The Climate and Sustainability Impact Lab advances research at the intersection of technology, climate, and sustainability, and how companies can decarbonize.

We are guided by the core research question: What are the best practices and supporting conditions that allow a company to achieve meaningful reductions in its lifecycle emissions or commercialize scalable climate solutions?

Team

Principal Investigators

George Serafeim George Serafeim  is Charles M. Williams Professor of Business Administration. He holds a DBA from 性视界 University.

Michael W. Toffel Mike Toffel  is Senator John Heinz Professor of Environmental Management. He holds a PhD from the University of California Berkeley.

Peter Tufano Peter Tufano  is Baker Foundation Professor. He holds a PhD in Economics from 性视界 University.

Faculty

 is Assistant Professor of Business Administration at 性视界 Business School.

 is Assistant Professor of Business Administration at 性视界 Business School.

is Assistant Professor of Business Administration at 性视界 Business School.

Postdoctoral Fellows

. Simon鈥檚 research interests are sustainable finance and corporate social responsibility, with links to financial intermediation, corporate finance, and banking.

Franziska鈥檚 research is in the area of corporate sustainability, with a focus on topics related to corporate environmental pollution and environmental disclosure practices.

. Tom’s research interests include electric vehicles, energy planning, electric utility investments, renewable energy, electricity markets, and other topics in sustainable operations.

Doctoral Student Affiliates

鈥檚 research interests are at the intersection of corporate governance and climate disclosure & management. Her research focuses on evaluating firms’ strategies in decarbonization, understanding ESG disclosure, and investigating shareholder actions that influence firms’ climate-related performance.

is interested in corporate climate action and decision-making.

Staff

Christina Jarymowycz Christina Jarymowycz is an Assistant Lab Director at the 性视界 Business School AI Institute. 

Project Highlights

Climate Solutions

Lab affiliates: George Serafeim, Shirley Lu, Simon Xu.

Climate change is often viewed as a risk, but it is also a driver of innovation. This project adopts a business opportunity perspective by exploring Climate Solutions鈥攑roducts and services that foster the transition to a low-carbon economy. Examples include solar panels, wind turbines, electric vehicles, battery storage, heat pumps, energy efficient equipment and buildings, and plant-based food products.

We utilize Large Language Models to analyze financial filings, identifying how firms and industries advance climate solutions. This measure of climate solutions allows us to address several crucial questions: What are the risks and financial implications for firms engaging in climate solutions? How do firms that transition their product portfolio towards climate solutions attract and develop the human capital needed to lead a product portfolio transition? What role do different capital providers play in incentivizing or disincentivizing such transitions?

Energy Transition in Transportation

Lab affiliates: Christian Kaps, Michael W. Toffel, Thomas Palley.

This research is analyzing how the electric vehicle charging infrastructure is evolving and can be optimized to reduce cost and climate impacts via smarter one-way charging (V1G) and two-way charging (V2G). This workstream includes  (with teaching note), and several ongoing scholarly research projects using telemetry data from an automotive original equipment manufacturer (OEM).

Electric Vehicle Charging Service Reliability

Lab affiliates: Michael W. Toffel

This research aims to understand and improve the operational reliability of public electric vehicle (EV) charging stations in the U.S., which is typically offered as a supplementary service by service companies such as hotels, retailers, and parking facilities.  The project examines the role of market competition from different types of EV charging stations. Using large language models with expert-in-the-loop prompting, the study analyzes hundreds of thousands of user reviews to measure charging reliability as experienced by drivers, rather than relying on technical specifications or self-reported metrics. 

This study seeks to help policymakers and industry stakeholders make informed decisions about funding allocations and operational improvements to enhance the effectiveness of EV charging infrastructure.  This project is a collaboration with Georgia Institute of Technology鈥檚  and  (former HBS BIGS Fellow).

Related Article:, Institute for Business in Global Society, 性视界 Business School.

Voluntary Carbon Markets

Lab affiliates: Michael W. Toffel, Franziska Hittmair.

Voluntary carbon markets, which entail the creation and trading of carbon credits, represent an important opportunity to foster decarbonization at a lower cost than relying solely on internal corporate efforts. Many companies plan to use carbon credits to meet their net zero targets, but persistent challenges, including measurement, permanence, and additionality, have led to major questions about the viability of this market. This workstream includes a , as well as a  (with teaching note) and a  on the carbon credit rating agency Calyx Global, and ongoing research by Mike Toffel and Franziska Hittmair on how media scandals are affecting supply and demand for carbon credits.

