Flex MBA Specialization in Artificial Intelligence for Business

Add a specialization in Artificial Intelligence for Business.

Artificial intelligence is transforming business across the industries, creating unprecedented opportunities for innovation and business growth. With AI technologies such as machine learning, deep learning, and generative AI now driving automation and augmenting decision-making processes, businesses are increasingly reliant on professionals who can harness these tools to solve complex problems. As AI continues to evolve, its integration into business areas, from customer engagement to healthcare, demands leaders who understand both the technical aspects and the strategic implications on organizations. 

With this growing reliance on AI comes the need for strong governance. This ensures that AI systems are designed, implemented, and managed with clear framework to mitigates risks and promote transparency. Also, leaders must develop strategies for human-AI collaboration, focusing on AI systems designed to enhance human interaction and optimize user experience. This strategic approach ensures that AI technologies are not only efficient but also aligned with human-centered goals, driving innovation and organizational success. 

Artificial intelligence for business curriculum highlights

Required courses

BU.520.710 AI Essentials for Business (2 Credits)

Artificial Intelligence (AI) is making substantial inroads into our everyday lives, demonstrating tremendous potential across numerous domains, including business and healthcare. This course aims to impart learners with crucial understanding of AI's core technologies, such as neural networks, deep learning, computer vision, natural language processing, and generative AI. We will also provide a succinct introduction to the burgeoning areas within AI. This course takes a hands-on, experiential learning approach, making use of the latest cloud computing platforms to provide practical understanding and application. Through this course, students will establish a robust comprehension of vital AI technologies, equipping themselves with the working knowledge required to harness these technologies in driving business growth and creating value. 

BU.142.775 Machine Learning for Management (2 Credits)

This course is a forward-thinking one designed for managers and business leaders who want to understand the power and potential of machine learning (ML) to drive data-informed decisions. Topics covered include key machine learning algorithms and their applications in business, model evaluation and improvement, machine learning projects and pipelines, biases in machine learning algorithms and other ethical concerns, user trust, business case studies, and future trends. Upon completion, students will possess the essential knowledge and skills required to leverage machine learning for strategic advantage in their business operations. 

Electives

BU.450.760 Customer Analytics (2 Credits)

Immerse yourself in the modern practice of customer analytics. And learn how marketers and business analysts can improve decision making by leveraging scientific approaches in the analysis of big data. Leading analytical techniques and data structures are illustrated in the context of their most prominent applications – for example, predicting customer responses to marketing campaigns and managing customer churn. The class has a strong hands-on component, enabled by several in-class examples and group assignments (implemented on Microsoft Excel and the statistical language “R”). You are not expected to become expert programmers or statisticians, but you will acquire basic skills and knowledge to orchestrate an effective analytics strategy.

BU.510.650 Data Analytics (2 Credits)

Gather, describe, and analyze data, using advanced statistical tools to support operations, risk management, and responses to disruptions. Analysis is done by targeting economic and financial decisions in complex systems that involve multiple partners. Topics include probability, statistics, hypothesis testing, regression, clustering, decision trees, and forecasting. 

BU.350.620 Digital Transformation of Business (2 Credits) 

This course addresses how markets, market mechanisms, and channels of product and service delivery are impacted and often transformed by information and communication technologies. Students will learn how technology, brought together with people and processes into systems, contributes to leveraging the creation of business value. The course considers different elements of the information architecture of the corporation and its impact on the nature of the work and the structure of the corporation. 

BU.330.760 Generative AI (2 Credits)

With the enterprises’ usage of Information and Communication Technology (ICT), a huge amount of data is being generated every second. Much of this big data is unstructured and loosely connected. Enterprise technology managers are often called upon to support decision making based on information that resides in this unstructured data. Managers of technology need to be able to support such decision making by delivering analytical applications via enterprise wide APIs and secure corporate networks. The ability to organize large repositories of unstructured data and run analytical applications on them is key creating an effective information architecture for the modern corporation. This course prepares students to manage enterprise technology needs by acquiring advanced data analytics skills for driving business insights from large amounts of unstructured data using network analysis and deep learning. The technology function in corporation is increasingly called upon to involve both managers and analysts to support and participate in data driven decision making. Therefore, this course uses a hands-on, learning-by-doing approach. Topics include: organization of corporate data warehouses containing unstructured data, unstructured data distribution through enterprise APIs, graph theory, network evolution and block models, API-based visualization methods, graphical models, deep feedforward network, regularization, convolutional neural network, and recurrent neural network. Students will use Python packages such as NetworkX, graph-tool, TensorFlow, Theano and Keras. Students will also use Gephi, an open source software for exploring and manipulating networks. The focus is on creating awareness of the technologies, allowing some level of familiarity with them through assignments, and enabling some strategic thinking around the use of these in business.  

