Two decades of technology and management leadership.
I founded and lead the AI/ML & Business Analytics department at NDIM, New Delhi, and serve as Dean of Research & Development. My work spans machine learning and generative AI, agentic systems, business analytics and intelligent automation — built first inside engineering faculties, then inside management faculties, and applied with industry.
A multidisciplinary path from technology to business transformation
My academic journey began with Philosophy and Psychology at the University of Delhi and progressed into Computer Science - MCA from and a doctorate from Bharathiar University.
I later strengthened this foundation with an MBA from Liverpool Business School and a postgraduate programme at IMT Ghaziabad. My doctoral research focused on hybrid AI models for improving software reliability.
My professional journey began as a software engineer, where I gained practical exposure to developing and deploying enterprise applications for large-scale infrastructure projects. At present, I am also associated with industry as a consultant in digital transformation and process automation, helping organisations adopt technology-driven solutions to improve their processes and operational efficiency.
My academic career then evolved across the complementary domains of technology and management. After nearly a decade of leading a computer science and technology department for engineering programmes, I founded and led the AI/ML and Business Analytics Department at a management institute. This role enabled me to integrate emerging technologies into business education and develop a deeper understanding of how AI, analytics, and digital systems can support business strategy, decision-making, and transformation.
Together, these experiences have shaped my ability to connect technical knowledge with managerial perspectives and translate technology into meaningful business value.
From predictive models to autonomous systems—a curriculum designed to connect emerging technology with business practice.
TATTVA — A Responsible AI Framework for Autonomous Business Systems
AI in business is moving beyond prediction and recommendation. Autonomous and agentic AI systems can now interpret information, make decisions, initiate workflows and complete actions—often without human review at every stage.
This creates a new governance challenge. For a business, accuracy alone is no longer sufficient. Before an AI system is authorised to act, the organisation must understand its purpose, define the limits of its authority, assign human accountability, test its reliability and ensure that its decisions can be traced and audited.
TATTVA—meaning essence or constitutive principle—is a six-part Responsible AI framework designed to assess whether an autonomous system is ready for business deployment and to define what it may decide or execute independently.
The system’s business mandate, intended outcomes and operating context must be clear to both technical and business stakeholders.
A named human or business function must remain responsible for the system’s decisions and consequences.
Every significant decision and action must be logged, explainable and reconstructable for audit and review.
Trust must be demonstrated through evidence, user experience and stakeholder confidence—not assumed because the technology performs well.
The system must be tested against business risks, exceptional situations and potential failure modes—not merely evaluated for accuracy.
Clear limits must determine what the system may perform independently, when approval is required and when control must return to a human.
TATTVA enables organisations to move from asking, “Can this AI system perform the task?” to the more important business question: “Should it be authorised to act, under what conditions, and with whose accountability?”
Two decades of building and leading
From enterprise software and engineering education to management education—culminating in the creation of an AI/ML and Business Analytics department that connects emerging technology with business practice.
Areas of work
Applied across healthcare, financial services, agriculture, energy, retail, manufacturing, supply chain and human resources.