How can we scale AI visual production for marketing?

Build generation into the production system rather than treating image creation and animation as separate tasks. For G42, production began by establishing how navigation and storytelling would work, then building a custom content management system (CMS) to hold the experience's structure. AI generation and video-processing automation were integrated into the CMS, allowing editors to suggest prompts, generate multiple images and animate them within a connected pipeline.

AI coding software helped build the platform in weeks. Once operational, the pipeline could create more than 150 videos in a matter of days. The practical lesson is to design the content structure and production workflow together, with editors directing generation. Gartner argued in 2025 that generative-AI creative systems require substantial initial setup and ongoing oversight, while computational expenditure can offset traditional production savings.

Scale the options, then curate the final experience: generation capacity and editorial selection serve different purposes. For G42's Intelligence Grid 2.0, the production pipeline generated AI images, converted them into 3D models and animated them on green screens. Work that would normally take hours per video took minutes, enabling thousands of videos to be created and around 170 to be curated for the final experience.

The useful production model is a broad pool of options followed by a deliberate selection for publication. G42's experience launched across six screens at GITEX GLOBAL in Dubai and continued as an online platform. Plan review capacity alongside generation capacity: a 2025 Adobe survey found that 58% of surveyed marketers said more than 40% of content-production time was spent managing reviews and approvals.