When AI Becomes Your Knowledge Partner: Two Approaches to Smarter Documentation

The Eternal Challenge of Technical Documentation

If you have ever worked as a Technical Writer, you know the scenario well: a project is in full swing, documentation is due, and the subject matter experts (SMEs) who hold the critical knowledge are simply unavailable. Their calendars are blocked, their inboxes are overflowing, and every meeting request gets pushed to "next week." Meanwhile, the information you need is scattered across Jira tickets, Slack threads, emails, and shared drives — visible to some, invisible to many.

This is not a rare edge case. It is the norm. And it raises a pressing question: how do we produce accurate, useful documentation when the traditional pipeline is broken?

Over the past year, I have had the opportunity to work through this challenge in two distinct ways — first improvising a solution under pressure, and then witnessing a more mature, systematized version of the same idea. Both experiences point to the same conclusion: AI is not replacing the Technical Writer or the SME. It is filling the gap between them.

Approach 1: Using AI to Unblock a Stalled Documentation Process

On a previous project, I found myself in a familiar bind. SMEs were not available to define the scope of documents or share the reference information I needed to get started. Without that input, the documentation process was at a standstill — and deadlines were not moving.

The workaround I landed on was to build a detailed, structured prompt — roughly two pages long — and feed it into an AI assistant with access to the organization's connected tools. That assistant had visibility into Jira tickets, Google Drive files, Slack conversations, emails, and other internal channels that I either could not access directly or had not known to look in.

The results were striking. Slack, in particular, turned out to be a goldmine. Countless technical decisions and resolutions had been discussed and resolved there — but never formally documented. The AI surfaced that information, synthesized it, and helped me produce a working first draft.

That draft was not perfect, and it was never meant to be. Its purpose was to give SMEs something concrete to react to. Rather than being asked "what should this document say?", they were asked "does this accurately reflect what we decided?" — a far easier and faster conversation. The documentation moved forward. Deadlines were met.

The key insight: when human availability is the bottleneck, AI can act as an intelligent proxy — not by replacing expert judgment, but by doing the legwork of gathering and synthesizing distributed knowledge so that expert time is used more efficiently.

Approach 2: A Systematic, Persona-Driven Documentation Workflow

The second experience came at a different organization, where I observed a more formalized version of this approach — one that had evolved from an ad hoc workaround into a repeatable, scalable process.

Here, the AI was not just searching for existing information. It was being used to evaluate and improve draft documents against the needs of specific reader audiences.

The process works like this:

1. A technical team produces an initial draft. This draft contains accurate information, but it is written from the perspective of those closest to the technology — meaning it often assumes knowledge the reader may not have.

2. Reader personas are applied. The organization has defined a set of personas representing different roles that interact with their documentation — ranging from deeply technical users to those with little or no technical background. These personas are loaded into the AI as context.

3. The AI evaluates the draft through each persona's lens. It identifies gaps — places where the document assumes knowledge a particular reader would not have, uses terminology without explanation, or skips steps that a non-expert would need. Crucially, it does not just flag problems; it proposes content to fill those gaps.

4. Style, formatting, and compliance rules are applied simultaneously. Alongside the personas, the AI is given the organization's style guide, documentation standards, and formatting requirements. The output is therefore not just more complete — it is also correctly formatted and on-brand.

The result is a draft that is, by internal estimates, roughly ready for SME review — with the remaining gaps clearly flagged and framed as specific questions for subject matter experts to answer.

The key insight: when you move from "AI as search tool" to "AI as structured reviewer," you shift the entire dynamic of the documentation process. SMEs spend less time generating content from scratch and more time validating and refining a nearly complete document.

Looking Ahead

As AI tools become more deeply integrated into enterprise workflows, the Technical Writer's role is evolving. The ability to craft effective prompts, define clear personas, and design structured AI-assisted review processes is becoming as important as traditional writing and editing skills.

The organizations that will benefit most are those that treat AI not as a shortcut, but as a systematic part of the documentation lifecycle — one that respects the knowledge of subject matter experts while dramatically reducing the friction of capturing and sharing that knowledge. 

The blank page problem is not going away. But the tools we have to face it are getting considerably better.

Have you experimented with AI in your documentation or knowledge management workflows? We would love to hear what approaches have worked — or not worked — in your experience.