If you’ve been following conversations about AI in technical communication, you’ve probably noticed a pattern by now. The loudest claims are often the least useful. AI will replace writers. Prompting is strategy. Automation will solve documentation bottlenecks on its own. Quality issues are just a temporary inconvenience.
In other words, there are still plenty of AI myths in tech comm shaping how people think about documentation work.
That’s one reason I’m excited to be presenting at Information Energy 2026, a practice-driven, community-focused online conference for technical communication professionals that runs April 22–24, 2026. The event is designed around immediately applicable ideas for technical writers, documentation managers, content strategists, AI leads, and information architects, with more than 35 sessions and recordings included with registration.
My session is called “AI Fallacies in Documentation: Lessons Learned When Automation Didn’t Deliver.” It is scheduled for April 22, 2026, from 18:30 to 19:10 CEST. In the session, Jackie Damrau and I unpack five common AI fallacies that derail documentation efforts, including the tendency to treat AI as a writer instead of a collaborator and the assumption that prompting can substitute for content design. The talk draws on real pilot implementations and a survey of nearly 90 technical writers, and focuses on how these misconceptions create workflow, governance, and quality problems.
Why AI Myths in Tech Comm Matter
One of the reasons I wanted to give this talk is that the current conversation around AI in documentation is often stuck at the level of abstraction. There is no shortage of hot takes. What there is still a shortage of, in my opinion, is grounded discussion about what happens when automation enters real documentation environments with real constraints.
That is where AI myths in tech comm become more than just bad ideas. They start to shape budgets, workflows, expectations, and hiring decisions.
When leaders assume AI can produce publishable documentation with minimal oversight, the result is often not efficiency. It is rework. When teams assume prompting is enough without structured content, metadata, or governance, they end up discovering that poor systems do not become good systems just because a new tool is layered on top. And when organizations frame AI as a replacement for human expertise instead of a support for it, they tend to create exactly the kind of documentation problems they were hoping to eliminate.
These are not hypothetical concerns. They are familiar patterns, and they are showing up across the field.
Why I’m Looking Forward to Information Energy
What makes Information Energy especially interesting to me is that it is explicitly focused on the real transformation of technical communication. The conference is online, international, and built around practical sessions that people can use in their day-to-day work. The 2026 program includes content on AI, future workflows, content strategy, metadata, and the evolving role of technical communicators.
That makes it a particularly good place to have a conversation about AI myths in tech comm.
Too many conference discussions about AI still begin from the assumption that adoption itself is the goal. I’m more interested in what happens after that. What actually changes? What breaks? What turns out to have been oversold? What kinds of content infrastructure make automation more viable, and what kinds of assumptions lead teams into trouble?
Those are the questions I’m hoping to explore in this session.
What I’ll Be Sharing in the Session
In “AI Fallacies in Documentation: Lessons Learned When Automation Didn’t Deliver,” Jackie and I will be focusing on the gap between AI enthusiasm and documentation reality. According to the session description, we examine five recurring fallacies and show how teams recover through stronger structure, metadata, and human review.
That last point is especially important to me.
I’m not interested in simply dismissing AI. I’m interested in helping technical communicators develop a clearer and more sustainable understanding of where AI fits, where it fails, and what kinds of human-centered practices still matter. If we want better conversations about AI myths in tech comm, we need to move beyond both hype and backlash. We need to look carefully at implementation, collaboration, and design.
That’s what this talk is really about.
Join Me There
If you’re working through questions about AI, documentation, governance, workflow redesign, or the future of technical communication, I’d love for you to join me at Information Energy 2026. The conference runs online from April 22–24, 2026, and registration includes access to the full event along with recordings of all presentations.
And if you’ve been wrestling with the gap between what AI promises and what it actually delivers in documentation work, I think this session will resonate.
Because the real issue is not whether AI matters. It does.
The real issue is whether we are willing to challenge the AI myths in tech comm that keep getting in the way of better decisions.
If you see this post and want to attend, contact me for a discount code!