Whilst AI is changing documentation work, organisations are seeing very different results. The most successful results come when you know what you ultimately want AI to achieve, and what “good” looks like. In an organisational context, you also need to know where AI belongs in the process, and how to keep control of quality.
That is why we have created a new self-paced e-learning course: Managing documentation projects with AI.
It is for people responsible for documentation projects, technical communication teams, policies and procedures, or content operations. The course takes a practical look at how AI can support the work across a documentation project, from research and planning through to checking, publishing, and maintenance.
Early-bird pricing is now available.
Why we chose e-learning
We have delivered this topic as a live, trainer-led course since the start of the year. The delegate feedback has been very positive; the discussions, exercises, and opportunity to compare approaches have been valuable both to the delegates and us.
However, there are limitations to a live course: there is only so much material we can cover in a day.
The e-learning format gives us room to include more of the practical detail: how to choose an AI opportunity, evaluate a workflow, decide where conventional automation is a better fit, define responsibilities, and build in evidence and approval points.
It also gives delegates time to pause, revisit a module, and work through the exercises properly. They can fit the course around their work demands and choose much of the order in which they take the modules. This can help when the goal is to make a sound decision about, and implement, an AI-assisted documentation workflow.
The course includes monthly “office hours” sessions so that students still have the ability to have a live discussion with the trainer.
Use the tools that fit your work
Documentation teams do not all use the same systems, content formats, or come to that, AI tools.
With a self-paced course, delegates have more opportunity to use the tools they prefer and test the ideas against their own working environment.
You can explore chatbots, AI assistants, locally installed tools, no-code applications, or automated workflows, and compare them.
The course focuses on the management decisions behind the tool choice. For example:
- Is this a suitable problem for AI, fixed automation, or a process change?
- What evidence is needed before content can be drafted or published?
- Who reviews the output and remains responsible for the decision?
- How will you assess whether the workflow is worth scaling?
A course for planning, implementing and making decisions, not collecting prompts
The course includes a project plan, AI evaluation, roles and responsibilities document, and total cost of ownership assessment.
By the end, you will be able to make an evidence-based recommendation: pilot the workflow, scale it, revise it, restrict it, or stop it.
We know AI can produce content quickly. But someone still needs to decide whether the information is accurate, appropriate, secure, and useful.
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