The process of strategic decision-making within a corporation is complex resulting in various risk factors. It requires an appropriate analytical framework to assess these risk factors which is fundamentally crucial. This paper proposes a new Bayesian network approach for modelling decision entropy which is a measure of uncertainty in corporate strategy models. Using the available data, we run simulations to show how decision entropy affects different strategic results in various corporate scenarios. We also provide proof using real data that our Bayesian approach using case studies is better than the traditional deterministic way of modelling. The results give the business people and corporate leaders more practical and data-driven ways of getting strategically accurate results. The research solves the gaps in the literature about decision making science and practical gaps in optimizing corporates strategy.