Imagine two worlds that dwell within the realm of management studies, separated by a narrow yet very deep crevice.

On one side is ‘academia’, where there is no concept of time. Rigour rules here supported by careful reasoning, and methodological discipline. Progress here is slow, deliberate, and often cumulative. Knowledge flows cautiously often navigating the dense forests of peer reviews, robustness checks, theoretical grounding, and scholarly consensus. Relevance often sacrifices itself at the altar of rigour.

On the other side of the crevice is ‘industry’, built on immediacy, constraint and consequence. Decisions are often made with imperfect information and real stakes. Rigour exists, but under the tight leash of economic rationale. Too much slows action. Too little invites disaster. What matters is not whether a model is elegant, but whether it works well enough, fast enough, and cheaply enough. Both these worlds frequently lament about each other’s shortfalls. For academia, industry appears impatient, overly pragmatic and even careless. For industry, academia feels distant, slow, and difficult to translate into action.

For decades, several well-intentioned efforts have been made to bridge the divide between these two worlds. Executive education programmes promise translation. Consulting projects promise application. Internships promise exposure. Research partnerships promise mutual benefit. Each has value, but none has fully resolved the structural separation between the two worlds. Many of these efforts succeed locally and temporarily. But few endure. They connect people but rarely connect ways of thinking. The divide remains.

The twain does not meet

Most traffic continues to flow within each side. Over time, academia has become increasingly sophisticated in how it speaks to itself, developing dense languages, specialised methods, and tightly gated publication outlets that reward precision but often render high-quality research inaccessible to general practitioners. Insights in top journals are rigorous, but they are also remote, circulating within narrow scholarly communities and rarely translated into forms that industry can readily engage with. At the same time, industry has moved in the opposite direction, becoming intensely solution oriented, packaging knowledge as proprietary frameworks, tools, and playbooks that privilege speed and competitive advantage while limiting access to outsiders.

This is not a story of superiority or failure. It is not that academia has abandoned relevance or that industry has abandoned rigour. Both worlds continue to produce excellence. But both are also equally guilty of tolerating, and at times rewarding mediocrity within their own silos, insulated from meaningful external scrutiny. What is missing is a shared structure that allows ideas, feedback, and learning to flow in both directions with equal ease.

And then, Generative AI arrived, shaking up both these worlds, albeit differently. In academia, GenAI has triggered deep unease. Teaching feels harder when students can generate polished responses instantly. Long standing practices around assessment, authorship, and originality are being re-examined. In research, debates have emerged around methodology, writing ethics, and what constitutes scholarly contribution in an AI assisted world. On the industry side, this has triggered discussions around job displacement, skill shifts, and whether firms were investing too much or too late. Leaders are struggling to separate genuine productivity gains from hype. While GenAI is promising scale and speed, its long-term value remains uncertain across both sides of the crevice.

GenAI as a bridge

As both sides grapple with their respective GenAI induced uncertainties and try to reinvent, generative AI offers a more hopeful possibility. Beyond being a tool to manage or a risk to mitigate, it has the potential to function as a “bridge”. GenAI is unusually good at translation. It can absorb abstract frameworks and express them as usable artifacts. It can take messy, experience driven practices and surface their underlying logic. In doing so, it creates a shared language that neither side has fully possessed before.

For academia, this opens a way to operationalise rigour without abandoning it. Theories need not remain confined to journals and classrooms. They can be embedded in simulations, decision aids, and conversational systems that practitioners can actually use. When industry engages with these systems, departures from theory become visible and informative. Use becomes a form of testing, not dilution.

For industry, this offers a way to reflect without slowing down. Interaction with theory-informed GenAI systems allows organisations to examine assumptions, explore alternatives, and make implicit trade-offs explicit. Judgment is not replaced. It is sharpened. Learning happens in the flow of action rather than after the fact. Education may be where this bridge becomes most tangible. When students learn with GenAI systems that encode academic rigour while responding to real world constraints, the familiar divide between theory and practice narrows naturally. Learning becomes a rehearsal for decision making, grounded in concepts but shaped by context. Relevance is no longer an afterthought. It is built in.

Obviously, none of this is automatic. A poorly designed bridge can collapse or worst mislead. Generative AI systems can oversimplify, reinforce bias, or privilege speed over thought if left unchecked. This is precisely why the bridge must be co-designed. Academia brings the discipline to ask what should be built and why. Industry brings the discipline to test whether it holds under real conditions. The crevice between academia and industry is unlikely to disappear, nor should it anyway. Difference has value. But generative AI offers an opportunity to make crossing ordinary rather than exceptional. Not a dramatic leap, but a steady movement back and forth. If approached thoughtfully, GenAI may finally allow rigour and relevance to meet not as rivals in disparate worlds, but as partners walking the same bridge.

(The writer is Associate Dean (Accreditation & Rankings), Institute of Management Technology Hyderabad)

Published on January 27, 2026