could also pull relevant information from internal wikis, JIRA tickets, Slack conversations (if integrated), and developer notes, synthesizing it into a structured format, saving hours of manual data collection.
AI could help generate targeted questions for engineers and product managers based on its initial analysis, making SME interviews more productive and focused on nuanced details rather than basic facts.
Kickstart the content generation process
You could use prompt engineering skills to guide an AI model like to generate initial drafts of new content. Prompts would include details from the product specifications, target audience, and desired output format, such as:
“Write a user guide section detailing how to set up the reporting feature for customer support professionals, assuming basic technical knowledge. Include steps and a brief conceptual overview.”
AI can take dense technical descriptions and rephrase them into simpler, more digestible language suitable for the target audience, significantly speeding up the conceptual explanation phase.
AI tools could also potentially integrate with your product development environments to automatically capture relevant UI screenshots and even suggest annotations, reducing a traditionally manual, time-consuming task.
Elevate the content experience & maintenance
Once you have a draft, AI-powered translation tools (like Help Center Translate) could quickly translate the content into multiple languages, maintaining context and nuance more effectively than traditional machine translation.
This saves significant time, while also creating a localized customer experience at scale.
AI could monitor changes in the product’s functionality or related systems and proactively suggest updates to the corresponding documentation, ensuring it remains current.
With AI making these processes and therefore you as a technical writer much more efficient, how would this shift your mindset?
You become a content strategist, editor, and prompt engineer. Your role shifts from laboriously creating every piece of content to curating, refining, and optimizing AI-generated outputs, ensuring accuracy, user-centricity, and strategic alignment, all while significantly accelerating the time-to-documentation.
In my own experience as a knowledge manager for both external and internal knowledge bases, it has allowed me to unlock new opportunities in becoming more user-centric.
I can now spend more time on researching user needs deeply. I can do more thinking about the customer journey and other ways to engage users with knowledge. Plus, I can find more effective ways to collaborate across my company to drive better business outcomes.
Leveraging AI for technical writing and knowledge management tasks has allowed me to have a bigger impact on our customers and our company.
What Tools Can Help Take Your AI Technical Writing to the Next Level?
There are so many AI tools out there today. So how do you know what is possible to leverage?
Here are some tools to consider when jumping in:
- AI writing assistants (content generation & drafting): Claude AI, Perplexity AI, Copilot, ChatGPT, Gemini: These large language models are excellent for generating initial drafts of content, explaining complex technical concepts in simpler terms, brainstorming ideas, and even summarizing extensive technical documentation. They can rapidly produce content that then needs to be refined and fact-checked by a human.
- Grammar, style & readability checkers: Grammarly: While not exclusively for technical writing, AI-powered grammar and style checkers like Grammarly are crucial. They go beyond basic spell-checking to offer insights into readability, conciseness, tone, and sentence structure, ensuring your technical documentation is clear, professional, and easy for your audience to understand.
- Translation Tools: DeepL / Help Center Translate: For global companies, AI translation tools are indispensable. They can quickly translate documentation into multiple languages, enabling wider reach and ensuring all users have access to information in their native tongue. While human review is still recommended for critical translations, AI significantly accelerates the initial translation process.
Explore and try your own! With so many tools and capabilities out there, it is important to experiment and try out ways to leverage these technologies in the most effective ways that work for you.
AI Technical Writing Is Here to Stay
The technical writing role is changing quickly because of AI, and there’s no going back now. Leveraging AI well enables you to spend more time finding effective ways to act as a translator between your customers and your company. By developing these skills and abilities now, you can stay ahead of the curve.
You may just be surprised at what you’re able to accomplish.
Written by Neal Travis
Curious learner and builder of customer experiences in scale-ups. Neal is the Head of Customer Experience at the Academy to Innovate HR (AIHR) and Host of Growth Support.
