AI Management Professional Certificate

This course introduces Artificial Intelligence (AI) from both business and systems perspectives, emphasizing architecture and strategy for real-world deployment. Students will explore the fundamentals of AI, its disruptive impact on organizations, and its business architecture. The focus then shifts to AI systems architecture and the underlying principles, including data conditioning, machine learning, modern computing, human-machine teaming, and AI vulnerability and mitigation. The course also covers the AI Strategic Development Model, guiding students in creating well-grounded implementation roadmaps. It concludes with a case study that enables participants to apply their newly acquired knowledge to practical, real-world scenarios.

This course explores the concept of responsible AI, encompassing ethical, legal, social, and technical dimensions. Students will learn to navigate the complexities of AI development, deployment, and use, considering issues such as bias, fairness, transparency, accountability, and the potential impact on individuals and society. Students will develop an understanding of responsible AI principles and practices, preparing them to contribute to the ethical and beneficial use of AI in various domains.

This course examines the popular cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud. Participants will learn the infrastructure requirements for powerful AI models on the cloud, the use of cloud platforms to build/train/deploy powerful AI models, and security/governance best practices for cloud-based AI models. The course equips professionals with practical tools and hands-on skills to build cloud-based AI models to solve business problems.

This course provides a practical and hands-on introduction to implementing AI systems using an architectural and life-cycle approach. Participants will first review essential frameworks, including the AI Systems Architecture, Implementation Framework, and System Readiness Levels, before engaging in a series of guided labs that demonstrate how AI technologies are applied in real scenarios. Through no-code exercises in data conditioning, ML training, natural language processing, chatbot building, and automation adoption, learners will build and evaluate functional AI components. The course concludes with strategies for testing, monitoring, and sustaining AI systems in operational environments.

This course explores the concept of human-centric AI management, with a focus on how emerging AI technologies transform performance, decision-making, and creativity. Students will gain knowledge about frameworks for analyzing employee–AI interaction. Students will also develop practical insights into crafting jobs, developing self-regulation strategies, motivating teams, managing change, and strengthening emotionally intelligent leadership in environments where AI and human work intersect. The course guides students on how to pursue human-focused management practices that can harness AI systems to augment employees’ performance.

This course examines the threats, vulnerabilities, and organizational risks that emerge when AI is used improperly or in violation of privacy standards. Participants will analyze real-world cases of AI misuse, explore regulatory frameworks, and identify high-risk practices. The course equips professionals with practical tools to assess compliance, mitigate data risks, and implement safeguards that balance innovation with privacy, trust, and accountability.

This course teaches students to design and operate an AI coding agent that writes code and creates applications from user prompts, evaluates its outputs, and improves via iterative feedback. Building on recent developments in vibe coding and agentic AI, learners will practice prompt-to-code workflows, automated testing, and safe execution. Emphasis is placed on reliability, safety, and collaboration between human reviewers and intelligent code-generation systems.

This course focuses on the management of stakeholder and workforce accountability within the context of organization-wide artificial intelligence (AI) adoption. The emphasis will be on social, cognitive, and ethical consequences on an organization’s value offerings and responsiveness to stakeholders mediated by AI adoption and workforce performance associated with AI competencies. The emphasis will also be on balancing stakeholder accountability, employee job performance and career development practices with AI tools. Coursework will include foundational research, best practices, case studies, simulations, discussions, and project work.

This course examines how organizations create value from data amid rapid advances in AI. Participants will explore emerging models for data access, sharing, and licensing for model training, along with evolving economic and ethical considerations. Through case studies and strategic analysis, the course highlights how data monetization strategies are likely to evolve as AI, regulation, and governance mature.

This course equips participants with practical knowledge and frameworks to develop, implement, and govern AI policy in public-sector and enterprise settings. Drawing on CDT’s policy development approaches—strategy, standards, and oversight—participants will learn how to frame AI governance challenges, craft policy instruments, and design accountability mechanisms. Through case studies, interactive exercises, and group work, attendees will explore ethical, legal, operational, and technical dimensions of AI deployment and build ready-to-use governance tools for their organizations.