How AI is Changing the Role of Technical Writers

For technical writers like me, AI is already shaping how we approach documentation. It can draft processes, summarise user stories, rewrite legacy content, and support release notes. In my work at Thesis SM, a Student Information System provider working with universities and colleges across the UK, Ireland, and Canada, these tools are starting to influence how documentation is created and maintained. The shift is not about replacing technical writers, but about repositioning how we contribute.

Drafting is Faster but Responsibility is Greater

AI can produce a first draft in seconds. That saves time, particularly for repetitive or structured content such as process documentation, user guides, and release notes. But AI can also:

• Introduce inaccuracies.
• Misinterpret technical behaviour.
• Use inconsistent terminology.

The AI output is not finished documentation. It is source material that requires validation. Technical writers remain accountable for its accuracy, clarity, and compliance; and in regulated environments, that accountability is not trivial.

In practice, drafting becomes faster but review cycles become more critical.

In higher education, documentation errors do not just cause confusion - they can affect statutory submissions, student records, and audit trails.

Technical Writing Shifts Towards Content Engineering

As drafting becomes easier, value shifts elsewhere. Technical writers are increasingly responsible for:

• Designing structured, reusable components that can be maintained consistently across a large system.
• Defining metadata and taxonomies so content can be found, filtered, and governed.
• Creating and enforcing terminology standards to ensure consistency across teams and outputs.
• Setting structural and style standards that AI tools must operate within, not around.

AI does not design content models; it operates within them. Technical writers who understand content architecture are the ones who shaping how AI is used effectively and ethically.

For institutions running complex student information systems, this mirrors a broader truth: the value of any automation tool depends on the quality of the structure beneath it. A system that is built around clear, configurable processes, rather than one-off customisations, gives documentation teams something consistent to work with.

Where AI Adds Value

Within technical writing, AI is particularly useful for:

It is less reliable when nuance matters, for example, in regulatory wording, complex dependencies, or customer-impact statements, so human oversight remains essential.

For HE institutions, the stakes in these areas are high. Statutory reporting requirements, student lifecycle processes, and regulatory frameworks do not leave much room for ambiguity, whether in the system itself or in how it is documented.

Governance Becomes Critical

More generated content means a greater need for governance and control to mitigate risk. Technical writing teams working in or alongside edtech systems need to define:

Effective governance is what enables AI to be deployed at scale without creating downstream risks. Institutions and software providers that establish clear guardrails can extract significantly more value from AI tools while minimising legal, operational, and reputational exposure. At this stage, AI governance can no longer be treated as an “early adoption” issue, AI systems are already embedded in core workflows, making structured oversight a present requirement rather than a future consideration.

What This Means for Institutions Choosing or Managing a SIS

The documentation that surrounds a Student Information System is part of how an institution manages risk. When processes change, when new functionality goes live, or when regulatory requirements shift, that documentation needs to be accurate, maintained, and accessible.

A SIS that is built around configurable, standardised processes, rather than layers of custom code, makes that job more manageable. It gives technical writers a stable, coherent system to document. It gives institutions a platform where changes are traceable and processes are consistent.

In my work at Thesis SM, I see how our “configure, not customise” approach shapes the way institutions use and understand the system. Instead of building something that only a handful of people can explain, the goal is a system that can be easily understood, documented, and governed. Technical writing plays a key role in this, and doing it well is more important than ever. That isn’t really an AI story. It’s a good systems design story. But it’s exactly the kind of foundation that makes AI tools more useful and far less risky when adopted thoughtfully, responsibly and courageously.

Conclusion

AI reduces drafting effort, but it does not replace professional judgement. The technical writer's role is evolving towards content architecture, quality control, and publishing governance. Those who focus on structured, reusable, well-governed content will benefit most.

The same principle applies to the technical documentation. AI can help generate and refine text and cut through complexity, whilst human oversight ensures that documentation remains accurate, usable, and reliable. The real value comes from combining both.

Find out more about Thesis SM

Thesis SM is a cloud-based Student Information System built for UK, Irish and Canadian higher education. To see how we approach process design, integration, and institutional complexity, book a demo or ask an expert.

About the author: Lisa Morgan - Technical Writing Manager

Lisa Morgan is the Technical Writing Manager at Thesis SM. She has 35 years of experience in the software industry, 15 of those in Higher Education.