Online Master's in Artificial Intelligence Management
Curriculum

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Artificial Intelligence Management Courses Taught by Industry Leaders

Build a foundation of technical knowledge or enhance your existing skill set with AI-specific expertise. The online Master of Professional Studies in Artificial Intelligence Management features courses taught by faculty from leading tech organizations so you'll gain valuable insights you can immediately apply in your career.

This flexible program includes two focus area options created for students with diverse career goals. A one-year accelerated track is also available. You can specialize as a strategic leader managing AI teams or prepare to develop and integrate solutions as an AI specialist. Every student also completes an experiential capstone project by developing an idea or solving a problem relevant to their company or industry.

Our online artificial intelligence management courses take an interdisciplinary approach to AI integration that goes beyond computer science to incorporate concepts from a range of business areas, including:

  • Communications
  • Data analytics
  • Ethics
  • Governance and Compliance
  • Innovation
  • Strategic management

The online Master's in Artificial Intelligence Management consists of 30 credit hours. Students complete six required artificial intelligence management courses and choose four additional courses in their focus area.

Program courses include:

Artificial Intelligence Management Online Course Descriptions

Required Courses

Ethical AI: Moral Dilemmas & Biases

Artificial intelligence is directly related to ethics in the sense that it impacts the well-being of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, focusing on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements and the distinction between humans and machines and explore questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

Introduction to Artificial Intelligence

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing their strengths and weaknesses.

Innovation Management & AI Strategy

This course examines combining advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering market study, resource allocation, and risk management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change.

AI Governance & Compliance

This course explores the fundamental principles of artificial intelligence, focusing on critical aspects like the explainability and transparency of AI decision-making processes. Students will examine the security risks and privacy concerns associated with AI, understanding the technical hurdles in ensuring strict governance. The course guides students through the complexity of regulatory and legal frameworks shaping the incorporation of AI across different industries. A significant part of the course is dedicated to learning about established standards and best practices in the field, providing the knowledge necessary to develop and implement effective AI governance models. The course emphasizes the importance of considering global and regional perspectives, highlighting the varied strategies and policies in AI deployment worldwide.

Communication Strategies for Digital Transformation

This course explores the challenges of leading and communicating organizational change in an era increasingly dominated by artificial intelligence. It recognizes that successful digital transformation transcends mere technological implementation, deeply impacting organizational culture and human dynamics. Students learn how to develop a communication strategy and implement a communication plan addressing issues related to AI and organizational change. The course examines multiple communication approaches as well as tools and techniques that are appropriate for different audiences.

Capstone

The capstone course culminates the student's academic and professional experience in the artificial intelligence management program. Over the semester, students will be asked to apply the knowledge gained during the program to a project. With a focus on AI integration, students will incorporate the skills necessary for analyzing key issues, thinking creatively, and making sound decisions to develop an AI strategy, solution design, and implementation plan. Projects will address global and local challenges as well as issues of accessibility and inclusiveness related to artificial intelligence. Students will have the option to either undertake a self-directed project of their choosing or engage in a simulated scenario that replicates the real-world tasks and responsibilities of AI managers.

Focus Area Courses: AI Strategy & Leadership

AI Leadership & Market Analysis

This comprehensive course explores the rapidly evolving AI-driven business landscape. It combines essential leadership strategies with in-depth market analysis to prepare students for the challenges and opportunities of technological advancements. The course covers the impact of AI on leadership, emphasizing adaptable leadership styles for tech environments. It teaches managing diverse workforces, leading digital transformation, and developing problem-solving and decision-making skills for the tech industry. It also includes analyzing AI market trends, interpreting industry reports, and conducting competitive analysis to understand the strategies and dynamics of different market players.

AI & Data-Driven Decision Making

The course covers the essentials of AI in data analysis and teaches participants to use AI tools to uncover trends and make informed decisions. The course emphasizes practical applications and data strategy management and addresses ethical and privacy concerns in data-driven AI. Through real-world case studies and hands-on projects, students will learn to integrate AI insights with strategic business planning, overcome organizational challenges, and effectively communicate the value of AI-driven decisions. This course prepares students to lead data-centric initiatives in their organizations, leveraging AI for enhanced decision-making and competitive business advantage.