Data Commons

The  shares and describes datasets about climate and sustainability to support researchers creating scholarship with data. In this database, each dataset is labeled with information on coverage, resolution, usage, and variables, and may be browsed by categories including Emissions and Air Pollution, Company Disclosures, and more. 

You may  for us to include in the resource. 

 produced by Mike Toffel and the HBS Baker Library provides 45+ actionable summaries of academic research focused on improving working conditions in supply chains conducted by scholars from 20+ universities.

We aim for this resource to provide actionable insights for managers, practitioners, and organizations to assess and improve working conditions in supply chains.

Publications

Articles and Working Papers:

Climate Alliances

 by Peter Tufano, Chris Thomas, Knut Haanaes, Matteo Gasparini, Robert Eyres, and Chris Chapman, 性视界 Business Review, November 2023.

 by Peter Tufano, Chris Thomas, Knut Haanaes, Matteo Gasparini, Robert Eyres, and Chris Chapman, Social Science Research Network (SSRN), July 2023.

Circular Economy

 by Shirley Lu and George Serafeim, 性视界 Business Review, June 2023. 

 by Shirley Lu and George Serafeim, Social Science Research Network (SSRN), May 2023.

Climate Finance

 by Matteo Gasparini and Peter Tufano, 性视界 Business School Working Paper, No. 23-057, January 2023.

Climate Solutions

Lab affiliates: George Serafeim, Shirley Lu, Simon Xu.

by Shirley Lu, George Serafeim, and Simon Xu, 性视界 Business School Working Paper, No. 25-025, November 2024. 

by Shirley Lu, Edward J. Riedl, Simon Xu, and George Serafeim, 性视界 Business School Working Paper, No. 25-024, November 2024.

Course Materials:

Circular Economy

 by George Serafeim, 性视界 Business School Case 123-089, April 2023. ( available.) 

 by George Serafeim, Michael W. Toffel, Lena Duchene, and Daniela Beyersdorfer, 性视界 Business School Case 124-007, July 2023. ( available)

Climate Finance

 by Peter Tufano, Brian Trelstad, Matteo Gasparini, 性视界 Business School Case 324-008, November 2023. 

 by Mark Egan and Peter Tufano, 性视界 Business School Case 224-025, September 2023. ( available)

Business Models

 by George Serafeim and Michael Norris, 性视界 Business School Case 124-017, September 2023 (Revised November 2023). ( available)

AI and Climate Change

. by Michael W. Toffel, and Nabig Chaudhry 性视界 Business School Background Note 625-050, April 2025. 

 by Michael Toffel, Kelsey Carter, Amy Chambers, Avery Park, and Susan Pinckney,  性视界 Business School Background Note 625-014, August 2024. 

Programs and Events:

AI and Climate Change Short Intensive Program

Co-led by Michael W. Toffel and John Mulliken, this Short Intensive Program explores how artificial intelligence and machine learning can accelerate both climate mitigation and adaptation as well as the strategic, investment, and entrepreneurship opportunities emerging at this intersection. Drawing on the premise that climate adaptation and mitigation will require substantial investment over the coming decades, the program examines where AI can be a 鈥渕ultiplier鈥� that helps solutions scale fast enough to matter, while also grappling with the tradeoffs posed by AI鈥檚 own energy and resource demands.

Net Zero Systems Solutions Roundtable: Fleet Electrification

This workshop hosted by 性视界 Business School and the Environmental Defense Fund in May 2024 joined representatives of shippers and carriers to surface challenges and best practices to accelerate the decarbonization of cargo trucking.

The Circular Revolution D^3 Catalyst [1]

The Circular Revolution D^3 Catalyst in April 2023 was hosted to highlight the impacts and opportunities of circular economy. The event was attended by 100+ participants, experts, and industry leaders, and resulted in 性视界 Business School article titled , a white paper titled , and two HBS teaching cases  and .

