In this episode of AI Ignited, host Elif Baser sits down with Mehmet Efe Gurkan to explore how organizations design Human-in-the-Loop (HITL) AI systems that keep managers embedded in automated decision cycles.The conversation breaks down the technical foundations of HITL frameworks, including model explainability, confidence scoring, threshold-based overrides, and escalation protocols that allow human judgment to intervene when AI systems face uncertainty or high-stakes decisions. Rather than removing managers from control, these systems redefine their role as supervisors, interpreters, and stewards of AI-driven processes.
Drawing on ongoing research at Carnegie Mellon University, the episode highlights how interactive machine learning and real-time explanation tools are shaping the next generation of management control systems—where humans can query, challenge, and correct AI recommendations before action is taken.This episode offers a technical yet practical look at how organizations can scale AI responsibly, ensuring automation enhances decision-making without sacrificing accountability, transparency, or strategic judgment.