START
END
DURATION
16 weeks
16 weeks
HOURS OF EFFORT
8-10 Hours of Effort per Week
8-10 Hours of Effort per Week
LANGUAGE
English
English
FORMAT
Online
Online
PRICE
AI Leadership and the Future of Organizational Transformation
Agentic AI is creating new possibilities for workflow automation, operational efficiency, and business innovation. Realizing that potential requires leaders who can evaluate where AI creates value, understand its implementation requirements, and build the organizational capabilities to support adoption.
The AI and Leadership Bundle – Applied Agentic AI and AI-Driven Leadership from MIT Professional Education offers two individual, complementary online courses, each eight weeks, at a bundled price. Together, the courses help participants progress from identifying a promising AI opportunity to planning its responsible implementation, with business goals, people, processes, technology, and risk considered throughout.
Applied Agentic AI for Organizational Transformation develops practical capabilities to evaluate AI use cases and platforms, design agent-based workflows, and connect AI systems with existing digital ecosystems. Participants assess cost, cybersecurity, governance, and ethical considerations. The course culminates in an actionable AI adoption plan or executive presentation.
AI-Driven Leadership equips participants to assess how AI and other emerging technologies are reshaping business models, customer expectations, and the workforce. Through practical frameworks and real-world examples, participants examine how to strengthen organizational readiness, address adoption barriers, and lead change across culture, capabilities, and business operations.
82%
of respondents say their companies need digitally savvy leaders.
Source: MIT Sloan Management Review Survey
76%
of executives agree that organizations need to drastically redesign the experiences that unite technology and people in more human-centered ways.
Source: Accenture
87%
of CEOs agree that the benefits of AI to their business outweigh its risks.
Source: Gartner
Courses You Will Take as a Bundle
Course 1: Applied Agentic AI for Organizational Transformation | Online • 8 Weeks
Module 1: Foundations of Generative and Agentic AI
- Evaluate the strategic value of AI functionalities such as chatbots, reasoning, and multimedia
- Construct an evaluation of the cost of an AI system
- Distinguish between major AI model types and terminology
Module 2: The Rise of Agentic AI and Emerging AI Platforms
- Explain the most relevant AI platform or approach for a specific sector and its application to agentic AI use cases
- Evaluate the key factors influencing the selection of open-source versus proprietary AI platforms within a specific organizational context
- Develop a landing page using AI
- Prompt AI to create a visual mock-up and functional HTML code
- Activate the code by saving and reuploading
- Explain a new AI workflow in an organization
Module 3: Connecting Agents to Digital Ecosystems
- Construct a use case demonstrating agent-based interaction across integrated tools
- Write a structured email-style proposal that outlines a specific use case for an AI agent within an organizational context
- Analyze a business workflow to determine how an AI agent could improve efficiency, reduce costs, or enhance user experience
- Design an integration approach that specifies how the proposed agent would connect with existing systems, platforms, or application programming interfaces (APIs)
- Evaluate the potential risks, ethical considerations, and success metrics associated with deploying the proposed AI agent
Module 4: Cybersecurity: Classic Scenarios, Agent Risks, Disinformation, and Systemic Impact
- Analyze organizational AI systems and workflows to identify potential cybersecurity risks using the National Institute of Standards and Technology (NIST) Cybersecurity Framework categories
- Evaluate current security practices to identify gaps in access control, monitoring, response, and recovery capabilities
- Develop a structured AI risk and security plan, including stakeholders, training, and incident response procedures
- Recommend actions to improve organizational readiness across identify, protect, detect, respond, and recover domains
- Evaluate how accountability is defined and enforced alongside security practices and governance in AI systems
Module 5: AI Agents by Business Function
- Describe the organizational context, including industry, organization type, and department, relevant to a proposed AI-driven product design initiative
- Summarize the current product design workflow within an organization to establish a baseline for improvement
- Select an appropriate AI technology for integration into a product design process based on its capabilities and relevance
- Develop a structured plan outlining how AI can be integrated into a product design workflow to improve efficiency, effectiveness, or quality
- Identify an appropriate AI agent architecture for a given organizational context and explain key trade-offs
- Identify opportunities for AI-enabled BPO and describe their potential organizational impact
Module 6: The Last Mile – From Pilot to Practice
- Propose measurable key performance indicators (KPIs) that evaluate the effectiveness of an AI system in relation to business outcomes.
