Agentic AI for Business
Develop the strategic and technical fluency to guide, evaluate, and lead agentic AI initiatives—without writing a single line of code.
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Course Description
As organizations accelerate AI adoption, business leaders face growing pressure to understand what these systems can do, how they work, how they should be governed, and where they can create meaningful business value. Agentic AI for Business is an immersive executive training program developed by faculty at Columbia Engineering to help leaders build the strategic and technical fluency needed to evaluate, deploy, and lead agentic AI initiatives without requiring a technical background or coding experience.
Through a combination of lectures, hands-on labs, workshops, case studies, and industry discussions, participants will develop a practical understanding of how agentic AI systems are designed, integrated, and applied within enterprise environments. Over three intensive days, participants will move from foundational AI concepts to building functional AI agents and evaluating the organizational, operational, and governance implications of deploying AI at scale. Each day combines conceptual learning with immediate hands-on application, helping participants translate business challenges into AI-driven workflows and automation opportunities.
In this training, you will:
- Develop a practical understanding of generative and agentic AI systems, including how they reason, plan, and execute workflows
- Build functional AI agents through guided hands-on labs without writing code
- Learn how to translate business processes into AI workflow specifications that technical teams can implement
- Explore the data infrastructure, APIs, cloud tools, and enterprise systems that support real-world AI deployment
- Evaluate AI architectures, vendor proposals, model outputs, and automation strategies with greater confidence
- Examine the operational, governance, ethics, and risk considerations associated with deploying AI systems at scale
- Analyze real-world use cases and enterprise deployment strategies through case studies and industry panels
- Build a network of peers and leaders navigating AI transformation across industries
The program is designed for executives and decision-makers responsible for AI adoption, innovation, strategy, operations, and organizational transformation. No prior experience in coding, artificial intelligence, or data science is required.
This course is ideal for:
- C-suite and senior executives leading AI strategy and digital transformation initiatives
- Business unit leaders evaluating opportunities for AI-driven operational improvement
- Strategy, operations, innovation, and transformation professionals
- Senior managers overseeing data, product, analytics, or technology teams
- Consultants and advisors supporting organizations through AI adoption and implementation
- Professionals seeking a practical, executive-level understanding of agentic AI and enterprise automation
By the end of the program, participants will be better equipped to identify high-impact AI opportunities, communicate effectively with technical teams and vendors, and lead informed decisions about the responsible deployment of agentic AI within their organizations.
Course Prerequisites
The program is designed for professionals seeking to better understand how to evaluate, integrate, and govern agentic AI systems within enterprise environments.
There are no formal technical prerequisites for this program:
- No coding or programming experience required
- No prior AI or data science background required
- Participants should have familiarity with business operations, organizational decision-making, or strategic planning within their industry or functional area.
Prior to the start of the program, participants may be asked to complete a short pre-program reflection exercise to help contextualize discussions and hands-on activities throughout the training.
What You Will Learn
This program is designed to help business leaders develop the strategic and technical fluency to confidently evaluate, deploy, and lead agentic AI initiatives within their organizations. Through a combination of lectures, hands-on labs, case studies, and industry discussions, participants will build a practical understanding of how agentic AI systems function, how they are integrated into enterprise environments, and how to assess their opportunities, risks, and organizational impact.
By the end of the program, participants will be able to:
- Develop a framework for identifying high-value AI automation opportunities within their organization
- Build a practical understanding of how generative and agentic AI systems reason, plan, and execute workflows
- Translate business processes into AI workflow specifications that technical teams can implement
- Create functional AI agents through guided hands-on labs
- Understand the data infrastructure, APIs, cloud tools, and enterprise systems that support AI deployment at scale
- Learn how to evaluate AI vendor proposals, system architectures, model outputs, and automation strategies with greater confidence
- Explore enterprise AI considerations, including reliability, governance, ethics, bias, and operational risk
- Develop strategies for managing organizational change and AI adoption across teams and business functions
Instructors
Hardeep Johar holds a Ph.D. in Information Systems from NYU Stern and serves as Program Coordinator for the MS in Business Analytics and MS in Management Science at Columbia Engineering. He brings extensive industry experience as a quantitative proprietary trader at Deutsche Bank, Credit Suisse, and Morgan Stanley, and has advised and served on the management teams of multiple technology startups. His teaching and research center on the practical applications of AI and machine learning in business, and he has taught widely in executive programs and participated in AI-focused industry panels.
Tony Dear teaches courses in artificial intelligence, mathematics, and robotics. He is the Faculty Director of Columbia Engineering's Online Artificial Intelligence Executive Education certificate program and the creator of a graduate course in Data-Driven Decision Modeling. His research focuses on the intersection of robotics and reinforcement learning, and he holds a Ph.D. in Robotics from Carnegie Mellon University.
Yi Zhang teaches data science, simulation, and business analytics. His research focuses on the economic impact of digital platform adoption, and he is passionate about incorporating digital technology into active learning environments. Prior to joining Columbia, he taught Econometrics and Advanced Business Analytics at Carnegie Mellon University's Heinz College, where he received his Ph.D. in Information Systems and Management.