Why AI-generated images matter
For many organizations, producing consistent and engaging visuals for websites, presentations, or social media can be resource-intensive. Traditional photoshoots often require significant time and budget. AI-generated images provide a practical alternative, enabling businesses to create professional, tailored visuals more efficiently.
Case study: Testing AI with a sustainable product example
To demonstrate how different AI tools can support product branding, we use a sample product called Zyra—a line of sustainable laundry strips. As consumer demand for eco-friendly household products continues to rise, Zyra serves as a useful case study to test how AI models interpret product descriptions and generate visuals that align with a brand identity.
Our test prompt was:
"Create an image of someone doing laundry with laundry strips. The strips are from the brand Zyra (vivid orange, blue, and purple abstract design). The woman is looking happy."
This allows us to evaluate which AI platforms produce realistic, usable results for marketing materials.
Which AI image generation platforms exist
We tested several leading AI image generation platforms, each of which is available via API for automation or integration into business workflows:
- GPT-image-1 (OpenAI Playground)
- DALL·E (Azure OpenAI)
- GPT-image-1 (Azure OpenAI)
- Gemini Flash 2.5 (Google)
- Grok
The key questions are: Which models produce the most realistic images, and how much manual tweaking is needed to achieve the perfect result? Some models let you edit existing images, while others generate visuals entirely from scratch. Many also require a subscription for full access.
Our goal is to evaluate how effectively these models can generate images automatically from AI-generated prompts — for instance, to create eye-catching visuals tailored for social media.
How did each AI tool perform on image generation
Below, we compare how each model interpreted this prompt and the level of realism, style, and manual adjustments required.
DALL·E-3 (Azure OpenAI)
- Strengths: Accessible through Azure, straightforward to use.
- Limitations: Struggles with realism, especially in human figures and product text placement.
Grok
- Strengths: Slightly improved results with human movement compared to DALL·E.
- Limitations: Weak at niche product details (such as laundry strips). Text integration remains a challenge.
GPT-image-1 (Azure OpenAI)
- Strengths: Generates more realistic images overall. Also supports editing existing visuals.
- Limitations: Text integration is not always accurate.
Gemini Flash 2.5 (Google)
- Strengths: Produces realistic people and packaging; handles text better than DALL·E.
- Limitations: Inconsistent results with varied prompts; regional availability is currently limited.
OpenAI Playground API (Most Versatile Overall)
- Strengths: Supports DALL·E-2, DALL·E-3, and GPT-image-1. By combining models, results are more realistic and context-aware. Allows advanced features such as editing, logo placement, and combining multiple images.
- Limitations: Requires credits for full use.
Practical example: Branding, fidelity, and social media feeds
In our Zyra test case, we demonstrated two useful applications of AI-generated imagery:
- Logo Placement and Branding: We were able to take an AI-generated lifestyle image and add the Zyra logo directly onto the packaging with a single adjustment. This illustrates how AI tools can replicate real-world branding tasks without requiring custom photography or graphic design.
- Fidelity Adjustment: Another option is to adjust the image's fidelity. Higher fidelity preserves more detail from the original and produces visuals that closely resemble traditional photography. While this comes at a slightly higher cost, it is particularly valuable when refining brand concepts or ensuring consistency across campaigns.
- Instagram Feed Example: To demonstrate how these capabilities translate into real-world marketing, we created a mock Instagram feed for Zyra entirely using AI. The feed includes product-focused images, lifestyle content, and branded visuals — demonstrating how a business can quickly establish a professional social media presence without large-scale photoshoots.
Together, these examples highlight how AI can support both tactical branding needs (logos, packaging design, fidelity refinements) and strategic marketing goals (consistent, engaging social content).
Which tool wins on AI-generated visuals
For maximum versatility, the OpenAI Playground API offers advanced features such as image editing, logo placement, and contextual adjustments. Reliable alternatives include GPT-image-1 (Azure) and Gemini Flash 2.5, which provide consistent performance for diverse industries.
While AI image generation continues to improve, certain challenges remain, including producing realistic human figures and achieving seamless text integration across all platforms and models. One interesting move we are expecting next months is visual editing tools like Canva & Photoshop to further embrace these AI-generated visuals while allowing for further editing as well (e.g., tuning the text). Combining AI generation & flexible editing would surely save many companies a lot of time.
Conclusion
AI image generation is not a replacement for every visual need, but it is becoming a valuable tool for marketing teams, consultants, and businesses. It allows faster production of visuals, cost savings, and greater flexibility in testing brand concepts.
For companies exploring AI adoption in marketing, integrating these tools into content workflows is absolutely something to look into. While the technology remains new and not yet always production-ready, we are looking forward to some of the leading marketing visual software (e.g., Canva) to adopt these AI-generated images further & to allow for flexible editing. Combining AI generation & flexible editing would for sure save many companies a lot of time and will further disrupt the marketing industry.
If you are keen to learn more, do reach out via the element61 contact form.
