Image generation (Non-Product Images)

Retailers and advertisers can boost creativity and productivity by generating banner creative with ease, thereby improving banner ads’ performance and revenue. AI-powered tools support the full breadth of their advertisers, especially smaller ones who may not be equipped to run effective banner campaigns. Traditional approaches to generating marketing imagery require extensive coordination between internal design teams, external agencies, and stock photography providers, creating dependencies that slow campaign velocity and limit personalization capabilities. The challenge intensifies as organizations expand into new geographic markets, where cultural nuances demand localized visual content that resonates with regional audiences while preserving global brand identity.

The financial implications of manual image-creation processes extend beyond direct production costs to encompass opportunity losses from delayed campaign launches and an inability to capitalize on trending moments. Brands and agencies are turning to generative AI to create ad copy, social media posts, promotional videos, and email campaigns that can be tailored to specific audiences in real-time. AI tools enable marketers to rapidly test variations of content, optimize messaging, and reduce campaign turnaround times, enhancing both efficiency and ROI. Organizations face mounting pressure to produce visual content at an unprecedented scale while controlling costs and maintaining quality standards across all customer touchpoints. The ability of AI tools like DALL-E, Adobe Firefly and Midjourney’s Discord to create images, and now video, represents a challenge to keep up and an opportunity to produce graphic content far more quickly and cost-effectively than ever before. To give an example of the scale at which this is occurring, it’s estimated that in 2024 people were creating 34 million images daily on AI-powered tools. While many of those images are created by individuals for their personal use, AI is now used by 75% of public relations professionals, with 51% using it every day.

But using these powerful tools for image-creation isn’t always easy. Marketing teams struggle with fragmented workflows across multiple design tools, version control issues, and inconsistent application of brand guidelines. Human factors, including designer burnout from repetitive tasks, knowledge transfer gaps when team members leave, and resistance to adopting new technologies, create additional friction. The convergence of these challenges—with the opportunity created by the emergence of these powerful creative tools—necessitates a fundamental shift from manual design processes to AI-augmented workflows that preserve creative control while dramatically accelerating production capabilities.

Modern AI-powered image-generation platforms leverage advanced diffusion models and neural networks to transform text descriptions into high-quality marketing visuals, fundamentally reimagining the creative production pipeline. These systems employ sophisticated style-conditioning algorithms that learn from brand asset libraries to ensure generated images maintain visual consistency with established guidelines. The architecture typically combines multiple AI models, including text encoders for understanding prompts, image-synthesis networks for generation, and quality-assessment modules that filter outputs before human review.

AI tools assist in generating concepts, storyboards, and packaging visuals while speeding up content production through prompt-based inputs. Core technical components include latent diffusion models that operate in compressed representation spaces for computational efficiency, cross-attention mechanisms that align textual descriptions with visual features, and fine-tuning capabilities that adapt base models to specific brand aesthetics.

Generative AI platforms use models that begin by analyzing content, followed by creating an initial composition, then fine-tuning the composition and applying appropriate colors. Designs undergo a scoring process based on aesthetics where only designs that meet quality standards are approved and displayed. Research efforts focus on improving current models and evaluating both available and custom methods on enterprise-style images. The goal is to help marketers spend less time on design review and editing AI-generated images by allowing them to input reference images and have AI generate new images for enterprise marketing campaigns that consistently maintain brand style. Integration architectures must accommodate existing digital asset management systems, content management platforms, and marketing automation tools while providing APIs for programmatic image generation at scale.

Implementation challenges encompass both technical and human dimensions, requiring careful orchestration of infrastructure, governance, and change management. Organizations must address computational resource requirements for on-premise deployments versus cloud-based solutions, establish quality control workflows that balance automation with human oversight, and implement feedback loops that continuously improve model performance based on campaign results. The technology stack must also incorporate safety filters to prevent the generation of inappropriate content, version control systems for managing prompt libraries and model iterations, and monitoring capabilities to track usage patterns and identify optimization opportunities.

Risk mitigation strategies require multi-layered approaches addressing copyright concerns, brand safety, and operational dependencies on AI systems. Adobe’s Firefly, designed to be “safe for commercial use” with IP indemnification for legal issues, was trained on stock images owned by the company, public domain content, and other openly licensed or non-copyright material. It automatically generates a “content credentials” tag for all created 267 3.3 Design images, listing the name, date created, and tools used, meant to act as a “digital nutrition label”. Similarly, Google’s generated output indemnity means customers can use content generated with their products knowing Google will indemnify them for third-party IP claims, including copyright.

However, companies must verify that the AI tools being used offers copyright protection. What’s more, limitations persist in areas such as generating photorealistic human faces without uncanny valley effects, maintaining consistency across image series, and adapting to highly specific brand requirements that deviate from training data distributions.

The Coca-Cola Co. is a prime example of a major brand using AI image-generation tools in marketing campaigns. In 2023, it used OpenAI’s ChatGPT-4 and its DALL-E image creator in its “Create Real Magic” campaign that enabled digital artists to create and submit work to be featured on Coca-Cola’s digital billboards. Coke also used image-generation when introducing its limited-edition Y3000 flavor that encouraged consumers to image what the world would be like in the year 3000. Besides using image-generation to create the futuristic can design, it also put a QR code on the can that links people to the Coca-Cola Creations Hub, allowing them to scan photos of their surroundings to see how they might look in the future.

Newell Brands has used Adobe’s Firefly image-creation tool for several of its brands, including Elmer’s glue. For a back-to-school marketing campaign, Elmer’s needed images for social media, ecommerce product detail pages and retailer media campaigns, an effort that in the past would have taken months of planning, shooting, editing, and producing. Now Elmer’s can product digital assets five times faster. For another Newell brand, the European team at Yankee Candle generated dozens of variations of a bow wrapped around a candle in seconds, making it possible to test how the various performed with and without a bow.

The global AI image generator market size was valued is projected to grow from USD $299.3 million in 2023 to $917.4 million by 2030, a CAGR of 17.4%, according to Fortune Business Insights. According to statistics compiled by Mconverter, an online document-management company, 46% of online retailers use AI tools for image generation and AI-generated images are expected to account for 15% of all digital content online in 2025, up from less than 2% in 2022.

Providers of AI image-generation technology include established technology giants, specialized AI startups, and integrated marketing platforms. Differentiation occurs across multiple dimensions, including model sophistication, integration capabilities, pricing structures, and specialized features for commerce applications. Enterprise buyers must evaluate solutions based on scalability requirements, brand safety controls, API flexibility, and vendor stability.

Evaluation criteria should prioritize indemnification provisions, data privacy guarantees, and a proven ability to maintain brand consistency at scale. Organizations must assess vendor roadmaps for continued innovation, support quality for mission-critical deployments, and ecosystem partnerships that enable seamless workflow integration.

Future market evolution will likely emphasize specialized models for specific industries, enhanced control mechanisms for brand consistency, and deeper integration with marketing technology stacks.