Chapter 4: Creative Automation—Text, Image, and Video Generation and Brand Governance
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4-1. The Wave of Generative AI Reaching Creative Work
Generative AI is rapidly expanding beyond text into all areas of creative production, including images, video, audio, and 3D. According to estimates by OG Analysis, the generative AI market in the creative industries is projected to reach approximately $4.7 billion in 2025 and grow to $44.4 billion by 2034 (roughly 9x growth). Industries such as advertising, design, entertainment, and fashion are beginning to adopt AI as a creative partner. Global surveys indicate that nearly 80% of content creators already use AI tools at some stage of their workflows, including scriptwriting, storyboarding, thumbnail creation, and subtitle generation. In Japan as well, advertising agencies and production companies are increasingly using generative AI in the “sketch,” “mock,” and “first draft” stages, enabling them to test variations at a scale and speed that would be impossible manually.
4-2. Text Generation: A “Draft Engine” for Copy, Scripts, and Scenarios
Text generation AI has become an essential tool in copywriting and script development. As of 2025, around 70% of marketers are using generative AI for copywriting tasks, generating headlines, ad copy, email content, and landing page text before refining them manually. By training AI on brand tone and past successful content, companies can partially reproduce their “own voice.” This enables:
Generating dozens to hundreds of A/B testing copy variations at once
Automatically adapting tone and messaging for different personas
Creating multiple versions of video narration scripts
These tasks can now be completed in a fraction of the time. Some case studies report that AI-powered content marketing has led to traffic increases on the order of several hundred percent, demonstrating that text generation can contribute directly to top-line growth.
In Japan, challenges remain in areas such as honorific language, nuance, and industry-specific terminology. As a result, a “co-creation” model—where AI handles ideation and drafting, and humans handle final phrasing and fact-checking—has become the most practical approach.
4-3. Image Generation: Scaling Banners, Key Visuals, and Product Images
Image generation AI (text-to-image) is transforming the speed of creative production, particularly for banners and social media visuals. Globally, around 70% of marketers report using generative AI for image creation, especially for producing multiple variations in social and display advertising (with some variation depending on how usage is defined).In practice, industries such as fashion and entertainment have used AI to generate hundreds or thousands of customized images in short periods. For example, fintech company Klarna has reported using AI to automate marketing image production, reducing production lead time from six weeks to about seven days, generating over 1,000 images within a short timeframe, and achieving annualized cost savings of approximately $10 million.In Japan, while regulatory and cultural sensitivities must be considered, AI image generation is increasingly used for:
Visuals illustrating product usage scenarios
Backgrounds and design elements for banner ads
Illustration-style visuals for social media
A key requirement is maintaining a two-layer process in which human designers review final outputs to ensure cultural appropriateness and avoid reputational risks.
4-4. Video Generation: Low-Cost, Multilingual Content with Platforms like Synthesia
Video has traditionally been the most resource-intensive form of content, but AI video generation is changing that. Companies can now produce multilingual videos with small teams and short timelines.Platforms such as Synthesia allow users to generate videos with avatars and voiceovers simply by inputting text, and are used for training materials, advertisements, and personalized messaging. Reported benefits include reduced production costs, shorter lead times, and improved engagement through easier localization.For Japanese companies, AI video is particularly effective in areas such as internal training, product demonstrations, and customer success content—where information density is high but budgets are limited. A common model is to produce an English version at headquarters and then use AI to generate localized Japanese voiceovers and subtitles.
