Proactive Hybrid In-Stream Advertising in Generative AI: A First-of-Its-Kind Framework for AI-Initiated Integration of Organic and Sponsored Content Across Any Applicable Media Stream
Year: 2026
Type: preprint
Abstract: This paper proposes Proactive Hybrid In-Stream Advertising (PHISA), a conceptual advertising framework for generative artificial intelligence (AI) environments that introduces an AI-initiated approach to integrating organic content and advertiser-sponsored content within ongoing media streams across different applicable media types. Unlike conventional in-stream advertising, which commonly relies on predefined advertising inventory, scheduled commercial breaks, or established pre-roll, mid-roll, and post-roll placements, PHISA enables a generative AI system to proactively identify and initiate an advertising opportunity within an ongoing content experience, even when the user has not explicitly requested an advertisement, product recommendation, or commercial message. The framework is not restricted to video or audio advertising. It encompasses any applicable media stream capable of supporting continuous, sequential, or dynamically updated content delivery, including text-based, audio, video, interactive, multimodal, and future streaming formats. Its defining principle is the integration of two distinct content categories within a unified in-stream experience: organic content, independently generated or selected to serve the user's informational, functional, or entertainment context, and sponsored content, whose inclusion or presentation is materially influenced by an advertiser's paid commercial arrangement. Rather than requiring a fixed advertising slot, predetermined insertion point, standardized sequence, or medium-specific presentation structure, PHISA allows these content categories to be integrated through flexible in-stream arrangements while preserving their distinct commercial provenance. To the best of the author's knowledge, this paper is the first to formulate Proactive Hybrid In-Stream Advertising as a dedicated generative-AI advertising framework specifically combining AI-initiated advertising within ongoing media streams, the integration of organic and advertiser-sponsored content into a unified in-stream experience, and applicability across different media-stream types without dependence on a fixed advertising format. The framework distinguishes this combination from conventional in-stream advertising, native advertising, AI-generated advertisements, sponsored recommendations, and proactive conversational AI. It establishes a conceptual definition, architectural model, formal representation, theoretical propositions, evaluation framework, and responsible implementation principles. Sponsorship transparency, user autonomy, privacy protection, and content integrity are treated as fundamental requirements. This paper is conceptual and does not claim experimental validation, commercial effectiveness, or performance superiority.
Author: Zhang, Jincheng
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225750339
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