Competing in the Age of AI

Artificial intelligence (AI) is revolutionizing the way today’s businesses compete and operate. By putting AI and data at the center of their capabilities, companies are redefining how they create, capture, and share value—and are achieving impressive growth as a result. This course will delve into the history of AI and how it has evolved over time before moving on to new AI-based business models and operational approaches. Through global case studies on market leaders and innovative startups in diverse industries, the course will explore topical issues such as academic freedom, corporate responsibility, and bias in AI systems, and equip you with a range of capabilities that can help your business succeed in today’s data-driven environments. The course will expand on generative AI, including how large language models are developed and how they can be used by individuals and businesses. Additionally, the course faculty will provide practical examples of how to use this technology by demonstrating how to converse and perform data analytics with ChatGPT.

Taught by Karim Lakhani (HBS), Antonio Moreno (HBS), and Feng Zhu (HBS)

Weekly Time Expectations: 

Foundations of Quantitative Analysis

This course is an introduction to using statistical approaches to solve business problems. It introduces statistical concepts via a management perspective and places special emphasis on developing the skills and instincts needed to make sound decisions and become an effective manager. The main components of the course include methods for describing and summarizing data, the fundamentals of probability, the basics of study design and data collection, and statistical inference. Data analyses, simulation, and design issues are implemented in the statistical computing package R run within the Posit interface.

Taught by Mark Glickman (FAS), Mike Parzen (HBS), and Kevin Rader (FAS)

Weekly Time Expectations: 

Leadership, Innovation, and Change

An emphasis on data analytics and algorithms at the center of an enterprise also means that leaders will have to drive both innovation and large-scale organizational change. This course will focus on the leader’s role in both executing their current strategy better than their competitors as well as their role in shaping strategic innovation. We employ the congruence model that links strategy to execution through alignment of culture, people, tasks, structure, and executive leadership. We also explore the inertial characteristics of aligned organizations and the strategic importance of driving innovation streams. We explore building ambidextrous organizations, organizations that can both exploit their existing strategy as well as explore into new strategic domains. Because ambidexterity requires leaders that can deal with punctuated change and paradoxical strategies, our course concludes with what we know about ambidextrous leadership and leading large system change.

Taught by Michael Tushman (HBS) and Rory McDonald (HBS)

*You must complete Competing in the Age of AI before you take this course.

Weekly Time Expectations: 

In-Person Immersion at Harvard Business School Campus in Boston

During these in-person experiences, you will meet face to face with your classmates, network with faculty and industry leaders during nightly events, tour the Harvard campus, and participate in hands-on guided learning exercises. You will also gain an in-depth understanding of deep learning and neural networks, and use the HBS case method to formulate solutions to real-world business scenarios as a way to understand relevant challenges in the industry. Recent topics include fairness in algorithms, data privacy, leading transformational change, and the evolving landscape of AI. Immersion dates are subject to change.

Operations and Supply Chain Management

Digital technologies and data analytics are radically changing the operating model of an organization and how it connects to its broader supply chain and ecosystem. This course emphasizes managing product availability, especially in a context of rapid product proliferation, short product life cycles, and global networks of suppliers and customers. Topics examined include inventory management, distribution economics, demand forecasting, supplier management, and the potential benefits of using AI and machine learning to work with data. The course emphasizes the “general manager’s perspective” in supply chains. Cases in the course illustrate that barriers to integrating supply chains often relate to behavioral issues (e.g., misaligned incentives or change management challenges) and operational execution problems that fall squarely in the domain of the general manager.

Taught by Ryan Buell (HBS), Dennis Campbell (HBS), and Jan Hammond (HBS)

*You must complete Foundations of Quantitative Analysis before you take this course.

Weekly Time Expectations: 

Programming and Data Systems

Modern business analytics requires executives and managers to be conversant with programming, AI, and data architecture. The aim of this course is to provide participants with the fundamental knowledge and practice needed to appreciate the challenges and opportunities related to developing robust and scalable systems that are at the core of business analytics by emphasizing mastery of high-level concepts and design decisions. Through a mix of technical instruction, discussion of case studies, and weekly programming projects, this course empowers participants to make technological decisions even if not technologists themselves. Topics include Artificial Intelligence, cloud computing, networking, privacy, scalability, security, and more, with a particular emphasis on web and mobile technologies. Participants will learn the basics of coding with SQL and Python, and be introduced to fundamental concepts in decision trees, neural networks, LLMs, other types of AI models, and generative AI in order to understand what AI can do for their organization. PDS participants also have access to a custom AI-chatbot, which was built by the teaching team and designed to guide and aid participants, as a teaching assistant would.

