– Dr. Kartikeya Bolar, Associate Professor, Information Systems and Analytics T A Pai Management Institute ,Manipal Academy of Higher Education, Manpal

People are constantly told that AI will revolutionise how firms operate. It is described as the biggest development in a generation, from making banking more efficient to transforming marketing. Yet amid all this excitement about the future, one critical question often goes unasked: how are the business schools and finance training programmes responsible for preparing the next generation of corporate leaders actually adapting to this new world?

The headlines suggest that a tech-driven revolution in education is already well underway. But the real problem is not the technology itself—it is how we prepare and teach people to use it. The real story is not about adopting new software; it is about fixing long-standing issues in behaviour, institutional readiness, and teaching practice.

The real AI gap is not between companies, but between deans and faculty: Institutional leaders frequently speak about ambitious plans for AI adoption, while faculty members who are already experimenting with AI often describe a very different reality. Deans may articulate bold visions, but instructors operate under practical constraints that make those visions difficult to realise. This disconnect makes it extremely hard to develop a coherent AI strategy.

A recent AACSB survey highlights how significant this gap is. According to 85% of deans, their institutions encourage faculty to use AI in the classroom. However, only 63% of faculty agree. The divide widens further when it comes to actual classroom use: 80% of deans believe faculty should be using AI, but only 55% of faculty share that view.

This is not a minor disagreement. It reflects a deeper structural problem: aspiration far exceeds capability. Without adequate support, training, and alignment, faculty cannot help students move beyond the most basic uses of AI. The result is a top-down vision with insufficient bottom-up execution—one reason we see such inconsistent and often superficial AI use among students today.

Students are already power users—but not in the way we expect: Debating whether students should use AI is largely irrelevant; they already do. A Digital Education Council (DEC) survey found that 86% of students use AI in their academic work. However, the way they use it tells a more revealing story.

Most students are not using AI for creative thinking or complex strategic analysis. Instead, they rely on it for efficiency and convenience. The most common uses include: 69% using AI to find information, 42% using it to check grammar, and only 24% using it to write first drafts.

These figures are significant. Students are comfortable with AI tools, but there is a clear difference between using AI and using it well. This gap is reflected in the fact that 58% of students say they do not feel adequately prepared to use AI in the workplace, despite frequent use in their studies.

Forward-thinking institutions are addressing this gap by shifting the focus from basic tool usage to a more advanced capability: co-creation.

The most important new skill is not coding—it is thinking with AI: For years, the dominant narrative around AI education has focused on coding and data science. While these remain valuable, the emphasis is changing. Increasingly, the critical skill is not building AI systems, but using AI strategically as a thinking partner.

This requires a shift from technical execution to conceptual leadership: defining problems clearly, asking better questions, and engaging AI as a collaborator in reasoning and creativity. This “mindset before toolset” approach is essential for developing future leaders.

As the saying goes, AI is only as good as the person using it. That is why effective education now prioritises problem framing, critical evaluation, and iterative dialogue with AI systems. This shift is what separates leaders from mere users.

Some institutions are already demonstrating this in practice. At Queen Mary University of London, students co-create with AI during business simulations by generating and critically assessing multiple strategic options. At Nanyang Business School, students collaboratively design generative AI prompts and then analyse and debate the outputs. This process encourages them to challenge AI logic and bias rather than accepting results uncritically.

Faculty often argue that they lack the time or resources to teach this way. Ironically, AI itself is beginning to solve that problem.

AI is becoming a co-pilot for educators, not just a tutor for students: One of the most surprising developments in educational AI is that its greatest impact may be on teachers rather than students. While much attention has been paid to AI as a personalised tutor, leading institutions are now deploying AI as a faculty support system.

ESMT Berlin provides a strong example. The school has developed an internal AI tool with two distinct functions. One supports student learning through personalised guidance. The other is designed exclusively for faculty as a “course-level assistant and development partner”.

This faculty-focused tool helps instructors identify content overlap across courses, uncover curriculum gaps, design new teaching approaches, and even support the onboarding of teaching assistants by answering programme-specific questions. It directly addresses the two main barriers faculty cite when it comes to AI adoption: lack of time and lack of institutional support.

By automating administrative and routine tasks, AI frees educators to focus on what matters most—mentorship, meaningful dialogue, and deep engagement with students.

Integrating AI into business and finance education is not merely a technological upgrade; it is a fundamental shift in how institutions think, teach, and operate. Success requires a unified strategy that aligns leadership vision with faculty capability, enabling students to progress from task efficiency to strategic collaboration.

The institutions that succeed will not simply be those that adopt the latest tools. They will be the ones willing to undertake the harder work of transforming people, pedagogy, and purpose from the ground up.

As AI becomes ever more pervasive, the most valuable skill for future leaders will not be technical proficiency, but the human capacity for collaboration, critical thinking, and vision—the very qualities needed to direct AI rather than be driven by it.

Also Read: New-Generation IITs Driving Inclusive AI Education Across India