Enterprise AI Strategy for Tech Leaders
Scale AI initiatives to deliver measurable enterprise ROI.
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At a Glance
- Enrollment:
- Open enrollment
- Length:
- 8 weeks
- Format:
- Online
- Investment:
-
$3,200
Upcoming Dates
June Start
Students may register up to 7 days after the course start.
The University of Chicago’s eight-week Enterprise AI Strategy for Tech Leaders equips technical leaders with a practical framework to move AI initiatives beyond early experimentation and into scalable, enterprise-wide impact. As generative and agentic AI become central to products and operations, leaders are accountable for how initiatives are selected, governed, and scaled. This course focuses on translating AI capabilities into business-relevant opportunities, establishing effective governance, and enabling responsible adoption aligned with organizational priorities and long-term value creation.
Designed For
This course is designed for professionals responsible for moving AI from experimentation to scalable, organization-wide adoption. It is particularly relevant for those accountable for defining enterprise AI strategy, justifying investments with clear business value, and guiding initiatives from proof of concept to production.
Learning Objectives to Lead Enterprise AI
Organizations across industries are embedding generative and agentic AI into core products, platforms, and operations. But how should enterprise leaders determine where AI creates real value, and how initiatives can be scaled responsibly amid technical, organizational, and human constraints? This course helps leaders distinguish between disconnected pilots and enterprise-level opportunities, with a focus on selecting, governing, and measuring AI initiatives that deliver sustained business impact.
After completing the course, you will be able to:
- Assess how generative and agentic AI drive enterprise value creation and competitive advantage.
- Align major AI model types with high-impact enterprise use cases across key business functions.
- Identify and address technical, organizational, and human barriers to AI adoption, including non-determinism, overconfidence, vendor lock-in, jagged intelligence, and employee resistance.
- Prioritize initiatives and build a practical, feasible enterprise AI roadmap.
- Define meaningful success metrics and governance models for scaling AI beyond pilots.
Enterprise AI Curriculum
You will learn to:
- Identify the enterprise capabilities, operating models, and skills required to scale generative and agentic AI responsibly.
- Develop a practical, organization-wide strategy to guide AI adoption across teams, functions, and workflows.
- Evaluate the risks, trade-offs, and value creation potential of enterprise AI initiatives.
Enterprise AI Strategy for Technology Leaders online format features
- Live online, instructor-led sessions that bring you, your peers, and your instructor together to tackle real enterprise AI challenges. Recorded sessions are available for review anytime.
- Guided learning activities that let you explore concepts, practice decision-making, and reinforce understanding between sessions.
- Capstone project to demonstrate your skills by applying frameworks to a real enterprise AI case.
Weekly course schedule
Learning objectives
- The evolving enterprise AI landscape and major shifts in recent years
- How AI-driven change compares to prior technology adoption cycles
- The role of technical leaders in shaping enterprise AI direction
Learning objectives
- The modern AI ecosystem, where generative and agentic AI fit, and key model types.
- Representative AI use cases across enterprise functions.
- Connecting model capabilities to practical business problems.
Learning objectives
- Common patterns that slow or derail AI initiatives.
- Technical, organizational, and cultural sources of friction.
- Framing risks and challenges so they can be surfaced and addressed.
Learning objectives
- How AI changes roles, skills, and expectations for teams.
- Patterns of technology adoption across early adopters, the majority, and laggards.
- Leadership communication and collaboration with HR and people leaders.
Learning objectives
- Identifying promising tasks and workflows for AI support for generative and agentic AI support.
- Differentiating between task assistance, augmentation, and deeper automation.
- Assembling an initial portfolio of AI initiatives aligned with enterprise goals.
- Navigating the J-curve by understanding why metrics often decline before improving during AI adoption, and how to set expectations with stakeholders.
Learning objectives
- Principles for choosing meaningful success metrics at different stages of AI adoption.
- How measurement evolves as AI initiatives move from pilots to scaled deployment.
