Beyond Bounded Rationality: A Dual-Engine (Quantitative–Qualitative) Hybrid Intelligence Framework for Strategic Enterprise Decision-Making

Enterprise decision-making has hit a wall. For seventy years, management theory accepted Herbert Simon's foundational thesis of bounded rationality. Human executives operate under physiological cognitive constraints, finite memory, and computational bottlenecks. Faced with complexity, leaders satisfice. They settle for outcomes that meet acceptable thresholds rather than optimize for theoretical maximums. Then behavioral psychology exposed the systemic costs of that compromise. Amos Tversky and Daniel Kahneman revealed that intuitive managerial shortcuts (System 1 cognition) systematically distort judgment. Corporate leaders anchor on irrelevant reference points, double down on sunk costs, fall prey to confirmation bias, and allow overconfidence to blind them to catastrophic downside exposure. To overcome these biological frailties, enterprise organizations turned to artificial intelligence. Yet the initial wave of enterprise AI failed to deliver on its transformative promise. By treating AI as a monolithic tool, corporations created severe new vulnerabilities. On one hand, standalone predictive algorithms generated opaque mathematical black boxes. As seen during the Knight Capital algorithmic collapse and the Long-Term Capital Management crisis, pure quantitative models remain blind to narrative context, geopolitical contagion, and human organizational dynamics. On the other hand, the rapid rollout of conversational Large Language Models introduced plausible confabulation, synthetic hallucination, and automation complacency. Executives frequently trade biological bounded rationality for ungrounded synthetic eloquence. In this study, I formulate an operational architecture: the Dual-Engine Hybrid Intelligence Framework. Moving beyond exploratory startup studies that treat AI as a generic conversational helper, this paper architects an enterprise-grade decision system that separates computational analysis into two complementary, mutually cross-verifying engines: (1) The Deterministic Quantitative AI Engine (System 2 Augmentation), implemented in Python to automate Bayesian risk distributions, econometric time-series forecasting, and Monte Carlo scenario stress-testing over structured ERP and financial data; and (2) The Generative Qualitative AI Engine (System 1 Augmentation), built on Retrieval-Augmented Generation (RAG) and domain transformers to ingest unstructured corpuses—commercial contracts, board minutes, regulatory directives, and employee sentiment—to extract latent strategic risks. Crucially, neither engine operates in isolation. Both streams converge within a centralized Executive Managerial Control Tower, where the human leader remains the final fiduciary arbiter. By instituting explicit cognitive guardrails, the framework eliminates algorithmic anchoring, suppresses hallucination risk, and elevates managerial rationality without surrendering human agency.