- Describe the organizational context including sector, organization type, and department relevant to an AI implementation
- Summarize the purpose and functionality of a proposed AI system within a business workflow
- Write three to five key performance indicators (KPIs) that measure the effectiveness of an AI implementation
- Evaluate how the selected KPIs align with business goals, and indicate whether the AI system is achieving its intended outcomes
Module 7: Governance, Compliance, and Agent Testing
- Identify applicable regulatory frameworks (e.g., GDPR, CCPA, HIPAA) relevant to a specific AI use case
- Analyze the risks associated with deploying AI systems, including both compliance and operational risks
- Apply appropriate testing strategies (e.g., sandboxing, A/B testing, safety checks) to evaluate AI system behavior
- Develop a comprehensive AI governance plan that integrates regulations, testing, risk mitigation, and documentation practices
- Create guiding questions that identify key regulatory and implementation considerations in real-world AI healthcare scenarios
- Classify AI use cases using the risk–speed quadrant framework
Module 8: Ethics and Capstone
- Explain how AI can be strategically integrated into organizational functions to create business value
- Evaluate the suitability of AI technologies for specific organizational use cases
- Analyze the cost, security, and operational implications of AI adoption
- Assess the human and organizational factors that influence successful AI implementation
- Synthesize course concepts into a structured approach for organizational AI adoption
- Evaluate ethical risks in a proposed AI system by identifying a potential issue, assessing its business impact, and recommending an appropriate mitigation strategy
Capstone Project
The course culminates in a capstone project where participants develop a structured AI adoption plan or executive presentation for their organization. They apply course concepts to identify an AI opportunity, evaluate its suitability and implementation considerations, and recommend an approach to responsible adoption. Hands-on assessments and expert guidance from MIT instructors support the project.
Course 2: AI-Driven Leadership | Online • 8 Weeks
Module 1: The New Era of Digital Transformation
- Identify the key capabilities necessary for thriving in a digitally powered business environment
- Analyze the role of technology in driving organizational transformation across various industries
- Explore examples of successful digital transformation in sectors like healthcare, finance, and manufacturing
Module 2: Making Sense of Artificial Intelligence
- Understand the distinctions between traditional AI, machine learning, deep learning, and generative AI, and how each can contribute to business transformation.
- Apply the “Gen AI Cost Equation” to identify business scenarios where the use of generative AI can drive productivity.
- Gain insight into emerging AI trends and how they can benefit businesses
- Module 3: Thriving in Webs of Connectivity: The Internet of Things and Digital Twins
- Understand the principles of IoT and digital threads in creating interconnected business environments
- Evaluate how real-time data can be used to diagnose issues and personalize solutions
- Examine case studies on the implementation of connected systems in different industries
Module 4: Living and Working With Non-Human Collaborators
- Assess the current capabilities of robots and autonomous systems in both hardware and software forms
- Analyze the implications of human-machine collaboration for the future of work
- Explore case studies of robotic systems transforming industries like logistics and manufacturing
Module 5: Driving Transformational Change
- Examine the organizational culture, skills, and systems required for effective digital transformation
- Identify barriers to adopting digital innovations and ways to overcome them
- Design strategies for embedding technology into business operations for long-term value creation
Module 6: Building the Capability to Thrive
- Analyze changing customer behaviors and preferences in the digital age
- Explore the role of personalized experiences and responsive services in driving customer engagement
- Investigate innovative business models that cater to evolving consumer demands
Module 7: Peeking over the Technology Horizon
- Explore emerging technologies such as quantum computing, blockchain, and smart sensors
- Evaluate the potential for these technologies to drive future business innovation
- Develop a basic understanding of the timeline for the broader adoption of these emerging tools
Module 8: Co-creating the Future
- Assess the long-term societal impacts of digital innovations and transformations
- Discuss the challenges and opportunities presented by new technologies for organizational growth
- Develop a roadmap for implementing a transformative digital innovation within your organization
*Curriculum and course modules are subject to change.
