Ramesh Bhat is a former Professor at the Indian Institute of Management, Ahmedabad, and the former Vice Chancellor of NMIMS University, Mumbai. Over four decades, his work has spanned health economics, financial analysis, public policy, and management development, with a sustained interest in the question of how senior practitioners actually make the judgements they make.
The Practice Lead
Prof. Ramesh Bhat
Formerly: Professor, IIM Ahmedabad. Vice Chancellor, NMIMS University.
He has previously held independent director positions at the National Small Industries Corporation (NSIC), BEML, ITI Ltd, Corporation Bank, Ahmedabad Stock Exchange, and several other Indian companies. He currently serves as a board member of Basic Healthcare Services.
He continues to teach, advise, and write — and now leads Skillprocess.in, a management development and advisory practice that decodes tacit managerial skill and embeds it in AI agents.
Selected publications and frameworks
Framework
BIRD — Business Interlinkages for Results and Development
A structured methodology for diagnosing where a company's financial chain is breaking and what to do about it.
Model
The Financial Linkages Model
A framework for tracing the relationship between operations, capital efficiency, and sustainable growth.
Other
Books, papers and case studies
In health economics, public finance, and managerial decision-making. Full list available on request.
A note on the practice
The practice is deliberately small. Skillprocess.in operates by invitation because the work it does — surfacing a senior manager's tacit judgement and rendering it operational — does not scale through volume. It scales through fidelity. Each engagement is preceded by a discussion to ensure the context fits. Fees and terms are shared at that point.
The work begins where the manager's skill is most valuable and least documented. That is where AI is most likely to add disproportionate leverage, and where it is most likely to do harm if applied without care. The framework exists to make the difference visible.
It does not scale through volume. It scales through fidelity.