BU.883.710 Health Analytics (2 Credits)

This course focuses on designing, measuring, and improving processes that deliver care in both inpatient and outpatient settings. The course provides an introduction to process analysis, queueing theory, capacity management, cost measurement, and the metrics of process flow. BU.883.710 Health Analytics (2 Credits)

BU.330.765 Human-AI Fusion (2 Credits)

This cutting-edge course is designed for professionals, technologists, and leaders who are eager to harness the combined power of human creativity and artificial intelligence. The course offers an in-depth exploration of how AI can augment human capabilities, enhance decision-making processes, and transform industries. Topics covered include the comparison and combination of human and AI capabilities, AI as a service to human users, augmented intelligence, brain-computer interface, human-AI teaming, autonomous intelligence, social AI, and the business of human-AI fusion. Upon completion, students will have acquired the essential knowledge and skills to develop strategies for human-AI collaboration, focusing on roles, tasks, and processes that maximize the strengths of both. 

BU.330.735 Responsible AI (2 Credits)

In light of the accelerating and ubiquitous impact of artificial intelligence (AI) on all sectors of the economy, the significance of responsible AI, i.e., systems that conform to principles of safety, equity, fairness, etc. is paramount. This course is crafted to equip business leaders, AI practitioners, and policymakers with an in-depth understanding of the ethical, safety, and governance issues associated with AI technologies. Topics include ethical frameworks in AI, AI safety, the current regulatory landscape and governance roles, sources of bias in AI, transparency and accountability in AI systems, and real-world case studies in the finance, healthcare, marketing, and management domain . Upon completion, students will have acquired the essential knowledge and skills to formulate organizational strategies that integrate responsibility throughout the AI development lifecycle. 

BU.450.740 Retail Analytics (2 Credits)

The retail and service sector is the largest of all economic activities and evolving rapidly in the age of big data and Artificial Intelligence. This course will leverage data-driven tools and theoretical models to analyze decisions of retail firms. We will cover a wide range of topics in strategic decisions in retailing: pricing, location, franchising, and omni-channel retailing. Using the real data in retailing, we will demonstrate and implement a wide range of statistical methods in econometrics and machine learning: single and multi-variate linear regressions, logistic regressions, classification trees, random forest, and multi-layer neural network. The focus is on predicting the effects of marketing decisions on profitability, although we will touch on causality as well. 

This class is practical and hands-on. All strategic decisions in business require a quantitative assessment of cause and effect. Each week we will introduce a new data set and data-driven tool that is valuable in the context of data scientists in retailing. You will learn how to perform convincing data analyses to answer specific questions. We will use R and ArcGIS for analyzing data. We do not assume that you have used R or ArcGIS, software for statistical and geographical analyses, respectively, in a previous class. For potential overlaps with other courses, we will cover them at a faster pace and emphasize techniques that are not covered in other courses. 

BU.610.750 Supply Chain Analytics (2 Credits)

For a firm to execute its competitive strategy successfully, its supply chain must be able to deliver on the firm’s promise to its customers. Therefore, it is important for all managers to have an understanding of key supply chain concepts. With this in mind, this course introduces the main trade-offs involved in supply chain management, and provides analytical, data-driven tools that can be used to evaluate supply chain trade-offs. The course emphasizes (i) building spreadsheet-ready models that capture supply chain challenges, (ii) using these models to ask what-if questions by applying simulation and optimization tools (e.g., @Risk, a powerful Excel add-in for simulations), and (iii) distilling managerial insights from what-if questions and communicating recommendations based on those insights.