Podcasts

Digital, AI, and IoT applications to address climate change

 featuring Hamid Maher and Charlotte Degot (March 1, 2023)

 featuring Yossi Matias (March 15, 2023)

 featuring Apoorv Bhargava (March 29, 2023)

 featuring Gary Agnew and Kim Lawrence (April 12, 2023)

 featuring Jim Hayden (April 26, 2023)

 featuring Paul McDonald (May 10, 2023)

 featuring Sagewell CEO Pasi Miettinen (Nov 20, 2024)

 featuring Dryad CEO Carsten Brinkschulte (Dec 4, 2024)

, co-founder NASA Harvest, co-founder Harvest SARA, University of Maryland professor (Dec 18, 2024)

, Deputy Scientist at Argonne National Laboratory (Jan 1, 2025)

Contact Us

You can reach us by contacting Christina Jarymowycz.

[1] Please note: The Digital Data Design Institute at 性视界 (D^3) was renamed the HBS AI Institute in April 2026.

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AI and digitalization have fundamentally changed the way companies interact with customers and vice versa. Wide data availability and the advent of AI give businesses access to tools which provide more nuanced insights, allowing them to personalize their offerings to individual customers. It鈥檚 an enticing proposition, but not one that comes without risks. How can we ensure that companies use their customer data ethically, and in a way that does not harm customers and society?

The Customer Intelligence Lab at the HBS AI Institute is set to become the world鈥檚 leading academic research lab for customer insights, as it will help organizations use their valuable customer data effectively and responsibly.

Faculty

The Customer Intelligence Lab is led by:

• Ayelet Israeli Ayelet Israeli , Marvin Bower Associate Professor of Business Administration. Ayelet received her PhD in marketing from the Kellogg School of Management at Northwestern University.
• Eva Ascarza Eva Ascarza , Jakurski Family Associate Professor of Business Administration. Eva earned a PhD in marketing from London Business School.

Activities/Research Focus

The goal of the Customer Intelligence Lab is to help companies to use customer data to improve outcomes for themselves, their customers, and society at large. The Lab鈥檚 research themes focus on:

How to use the customer data to address topics such as:

• Impact of AI on decision making, technology, and platform regulation.
• Deriving value from unstructured data such as video, audio, image, and text.
• Personalization and marketing interventions.
• The value of customer trace data.
• Discrimination and fairness in personalization.
• Customer protection.

How the digital world is changing consumption, the emergence of the creator economy, and the competition for customer engagement. Questions include:

• Can firms directly manage customer attention?
• What are new ways for companies to engage with their customers?
• How do creators, brands, and platforms jointly create, capture, and distribute value?
• How will advertising evolve in the engagement economy?

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A unique aspect of the lab is it fosters collaborations between management scholars, statisticians, and computer scientists to overcome the methodological challenges that arise in the operationalization of AI due to the misalignment between statistical theory underpinning modern data science that was developed for significantly different contexts and applications than current business use cases. For example, the fundamentals of experimental design were first introduced a hundred years ago for agricultural settings with few experimental units and outcomes; today, companies run hundreds of experiments on millions of connected people, tracking thousands of outcomes. 

Given the applied nature of the lab’s agenda, we often closely collaborate with industry partners. If you are interested in learning more about potential research collaborations, please reach out.

People

The Data Science and AI Operations Lab is led by: 

Iavor Bojinov
Assistant Professor of Business Administration,
性视界 Business School

Iavor Bojinov is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at 性视界 Business School. He is the co-PI of the Data Science and AI Operations Lab and a faculty affiliate in the Department of Statistics at 性视界 University and the 性视界 Data Science Initiative.
Professor Bojinov’s research focuses on developing novel statistical methodologies to make business experimentation more rigorous, safer, and efficient, specifically homing in on the application of experimentation to the operationalization of artificial intelligence (AI), the process by which AI products are developed and integrated into real-world applications.

Edward McFowland III
Assistant Professor of Business Administration,
性视界 Business School

Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at 性视界 Business School. He is the co-PI of the Data Science and AI Operations Lab and teaches the first-year TOM course in the required curriculum. Professor McFowland鈥檚 research interests lie at the intersection of Anomalous Pattern Detection, AI, and the Social Sciences (e.g., management, economics, public policy). This includes the development of computationally efficient algorithms for large-scale and robust AI systems, and evaluating the impact of their deployment on managerial decision-making.

