Designing Human-to-AI Negotiation Strategies through the Lens of Negorealism

This article introduces Negorealism—a conceptual framework grounded in business realism and inspired by geopolitical theory—as a strategic lens for understanding the rise of automated negotiations in Human-to-AI (H2AI) environments. Developed within the AINA (Artificial Intelligence Negotiation Algorithms) project, the concept addresses how AI systems are transforming negotiation dynamics in retail, particularly between Key Account Managers (KAMs) and Category Managers (CMs). By integrating elements of pragmatism, competitive positioning, and strategic adaptability, Negorealism offers a model for AI-supported negotiation practices that balance efficiency with relationship sustainability. The study draws on a conceptual-heuristic methodology, combining theoretical synthesis with practical insights from algorithmic design and negotiation training. Key findings highlight nine principles of Negorealism, including strategic manipulation, data-driven decision-making, and simulated partnership building. These rules serve as a bridge between traditional human negotiation values and the logic of algorithmic systems. The paper emphasizes the growing need to align negotiation philosophy with emerging AI realities. It proposes Negorealism not only as a tool for business strategy but as a framework for evaluating the ethical and practical boundaries of automation in decision-making. Future research directions include testing AINA-based negotiation tools in real-world B2B settings and exploring cognitive limits of human acceptance of AI-optimized outcomes.