In 2024, people often counted how many marketers were already using AI. That percentage says little about work quality. More interesting questions are where it helps, where it introduces risk and who approves the final result.
Divide the work into tasks
AI can help summarise interviews, organise data categories, produce headline variants or spot unclear wording. Product facts, customer data, competitor comparisons and public promises require human checks against original sources. Do not put sensitive material into a tool without an agreed processing procedure.
Agree on quality
Define an input source, expected output, editor and error checklist for each task. Measure time to publishable material, not the speed of the first draft. If a model produces many convincing but inaccurate claims, the apparent saving disappears into corrections.
Keep the experience
Record which questions need the company's own observations and examples. AI can improve a text's structure, but cannot itself substantiate work performed by the team. At publication, the author should be able to explain the basis of the main claims.
What to check in practice
For example, when a team prepares an article from a customer interview, AI can suggest topics and a structure. An editor checks every quote against the recording, verifies the company's permission and decides what can be disclosed publicly. Publication follows those steps. This takes longer than one prompt, but produces a text the business can stand behind.