Michael Lingzhi Li
Assistant Professor of Business Administration,
性视界 Business School

Michael Lingzhi Li is an Assistant Professor in the Technology and Operations Management unit at HBS. He teaches the first-year TOM course in the required curriculum. Professor Li鈥檚 research focuses on the end-to-end development of decision algorithms based on machine learning, causal inference and operations research. He examines the implementation of such algorithms in hospitals, pharmaceutical companies, and public health organizations, and their potential to fundamentally transform healthcare operations. 

Publications:
1.

2.

The following faculty, doctoral, and staff students are active researchers in the Data Science and AI Operations Lab:

Jafer Hasnain
Research Associate,
性视界 Business School

Jafer is a Research Associate under Professor Edward McFowland III.
He is interested in leveraging new approaches in mathematical statistics to develop robust algorithms suitable for real-world data 

Shaolong “Lorry” Wu
Doctoral Student,
性视界 Business School

Lorry is a PhD student at HBS. He obtained a M.S.E. in Electrical Engineering from Penn Engineering and a B.S. in Economics from the Wharton School of University of Pennsylvania. Lorry had a stint at Bridgewater before his PhD. He is broadly interested in innovation and entrepreneurship and the impact of digital technologies in business.

Publication:

Shirley Huang
Doctoral Student,
性视界 Business School

Shirley is a doctoral student in the Technology and Operations Management Unit at HBS. Shirley is interested in human-AI collaboration and designing algorithms to more effectively support human decision-making.

Paul Hamilton
Doctoral Student,
性视界 Business School

Paul is a doctoral student in the Technology and Operations Management Unit at HBS. Paul is interested in two topics: (i) the dynamics of skills and labor markets for software engineers and IT workers, and (ii) the tradeoffs between fairness, privacy, and transparency in AI systems. 

Publication:

Tu Ni
Postdoc Research Fellow,
性视界 Business School

Tu is a Postdoc Research Fellow at the HBS AI Institute. His research is on the design and analysis of experimentation in operations, making it effective and efficient. This is mainly related to the evaluation of data science and AI solutions in companies.

Publication:

Ruru Hoong
Doctoral Student,
性视界 Business School

Ruru Hoong is a doctoral student in the Business Economics programme at HBS/性视界 Economics. Her current research agenda concerns the economic impacts of AI 鈥� in addition to several strands on data privacy and problems surrounding social media use. Investigating the efficient design and use of AI in human collaboration underlies much of her PhD research 鈥� including designing optimal human-AI decision-making systems in loan approvals and hiring, and exploring the impact of labour and technological shocks on organisational management in the AI data annotation industry. 

Publication:

Jenny Wang
Doctoral Student,
性视界 Business School

Jenny is a doctoral student in the Technology and Operations Management Unit at HBS. Jenny is broadly interested in interpretable and explainable machine learning (ML), identity and inequality, and improving existing methods used to answer social and policy-relevant questions, and consequently, business will be affected as a result of social/policy outcomes. More specifically, Jenny’s recent research explores how LLMs are reshaping human interactions with technology, and how trust in these systems can lead to better/more efficient learning outcomes (e.g. improve news consumption). 

Biyonka Liang
Doctoral Candidate,
性视界 Department of Statistics

Biyonka is a doctoral candidate in the Department of Statistics at 性视界 University. Biyonka’s research focuses on developing statistical methods for complex experiments, with a particular focus on adaptively collected data, large-scale online experiments, and health applications. 

Publications:
1.
2.

Matt DiSorbo
Doctoral Student,
性视界 Business School

Matt is a doctoral student in the TOM Unit at HBS. Matt’s research focuses on Human-AI Collaboration.

Publication:
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift

Luca Vendraminelli
Postdoctoral Researcher,
Stanford University

Luca is a postdoctoral Researcher at the Digital Economy Lab at Stanford. I study the dynamics of AI diffusion in organizations to understand why some AI projects fail to improve employee performance and well-being.

Publications:
1.
2.

Annika Hildebrandt
Research Associate,
性视界 Business School

Annika is a research associate working with Professor Bojinov and Professor McFowland. Annika is interested in human-AI collaboration and how AI adoption affects individuals, teams, and organizations, particularly in the software engineering context. 

