Innovation-focused enterprises increasingly adopt generative AI tools (e.g. large language models, autonomous agents) to augment strategic decision-making processes. This article presents a conceptual framework for integrating generative AI into enterprise strategy workflows, synthesizing literature on AI-enabled forecasting, planning, product development, and customer engagement. We review recent studies on AI’s impact in decision contexts, including improved operational efficiency, decision accuracy, and creativity, as well as documented challenges of data quality, bias, and ethics. We propose a multi-layer framework encompassing data management, AI agents, human AI collaboration, and governance. Illustrative use cases market forecasting, scenario planning, product innovation, and personalized customer outreach demonstrate how generative AI can support strategic tasks. We compare AI-driven and human-driven decision metrics (e.g. speed vs. contextual insight) and outline enterprise readiness and AI maturity levels (data, skills, processes, governance). Finally, we discuss results and outcomes from early adopters (improved speed, creativity, ROI) and address challenges such as model risk, explainability, and ethical governance. The framework and analysis offer both theoretical and practical guidance, with implications for managers aiming to harness AI innovations responsibly.