From Data Insight to Foresight: The Collaborative Logic and Boundary Constraints of Embedding Generative AI in Corporate Strategic Decision-Making

Abstract

The entry of generative artificial intelligence into corporate strategy fundamentally transforms how organisations connect problem identification, evidence organisation, option development, and scenario exploration. Generative AI can convert dispersed materials into discussable strategic hypotheses and widen the search space for both opportunities and risks. However, its fluent output may also conceal hallucinated facts, misplaced causality, contaminated data, and ambiguous responsibility, thereby introducing new vulnerabilities into the strategic decision-making process. This paper examines how generative AI can support a progression from data insight to forward-looking judgement without being mistaken for an autonomous forecasting authority. Drawing on a conceptual analysis of human-AI interaction in strategic contexts, it argues that effective embedding requires a decision architecture built on verified evidence, competing scenarios, cross-examination between humans and models, and continuous feedback from realised outcomes. Access rights should vary according to the materiality, reversibility, and information sensitivity of each decision. Source attribution, human review, version records, dissent logs, and accountable sign-off must therefore operate within one auditable and transparent process. The study further proposes a governance framework that delineates the boundary conditions under which generative AI contributes to strategic foresight while preserving managerial accountability. Under these conditions, generative capability can become an enhanced strategic decision mechanism that remains open to verification, challenge, and correction rather than a substitute for managerial judgement. The findings offer actionable guidance for organisations seeking to integrate generative AI responsibly into their strategic planning and decision-making workflows.