The Skills You Will Develop
The AI and Leadership Bundle – Applied Agentic AI and AI-Driven Leadership from MIT Professional Education offers two individual, complementary online courses, each eight weeks, at a bundled price. Together, the courses help participants progress from identifying a promising AI opportunity to planning its responsible implementation, with business goals, people, processes, technology, and risk considered throughout.
1.
Analyze the impact of emerging digital technologies like AI, robotics, and IoT on business models and organizational change.
2.
Develop strategies for integrating digital transformation into various industries to improve operational efficiency and customer engagement.
3.
Discover how future technological innovations may affect business, workforce, and society, and create the frameworks for sustainable adoption.
4.
Assess how real-time data can be applied to identify problems and tailor solutions.
5.
Explore changing customer behaviors and preferences in the digital age.
6.
Examine the long-term societal impacts of digital innovations and transformations.
7.
Understand the fundamentals of generative and agentic AI, including their architectures, use cases, and future potential.
8.
Assess where and how AI can improve business outcomes, from operational workflows to customer engagement, while evaluating the viability and cost-effectiveness of AI adoption.
9.
Integrate AI systems into existing digital ecosystems, leveraging cloud infrastructure, APIs, and enterprise platforms.
10.
Evaluate AI platforms for strategic fit, capabilities, business value, and risk.
11.
Develop agent-based solutions to orchestrate multistep workflows and enhance automation.
12.
Navigate governance and compliance challenges, including ethical deployment and regulatory frameworks like GDPR, HIPAA, and CCPA.
13.
Build a strategic AI roadmap, culminating in an actionable adoption plan or executive pitch, supported by change management considerations for successful implementation.
Receive Two Certificates of Completion – One for Each Course Completed
Participants who successfully complete both courses in the AI and Leadership Bundle – Applied Agentic AI and AI-Driven Leadership will receive two MIT Professional Education Certificates of Completion, one for AI-Driven Leadership and one for Applied Agentic AI for Organizational Transformation. Participants will also earn MIT Continuing Education Units (MIT CEUs) for each completed course. *
To obtain MIT CEUs, participants must complete the accreditation confirmation available at the end of each course. MIT CEUs are awarded based on the number of learning hours, where one MIT CEU represents 10 contact hours of participation in a professional development program.
*The MIT Continuing Education Unit (MIT CEU) is defined as 10 contact hours of ongoing learning to indicate the amount of time participants have devoted to a non-credit/non-degree professional development program.
To understand whether these MIT 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.
These Courses Are Directed Toward
- C-suite executives, business executives, and senior leaders responsible for strategic planning, decision-making, and assessing how AI, IoT, and robotics affect their organizations and industries.
- Business leaders and functional heads driving digital innovation across operations, strategy, marketing, and product.
- Managers and team leads modernizing workflows and aligning teams with technology change.
- Technology leaders, IT professionals, and technical professionals connecting emerging technologies with business objectives and bridging engineering and transformation leadership.
- Change management and transformation leaders developing adoption strategies and cultivating a digitally ready organizational culture.
- Entrepreneurs and innovators applying advanced technologies to improve products and services, explore market opportunities, and respond to emerging trends.
- Consultants and advisors guiding clients through digital transformation, technology adoption, and organizational change.
- Mid- to senior-level professionals across industries, including healthcare, finance, manufacturing, mining, and other sectors undergoing significant digital disruption.
*No prior background in analytics, computer science, coding, or machine learning is required.
Meet the Course Instructors
Dr. Anthony is Director of MIT’s Master of Engineering in Manufacturing Program, Co-Director of the Medical Electronic Device Realization Center, and Associate Director, MIT.nano. With over 20 years experience in product realization—Dr. Anthony won an Emmy (from the Academy of Television Arts and Sciences) in broadcast technical innovation—Dr. Anthony designs instruments and techniques to monitor and control physical systems. His work involves systems analysis and design and calling upon mechanical, electrical, and optical engineering, along with computer science and optimization, to create solutions.
The focus of Dr. Anthony’s research is in computational instrumentation—the design of instruments and techniques to measure and control complex physical systems. His research includes the development of instrumentation and measurement solutions for manufacturing systems and medical diagnostics and imaging systems.
