"We must create AI commanders, not just AI users" - Dr S Arunachalam on future-ready management education
AI, data, and digital platforms have moved from optional to essential. Business schools either evolve with them or fall behind. Dr S Arunachalam, Dean at Badruka School of Management (BSM), lays out a clear mandate: build leaders who set direction for AI, not graduates who passively use it.
From AI users to AI commanders
The hardest part isn't software. It's mindset. Managers must learn to lead AI initiatives-asking better questions, framing the right problems, and translating outputs into business decisions.
- Prioritise strategic fluency: what AI can do, where it breaks, and how to scope viable use cases.
- Give hands-on exposure to GenAI and prompt engineering embedded across functions, not confined to electives.
- Build depth through sustained, cross-functional projects, not one-off demos.
- Embed ethics, privacy, bias mitigation, and security into every AI activity as default governance.
Outcome: graduates who can design, critique, and lead AI workstreams with accountability.
Designing a digital-first curriculum
Incremental updates won't cut it. Programmes need a full reset that blends strong foundations with practical delivery-analytics, digital marketing, fintech, and AI-enabled decision-making built into core learning.
- Co-create courses with industry practitioners to keep assignments, datasets, and cases current.
- Replace one-way lectures with flipped classrooms, live projects, simulations, and competitions.
- Keep industry in the loop throughout the year-reviews, critiques, and field problems, not just internships.
This approach builds creativity, adaptability, problem-solving, and critical thinking that transfer across roles and sectors.
New career tracks in data and platforms
Data has become the operating system of modern business. Roles are expanding beyond classic analytics into hybrid and platform-led paths. Data scientists alone are projected to grow by roughly a third this decade, reflecting market demand for applied analytics and AI fluency (Bureau of Labor Statistics).
- Data Strategy Manager: turns datasets into decisions and roadmaps.
- Business/Data Analyst: builds metrics, insights, and experiments that move KPIs.
- Platform Operations Manager: runs marketplaces, consumer apps, and partner ecosystems.
- AI/Digital Transformation Consultant: scopes use cases, change plans, and governance.
- Data Scientist: models customer, pricing, and risk problems for action.
The skills that matter: data literacy, analytical thinking, and a customer-first mindset. For course planning mapped to roles, explore courses by job that align curriculum design with real hiring needs.
Global readiness that goes beyond theory
Working across cultures demands more than domain knowledge. Students need practice operating under uncertainty with diverse teams and stakeholders.
- Use real-world projects with cross-border constraints and ambiguous briefs.
- Develop leadership, analytical thinking, and decision-making under pressure.
- Invest in language proficiency to build trust and collaboration-especially as growth tilts toward the Global South.
Meaningful international exposure
Exchange terms, immersion trips, and twinning programmes give students a direct view of how business practices differ by region and regulation. Faculty with diverse academic backgrounds bring comparative perspectives into the classroom.
Done well, these experiences produce graduates who can work with varied norms, frameworks, and market realities from day one.
Leadership that scales beyond the resume
Technical competence is table stakes. The edge comes from emotional intelligence, adaptability, ethical judgement, and self-awareness-leadership as practice, not position.
- Run workshops on communication, executive presence, resilience, and interpersonal effectiveness.
- Pair students with mentors from both corporate and startup settings for real-world feedback.
- Back student-led clubs across consulting, sustainability, entrepreneurship, technology, and culture.
This builds confidence and judgment through repeated, applied experience-not just grades.
Entrepreneurship as a transferable skillset
Encouraging entrepreneurship isn't only about launching startups. It's about building problem-solving, calculated risk-taking, creativity, and resilience that apply to any role.
- Use case challenges, pitch reviews, and practitioner critiques to test ideas fast.
- Leverage proximity to innovation hubs and startup networks for hands-on exposure.
- Teach students to evaluate market truth, not just deck polish.
The goal: experimentation with discipline and a focus on durable value.
What business schools must prioritise next
Digital fluency needs to be foundational, not a niche. AI literacy is now a leadership skill-framing questions, interpreting outputs, and deciding where AI should and shouldn't be used.
Teach AI in a strategic, non-technical context across disciplines. Pair that with ethics, governance, and the ability to turn technical results into business and societal value. Add emotional intelligence, adaptability, and purpose-and you get graduates ready to lead in a digital and inclusive context.
As Dr Arunachalam puts it: "We must create AI commanders, not just AI users." For hands-on classroom resources in prompt design and GenAI workflows, see this curated set on prompt engineering.
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