- Understanding why performance metrics may initially decline before improving, and how to set realistic expectations with stakeholders.
- Approaches for assessing internal initiatives and external AI offerings.
Learning objectives
- Key categories of risk introduced by enterprise AI.
- Governance structures and acceptable use guidelines for AI programs.
- High-level views of technical and process guardrails that enable responsible experimentation.
In the course wrap, participants engage with a realistic case scenario centered on a newly appointed CTO at a technology product company struggling to integrate AI into its operations. Using the knowledge, skills, and frameworks developed throughout the course, participants design a practical roadmap to guide AI adoption and execution.
Working with provided organizational context, including qualitative survey inputs and workflow analyses highlighting operational bottlenecks, participants develop a capstone deliverable that includes:
- A categorized workstream analysis identifying high-value opportunity areas.
- A focused project brief outlining the initial initiative to pursue and the rationale behind it, including opportunity cost considerations.
- A multi-stage plan to improve adoption and traction across teams.
- A prioritized backlog of additional AI opportunities beyond the initial initiative.
- A draft internal communication, such as a Slack post, announcing and framing the transition.
The course concludes with structured presentations demonstrating how AI pilots can be translated into accountable, scalable programs aligned with organizational goals.
Earn a Credential in Enterprise AI for Tech Leaders
After successful completion of this course, participants will receive credentials certified by the University of Chicago including a digital badge to recognize their achievement.
Start Your Journey
Meet Your Instructors
Our instructors are experienced industry leaders and rigorous thinkers who bring deep, current expertise in AI, technology, and enterprise practice. Drawing on real-world experience and cross-disciplinary insight, they translate complex concepts into practical frameworks that support informed decision-making and real organizational impact.
This course is taught regularly by qualified instructors. Please contact your enrollment advisor for information on the current instructor.
Arnab Bose, PhD
Associate Senior Instructional Professor, Program Director of MS in Applied Data Science Online Program; Chief Scientific Officer, UST AlphaAI
Dr. Arnab Bose is an Associate Senior Instructional Professor, Faculty Director of the online program, and Curriculum Director in the Master of Science in Applied Data Science program at the University of Chicago Data Science Institute. He has over two and a half decades of industry experience and...
James Janega
Adjunct Assistant Professor of Entrepreneurship
James Janega is Managing Partner at Growth Innovation Strategy Group (GIS). GIS helps organizations see how the future will develop around them, find wins in changing environments, and build growth paths using agile strategies and better innovations.
Janega specializes in bridging the gap between...
Career Outlook
Generative and agentic AI are increasing demand for leaders who can guide how AI initiatives are selected, governed, and scaled across the enterprise. As organizations invest heavily in AI, senior technology and AI executives are seeing strong compensation growth tied to their ability to move initiatives beyond experimentation and deliver measurable, sustained business impact. Global research highlighting AI’s contribution to economic growth reinforces the value of advanced enterprise AI leadership expertise.
Percentage increase in global economic output by 2035 due to widespread AI adoption.
The estimated average annual salary in 2031 for Chief Artificial Intelligence Officers in the United States.
Leaders with AI skills, on average, earn more than their peers globally.
Potential Job Titles for Professionals with Expertise in Enterprise AI Strategy
- Chief AI Officer
- Chief Data & AI Officer
- Chief Technology Officer
- Chief Digital Officer
- Digital Innovation Officer
- Enterprise AI Architect
- Product Manager
- AI Governance & Risk Lead
- Digital Product Officer
- Director of AI Strategy
- AI Program Manager
- Insights Analyst
- Technology Consultant
- AI Governance Consultant
How Do I Get Started?
- Complete the form on the registration page.
- Pay the tuition fee through our secure gateway.
- Receive a welcome email with your login information for the virtual campus.
- Gain access to the course content prior to the start date.
Of Interest
Artificial Intelligence and Data Science for Leaders
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