In addition to his academic work, he has extensive experience in market-driven technology innovation, product realization, and business entrepreneurship and commercialization at the intersection between information technology and advanced manufacturing. His teaching interests include the modeling of large-scale systems in a wide variety of decision-making domains and the development of optimization algorithms and software for analyzing and designing such systems. He has extensive experience in market driven technology innovation as well as business entrepreneurship.
His teaching, research, and advisory interests center on the practical application of Predictive and Generative AI techniques to problems and opportunities in industry and in the creation of intelligent products and services.
Prior to joining MIT Sloan, Rama was a tech entrepreneur and executive for over 20 years. He has founded or has been a senior executive in four software companies that have exited to technology titans: Oracle, Salesforce, and Demandware. He is active in the startup ecosystem as an advisor, angel investor, and board member.
Most recently, Rama was senior vice president at Salesforce (NYSE: CRM) and chief data scientist for Salesforce Commerce Cloud. In this role, he led Salesforce Einstein for Commerce—the analytics/machine-learning platform that powers Salesforce Commerce Cloud—and was responsible for product management, engineering, data science, and cloud production operations. The Einstein platform uses analytics techniques to predict and influence the shopping behavior of hundreds of millions of unique shoppers monthly.
The path that led Rama to Salesforce started in July 2010 when he founded a startup, CQuotient, to build a data-science-based personalization platform for retail and e-commerce. Backed by funding from Bain Capital Ventures, Rama built and grew the company to a successful exit to Demandware (NYSE: DWRE) in October 2014. As a member of the Demandware executive team, Rama was involved in the successful sale of Demandware to Salesforce in July 2016 for $2.8 billion. CQuotient technology, now known as Salesforce Einstein for Commerce, is one of the top B2C recommendation engines in the world and influences the shopping behavior of billions of consumers annually.
Prior to founding CQuotient, Rama taught analytics at MIT Sloan, was chief scientist and vp of R&D at ProfitLogic, was chief analytics officer and vp of R&D for the retail business of Oracle, founded two analytics companies, and was a consultant at McKinsey & Company.
Rama has a BTech degree from the Indian Institute of Technology, Chennai and MS and PhD degrees from MIT.
Dr. George Westerman is a world-recognized thought leader in leading transformation and competitive advantage through technological innovation. He works at the intersection of executive leadership and digital innovation to provide continuous valuable insights to leaders in all industries.
As a Senior Lecturer at the MIT Sloan School of Management, he researches digital transformation and innovation culture. As a pioneering researcher on digital transformation, George regularly conducts keynote presentations, senior executive workshops, and Board presentations worldwide. George is the author of three award-winning books, including Leading Digital: Turning Technology into Business Transformation. He has also written numerous contributions for publications such as Sloan Management Review, Harvard Business Review, and academic journals.
As a principal research scientist for workforce learning in MIT’s Office of Learning, George conducts transformative research aimed at improving job readiness and career advancement for workers around the world. His research on transforming corporate learning and development processes, bridging the gap from educators to employers, and improving career development has received widespread attention from leaders around the world. He is also the founder of the Global Opportunity Forum, which is building a community of like-minded academic and corporate leaders to co-create the future of workforce learning.
“Using research-backed frameworks and real-world examples, this course will help you build organizational capabilities for ongoing transformation and leverage business opportunities in advanced technologies.”
George is a member of the Digital Strategy Roundtable for the US Library of Congress, Co-Chairman of the MIT Sloan CIO Leadership Awards, and Faculty Director for several MIT executive courses.
- Pioneered some of the earliest research on digital transformation, including an award-winning book and numerous papers on the topic.
- Prior to returning to academia, he gained more than a dozen years of experience in product development and technology leadership roles.
- He has engaged closely with senior leadership teams in dozens of large companies to improve digital transformation and workforce learning.
Dr. Sanchez is the architect of the global network “The Internet of Things” and data analysis platforms for SAP, Ford, Johnson & Johnson, Accenture, Shell, Exxon Mobil and Altria. In cyber security, he has developed cyber-attack impact analyses for the U.S. Department of Defense and a password firewall for IARPA. He holds a PhD from MIT and teaches cyber security, engineering, blockchain and data science. He has contributed to educational software for Microsoft and the Accenture Technology Academy. He has produced over 150 educational videos and has 10 years of experience with LMS across the Americas, Asia and Europe.
