Bridging the Gap Between Business and AI in Organizations A key challenge in AI adoption is closing the gap between business needs and technical AI solutions. While data scientists are essential for preparing and ensuring high-quality data, business stakeholders must be equally involved in model development to ensure the solution addresses real use cases. This collaboration allows business experts to embed their domain knowledge into transparent model logic, while data scientists focus on technical accuracy—ultimately leading to better outcomes and AI democratization across the organization. In high-stakes decisions, explainability is critical. Regulator, Risk, and operational demands require that AI models be interpretable. To address this, organizations should: 1.Use explainability methods 2.Maximize use of transparent models To fully benefit from AI, organizations should adopt business-friendly AI platforms that promote collaboration, transparency, and trust in decision-making.