Taught by Henry Leitner (SEAS) and David J. Malan (SEAS)

Weekly Time Expectations: 

Leadership and People Analytics

People analytics is designed to help practitioners use data to improve people-related decisions. Participants will build hands-on skills to analyze data in ways that complement the frameworks and intuitions they would normally use to guide their managerial actions on people issues. At a deeper level, participants in any job, organization, or industry context will sharpen their ability to think critically through the lens of rigorous analytics. Anchored in data, this course will equip participants with an analytic approach to diagnosing the varied forces that influence individual, team, and organizational performance, leading to more effective interventions and actions. While developing analytic skills and trying out tools and techniques, participants will come to appreciate the opportunities, limits, and tensions involved in using data analytics to inform people issues, while simultaneously gaining deeper insight into the substance of the business issues in question.

Taught by Jeff Polzer (HBS)

*You must complete Foundations of Quantitative Analysis, Programming and Data Systems, and Leadership, Innovation, and Change before you take this course.

Weekly Time Expectations: 

Data-Driven Marketing

Marketing has been revolutionized and forever changed by data analytics. What used to be a qualitative and instinct-driven business function (think Mad Men) has now become a data-driven profession that relies on quantitative insights on how best to optimize ad creation and placement and influence consumer purchase behavior. It also focuses on the benefits that AI and machine learning bring to marketing including enhanced personalization and customization, as well as pricing optimization and automation. You will also learn how AI affects change management and algorithmic bias. This course will examine the ways in which marketing has changed and the new skills and capabilities needed to succeed in this function.

Taught by Ayelet Israeli (HBS) and Flavio Calmon (SEAS)

*You must complete Competing in the Age of AI, Foundations of Quantitative Analysis, Operations and Supply Chain Management, Programming and Data Systems, Immersion 1, and Leadership, Innovation, and Change before you take this course.

Weekly Time Expectations: 

Data Science Pipeline and Critical Thinking
This course will take a holistic approach to helping participants understand the key factors involved in the data science pipeline, from data collection to analysis to prediction and insight. The curriculum will expand on the application of AI in data science by looking at the role of machine learning. Topics such as large language models, supervised learning, unsupervised learning, Bayes’ Theorem, and deep learning will be explored throughout the course. Projects will give participants hands-on experience developing and running a data science pipeline to ensure that the correct business predictions are being made.

Taught by Hanspeter Pfister (SEAS), Iavor Bojinov (HBS), and Mark Glickman (FAS)

*You must complete Competing in the Age of AI, Operations and Supply Chain Management, Programming and Data Systems, and Foundations of Quantitative Analysis before you take this course.

Weekly Time Expectations: 

In-Person Immersion at Harvard Business School Campus in Boston

During these in-person experiences, you will meet face to face with your classmates, network with faculty and industry leaders during nightly events, tour the Harvard campus, and participate in hands-on guided learning exercises. You will also gain an in-depth understanding of deep learning and neural networks, and use the HBS case method to formulate solutions to real-world business scenarios as a way to understand relevant challenges in the industry. Recent topics include fairness in algorithms, data privacy, leading transformational change, and the evolving landscape of AI. Immersion dates are subject to change.

Competing in the Age of AI

Artificial intelligence (AI) is revolutionizing the way today’s businesses compete and operate. By putting AI and data at the center of their capabilities, companies are redefining how they create, capture, and share value—and are achieving impressive growth as a result. This course will delve into the history of AI and how it has evolved over time before moving on to new AI-based business models and operational approaches. Through global case studies on market leaders and innovative startups in diverse industries, the course will explore topical issues such as academic freedom, corporate responsibility, and bias in AI systems, and equip you with a range of capabilities that can help your business succeed in today’s data-driven environments. The course will expand on generative AI, including how large language models are developed and how they can be used by individuals and businesses. Additionally, the course faculty will provide practical examples of how to use this technology by demonstrating how to converse and perform data analytics with ChatGPT.

Taught by Karim Lakhani (HBS), Antonio Moreno (HBS), and Feng Zhu (HBS)

Weekly Time Expectations: 

Foundations of Quantitative Analysis

This course is an introduction to using statistical approaches to solve business problems. It introduces statistical concepts via a management perspective and places special emphasis on developing the skills and instincts needed to make sound decisions and become an effective manager. The main components of the course include methods for describing and summarizing data, the fundamentals of probability, the basics of study design and data collection, and statistical inference. Data analyses, simulation, and design issues are implemented in the statistical computing package R run within the Posit interface.

