Content teams face a specific version of the AI problem: the tools can produce text quickly, but the output often requires more editing than writing from scratch would have taken. This course looks at where AI assistance genuinely reduces workload and where it tends to create new ones.
Mapping the editorial process
Day one begins with participants mapping their current workflow — from brief to published piece — and identifying where delays and bottlenecks actually occur. AI tools are then introduced at specific points in that map rather than as a general replacement for writing.
Common use cases examined include brief expansion, research summarization, headline variation testing, and first-draft generation for templated content types like product descriptions or event announcements.
Quality control without slowing down
The second day focuses on review processes. Participants build a lightweight checklist for AI-assisted content: factual verification steps, tone consistency checks, and a simple flagging system for outputs that need heavier editing. The goal is a repeatable process, not a perfect one.
What participants leave with
Each participant produces a written workflow document for their specific team context. These are reviewed in pairs and refined based on peer feedback. Facilitator Bogdan Iliev, who has managed editorial teams at three digital publications, provides written notes on each document before the end of day two.