AI Economics and Business Value
Return on Investment, Quick Wins, and Strategic Options
The complete chapter, including its value frameworks, cases, applied exercise and references.
Read sample chapter →
Enterprise AI Transformation
A management guide to turning AI investment into business value, from choosing opportunities to building, governing, and sustaining enterprise transformation.
Approving a budget and launching a pilot are only the beginning. The harder management task is to connect AI to a decision that matters, prepare the data and people it depends on, and sustain the work after the first demonstration.
This book follows that task from foundations and strategy through portfolio choices, operating models, adoption, and governance. It then examines generative and agentic AI before bringing those decisions together in a transformation roadmap.
Indian enterprise contexts sit alongside global cases. The emphasis is on managerial judgment: where AI belongs, what it costs, how it changes work, and who remains accountable.
Used in graduate management teaching. Chapter learning outcomes support session planning, while applied exercises and reflection questions support seminars, assignments, and independent study.
Read a representative chapter, then see how the companion Study Guide supports revision and practice. Both samples can be read online.
Return on Investment, Quick Wins, and Strategic Options
The complete chapter, including its value frameworks, cases, applied exercise and references.
Read sample chapter →The AI Dividend: Enterprise AI Transformation
Explore the chapter summary, four MCQs with answers, three short-answer questions with model answers, and two long-answer questions with hints.
Read Study Guide Sample →Identify how an AI initiative creates value before deciding how to measure its return.
Example: Predictive maintenance may reduce downtime, release technician capacity and improve quality. Name the primary value mechanism and avoid counting the same benefit twice.
Challenge an AI proposal before committing resources. An unresolved stop condition calls for rework, deferral or rejection.
Example: A proposal for a learned model should be reconsidered if a rule engine can capture most of the value with lower cost and greater explainability.
Read the sample chapter for the full argument, cases, application guidance and references. Read Chapter 4 →
Appendix A — Flagship Cases of Enterprise AI Transformation
Appendix B — Frameworks of Enterprise AI Transformation
Appendix C — Glossary of Enterprise AI Transformation
Explore what accompanies the book. Public samples support evaluation; complete student and faculty files are supplied through the appropriate access route.
20 chapter guides with summaries, MCQs and answer keys, short-answer questions with model answers, and long-answer questions with hints.
Read the Study Guide Sample →A framework map, cross-chapter synthesis questions, answer hints and common pitfalls for revision.
20 chapter decks for postgraduate and executive teaching. Complete decks are reserved for verified faculty access.
Applied exercises, reflection questions, executive briefings and cases within the book, plus case and framework appendices.
Role-to-course pairings and pathways for postgraduate courses, executive education and workshops.
Faculty considering the title for a course can request an inspection copy and discuss teaching-resource access.
Request academic access →Public evaluation: read the sample chapter, study-guide sample and framework explainers online.
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Faculty resources: teaching decks and restricted assessment materials require faculty verification. Inspection copies for course evaluation can be requested below.
Considering this book for a course? Request an inspection copy or ask about student and faculty resources. Faculty access is reviewed using your institution, role and intended teaching use.
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Explore the briefings →Explore Leadership in the AI Era, The Chief AI Officer, and AI in Financial Services in the Press catalog.
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