Here’s something that doesn’t make sense.
You work with engineers every day. Smart people. They build complex systems. They understand workflows, pipelines, dependencies.
But ask them to map how they actually write documentation, and most of them can’t do it.
They’ll describe the end result. They’ll tell you what the doc should contain.
But the actual workflow? How information moves from subject matter expert interview to final publication? The tools, the handoffs, the format conversions, where things get stuck?
Blank stare.
Meanwhile, you can map that workflow in your sleep.
You know exactly where the bottleneck is—usually in review cycles, right? Or maybe it’s getting engineers to actually respond to edit queries. Or it’s that one legacy system that doesn’t integrate with anything.
You see the system because your job depends on seeing the system.
But here’s what I realized recently: Most people were literally taught NOT to see what you see.
And that’s not a small thing. That’s the difference between people who are prepared for AI integration and people who are scrambling.
I’m Lance Cummings, and you’re listening to Deep Reading, where we look at research that changes how we think about writing and AI.
Today I want to show you a piece of research from 1996 that explains why you’re positioned to lead AI integration in your organization—even though nobody’s probably told you that yet.
And why the engineers who are supposed to be “tech people” are actually starting from behind.
The Invisible Tool Myth
In 1996, a researcher named Christina Haas published a study called “Writing Technology: Studies on the Materiality of Literacy.”
She was investigating something she called the transparent technology myth.
Here’s what she found: Most people are taught to treat writing tools as neutral instruments that don’t change the writing itself.
The tool is just a conduit. The thinking happens inside your head, and the tool just captures it.
This is how most of us were taught. “It doesn’t matter if you write by hand or on a computer—the writing is the same.”
Except that’s not true at all.
Haas showed that tools fundamentally shape not just the final product, but the process of composing itself.
Writing in Microsoft Word feels different than writing in MadCap Flare. Not because one is better, but because they organize information differently, which changes how you think about structure.
Managing documentation in Confluence creates different workflows than managing it in SharePoint.
Authoring in DITA with Oxygen XML forces you to think modularly in ways that a traditional word processor doesn’t.
The tool isn’t transparent. It’s active. It shapes the work.
But if you’ve been taught the transparent technology myth—that tools don’t matter, only ideas matter—then you literally can’t see how tools shape your process.
In the Classroom
I saw this play out exactly as the research predicts in a classroom last week.
I asked twenty students—computer engineers, English majors, cybersecurity students—to map their writing workflows.
The engineers were the most striking. These are people who live in version control systems. They can map a deployment pipeline in their sleep.
But their writing workflow? “I just... write it until it’s done.”
The English majors could describe intellectual moves—brainstorming, researching, drafting—because that’s what they’d been taught to name.
But the operational reality? The actual tools, formats, handoffs, file management, version control? That was supposed to be background noise. Irrelevant to “real” writing.
They couldn’t map their workflows because they’d been taught not to see the tools.
And this matters now because you can’t integrate AI into workflows you can’t see.
From Process to Workflow
In 2020, two researchers named Tim Lockridge and Derek Van Ittersum published a framework specifically about writing workflows.
They defined a workflow as “the tools and the process used for a writing task.”
Not just the cognitive process—brainstorm, draft, revise.
But the tool sequences. How work actually flows through systems.
They argued that you can’t understand contemporary writing without examining these tool sequences rather than treating technology as transparent.
Then in 2024, a researcher named Alan Knowles extended this to AI specifically.
He merged workflow thinking with something called Human-in-the-Loop principles.
The question isn’t “what can AI do?”
The question is “where does AI fit within existing work practices?”
Does it reduce friction? Does it open up new relationships with tools and tasks?
You can only answer that if you can see the workflow first.
Here’s where it gets interesting for content professionals.
In 2025, three researchers—Getto, Kelley, and Vance—applied this specifically to technical communication.
They pointed out something crucial: Technical communicators don’t operate under the transparent technology myth.
They never could.
Because technical communicators routinely attend to how tools shape content.
Style guide enforcement software. Content management systems. Structured authoring environments. XML editors. Publication pipelines.
Technical editors and content professionals have always had to think about where human judgment enters a production sequence and where automation can handle routine operations.
That’s the job.
You can’t manage content through production systems while pretending tools are transparent.
So when AI shows up, technical communicators are already prepared.
They already ask: Which tasks, at which stages, under what oversight conditions?
That’s exactly what Human-in-the-Loop AI collaboration requires.
Understanding AI Workflows
When most people try to integrate AI, they treat it like the transparent technology myth: just another neutral tool that captures thinking.
“Help me write this.”
But AI isn’t transparent. It’s deeply shaped by how you structure information, how you sequence prompts, what format you give it, what stage of the workflow it enters.
Getto, Kelley, and Vance describe the Human-in-the-Loop role as “manager of the process, validating outputs for whatever criteria they are aiming for.”
That’s familiar work if you’re a technical editor.
You already define tasks. Evaluate outputs against rhetorical criteria. Iterate based on results.
You already manage content through production systems.
AI is just applying the same analytical framework you’ve been using for structured authoring, content management, and publication workflows.
The question isn’t whether AI changes writing. Of course it does—tools always do.
The question is: Can you see well enough to manage that change strategically?
Here’s what this research tells us:
Most writers can’t map their own workflows because they were taught not to see tools as shaping work.
But content professionals—especially technical communicators and editors—were never trained that way.
You’ve always had to see the systems.
You understand that tools aren’t neutral. They shape how information flows, where friction happens, what’s easy and what’s hard.
You know where human judgment matters and where automation helps because you’ve been making those decisions for style guides, CMSs, and structured content for years.
That’s not a nice-to-have skill anymore. It’s the essential skill for AI integration.
Because you can’t integrate AI into workflows you can’t see.
And you can already see them.
The Value of Operational Thinking
The panic narrative says AI replaces writers.
But the research suggests something different: AI integration requires exactly the operational thinking that content professionals have been developing all along.
You’re not behind. You’re prepared.
You just might not have recognized workflow thinking as the strategic advantage it actually is. Because now that you can see the workflow, we need to understand what AI can actually do within it.
I’m Lance Cummings. Thanks for listening to Deep Reading.
If this changed how you think about AI and writing, share it with someone who needs to hear it.
And if you want the practical framework for mapping your workflows and integrating AI systematically, check out my newsletter Cyborgs Writing at http://www.isophist.com.
I’m also releasing a beta version of my Writing with Machines course soon for paid subscribers. For more information, click here.