4-5. Case Studies: How AI Creative Impacts ROI
Several case studies illustrate the impact of creative automation on return on investment (ROI).Barbie Movie Selfie GeneratorWarner Bros. launched a selfie generator that transformed user photos into movie-poster-style visuals, enabling a participatory campaign where users became creators themselves. This resulted in large volumes of user-generated content (UGC) in a short time.Klarna’s AI Image ProductionKlarna automated its marketing image production using AI, reportedly reducing costs by approximately $10 million annually. Production time was shortened from six weeks to about seven days, and over 1,000 images were generated within a short period, significantly accelerating testing cycles.EdTech Company AI Video AdvertisingEdTech company Headway used AI tools to create UGC-style video ads and static creatives, reporting improvements in advertising ROI and achieving over 3.3 billion impressions within six months.These cases highlight a new reality: creative production is no longer the bottleneck. The competitive edge lies in how quickly hypotheses can be tested and refined.
4-6. The Risk of “Brand Collapse” Without Governance
At the same time, unstructured adoption of generative AI can lead to serious risks, including loss of brand consistency. With multiple AI tools embedded across content creation workflows, inconsistent interpretations of brand guidelines can result in fragmented messaging.Generative AI accelerates content production but also increases the complexity of brand management. If brand guidelines remain static documents, inconsistencies in logos, colors, tone, and terminology can proliferate, leading to “brand drift.”In Japan, strict regulations such as the Pharmaceuticals and Medical Devices Act and advertising laws further increase the need for governance, making compliance as important as consistency.
4-7. “Brand AI”: From Static Guidelines to Machine-Readable Intelligence
To address these challenges, approaches such as “AI brand management hubs” or “brand LLMs” are emerging. These involve converting traditional brand guidelines into machine-readable rules and embedding them directly into generative AI systems.This enables:
Automatic detection and correction of prohibited or inappropriate language
Enforcement of logo placement, color palettes, and layout rules
Compliance checks against regional regulations
In essence, brand guidelines evolve from static references into embedded intelligence within AI systems.
4-8. Changing Roles of Creative Professionals
The rise of generative AI does not eliminate creative roles but transforms them. Routine tasks are increasingly automated, while new hybrid roles emerge that combine creativity with AI literacy.Designers shift toward art direction and visual strategy, copywriters toward narrative and information design, and video creators toward structure and direction.The key shift is from “execution” to “designing the system of creation.”
4-9. Practical Implementation Steps in Japan
For Japanese companies, a phased approach to creative automation is most practical:Start with draft generation for text and images
Blog posts, landing pages, and ad copy drafts
Rough concepts for social media banners
Video outlines and narration scripts
Adapt brand guidelines for AI
Define prohibited and recommended language
Establish visual rules for logos, colors, and fonts
Build a governance layer
Define internal approval workflows
Align with legal and advertising compliance processes
Create feedback loops
Continuously learn from high-performing creatives
Store and use negative examples to improve outputs
This approach allows companies to balance speed and scale without compromising governance.
4-10. Bridge to the Next Chapter: Autonomous Customer Support and CX
As seen in this chapter, generative AI significantly enhances the speed and scale of creative production while introducing new governance challenges. A similar dynamic is emerging in customer support and customer experience (CX).The next chapter explores the evolution from FAQ bots to autonomous support agents, and how Japanese companies are working to balance “24/7 AI-driven support” with the country’s high standards of hospitality and service quality.
References
OG Analysis, “Generative Ai In Creative Industries Market Outlook 2025-2034”
https://www.marketresearch.com/OG-Analysis-v3922/Generative-Ai-Creative-Outlook-Share-42768277/
Reuters, “Klarna using GenAI to cut marketing costs by $10 mln annually”
Klarna, “AI helps Klarna cut marketing agency spend by 25% and run more campaigns”
Sounds Profitable, “AI in Content Creation 2025, a new report from Wondercraft reveals that over 80% of creators are now using AI in their workflows”
Digiday, “In graphic detail: How creators are using generative AI to shape video and design”
Synthesia, “Case Studies”
Synthesia, “How Cision uses AI video to scale multilingual support and improve client experience”
Synthesia, “How Evonik creates training videos in multiple languages with Synthesia”
Digiday, “How Klarna is using AI for cost savings, changing ‘extremely frustrating’ creative processes”
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