John R. Williams is a globally recognized leader in AI-driven systems, high-performance computing, and energy infrastructure, with a career at the intersection of advanced computation and the energy sector. His work has consistently focused on enabling large-scale, mission-critical energy systems—from subsurface modeling to modern AI-enabled infrastructure.
He is co-inventor of the Discrete Element Method (DEM), a foundational simulation capability used extensively across oil & gas, mining, and energy-intensive industries to model granular and discontinuous materials. DEM remains a core technology for understanding subsurface behavior, drilling dynamics, and material flows at industrial scale.
Prior to academia, Dr. Williams served as Vice President of Applied Mechanics, where he led consulting engagements for the oil and gas sector, applying advanced numerical methods and simulation to real-world energy challenges. This industry grounding has shaped a career in which all research is explicitly aligned with energy systems, infrastructure resilience, and large-scale industrial operations.
His work now directly addresses one of the most critical constraints in the U.S. economy:
the need to rapidly scale energy supply to support AI-driven data center expansion and gigawatt-class infrastructure.
Dr. Williams’ expertise spans AI, agentic systems, and high-performance distributed computing, with a focus on:
- Energy-aware computation and optimization of data center workloads
- Grid-integrated AI systems for real-time control and resilience
- Latency- and fault-tolerant architectures for energy-critical operations
His innovation in fault-tolerant computing via container-level migration—enabling seamless recovery without code modification—received the 2019 IEEE High Performance Extreme Computing Award for Best Innovation, with direct relevance to high-availability energy and compute infrastructure.
At Massachusetts Institute of Technology, he has led programs that connect engineering, computation, and large-scale systems deployment:
- Co-Director, Systems Design and Management (SDM) Program — training leaders to architect complex energy and infrastructure systems
- Director, Auto-ID Laboratory (2006–2012), where foundational work on the Internet of Things (IoT) enabled large-scale sensing and control—now critical for modern energy systems
He has authored 250+ publications, multiple books, and contributed to national strategy, including the UK government’s Future of Manufacturing Foresight Report.
Alongside Bill Gates and Larry Ellison, he was named one of the 50 most powerful people in computer networks, reflecting the global impact of his work on distributed and infrastructure-scale systems.
He delivered the introductory two-day executive workshop on “Energy and AI” at CERAWeek 2026, engaging senior leaders on how AI is reshaping energy production, grid operations, and data center demand.
Energy-Centric Industry Impact
Dr. Williams has advised leading global energy companies and institutions, including:
- Shell, ExxonMobil, Total, ARAMCO, Schlumberger
- National laboratories and defense agencies (Sandia, Lincoln Laboratory, IARPA)
His work focuses on deploying computation and AI into operational energy systems, including:
- Subsurface and materials modeling for extraction and production
- Smart grid and energy market systems (e.g., SAP smart grid architectures)
- Energy-intensive supply chain and industrial system simulation
- Cybersecurity and resilience for critical infrastructure
Strategic Relevance: Energy × AI × Infrastructure
Dr. Williams’ current work is directly aligned with the structural shift underway:
AI is no longer just a compute problem—it is an energy systems problem.
He is focused on enabling:
- Co-design of data centers and power systems (compute + generation + grid integration)
- AI-driven optimization of gigawatt-scale energy assets
- Agentic AI for autonomous energy system operation and control
- Resilient architectures for always-on infrastructure at national scale
Current Focus: Agentic AI for Energy Systems
His work positions Agentic AI as core infrastructure for the energy transition:
- Multi-agent systems for real-time grid and energy asset management
- Integration of AI into physical energy systems and industrial operations
- Design of principled, scalable AI architectures for high-consequence environments
He teaches at MIT:
- Architecting Software Systems
- Engineering Computation and Data Science
and delivers executive programs in Applied Generative AI and Agentic Computing, with a strong emphasis on energy and infrastructure applications.
Industry Positioning
- Energy-first systems architect: Career-long focus on oil & gas, energy systems, and infrastructure
- AI × Energy convergence leader: Deep expertise in linking compute demand with power generation and grid systems
- Builder of mission-critical systems: Proven delivery in high-reliability, high-scale environments
- Trusted advisor: Global energy companies, national labs, and government agencies
