The case study explores the critical challenges faced by MyRobo, an EdTech startup that relied heavily on AI-driven decision-making from its inception. Initially, MyRobo’s machine learning models provided a competitive advantage by optimizing resource allocation and forecasting student enrolment. However, as the company scaled, the predictive models began to fail, leading to overstocked schools, declining enrolments, and operational inefficiencies. This deterioration raised fundamental questions about the sustainability of algorithmic decision-making and the trade-offs between AI efficiency, explainability, and long-term organizational goals.

The core dilemma revolves around whether MyRobo should refine its AI models, integrate human oversight into decision-making processes, or abandon AI altogether. These decisions highlight the broader challenges of lifecycle management in AI systems, including model drift, algorithmic bias, and the black-box nature of complex algorithms. The case emphasizes the importance of balancing data-driven insights with human judgment to achieve sustainable business outcomes.

Learning Objectives

• Formulate optimal AI adoption timing criteria based on ecosystem readiness and organizational maturity.
• Evaluate real-world failures in algorithmic decision-making and propose strategies to mitigate biases and model drift.
• Develop approaches to integrate human-centered decision-making with data-driven insights for practical applicability.
• Analyse trade-offs between model transparency, explainability, and accuracy to align with stakeholder trust.
• Design strategies for effective lifecycle management of AI/ML models to ensure sustained operational effectiveness.