Agentic Multimodal AI for Hyper-Personalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework

Sagar Srinivas Sakhinana, Akash Das, Shivam Gupta, Venkataramana Runkana

Keywords: Agentic AI, Multilingual and Multimodal Ad Generation, Hyper-Personalized Advertising

Abstract: The increasing deployment of foundation models (FMs) in real-world applications necessitates strategies to enhance their adaptivity, reliability, and efficiency in dynamic market environments. In the chemical industry, AI-discovered materials are driving innovation in new chemical products, but their commercial success depends on effective market adoption. This requires FM-driven advertising frameworks capable of operating in-the-wild, adapting to diverse consumer segments and competitive market conditions. We introduce an AI-driven, multilingual, multimodal framework that leverages foundation models for autonomous, hyper-personalized, and competitive advertising in both Business-to-Business (B2B) and Business-to-Consumer (B2C) markets. By integrating retrieval-augmented generation (RAG), multimodal reasoning, and adaptive persona-based targeting, our framework generates culturally relevant and market-aware advertisements tailored to dynamic consumer behaviors and competitive landscapes. Our approach is validated through a combination of real-world experiments using actual product data and a Simulated Humanistic Colony of Agents to model consumer personas and optimize ad strategies at scale while maintaining privacy compliance. This ensures market-grounded and regulatory-compliant advertising. Synthetic experiments are designed to mirror real-world scenarios, enabling the testing and optimization of advertising strategies by simulating market conditions, consumer behaviors, and product scenarios. This approach helps companies avoid costly real-world A/B tests while ensuring privacy compliance and scalability, allowing them to refine strategies through simulations before actual deployment. By combining structured retrieval-augmented reasoning with in-context learning (ICL) for adaptive ad generation, the framework enhances engagement, prevents market cannibalization, and optimizes Return on Ad Spend (ROAS). This work presents a scalable FM-driven solution that bridges AI-driven novel product innovation and market adoption, advancing the deployment of multimodal, in-the-wild AI systems for high-stakes decision-making environments such as commercial marketing.

Submission Number: 75