Why Generative AI Depends on Technical Communications

While generative AI can write documentation quickly, it cannot replace the work of technical communications. In fact, AI depends on strong technical communications to be useful, accurate, and trustworthy at scale.

AI can write documentation.

It can't replace technical communications.

As generative AI becomes more capable, it's natural to ask what this means for technical communications. AI can now draft documentation in seconds, summarize APIs, rephrase content, and surface answers quickly. And in many cases, it does these things quite well.

It's tempting to assume that this level of automation makes the role of technical communications less necessary.

In practices, the opposite is happening.

Generative AI is very good at producing language. Technical communications, however, is about producing understanding. That distinction matters more as documentation becomes more visible, more automated, and tightly integrated into the product experience.

Technical communicators aren't just writing pages of content. They're making decisions about what needs to be documented, how information should be structured, and how much context users need to successfully use a product. They balance clarity with accuracy, usability with compliance, and speed with risk. Those decisions shape how documentation is experienced long after the words are written.

AI can assist with execution, but it doesn't replace that judgement.

One of the less discussed aspects of generative AI is how dependent it is on the quality of existing documentation. AI performs best when it can rely on clear terminology, consistent concepts, and up-to-date source material. When documentation is well structured and actively maintained, AI tools can surface helpful answers quickly and at scale.

When that foundation isn't there, the results degrade. AI fills gaps with assumptions, blends outdated information with current behavior, and produces responses that sound confident but aren't reliable. In this way, AI doesn't solve documentation problems; it makes them more visible.

There's also a fundamental mismatch between how AI works and how Engineering work actually happens. Engineering teams don't operate solely through finalized artifacts. Much of the real work lives in conversations, design reviews, Slack threads, and evolving decisions. Features change mid-development, behavior shifts as constraints emerge, and tradeoffs are made that never show up cleanly in a spec.

Technical communicators operate within that reality. They participate in planning discussions, ask clarifying questions, and extract context from Engineers before it's lost. They notice when what's shipping doesn't quite match what was originally intended, and they adjust documentation accordingly. That ongoing interaction is something AI can't replicate.

AI can summarize what has already been written. It can't engage in the conversations that determine what should be written next. AI can also transcribe or summarize meetings, but it tends to capture what was said, not what was decided. It struggles to recognize what details matter, which assumptions are challenged, and which statements reflect tentative thinking versus final direction.

Accountability is another important factor. Documentation isn't just content; it's part of the product experience. When documentation is unclear or incorrect, the impact is tangible. Customers misconfigure systems, support costs increase, and trust erodes. Someone has to be responsible for preventing and correcting those outcomes.

AI doesn't own that responsibility, technical communications teams do.

As AI becomes more embedded in documentation workflows, the role of technical communications is shifting rather than disappearing. Less time is spent drafting from scratch. More time is spent designing documentation systems, setting standards, validating AI output, and partnering earlier with Product and Engineering teams. The work moves upstream, where it has greater influence on product clarity and adoption.

This shift aligns with a broader change in how documentation is valued. Documentation is no longer just a support artifact. It plays a direct role in how users evaluate, adopt, and trust a product. AI can help surface documentation in new ways, but only if the underlying content is accurate, intentional, and well maintained.

Generative AI can write documentation faster than any human. Speed, however, isn't the primary goal. Clarity, accuracy, and trust are. Those still require humans.

Technical communicators don't just make products usable. They create the foundation that allows AI to be useful as well.