Knowledge Is at Our Fingertips — Now Comes the Real Challenge

You have worked across multiple industries, from software development to analytics, and now academia at top institutes like IIMs and XLRI. What motivated your transition from industry to teaching, and how has your professional experience influenced your approach to Management Information Systems (MIS) education?
The key term in my transition from industry to academia is impact. I knew I would still be connected to the core concepts of technology, as I was moving into the information systems domain, which inherently involves technology. The second aspect is the opportunity to engage in discussions with future leaders in the classroom. Many of these students will go on to start their own startups, join top companies, or even become CEOs. The chance to shape their minds, learn from them, and hear their perspectives is incredibly rewarding. I’ve always had a positive inclination towards discussions, case-based learning, and the dynamic nature of management as a field. This is what drew me to academics. As for how my industry experience has influenced my teaching, it has multiple advantages. First, I can bring real-world examples into the classroom. Whether we’re discussing AI, automation, blockchain, or cloud computing, I can easily connect these technologies to practical applications. I’ve seen these technologies in action, so I can explain not just the technical aspects but also the challenges, pros, and cons of each approach. Additionally, my industry background allows me to innovate in pedagogy. I can design projects that connect students to the industry, ensuring they gain exposure beyond the classroom. This industry connection is a crucial part of my teaching approach, and it’s something I emphasise in my classes.

Artificial intelligence is reshaping business models, decision-making, and operations. In your view, how is AI influencing managerial roles, and what key technological trends should businesses focus on to stay competitive?
The biggest shift I’ve observed is the evolution of the information revolution. Earlier, with tools like Google, data became accessible at our fingertips. Now, with the AI revolution — especially in the last three years with generative AI tools like ChatGPT, Bard, and DeepSeek — knowledge itself is at our fingertips. You no longer need to visit individual websites or sources; these systems aggregate and present knowledge for you. The next step for businesses is to move beyond knowledge. The challenge now is managing this vast amount of knowledge. With big data, we’re dealing with issues of velocity, variety, and volume. The key question is: How do you filter, capture, and derive value from this plethora of knowledge? Soft skills will become increasingly important. While AI will take over many technical and skill-based jobs, the ability to manage, interpret, and apply knowledge will be critical. Leadership, creativity, and the ability to communicate effectively will take centre stage.

With increasing automation and AI integration, traditional job roles are evolving. What fundamental shifts do you foresee in the future of work, and how should businesses and professionals prepare for these changes?
For professionals, there are three key areas to focus on: leadership, discipline, and resilience. Leadership doesn’t necessarily
mean being in a leadership role; it’s about having the ability to think creatively, communicate effectively, and address problems holistically. Discipline is what will help you execute your vision, and resilience is crucial because failure will happen, especially in
a competitive environment where you’re not just competing with other professionals but also with AI agents. Businesses, on the other hand, need to focus on upskilling their workforce and fostering a culture of adaptability. The pace of change is accelerating, and organisations must be prepared to continuously evolve.

With advancements in machine learning and predictive analytics, AI-driven decision support systems (DSS) are transforming business strategy. How do you see AI enhancing MIS frameworks, and what are the key technical challenges in integrating AI into enterprise decision-making systems?
AI is moving us from rule-based systems to automated, AI-driven systems. However, this shift brings challenges, particularly
around trust and accountability. Who is accountable for decisions made by AI? Is it the company, the developer, or the AI itself?
To address this, we need to focus on AI-HI integration — artificial intelligence and human intelligence working together as
partners. This isn’t just about automation or assistance; it’s about augmentation, where both human and machine intelligence
complement each other. There are also technical challenges, such as data quality, fairness, biases, and privacy. With IoT (Internet of Things) and other technologies generating massive amounts of data, questions around data control and interoperability become critical. Additionally, integrating AI with legacy systems and managing the change process within organisations are
significant hurdles.
MIS plays a crucial role in connecting technology with business strategy. As AI and data analytics gain prominence, how do you see the role of MIS evolving in B-schools, and what changes are necessary to make MIS education more industry-relevant?
The pace of change in technology is accelerating, and this will continue for the foreseeable future. Rather than just updating the
content of MIS courses, we need to change the nature of learning itself. Continuous learning, upskilling, and adaptability are
becoming essential. MIS education must focus on preparing students for a world where change is constant. This means fostering a mindset that embraces change and equipping students with the skills to navigate a rapidly evolving digital landscape.

For current MBA students looking to build careers in a rapidly evolving digital landscape, what skills and mindsets should they develop to effectively leverage MIS, AI, and emerging technologies in their careers?
Two key mindsets are crucial for MBA students: data-driven decision-making and a digital-first mentality. Data-driven decision-
making has been important for decades, but now it’s more critical than ever. Students must learn to interpret and leverage data effectively. A digital-first mentality means thinking about personalisation and omnichannel experiences, not just from a customer perspective but also in everyday life, government, and the workplace. This mindset will be essential as businesses
increasingly rely on digital technologies. Additionally, students should focus on developing leadership, discipline, and resilience — skills that will help them thrive in a competitive, AI-driven world.