IMI Delhi: Dynamic Learning

 LEADERSHIP IS increasingly a race against the half-life of knowledge. In a VUCA world— volatile, uncertain, complex, and ambiguous—content gets outdated quickly while judgement, ethical clar­ity, and the ability to work productively with artificial intelligence gain value. As a business school, I view our job not as protecting an old canon, but as groom­ing leaders who can convert flux into ad­vantage: people who ask better questions, structure messy problems, and build re­sponsible AI-enabled solutions that stand up to scrutiny. This is the organising prin­ciple behind how management education must evolve in the coming decade.

Content is abundant but capability is scarce. Our programmes therefore must emphasise problem formulation, data literacy, experimentation, and iterative thinking with AI as a partner across mar­keting, finance, operations, HR, and strat­egy. The practical question has changed from “What do you know?” to “What can you build, defend, and deliver?”

Learners want modular credentials, micro-certifications, and periodic reskill­ing. Executive education, alumni upskill­ing, and the MBA now sit on one learning spine. B-Schools must support a multi-year journey: entry into the workforce, transi­tions across roles, and periodic reinvention.

Live projects, simulations, and co-taught industry courses are mainstream. Assessments must measure perfor­mance—an analysis, a model, a product demo, or a policy memo—often produced in an AI-enabled workflow. The centre of gravity is shifting from lectures about practice to learning-by-shipping.

These shifts necessitate upgrades in digital infrastructure, clear gover­nance for responsible AI, and rigorous integrity frameworks that reward disclo­sure and reproducibility over polished but opaque output.

Curricula are adapting on two essential dimensions—AI-across-the-curriculum and data plus decisions. The first instance is not a solitary “AI for Busi­ness” elective, but AI as a horizontal see­ing discipline-level integration—prompt strategies in marketing research, model risk awareness in finance, optimisation and simulation in operations, and people analytics in HR. The goal is confidence with AI as a teammate. In the second case, we inculcate basic coding comfort, analytics, and dashboards, paired with managerial judgement and communi­cation. The objective is not to turn every manager into a data scientist but to build leaders who can frame hypotheses, inter­rogate outputs, reason about trade-offs, and communicate decisions with evi­dence and humility.

The human element is not a counter­point to technology; it is the multiplier. Baseline technical fluency—data literacy, digital business models, comfort with AI tools—is now non-negotiable for entry into the conversation. Technology alone does not win buy-in, change behaviour, or resolve value conflicts. That requires em­pathy, communication, coaching, cross-cultural sensitivity, and ethical judgement.

Collaboration in India is improving— more live projects, guest sessions, and joint certifications. The most meaningful engagement is artefact-oriented: compa­nies bring a scoped problem and data; fac­ulty co-teach; student teams deliver usable outputs—dashboards, demand forecasts, pricing engines, risk models, or compli­ance prototypes—with clear success metrics. Flexible NDAs and sandboxed environments make this possible. These projects also strengthen internships-to-offers conversion and act as credible evi­dence of job readiness.

The alumni also step in. Beyond being brand ambassadors, they co-design cur­riculum elements, run AI clinics and port­folio reviews, and help stand up corporate labs with sanitised datasets. This alumni flywheel shortens the distance between classroom and relevance, ensuring the toolchains students learn match produc­tion environments.

THE TRAJECTORY IN leading In­dian B-Schools is positive—output has grown and quality concentration is visible. The next frontier is quality with relevance. We need more scholarship that clears rigorous bars (FT50, UTD24, ABS-4*, ABDC-A*) while staying anchored to problems that matter to practice and policy. That requires better data access through industry MoUs, incentives that value quality and impact (not just counts), and a stronger culture of replicability and open methods.

Across recent cycles, placements have been resilient, though the role mix is shift­ing. Recruiters increasingly demand evi­dence of AI-augmented productivity— students who can show how analytics or automation improved a decision or process. Consulting, BFSI, analytics, and B2B/SaaS-oriented roles remain steady. Internships and live projects matter more to final conversions. Consequently, port­folios now matter as much as CVs: code notebooks, dashboards, demo videos, and reflective memos that demonstrate capa­bility in action.

So, what must future managers actu­ally develop? First, problem formulation and critical thinking. AI can produce options at scale; the right question and constraints remain human responsibili­ties. Second, data and AI collaboration. Beyond tool familiarity, they must know how to interpret outputs, understand limitations, design guardrails, and com­municate uncertainty. Third, learning agility. They must have comfort with un­learning and reskilling as the half-life of skills shortens. Fourth, stakeholder intel­ligence. Tomorrow’s managers must navi­gate regulators, platforms, supply-chain partners, and cross-cultural teams; build coalitions that get things done. These are not optional extras. They form the bed­rock of employability and leadership in the decade ahead.

We should elevate learning outcomes over inputs: demonstrable gains in assur­ance of learning, alumni mobility, and re­peat hiring from employers tell us more than faculty counts or spend. We should prioritise research quality over quantity, using field-weighted journal tiers and wid­ening subject categories to reflect interdis­ciplinary work. And we should reduce reliance on perception scores that erode methodological validity. International accreditation bodies are already moving this way; our national frameworks can lead with clarity and confidence.

Practical challenges remain. Faculty upskilling at scale is non-negotiable; the median must move from tool curiosity to assessment redesign and model-aware pedagogy. Assessment integrity must evolve so AI is a partner in learning, not a shortcut around it: vivas, reproducible workflows, and oral defences help. Data-centricity requires secure, affordable en­vironments with real datasets. Student well-being matters in an era of cognitive overload and career anxiety. Financial sustainability demands sharper choices on digital infrastructure while preserving access and affordability. Regulatory clarity on AI use and data privacy must keep pace with classroom realities.

Ultimately, grooming leadership for a VUCA decade is not about mastering an ever-longer list of tools. It is about cultivat­ing judgement, ethics, and the confidence to learn fast in ambiguity. If we get the fundamentals right—asking better ques­tions; building and critiquing AI-enabled solutions responsibly; communicating decisions with evidence and humility— then Indian management education will not only keep up with change; it will help shape it.