Research Focus

• Applications of AI and its development
• Experimentation & Causal Inference in the age of AI

Educational and Practitioner Materials

• (Case)
• (Case)
• (Case)
• (Teaching Note)
• (Teaching Note)

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The Digital Emotions Lab is located at the heart of these exciting developments with the hope of utilizing technology to understand, predict, and change emotions. Our first focus is on mood detection and prediction, both at the individual and the collective level.  Our second focus is on emotional change, to identify when emotions are unhealthy or unhelpful in achieving certain goals. We utilize insights from psychology, management, and computer science to answer questions in these domains.

Faculty

Digital Emotions Lab is led by: 

• Amit Goldenberg Amit Goldenberg , Assistant Professor of Business Administration. Holds a PhD in Psychology from Stanford University.

Activities/Research Focus

The approach of the Lab will combine experiments, computational modeling, deep learning, and analysis of large-scale digital data. It will seek to answer questions such as:

• How can individual emotions be evaluated? 
• Is it possible to predict the occurrence of strong emotional responses, both at the individual and organizational level? 
• How can technology be used to help people and collectives change their emotions when they are unhelpful? 

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Across the globe companies and governments are starting to invest in initiatives aimed at retraining and reskilling people. In some instances, they do so with the intention to prepare the future workforce for jobs that don鈥檛 yet exist. But what has been done to date, and the effectiveness of past and ongoing initiatives is largely unknown or unrecorded. If it is understood, its success can be measured, best practices can be replicated, and errors corrected.

The Digital Reskilling Lab will gather this precious information, and work to provide new and innovative solutions to these problems. It will first examine how firms deal with the recruitment and training of 鈥渁t risk鈥� workers, focusing on whether and how retraining initiatives reach the workers who most need them, what approaches are being used to upskill and reskill the workforce to use the new digital technologies and their perceived effectiveness. It will use these data to provide a sense of 鈥渂est practices鈥� and their adoption across firms. Second, it will build on this knowledge and partner with organizations to test and experiment with different approaches to make sure that upskilling and reskilling initiatives reach workers that really need them, and deliver tangible benefits to them in the most effective way.

Faculty

The Digital Reskilling Lab is led by: 

• Raffaella Sadun Raffaella Sadun , Charles Edward Wilson Professor of Business Administration. She received her PhD from the London School of Economics and Political Science. 
• Jorge Tamayo, Assistant Professor of Business Administration. He holds a PhD from the University of Southern California.

Activities/Research Focus

The Lab partners with organizations to test the feasibility, scalability, and effectiveness of different approaches to the skills challenge. Its work will be relevant to large, digital-immigrant organizations and to young organizations offering retraining services.

The Digital Reskilling Lab has two objectives:

1. Close the knowledge gap. To do this we will:
   • Understand retraining initiatives within firms to:
     • Learn from what already exists. This involves capturing the knowledge that has been developed, at HBS and elsewhere.
     • Expand knowledge. Gaining deeper insights from existing case studies, and extending our search to other firms. 
     • Compare data. Synthesizing the knowledge from various approaches and cataloging best practices.

2. Close the implementation gap. To do this we will:
   • Recruit partner organizations to:
     • Evaluate the costs and benefits of retraining programs.
     • Structure the roll out of these programs and the data to measure take-up and ROI. 
     • Study how key program characteristics relate to individual and organizational outcomes. 
     • Assess the scalability of different approaches and whether they can be replicated across different subsidiaries within firms, or across firms?

We will determine the boundary conditions of various reskilling strategies. This knowledge and best practices will be broadcast across our academic and business communities.

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Faculty

LISH is led by a team of principal investigators from diverse backgrounds: 

• Karim R. Lakhani Karim R. Lakhani , Dorothy and Michael Hintze Professor of Business Administration. He is the founder and co-director of LISH and holds a PHD from MIT. 
• Eva C. Guinan, Professor, Radiation Oncology, 性视界 Medical School. She is the director of the 性视界 Catalyst Translational Innovation program. Eva earned her MD from 性视界 Medical School.
• Kyle R. Myers Kyle R. Myers Assistant Professor of Business Administration. He has a PhD from the University of Pennsylvania. He studies the economics of the rate and direction of innovation. 
• Alberto F. Cavallo Alberto Cavallo , Thomas S. Murphy Professor of Business Administration. Pioneer of measuring inflation, he is the creator of Inflacion Verdadera and co-founder of The Billion Prices and PriceStats. Alberto earned a PhD in Economics from 性视界 University.
• Iavor I. Bojinov Iavor Bojinov , Assistant Professor of Business Administration. He is a Richard Hodgson Fellow and holds a PhD from 性视界 University. 
• Edward McFowland III Edward McFowland III , Assistant Professor of Business Administration in the Technology and Operations Unit of HBS. Edward holds a PhD from Carnegie Mellon University. 
• Frank Nagle Frank Nagle , Assistant Professor of Business Administration. Frank earned his DBA in Technology and Operations Management from 性视界 Business School
• Jacqueline Ng Lane Jackie Lane , Assistant Professor of Business Administration. She holds a PhD from Northwestern University and studies the impact of new technologies on performance outcomes. 

