AI-generated imagery has sparked debates about authenticity. Critics argue that AI visuals often feel fake, contain errors, or misrepresent reality. These concerns aren’t unfounded—AI hallucinations have led to everything from extra fingers on human models to entirely fabricated product features.

Yet, paradoxically, AI also has the potential to maintain authenticity. When trained correctly, AI can generate images that faithfully represent products, settings, and branding, ensuring that what customers see is what they get. The difference comes down to how the model is trained and whether it operates within a defined, structured dataset.

Beyond creativity, AI-generated imagery is increasingly being used to solve practical challenges across industries like automotive, real estate, and fashion. As Chris Zacharias, CEO of imgix, noted in a recent webinar, "Companies don’t need AI that generates anything and everything. They need AI that’s purpose-built for their business." 

This shift toward hyper-specialized AI models ensures that AI-generated visuals remain both accurate and trustworthy. By moving from general-purpose AI models to specialized ones, businesses can ensure that AI-generated images remain both accurate and trustworthy.

Why general AI models hallucinate images

Many AI-generated images look convincing at first glance but contain subtle inaccuracies. These hallucinations occur because most models are trained on vast, uncurated datasets that include conflicting or irrelevant information. The result? AI might generate images that include impossible features or details that don’t align with reality.

For example, AI-generated furniture images might depict a sofa in a fabric or color that the manufacturer doesn’t actually offer, leading to customer confusion and disappointment. These inaccuracies undermine trust—especially in industries where precision and realism are critical, such as e-commerce, real estate, and automotive marketing.

Hyper-specialized models eliminate inaccuracies

AI doesn’t have to be inaccurate. The key to generating authentic visuals is specialization. When an AI model is trained exclusively on a company’s own catalog—complete with specifications, available colors, and product details—it produces results that reflect reality.

• A car manufacturer using a general AI model might see vehicles generated in non-existent colors or with incorrect features. A model trained solely on the brand’s real product catalog, however, will only generate images of cars in available configurations.
• A luxury watch brand using a specialized AI model will only generate images of its timepieces with the correct materials, dial designs, and strap options that are actually available, ensuring consistency across marketing campaigns and product catalogs.
• A home decor brand training AI on its own inventory can generate lifestyle images that accurately showcase its products in realistic room settings, without AI inserting imaginary textures or materials.

By using well-defined training datasets, AI becomes a tool for creating scalable, on-brand imagery—rather than a source of misinformation.

How brands can train AI for authentic images

To use AI effectively while maintaining trust, brands should follow a structured approach to training and deploying AI-generated images.

1. Use proprietary datasets. Instead of relying on broad internet-trained AI, train models exclusively on your own product images and data.
2. Define strict constraints. Prevent the AI from generating elements that don’t exist within your inventory.
3. Apply human oversight. AI should assist creative teams, not replace them. Designers and marketers should review AI-generated visuals to ensure accuracy.
4. Leverage industry-specific models. Rather than using a generic image generator, work with AI models tailored to your industry and use case.

With the right approach, AI-generated visuals can reinforce trust, enhance efficiency, and ensure brand consistency—all without sacrificing authenticity.

AI-generated visuals should maintain, not replace, authenticity

AI is neither inherently deceptive nor inherently authentic—it all depends on how it’s trained and used. By moving away from broad, one-size-fits-all models and embracing hyper-specialized AI, businesses can create accurate, reliable visuals that align with reality.

The paradox of AI is that, despite being artificial, it can create some of the most precise and scalable imagery available—when used correctly. As Zacharias put it, "2025 will be the year when people internalize these tools and integrate them into their creative workflows." This shift marks the beginning of AI being truly embedded into real-world creative and marketing strategies.

Want to see how AI is transforming image processing? Watch our on-demand webinar, “Revolutionizing Visual Experiences: AI-Driven Image Processing in the Digital Age”, to explore the latest advancements in AI-driven image generation and optimization.