B-Schools Add AI Labs for Data-Savvy Managers
Business schools are adding AI labs to teach data-savvy managers. Discover how institutions are preparing graduates for AI-led decision-making.
Business schools are rapidly adding artificial intelligence labs to their curriculums as companies demand data-savvy managers who can handle the shift toward AI-led decision-making. Institutions are moving beyond traditional lectures to create environments that mimic real-world analytics environments, aiming to produce graduates who are as comfortable with data as they are with strategy.
The Data Experience Lab (DEL) at the school serves as a primary example of this shift. There, students work on live projects involving customer churn, employee retention, demand forecasting, process optimisation and digital marketing performance. The focus is not strictly on technical execution, but on identifying business problems, interpreting findings and translating them into strategic recommendations.
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Preparing for the Hybrid Manager Role
As organizations adopt AI-led decision-making, the demand for professionals who can bridge business judgment and analytical reasoning is rising sharply. AI laboratories are also emerging as talent pipelines for startups and Global Capability Centres (GCCs), sectors that are among the largest recruiters of analytics and AI talent.
Employers are increasingly looking for candidates who can handle ambiguous business situations, work with complex datasets and operate in rapidly changing technology environments. This changing demand is giving rise to what Mittal describes as the “hybrid manager”—professionals who understand business realities while engaging confidently with data, systems and analytical outputs. “Managers can no longer remain disconnected from data-led decision-making,” he noted.
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Industry Partnerships and Curricular Changes
The evolution is reshaping the relationship between academia and industry. Partnerships with technology firms and analytics platforms are increasingly influencing curriculum design, classroom discussions and applied learning experiences. Such collaborations help ensure students work with contemporary tools and business challenges rather than relying solely on textbook examples.
Mittal cautions against viewing AI adoption purely through the lens of technology. As institutions standardise around similar tools and platforms, there is a risk that learning could become overly uniform, producing students who know how to operate software but lack the ability to think critically about business problems. To address this, educators are increasingly focusing on interpretation, problem framing and independent thinking, ensuring students become creators of AI-driven solutions rather than passive consumers of technology.