Taught by Mark Glickman (FAS), Mike Parzen (HBS), and Kevin Rader (FAS)

Weekly Time Expectations: 

Leadership, Innovation, and Change

An emphasis on data analytics and algorithms at the center of an enterprise also means that leaders will have to drive both innovation and large-scale organizational change. This course will focus on the leader’s role in both executing their current strategy better than their competitors as well as their role in shaping strategic innovation. We employ the congruence model that links strategy to execution through alignment of culture, people, tasks, structure, and executive leadership. We also explore the inertial characteristics of aligned organizations and the strategic importance of driving innovation streams. We explore building ambidextrous organizations, organizations that can both exploit their existing strategy as well as explore into new strategic domains. Because ambidexterity requires leaders that can deal with punctuated change and paradoxical strategies, our course concludes with what we know about ambidextrous leadership and leading large system change.

Taught by Michael Tushman (HBS) and Rory McDonald (HBS)

*You must complete Competing in the Age of AI before you take this course.

Weekly Time Expectations: 

In-Person Immersion at Harvard Business School Campus in Boston

During these in-person experiences, you will meet face to face with your classmates, network with faculty and industry leaders during nightly events, tour the Harvard campus, and participate in hands-on guided learning exercises. You will also gain an in-depth understanding of deep learning and neural networks, and use the HBS case method to formulate solutions to real-world business scenarios as a way to understand relevant challenges in the industry. Recent topics include fairness in algorithms, data privacy, leading transformational change, and the evolving landscape of AI. Immersion dates are subject to change.

Operations and Supply Chain Management

Digital technologies and data analytics are radically changing the operating model of an organization and how it connects to its broader supply chain and ecosystem. This course emphasizes managing product availability, especially in a context of rapid product proliferation, short product life cycles, and global networks of suppliers and customers. Topics examined include inventory management, distribution economics, demand forecasting, supplier management, and the potential benefits of using AI and machine learning to work with data. The course emphasizes the “general manager’s perspective” in supply chains. Cases in the course illustrate that barriers to integrating supply chains often relate to behavioral issues (e.g., misaligned incentives or change management challenges) and operational execution problems that fall squarely in the domain of the general manager.

Taught by Ryan Buell (HBS), Dennis Campbell (HBS), and Jan Hammond (HBS)

*You must complete Foundations of Quantitative Analysis before you take this course.

Weekly Time Expectations: 

Programming and Data Systems

Modern business analytics requires executives and managers to be conversant with programming and data architecture. The aim of this course is to provide participants with the fundamental knowledge and practice needed to appreciate the challenges and opportunities related to developing robust and scalable systems that are at the core of business analytics by emphasizing mastery of high-level concepts and design decisions. Through a mix of technical instruction, discussion of case studies, and weekly programming projects, this course empowers participants to make technological decisions even if not technologists themselves. Topics include artificial intelligence, cloud computing, networking, privacy, scalability, security, and more, with a particular emphasis on web and mobile technologies. Participants will learn the basics of coding with SQL and Python, and be introduced to fundamental concepts in decision trees, neural networks, LLMs, other types of AI models, and generative AI in order to understand what AI can do for their organization.

Taught by Henry Leitner (SEAS) and David J. Malan (SEAS)

*You must complete Foundations of Quantitative Analysis before you take this course.

Weekly Time Expectations: 

Data Science Pipeline and Critical Thinking

This course will take a holistic approach to helping participants understand the key factors involved in the data science pipeline, from data collection to analysis to prediction and insight. The curriculum will expand on the application of AI in data science by looking at the role of machine learning. Topics such as large language models, supervised learning, unsupervised learning, Bayes’ Theorem, and deep learning will be explored throughout the course. Projects will give participants hands-on experience developing and running a data science pipeline to ensure that the correct business predictions are being made.

Taught by Hanspeter Pfister (SEAS), Iavor Bojinov (HBS), and Mark Glickman (FAS)

*You must complete Competing in the Age of AI, Operations and Supply Chain Management, Programming and Data Systems, and Foundations of Quantitative Analysis before you take this course.

Weekly Time Expectations: 

In-Person Immersion at Harvard Business School Campus in Boston

During these in-person experiences, you will meet face to face with your classmates, network with faculty and industry leaders during nightly events, tour the Harvard campus, and participate in hands-on guided learning exercises. You will also gain an in-depth understanding of deep learning and neural networks, and use the HBS case method to formulate solutions to real-world business scenarios as a way to understand relevant challenges in the industry. Recent topics include fairness in algorithms, data privacy, leading transformational change, and the evolving landscape of AI. Immersion dates are subject to change.