Alumni Author: The AI-Centered Enterprise -Natarajan Balasubramanian, PGP 1996
As businesses grapple with the rapid rise of AI, IIMB alumnus Natarajan Balasubramanian is helping leaders cut through the noise with clarity. As co-author of The AI-Centered Enterprise, he draws on years of research and practice to provide organizations with a grounded roadmap for harnessing AI. Conceived just months after the launch of ChatGPT, the book moves beyond hype, combining theory, case studies, and strategy to show how enterprises can sense, reason, and act in an AI-driven world. With his multidisciplinary background and commitment to bridging technology with organizational transformation, Natarajan offers leaders a way to view AI not as a passing trend but as a fundamental shift in how work gets done.
What inspired you to co-author The AI-Centered Enterprise, and what gap were you hoping to address?
My coauthors and I began on the book project around May–June 2023, just six months after ChatGPT launched. At that point, everyone was talking about generative AI, but few were digging into what was really at stake for organizations. “Agents” weren’t yet part of the mainstream vocabulary, and while there was no shortage of buzz, there was a big gap in systematic, practical thinking. We wanted to go beyond hype and crystal-ball predictions to offer a clear roadmap for leaders—one that connects AI capabilities to actual business transformation, grounded in theory, case studies, and strategy, not just buzzwords and tech jargon. With our multidisciplinary background and years of research and experience, we felt we were very well positioned to offer that roadmap.
The book argues that businesses must move beyond tactical uses of generative AI. Why do you think that shift has been slow?
The shift has been slow because most organizations still view AI as a tool to speed up tasks—not as a way to rethink how work gets done. It’s not necessarily about overhauling entire workflows but about reimagining them through an information processing lens. Where does information flow? Who interprets it? What decisions depend on it? These are deeper questions that demand more than just plugging in a chatbot. Tactical use is convenient, but transformational use requires leaders to rethink how their organizations sense, reason, and act—often in ways they haven’t before. That’s a bigger leap.
Can you explain the concept of Context-Aware AI and how it differs from current generative AI tools like ChatGPT?
Generative AI tools like ChatGPT are mostly generic—they don’t understand intent very well. These tools are rapidly getting better at picking up on context, especially with techniques like fine-tuning and smart prompting. But even then, it takes real effort to make them understand the specific context of a workplace—its data, workflows, language, and goals. Context-aware AI is the next step and aims to interpret both content and intent within an organization’s environment. Getting there often involves combining other tools such as retrieval-augmented generation (RAGs), knowledge graphs and recently, an agentic architecture to tailor responses to a user’s task, role, or situation.
You introduce the 3Cs framework—Calibrate, Clarify, and Channelize. How can this help leaders integrate AI more effectively?
The 3Cs are a practical way to bring structure to what can feel like a chaotic AI landscape. The framework provides leaders with a structured roadmap to integrate AI with clarity and purpose—avoiding the pitfall of scattershot experimentation. Calibrate encourages a clear-eyed assessment of which AI technologies are mature enough for different tasks, aligning technical capabilities with business needs. Clarify helps leaders map out where AI can deliver true strategic value, distinguishing between table-stakes automation and areas of competitive advantage. Finally, Channelize guides the organization through adoption, ensuring initiatives are not only technically sound but also embedded in workflows and embraced by people. Together, the 3Cs help transform AI from a shiny object into a source of lasting impact.
How do agentic systems fit into the future of enterprise AI, and can you share a practical example of their use?
Agentic systems—AI models that reason, coordinate, and act based on both structured and unstructured data—are central to the next evolution of enterprise AI. Unlike basic generative models, agentic systems can handle complex tasks that require reasoning across multiple documents, data types, and decision layers. For example, a context-aware AI platform used by a logistics firm can process ambiguous contract terms, factor in package dimensions, and forecast future costs by chaining together agents that retrieve data, analyze contract clauses, and simulate pricing models. This multi-step reasoning, orchestrated by agents, allows organizations to automate and optimize decisions that previously required human intuition. So these systems can make AI a team member, not just a tool, making them central in the enterprise as we go forward.
What kinds of organizations or leadership mindsets are best positioned to adopt AI-centered approaches successfully?
Organizations with a strong grasp of their value-creation networks—and leaders willing to rethink how work gets done—are best positioned to thrive with AI-centered approaches. Success depends not just on technical readiness, but on a mindset shift: recognizing that unstructured data (emails, contracts, reports) is just as vital as structured metrics, and that context-rich reasoning can unlock value hidden in daily operations. Leaders who are comfortable experimenting, open to collaboration with tech partners, and committed to integrating AI into real workflows—not just dashboards—will lead the way in this new AI era.
As someone who studies innovation and organizational learning, how do you see those disciplines evolving in the age of AI?
In one of studies, my coauthors and I explored what may happen when machine learning substitutes human decision-making. What we predicted there was that while AI can improve efficiency, it can also lead to “myopia”—organizations become so focused on what the model says that they lose the diversity of nuance and contextual insights that human judgment brings. Recent work is beginning to confirm such a loss of diversity from using AI in some contexts. So, I think there is an opportunity to study and think about systems where AI and people learn from each other and build on feedback loops between AI-generated insights and human reasoning.
Lastly, as an IIMB alumnus, how has your experience at the institute influenced your thinking, research, or career path?
IIMB had a deep impact on how I think and do my research. It wasn’t just the academic rigor or the incredible faculty—though both were exceptional—but also the opportunity to collaborate with high-caliber peers, many of whom are leaders in their fields today. The environment pushed us to think critically, to work in diverse teams, and to tackle problems with both breadth and depth. That ability to frame broad questions across disciplines has stayed with me throughout my career.
Even this book reflects that. It’s co-authored by a very unusual multidisciplinary team—operations, strategy, and marketing—which isn’t easy to pull off. But that ability to bridge disciplines and think holistically has its roots in my experience at IIMB.
From introducing the 3Cs framework to explaining the promise of agentic systems, Natarajan Balasubramanian’s work reflects a deep understanding of both innovation and organizational learning in the AI era. At its heart, though, the book also carries the imprint of IIMB—rigor, critical thinking, and the ability to bridge disciplines. As he acknowledges, the institute shaped not only his academic journey but also his collaborative approach to research. With The AI-Centered Enterprise, Natarajan and his co-authors invite leaders to embrace AI with purpose, ensuring it becomes not just a tool, but a true partner in building smarter, more adaptive organizations.
Read more about The AI-Centered Enterprise here: Amazon link.
