AI Creative Production
AI creative production is now broadcast-credible for many use cases and limited for others. This module covers the tools, the workflows that produce quality at speed, and the rights and QC that protect brand.
What you will learn
- AI for creative production: where it works and where it does not
- Image generation: Midjourney, DALL-E, Stable Diffusion, Adobe Firefly
- Video generation: Runway, Sora, Synthesia, Pika
- Voice and audio: ElevenLabs, Suno, Play.ht
- Text-to-design tools: Galileo, Uizard, Canva AI
- Style consistency in AI-generated assets
- Production workflow: brief, generate, curate, refine
- The "good enough" question: when AI output meets brand bar
- Rights, ownership, and disclosure
- Quality control and editorial review
- Building an AI-augmented creative team
1. Where AI creative works
Image generation is production-ready for: stock-photo replacement, ideation, mood boards, social variants, internal comms, low-stakes background imagery. Less ready for: hero brand campaigns, photography requiring real-world specificity, anything requiring perfect text rendering.
2. Image generation tools
| Tool | Strengths |
|---|---|
| Midjourney | Aesthetic quality, style controllability |
| DALL-E 3 / OpenAI | Prompt adherence, available via ChatGPT |
| Stable Diffusion | Open source, customizable, on-device |
| Adobe Firefly | Commercial-safe training data, integrates with Creative Cloud |
| Ideogram | Strong text rendering in images |
3. Video generation
- Runway: The most production-mature; Gen-3 quality is broadcast-credible for short cuts.
- Sora (OpenAI): Long-form, high-quality; access expanding.
- Synthesia: AI avatars for explainer / training video.
- Pika, Luma Dream Machine: Consumer-grade ease.
4. Voice and audio
- ElevenLabs: Voice cloning and TTS, broadcast-quality.
- Suno: Full song generation including vocals.
- Play.ht, WellSaid Labs: Marketing-focused TTS.
- Descript: Audio editing with AI voice features.
5. Design tools
Galileo, Uizard, Canva AI, Adobe Express, Figma AI features: turn text descriptions into design comps. Output is iterating-quality, not finished-quality, for most use cases.
6. Style consistency
The hardest problem in AI creative production: generating multiple assets that feel like the same brand. Approaches:
- Custom model training on brand assets (LoRA, DreamBooth).
- Strict prompt templates.
- Style reference images (Midjourney --sref).
- Curation discipline at output review.
7. Production workflow
- Brief: clear concept, brand context, deliverables.
- Generate: 5 - 30 variants per output.
- Curate: human selection of best.
- Refine: in-tool or in Photoshop / video editor.
- QC: editorial and brand review.
- Approve.
8. Good enough
AI creative output is "good enough" for: paid social variants, A/B test creatives, internal materials, ideation, mood boards. Often not good enough for: hero brand films, packaging, signage, anything with high public visibility.
9. Rights and disclosure
- Commercial usage rights vary by tool (Midjourney commercial subscription required).
- Adobe Firefly trains on commercially-licensed images.
- Training-data lawsuits ongoing for most tools.
- FTC guidance: disclose AI-generated content where consumer might be deceived.
- Some clients require disclosure of AI use; some prohibit it.
10. Quality control
- Brand bar review.
- Text-in-image accuracy check.
- Compositional and proportional accuracy.
- Demographic representation review.
- Cultural appropriateness.
- Logo and brand-asset detection.
11. AI-augmented creative team
Roles that emerge: prompt engineer / AI specialist, AI-augmented designer, AI quality reviewer. Roles that compress: junior layout, stock-image researcher, basic variant production.
Sources & further reading
Part of the AI Marketing Tools series · RGM Training