Activities/Research Focus

The Lab focuses on three key areas of investigation:

Open Innovation: 

• How can organizations use contests, communities, and labor markets to solve complex problems? 
• How does open innovation transform access to talent? 
• What does the future of work look like in an era of open talent? 

Science of Science: 

• What are the drivers, behaviors, and motivation underpinning innovative work? 
• What makes a successful Lab? 
• How can science best be applied in solving business challenges?

Data Science and AI: 

• How is a data-first company built? 
• What is the role of data science in a firm’s operating model? 
• How can AI factories be used in organizations? 
• What impact do algorithms have on the innovation process?

Incubating within LISH are two other labs:

Pricing Lab, PIs: Alberto F. Cavallo Alberto Cavallo

The mission of the Pricing Lab is to conduct research on firm-level pricing decisions, underlying pricing capabilities, and the impacts of pricing on firm performance and the overall economy. The Pricing Lab promotes an understanding of current technologies and related policies through a combination of theoretical and empirical methods. 

Data Science and AI Operations Lab, PIs: Iavor I. Bojinov Iavor Bojinov and Edward McFowland III Edward McFowland III

The Data Science & AI Operations Lab focuses on enabling organizations to overcome the methodological and operational challenges that arise when employing data science for data-driven decision making. Successful applications of data science require the correct methodology, technology, process, and organizational culture to develop the capability. Methodological challenges arise because the statistical theory underpinning modern data science was developed for contexts and applications that are significantly different from the current business use cases.

Find out more about LISH .

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But what strategies and tools do they need to employ to enable this transformation? How should firms with innovative business models compete when a handful of behemoths dominate the world? How do we ensure that such business models enhance social welfare?

At the HBS AI Institute, the Platform Lab is building a research-based roadmap for businesses and policymakers. The aim is to give them insights and routes to innovation based on rigorous research.

The lab is built on the six principles that underpin the HBS AI Institute. By combining the perspectives of business and operating model transformation, organizational and workforce transformation, performance and metrics, algorithms, AI, ethics, and societal impact it will enable the translation from theory to practice and learn from practice to enable more rigorous science.

Faculty

The Platform Lab is led by: 

• Feng Zhu Feng Zhu , MBA Class of 1958 Professor of Business Administration. 
• Chiara Farronato Chiara Farronato , Glenn and Mary Jane Creamer Associate Professor of Business Administration. 

Activities/Research Focus

The lab is interested in the answers to three broad questions: 

1. The wellbeing of customers and society
   • What is the impact of new business models on market frictions?
   • How do new business models influence equality, inclusivity, and mobility?
   • Can platforms play a role in cities鈥� economic development?
2. Opportunities for innovation
   • What new business models will emerge in the coming years?
   • What role will platforms play in the metaverse?
   • How to launch and grow platform businesses? 
   • Should traditional or incumbent firms compete or partner with entrants with new business models?
   • How can products and services be transformed into platform offerings?
3. Role of policymakers
   • How should competition and market power be addressed?
   • How to create inclusive platforms and shared value?
   • What can digital leaders do to ensure privacy?

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The mission of the Pricing Lab is to conduct research on firm-level pricing decisions, underlying pricing capabilities, and the impacts of pricing on firm performance and the overall economy. The Pricing Lab promotes an understanding of current pricing technologies and related policies through a combination of theoretical and empirical methods. Our leadership team has brought microeconomic and macroeconomic insights to collaborations with outside organizations.

Faculty

The Pricing Lab is led by: 

• , Thomas S. Murphy Professor of Business Administration. He received his PhD in Economics from 性视界 University.

