The growing use of foundation models (FMs) in real-world applications demands adaptive, reliable, and efficient strategies for dynamic markets. In the chemical industry, AI-discovered materials drive innovation, but commercial success hinges on market adoption, requiring FM-driven advertising frameworks that operate in-the-wild. We present a multilingual, multimodal AI framework for autonomous, hyper-personalized advertising in B2B and B2C markets. By integrating retrieval-augmented generation (RAG), multimodal reasoning, and adaptive persona-based targeting, our system generates culturally relevant, market-aware ads tailored to shifting consumer behaviors and competition. Validation combines real-world product experiments with a Simulated Humanistic Colony of Agents to model consumer personas, optimize strategies at scale, and ensure privacy compliance. Synthetic experiments mirror real-world scenarios, enabling cost-effective testing of ad strategies without risky A/B tests. Combining structured retrieval-augmented reasoning with in-context learning (ICL), the framework boosts engagement, prevents market cannibalization, and maximizes ROAS. This work bridges AI-driven innovation and market adoption, advancing multimodal FM deployment for high-stakes decision-making in commercial marketing.

翻译：基础模型在现实世界应用中的日益普及，要求为动态市场制定适应性、可靠且高效的策略。在化学工业中，AI发现的新材料驱动着创新，但商业成功取决于市场采纳度，这需要能在真实环境中运作的、由基础模型驱动的广告框架。我们提出了一种用于B2B与B2C市场自主、超个性化广告的多语言、多模态AI框架。通过集成检索增强生成、多模态推理和基于自适应用户画像的定向技术，我们的系统能够生成与文化相关、具有市场意识、并针对不断变化的消费者行为和竞争态势量身定制的广告。验证工作结合了真实产品实验与一个模拟人性化智能体集群，以建模消费者画像、大规模优化策略并确保隐私合规。合成实验模拟了真实场景，使得无需进行高风险的A/B测试即可对广告策略进行成本效益高的验证。该框架将结构化检索增强推理与上下文学习相结合，从而提升了用户参与度，防止了市场蚕食效应，并最大化广告支出回报率。本研究弥合了AI驱动创新与市场采纳之间的鸿沟，推动了多模态基础模型在商业营销等高风险决策场景中的部署。