Leading AI Strategy: From Hype to Enterprise Reality
Deploy AI With Clear Strategic Intent
Lead enterprise AI from strategy to execution
The conversation around AI is often shaped by assumptions that do not hold up in real-world applications. This course brings clarity, offering a grounded understanding of what AI is, what it is not, and where its true capabilities and limitations lie in business contexts.
In this 8-week live online course, you will learn to evaluate AI as a practical, operational resource. You will assess what it can reliably deliver, what it costs to implement, and where it is most likely to fail. Through real-world case studies, in-depth modules, and a hands-on capstone project, the course builds a disciplined, evidence-based approach to integrating AI into strategy, decision-making, and organizational design.
Course takeaways
Differentiate between AI hype and strategic reality in business contexts
Analyze the technological and economic foundations shaping modern AI strategy
Evaluate AI as a source of competitive advantage within changing industry environments
Assess organizational approaches to AI integration
Design strategic approaches for aligning AI with organizational priorities
Develop a strategic framework for redesigning organizational structures through AI integration
Course modules
Differentiate between explicit and implicit knowledge
Explain the concept of artificial general intelligence (AGI) as it is commonly presented
Analyze the claim that AGI represents a near-term business reality
Analyze the historical and future cycles of AI development and failure
Apply the historical cycles of digital development and failure to organizational strategy
Distinguish deterministic programming from AI-based systems
Identify how neural networks enable AI systems to learn from data
Describe foundational mechanisms that enable modern AI systems to function
Evaluate the capabilities and limitations of AI systems
Compare the capabilities and limitations of AI systems
Analyze how AI systems function and where they may succeed or fail
Evaluate real-world examples of AI implementation for patterns of success and failure
Differentiate between tasks that are well suited for AI augmentation vs. those requiring human judgment
Differentiate between advisory, auditing, and agentic roles of AI in workplace contexts
Assess the hierarchy of risk associated with AI applications based on stakes and potential consequences
Determine appropriate configurations of human and AI roles within a given task or workflow
Identify the three types of economic value created by AI adoption
Identify how AI adoption, usage, and cost drivers contribute to the economic value of AI within an organization
Evaluate how AI adoption, usage, and cost drivers influence the economic value of AI within an organization
Explain how organizations move from individual AI use to organizational AI implementation
Evaluate the costs, benefits, and risks of using AI in an organizational workflow
Describe how human–AI hybrid systems reshape organizational roles and reporting structures
Differentiate between human-owned tasks and AI-executable tasks within a hybrid workflow
Explain the concept of task–risk alignment in assigning oversight within hybrid systems
Assign a task-risk human oversight “framework” to AI-driven tasks
Analyze how an AI-native organization structures roles, risk tolerance, and operations
Compare the structural constraints of AI-native organizations and legacy enterprises
Evaluate how risk tolerance influences organizational design decisions in hybrid systems
Differentiate between Layer 1 (transactional) and Layer 2 (relational) competitive advantages in the context of incumbents and startups
Evaluate which organizational tasks and processes can be transactionified versus those that require relational or contextual judgment
Assess AI use cases using a risk–impact framework to determine appropriate implementation pathways
Differentiate between corporate, ethical, and legal risks associated with AI implementation
Assess organizational exposure to risk across operational, reputational, legal, and environmental dimensions
Analyze how evolving regulatory frameworks and legal systems affect AI deployment
Explain how AI-driven societal impacts influence organizational decision-making
Identify credible sources for monitoring evolving AI regulatory, legal, and societal developments
Define an appropriate organizational response to AI-related risk under conditions of uncertainty
Analyze influences and biases in AI future-facing information
Evaluate the feasibility of a proposed AI or robotics development
Determine managerial actions required to operate in an evolving AI environment
Sequence AI initiatives from incremental adoption to organizational redesign
Industry examples
Note: All product and company names are trademarks or registered trademarks of their respective holders. Their inclusion is for educational illustration only and does not imply affiliation or endorsement.
What will you learn
Foundational AI understanding: Understand what AI is, how it works at a high level, and where its limits lie
Capability and constraint evaluation: Assess what AI systems can and cannot do in real-world contexts
Operational assessment of AI: Evaluate how AI systems perform in real operating conditions
Risk awareness: Identify failure points, risks, and implications of overreliance on AI systems
Economic understanding of AI: Interpret cost structures, value creation, and AI investment dynamics
AI integration into organizations: Understand how AI is incorporated into business systems, workflows, and roles
Capstone project
Develop a business strategy for implementing AI within a specific department of your organization through a structured, module-by-module approach.
Participants will work on a single job role or function and progressively build an AI-enabled strategy. Each module includes a required capstone checkpoint, enabling learners to apply concepts directly to their organizational context and build toward a final, integrated strategy.
Why enroll in this course?
Engage in weekly live sessions with an MIT instructor designed to build a clear and structured understanding of AI in business contexts.
Examine real-world case studies to understand how AI performs in practice, including its capabilities, limitations, and strategic implications.
Work on a capstone project to apply course concepts to real organizational challenges and strategic decisions.
Establish professional connections with industry practitioners and a diverse cohort of peers through interactive learning.
Join a global network of professionals engaged in evaluating and applying AI to business strategy and organizational transformation.
Get access to rich supplementary resources, including frameworks and additional materials, to support a more comprehensive learning experience.
This course is an excellent fit for
Professionals responsible for making real decisions about AI, where cost, risk, accountability, and organizational impact matter.
Mid- to senior-level leaders and functional heads seeking to understand how AI influences corporate strategy, competitive dynamics, and enterprise design.
C-suite and business unit leaders navigating AI-driven disruption and building long-term strategic advantage across platforms, ecosystems, and intelligent systems.
Innovation, strategy, and transformation leaders aiming to move beyond pilots and drive enterprise-wide adoption of AI initiatives.
Senior consultants and advisors supporting organizations on AI strategy, operating model redesign, governance, and responsible transformation.
Instructor
Fred Fort Flowers (1941) & Daniel Fort Flowers (1941) Professor in Mechanical Engineering
Sanjay Sarma is the Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor of Mechanical Engineering at MIT. He was the President and CEO of the Asia School of Busi...
Certificate
All participants who successfully complete Leading AI Strategy: From Hype to Enterprise Reality will receive a MIT Professional Education Certificate of Completion and Continuing Education Units (CEUs).
In order to obtain CEUs, participants must complete a required CEU accreditation form. CEUs are calculated based on the number of learning hours in each course.
MIT Professional Education in Numbers
+60K
+155
92%
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