Activities/Research Focus

The Pricing Lab partners with organizations to assess and improve pricing capabilities and to create generalizable knowledge about the consequences of new technologies and practices.

The approach of the Lab is to combine large-scale datasets with variation arising from changes internal and external to the firm. The Lab encourages collaborative experimentation with partner organizations. It will seek to answer questions such as:

• What are the impacts to the firm of adopting new pricing technologies and processes?
• How do customers react to dynamic pricing?
• What are the broader implications for the market when firms change their pricing systems?

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When investors play safe, developing countries lose out. So do women, people of color, and other minority groups. And when these groups are excluded, we all lose. 

The Tech for All Lab at the HBS AI Institute aims to promote accessibility. It will broaden the benefits of digital innovation to reach more emerging economies. By providing knowledge and tools across the stack, it will serve as a launchpad for start-ups and entrepreneurs, and help established companies and governments across the world to learn how to succeed in the digital age.聽

Faculty

The Tech for All lab is led by: 

• Rembrand M. Koning Rem Koning , Mary V. and Mark A. Stevens Associate Professor of Business Administration. With a PhD from Stanford University, he studies how entrepreneurs build and broaden the benefits of startup growth and innovation.
• Tarun Khanna Tarun Khanna , Jorge Paulo Lemann Professor. A co-founder of businesses across emerging markets and in the USA. He holds a PhD from 性视界 University. 

Affiliated Faculty and Researchers

• is an Assistant Professor of Strategy at INSEAD and a Research Affiliate at the Tech for All Lab. She studies how AI is changing strategy for new and established firms across the globe. She holds a doctorate from 性视界 University.

Activities/Research Focus

The lab鈥檚 research agenda will focus on: 

• Identifying the frictions that drive wedges between technologies and sub-populations who should be using these.
• How start-ups and incumbents can develop tech-enabled strategies to benefit the underserved and create value for the firm. 
• The impact of data and digitization on firm strategy, the workforce, and society. 
• How technology can be used to identify 鈥渓ost talent鈥� that existing labor markets overlook. 
• What types of consumers benefit from innovation and how companies can build innovation pipelines that are more inclusive.

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AI makes our lives easier, but also adds to our anxiety. 

The ethics of AI is an emerging area of academic research and corporate strategy. There are big questions to answer: How do we run companies with algorithms? We want to know what that means for leaders. Will AI and machine learning (ML) help us make responsible, data-driven, decisions? Does AI have a positive impact on equity and inclusion for minority groups? We will establish metrics. How do we balance the value of algorithmic insights with individuals鈥� privacy rights? 

These questions are worth exploring because they mark the new frontiers of business in a digital-first world, and will present some of the thorniest decisions facing modern managers.

The HBS AI Institute鈥檚 Trustworthy AI Lab is at the forefront of this investigation. It is breaking new ground in the way AI and ML are applied to problem-solving and operations within enterprises. Our insights will shape how a generation of managers thinks about the role of AI within organizations.

Faculty

The Trustworthy AI Lab is led by: 

• Marco Iansiti is David Sarnoff Professor of Business Administration at HBS. He is the co-author of 鈥楥ompeting In the Age of AI鈥�.
• Himabindu Lakkaraju is an Assistant Professor of Business Administration at HBS. She holds a PhD from Stanford University.
• Salil Vadhan, Vicky Joseph Professor of Computer Science and Applied Mathematics (SEAS) holds a PhD from the Massachusetts Institute of Technology. He leads 性视界鈥檚 Privacy Tools Project and co-directs the OpenDP Project for open-source privacy software. 

Activities/Research Focus

The lab will focus on: 

Operating improvements, worker efficiency, and managerial decision-making. It will consider:  

• Integration of AI tools and machine learning (ML) models into workflows. 
• Seamless interaction between humans and ML models to enable critical tasks.

Research on the algorithmic aspects of AI and ethics focuses on:

• Making AI/ML models easier for humans to understand and apply.
• ML models that are fair to minority groups and robust to adversarial manipulations.
• Use of AI to solve complex problems involving multiple objective functions across multiple time scales.
• Studying the privacy risks in machine learning models and developing privacy-preserving algorithms for building the models in way that is guaranteed to avoid these risks.

 The Trustworthy AI Lab is participating in the Generative AI Working Group.

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