In the context of the Residual Learning Framework, how can integrating learning algorithms enhance data-driven decision-making and skill development within a business environment?

Residual Learning Framework focuses on the gaps between what we predict and what actually happens, treating those gaps as learning signals. By adding machine‑learning algorithms, the system can automatically find hidden patterns in those gaps that humans might miss. Those patterns become actionable insights that help managers adjust processes or resources for better outcomes. Employees see how their actions change the gaps, turning everyday work into a continuous learning loop that sharpens their skills. For example, a retail chain uses a residual‑learning model to adjust inventory levels, learning from the difference between forecasted and real sales, which boosts sales accuracy and trains staff in data‑driven decision making.