Creative & Generative AI

This course provides an overview of generative AI, defining its role and capabilities in creating novel content. The course focuses on the practical applications of generative AI in various creative domains. Students will explore how AI is used to generate visual art, create music, design products, and even write creative literature. The course will showcase case studies and examples where generative AI has been implemented, offering insights into its transformative potential. Students will also engage with the creative process behind generative AI. This involves understanding the human-AI collaboration in creative work and exploring how artists and designers can guide AI to enhance creativity rather than replace it. Discussions will include the ethical considerations and implications of AI-generated content, particularly regarding originality, copyright, and authorship. We will also examine issues of bias and the future of work as generative AI reshapes the global market for creative content.

AI Security & Data Privacy

This course offers a comprehensive overview of the essential practices of securing AI systems and protecting data. The first part of the course focuses on the security threats unique to AI, including adversarial attacks, data poisoning, and model theft. Students will learn about the techniques used to exploit AI systems and the strategies to defend against these threats. This includes best practices in securing AI data, algorithms, and infrastructures. The second part of the course examines the core concepts of privacy, including data confidentiality, integrity, and the rights of individuals regarding their personal information. Students will explore issues such as data surveillance, profiling, and the potential for AI to infringe on personal privacy. Students will learn about various scenarios where AI's data processing capabilities might conflict with privacy norms and expectations. Practical strategies for preserving privacy in AI systems form a key part of the curriculum. Students will explore anonymization, data minimization, and secure data storage techniques.

Focus Area Courses: AI Development & Integration

AI Applications Integration

The course provides a deep dive into the landscape of available AI solutions, exploring various models and platforms developed for industry-specific applications. Students will learn about the strengths and weaknesses of these off-the-shelf AI solutions, gaining insight into how they can be effectively selected and adapted. A core focus of the course is on the process of tailoring AI models. This includes understanding the technical aspects of modifying existing AI algorithms, as well as aligning these modifications with the strategic goals and operational requirements of the organization. Students will engage with topics such as data preparation, model retraining, and fine-tuning AI systems for enhanced performance and accuracy. This covers the technical, operational, and organizational challenges of embedding AI solutions into existing business processes and systems. Key areas of discussion include compatibility assessment, integration planning, and change management strategies to ensure smooth adoption and minimal disruption.

Developing AI Solutions

This course offers a comprehensive overview of agile digital solution development, with a special focus on artificial intelligence applications. It delves into Agile, Lean, and Design Thinking methodologies, underscoring the importance of developing AI solutions that deliver exceptional user experience and user interface. Students will actively engage in the entire process of AI solution development, from conducting targeted user and market research to designing AI-driven user experiences. They will also learn to prototype AI components, create intuitive AI-powered interfaces, and enhance user interactions using AI. This hands-on approach ensures students understand theoretical frameworks and acquire practical skills in crafting user-centric AI solutions.

Data Engineering for AI

The course introduces students to the fundamentals of data engineering. Students will learn about the lifecycle of data, from its initial collection to its final usage in AI models, including the various challenges and best practices at each stage. Students will explore various methods for acquiring data, whether structured or unstructured, from diverse sources. This includes understanding the nuances of data quality, integrity, and the importance of ethical data collection practices. Students will learn about different data storage solutions like databases, data warehouses, and data lakes, learning how to choose and implement the right storage strategy based on the specific needs of AI applications. Finally, students will gain hands-on experience with tools and frameworks for processing large datasets. This includes learning about batch and real-time data processing, ETL operations, and data pipeline design and optimization.

AI System Design & Architecture

This course provides a comprehensive approach to designing and architecting AI systems, focusing on scalability and efficiency. It covers AI architecture, including layers, modules, and interfaces, and guides students in selecting appropriate algorithms and models for specific use cases. The course addresses the challenges of scaling AI systems to handle large data volumes and complexity and emphasizes performance optimization. Advanced topics include distributed computing, cloud technologies, and edge computing for improved AI system performance. Students will also learn about deploying AI systems in different environments, integrating AI with existing IT infrastructure, tackling compatibility issues, and ensuring seamless data integration.

Accreditation

All programs offered by Georgetown University are accredited by the Middle States Commission on Higher Education.

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