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ONLINE COURSE

Applied Agentic AI for Organizational Transformation

Leverage AI Agents to Elevate Efficiency and Innovate Models

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Empowering leaders to navigate the impact of Generative and Agentic AI

Digital transformation isn’t just digitizing workflows — it’s about reimagining operations, markets, and leadership with AI at the center. In this executive-level program, you’ll go beyond high‑level theory to acquire practical frameworks, tools, and governance strategies tailored for Generative and Agentic AI. Through hands-on exercises, real-world case studies, expert instruction, and a capstone project, you’ll learn to build AI‑powered systems responsibly and strategically — from prompt to impact. 

What you will learn?

  • Understand the foundations of Generative and Agentic AI - how they work, how they differ, and how they apply to real organizational functions.

  • Design AI-integrated strategies using cloud infrastructure, APIs, and enterprise platforms to deliver real-world impact.

  • Build practical skills through hands-on mini-projects, including text, image, audio, coding and video with no prior coding knowledge required.

  • Address the risks and responsibilities of AI, from deepfakes and misinformation to legal compliance and ethical governance.

Why enroll in this course?

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Engage in two live sessions with MIT instructors, and up to eight live sessions with learning facilitators, industry experts, and peers.

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Networking opportunities establish professional connections with industry experts and your cohort.

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Access to rich supplementary resources provides additional materials and content for a more thorough educational journey.

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Navigate AI’s ethical and regulatory landscape with confidence, from bias and data privacy to frameworks like GDPR, CCPA, and HIPAA. 

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Join a global network of peers and professionals engaged in reimagining how AI drives impact at scale. 

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Bridge the gap between AI and business outcomes, without needing a technical background. 

Certificate

Certificate

All the participants who successfully complete their program will receive an MIT Professional Education Certificate of Completion, as well as Continuing Education Units (CEUs)*.

To obtain CEUs, complete the accreditation confirmation, which is available at the end of the course. CEUs are calculated for each course based on the number of learning hours.

*The Continuing Education Unit (CEU) is defined as 10 contact hours of ongoing learning to indicate the amount of time they have devoted to a non-credit/non-degree professional development program.

To understand whether or not these CEUs may be applied toward professional certification, licensing requirements, or other required training or continuing education hours, please consult your training department or licensing authority directly.

Modules

  • Generative AI Fundamentals

  • AI Chatbots: Past, Present, and Future

  • Cost-Optimized Models and Performance Trade-Offs

  • Exploring Multimedia and Language Interaction Models

  • Advanced Applications of Generative AI Tools

  • Emerging Agentic Platforms 

  • Vibe Living 

  • Single vs. Multi-Agent Architectures 

  • Open-Source vs. Closed-Source AI Systems 

  • Building Agents into Existing Workflows  

  • Integrating Generative and Agentic AI with Existing Systems: Challenges and Solutions 

  • Spotify MCP and Other Edge-cases of Agent Integration 

  • Empathy and Response-Tuning for Customer-Facing Agents 

  • Classic and Current Cybersecurity Risks   

  • Cybersecurity for Agent Ecosystems 

  • Limitations of Agent Perception and Error Correction 

  • The Maturity Cycle of Agentic Implementation (Crawl-walk-run) 

  • Advantages Of Agentic AI for the Product Development Cycle (E.G. Accelerated Prototyping, Automation of Quality Assurance Pipelines, Reducing Product Launch Timelines) 

  • Functional Deployments: HR Bots, Finance Advisors and IT Copilots 

  • Agent Architecture in the Enterprise: Centralized Vs. Embedded 

  • Voice Agents: Synthesis, Phone Systems, and Real-time Applications 

  • Last-mile Integration: Why Pilots Succeed but Deployments Stall 

  • Internal Resistance and Change Management 

  • Monitoring Agent Performance (Metrics, KPIs, Feedback Loops) 

  • Regulation Overview: GDPR, CCPA, HIPAA, and Emerging Agent Rules 

  • Testing Agent Behavior: Sandboxing, A/B Testing, Safety Checks 

  • Agent Speed vs. Oversight: Where to Insert Guardrails 

  • Documentation and Compliance Readiness 

  • Strategic roadmap development (short-, medium-, long-term) 

  • Team structure, vendor choice, and internal capability building 

  • Organizational culture and leadership for agent adoption 

  • Summary of technical enablers and business opportunities 

  • Maturity assessment of agent strategy – change management 

*Modules and curriculum are subject to change 

Capstone Project

Apply your learning in a final capstone project designed to demonstrate real-world impact. Choose between two strategic paths:

  • Design a comprehensive AI integration plan tailored to a specific business function, or

  • Develop an executive presentation that outlines a transformative, agent-based AI initiative.

Support your project with a detailed risk-benefit analysis, cost implications, and measurable KPIs to demonstrate strategic value.

Who is the course for?

This program is designed for mid-to-senior working professionals who understand the urgency of AI and want to lead its adoption effectively across their organizations. Whether you're shaping digital strategy, managing transformation initiatives, or advising others on innovation, this course gives you the frameworks to turn AI into actionable outcomes.  This program is ideal for: 

  • C-suite executives (CEOs, CIOs, CTOs, CMOs, COOs) aiming to make informed decisions about AI strategy and integration 

  • Business leaders and function heads driving digital innovation in operations, marketing, product, or strategy 

  • Managers and team leads modernizing workflows and aligning cross-functional teams with emerging technologies 

  • Technical professionals moving into leadership roles in digital transformation or innovation 

  • Consultants and advisors supporting clients through AI-driven change and adoption planning 

Prerequisites: No prior background in analytics, computer science, coding or machine learning is required.

Instructors

MPE - Faculty - Abel Sanchez
Dr. Abel Sanchez

Executive Director, MIT Geospatial Data Center; Research Scientist, Center for Complex Engineering Systems, Sociotechnical Systems Research Center, under the Schwarzman College of Computing 

MPE - Faculty - John R. Williams
Prof. John R. Williams

Professor of Information Engineering, Civil and Environmental Engineering; Director, MIT Geospatial Data Center; faculty member, Center for Computational Science and Engineering, Schwarzman College of Computing 

MIT Professional Education in Numbers

+60K

Participants in our courses

+155

Countries represented by our participants

92%

Rate the experience as extraordinary

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