Leonardo AI Releases Brand Consistency Workflows for Enterprise Content Teams

Leonardo AI has published detailed workflows for maintaining brand consistency in AI-generated visual content, addressing one of the persistent pain points for enterprise marketing teams adopting generative AI tools.

The techniques center on using image references rather than text prompts alone to control specific visual variables—color palettes, typography, logos, and brand mascots. For video generation, Leonardo recommends Image-to-Video (I2V) and Start/End frame workflows to prevent the “identity drift” that causes subjects to warp or mutate during motion sequences.

The Technical Approach

The core insight: text prompts aren’t enough. When you ask an AI model to use “brand colors” or a “specific font,” you’re essentially asking it to guess from its training data. The result tends toward generic, middle-ground outputs.

Leonardo’s solution involves creating visual reference sheets—color swatches with HEX codes, font samples, logo files—and uploading them directly as image references alongside text prompts. For a UI mockup using a specific color palette, this means generating a color swatch sheet through tools like Canva’s palette generator, then feeding that image to the model while also including HEX codes in the prompt text.

Typography presents a harder challenge. Font replacement remains one of the most difficult tasks in AI image generation, according to Leonardo. Even models that render legible text struggle to match specific named fonts from prompts alone. The workaround: create a simple visual showing the font and use it as an image reference, then switch to models optimized for text handling—Leonardo recommends their Nano Banana Pro model for this task.

Video Consistency Requires More Control

Video generation compounds the consistency problem. Without anchoring frames, AI models must simultaneously invent visual style and calculate physics of motion—a recipe for glitches.

The Start/End frame workflow locks in exactly where a video begins and concludes, eliminating guesswork. Leonardo emphasizes upscaling images before feeding them to video models; low-resolution starting frames can cause the AI to misinterpret pixel noise as physical shapes, creating artifacts during animation.

Different models serve different purposes. Leonardo suggests Veo 3.1 for morphing animations and Kling 3.0 for character-driven sequences, though model selection depends on the specific creative application.

Why This Matters for Marketing Teams

The “generic output trap” isn’t just an aesthetic problem—it’s a brand dilution problem. Foundational AI models trained on massive datasets naturally output the statistical average of similar images. That average lacks the distinct character that differentiates brands.

Leonardo’s guidance includes building centralized prompt libraries so teams work from identical foundations rather than each member improvising their own approach. Without standardization, brand consistency breaks down quickly across campaigns.

The company acknowledges that technical workflows alone won’t produce truly on-brand content. “AI models are excellent at following structural instructions and matching colors, but they lack empathy,” the guide states. The human operator provides the emotional intelligence to connect brand messaging with audience expectations—AI handles execution speed and visual generation.

For enterprise teams evaluating AI content tools, these workflows represent the current state of the art for controlled generation. Whether competitors like Midjourney, DALL-E, or Runway offer equivalent brand control features may determine which platforms capture the enterprise market.

Image source: Shutterstock