If your agency’s AI content workflow is “paste a brief into ChatGPT and edit the output,” you are using about 10% of what is available.
ChatGPT is a starting point. The agencies producing genuinely good AI-assisted content are using a stack of tools and techniques across the full production workflow, from strategy to publishing.
The content production stack
Strategy and planning: Before any content is written, AI helps with topic research, keyword clustering, competitor content analysis, and editorial calendar planning. Feed your client’s topic area into Claude or ChatGPT and ask for a content gap analysis against their top competitors. You get a prioritised list of topics in minutes.
Briefing: The brief determines the quality of the output. A prompt that says “write a blog post about SEO” produces commodity content. A brief that includes the target keyword, search intent, audience description, tone requirements, competitive content to beat, internal linking targets, and a suggested structure produces something worth publishing. Good prompt engineering is what makes the difference here.
The best agencies spend more time on the brief than on editing the output. That is the right trade-off.
First-draft generation: This is where most agencies stop. But the tool choice matters. For long-form content, Claude tends to produce more natural, less formulaic prose than ChatGPT. For technical content, both work well with sufficient context. For content that requires specific brand voice matching, include 2-3 examples of existing content in the prompt.
Editing and refinement: AI-generated content needs human editing. Not just proofreading, but adding genuine insight, removing generic filler, and ensuring the piece says something that only someone with real expertise would say. The test: if a reader cannot tell this was written by someone in your industry, it needs more work.
Visual content: Midjourney and DALL-E handle blog graphics, social images, and presentation visuals. For agencies, the biggest win is speed: generating 10 image options for a hero graphic in minutes rather than briefing a designer for a stock photo search.
Repurposing: One piece of long-form content should become multiple assets. AI handles the conversion: blog post to LinkedIn carousel, to email newsletter section, to social posts, to client presentation slide. For social media agencies, this is where the biggest time savings live. Feed the original piece in and ask for each format with specific requirements.
What makes agency content different
The trap with AI content is homogeneity. Every agency producing AI-assisted content is pulling from the same training data. If you do not add something unique, your content sounds like everyone else’s.
The agencies producing standout content are adding three things that AI cannot:
- Original data or case studies. Real numbers from real projects. AI cannot fabricate these credibly.
- Genuine opinions. A point of view that someone could disagree with. AI defaults to balanced, non-controversial takes. Your content should not.
- Client-specific context. For client content, the details that come from actually knowing the business, the industry, and the audience. This is the layer that separates AI-assisted content from AI-generated content.
The workflow in practice
- AI generates the strategy and brief (10 minutes)
- AI generates the first draft (5 minutes)
- Human editor adds expertise, data, and voice (30-45 minutes)
- AI generates visual assets (10 minutes)
- AI creates repurposed versions (10 minutes)
- Human reviews all outputs (15 minutes)
Total: 80-95 minutes for a full content package (blog post, social assets, email section) that previously took a writer and designer a full day. To ensure consistency at scale, build a quality control process around this workflow.
This is part of Tool Drop, a series reviewing AI tools and approaches through an agency lens. Subscribe to the newsletter to get new articles weekly.
