Over-reliance on automated demand forecasting algorithms can lead to inaccuracies due to their limitations in capturing sudden shifts, overlooking qualitative factors, and potential biases. To mitigate these risks, organizations should balance automation with human expertise with the help of collaborative demand planning, continuously validating algorithms, and promote data literacy among stakeholders to make informed decisions. The planners should be actively involved in the forecasting processes to ensure that they can override the system generated engine if their expertise says that the forecast is not accurate. On achieving this type of data-driven decision making culture only, organizations can rely on the automated demand forecasting algorithms.