AI-generated content

What it is

AI-generated content covers any asset where a generative model creates the substance of the work, including blog drafts, product descriptions, meta descriptions, illustrations, voiceovers and video. Most practical use sits on a spectrum between AI assisted, where a person directs and rewrites, and fully synthetic, where output is published with little review. The distinction matters more than the label, because reviewers and search systems respond to quality and originality rather than to the tool used.

Why it matters

Generative tools have cut the cost of publishing, so volume alone no longer differentiates a brand and thin output can dilute a site's credibility. Search engines and AI answer engines reward content that shows genuine expertise, first hand evidence and clear sourcing, which pure model output rarely supplies on its own. Handled well, AI speeds up research, outlining and repurposing so specialists spend their time on the parts only they can write.

How it works

Teams usually build a workflow with defined inputs such as briefs, interview transcripts, product data and internal research, then use models to draft and restructure, then apply human fact checking and editing before publication. Controls include prompt and style guides, a factual review step, source citation rules and disclosure where the audience would reasonably expect it. Many organisations also log which assets were AI assisted so they can audit performance and fix problems at scale.

When it applies

It applies to any content operation using generative tools, and the controls matter most for material that carries claims about health, money, law, safety or product capability.

Examples

• A retailer generates first draft descriptions for thousands of catalogue items from structured attribute data, then has merchandisers review the top selling lines.
• A B2B team uses a model to turn a recorded customer interview into a case study outline, then a writer verifies the numbers with the client before publishing.
• A publisher uses AI to draft alt text and summaries for an archive, with an editor spot checking a sample of each batch.

How it is measured

• Share of published assets that are AI assisted versus fully human written, tracked in the CMS
• Edit distance or rewrite rate between model draft and published version
• Factual correction and retraction rate per hundred published items
• Organic and AI answer engine performance of AI assisted assets compared with a human written control set