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Chief AI Officer (CAIO) Program

Chief AI Officer (CAIO) Program

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Application closes 30th Sep 2026

Why Should You Participate in This Program?

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    Global Peer Network

    Learn alongside a global cohort of senior professionals with similar mindsets through structured networking opportunities throughout the program, during the on-campus immersion, and beyond the program

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    On-Campus Immersion

    Experience a 1-week immersion at Texas McCombs, gaining firsthand exposure to the University of Texas at Austin ecosystem and learning alongside faculty and peers

KEY OUTCOMES

How Will You Lead Enterprise-Level AI?

Drive measurable enterprise AI strategy and elevate your career as a Chief AI Officer

  • Identify the right business problems that will benefit from an AI solution, before investing resources

  • Understand how AI and ML work to evaluate technical claims, vendor pitches, and capability limits

  • Model AI ROI, including the hidden costs of governance, so your investment decisions hold up under scrutiny

  • Evaluate and approve technical decisions around models, data infrastructure, and agentic systems

  • Design AI governance and data-readiness programs that embed accountability and risk management

  • Scale a validated pilot to production backed by data maturity, governance, and MLOps standards that sustain it

Rankings

  • #1 (U.S., Big Data Management) in MS Business Analytics

    #1 (U.S., Big Data Management) in MS Business Analytics

    Eduniversal (2025)

  • #6 Analytics

    #6 Analytics

    U.S. News & World Report (2025)

  • #6 Business Programs

    #6 Business Programs

    U.S. News & World Report (2025)

  • #7 in MS - Business Analytics

    #7 in MS - Business Analytics

    QS World University Rankings (2022)

  • #7 in MS Business Analytics

    #7 in MS Business Analytics

    The Financial Engineer Times (2025)

  • #3 in Information Systems Graduate Programs

    #3 in Information Systems Graduate Programs

    U.S. News & World Report (April 2025)

  • #7 Public University in the U.S.

    #7 Public University in the U.S.

    U.S. News & World Report, 2026

  • #6 in Executive Education - Custom Programs

    #6 in Executive Education - Custom Programs

    Financial Times, 2022

What Program Benefits Will You Receive?

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Join the Texas Exes global alumni network and receive exclusive privileges designed to support lifelong learning and future skill development

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Gain access to the University of Texas Libraries, premium online databases, and the Career Resource Library to support leadership development and career advancement

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Join one of the world’s most influential alumni communities. Collaborate with senior leaders, mentors, and decision-makers across industries and geographies.

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Receive invitations to events, conferences, and cultural programs, along with exclusive alumni gatherings and high-impact networking opportunities

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Gain access to exclusive webinars and curated networking events designed to connect you with industry leaders, subject matter experts, and global alumni

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Engage and network on LinkedIn with fellow Texas Executive Education learners and the global Texas Exes alumni community of leaders

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Be part of the Texas McCombs tradition of excellence and global influence, and contribute to scholarships and initiatives that shape future leaders

Program Advantage

*All benefits are subject to change as per university decision

KEY PROGRAM HIGHLIGHTS

Why Choose This CAIO Program?

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    Blended Learning Format

    Delivered through 18 live virtual sessions led by Texas McCombs faculty, complemented by a one-week on-campus immersion at The University of Texas at Austin

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    On-Campus Immersion

    Learn alongside faculty and peers during a 1-week immersion at Texas McCombs, gaining firsthand exposure to the University of Texas at Austin ecosystem

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    Texas Exes Alumni Benefits

    Join the Texas Exes alumni network, a global community of 600,000+ alumni, and gain access to exclusive privileges, resources, and networking opportunities

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    Global Peer Network

    Build meaningful connections with a global cohort of senior professionals through structured networking throughout the program and during the on-campus immersion

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    Learning Support

    Receive personalized guidance and dedicated support from a Program Manager throughout your learning journey

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    Recognized Credentials

    Earn a Certificate of Completion and Continuing Education Units (CEUs) from the McCombs School of Business at The University of Texas at Austin, upon completion of the program

Skills You Will Learn

AI Strategy & Opportunity Diagnosis

AI & Machine Learning Foundations

Financial Valuation & ROI Modeling

Enterprise AI Architecture

Agentic AI Governance & Control

Data Strategy & AI Readiness

Decision Analysis & Portfolio Management

AI Scaling & MLOps Standards

Corporate Governance & Risk Oversight

Change Leadership & Boardroom Advocacy

AI Strategy & Opportunity Diagnosis

AI & Machine Learning Foundations

Financial Valuation & ROI Modeling

Enterprise AI Architecture

Agentic AI Governance & Control

Data Strategy & AI Readiness

Decision Analysis & Portfolio Management

AI Scaling & MLOps Standards

Corporate Governance & Risk Oversight

Change Leadership & Boardroom Advocacy

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  • Overview
  • Curriculum
  • Certificate
  • Faculty
  • Mentors
  • Fees

Who is This Program Ideal For?

Senior leaders responsible for AI transformation to move AI from pilots to production sustainably and at scale

  • C-Suite and Senior Technology Leaders

    Driving and expanding their mandates to include enterprise-wide AI strategy and governance

  • Business Heads and Functional Leaders

    Across functions such as operations, marketing, and finance who influence AI adoption, funding, and scalability in their domains

  • Leaders and Consultants

    Across transformation, product, and innovation, integrating AI into operating models, and culture while building business cases

  • Board Members and Senior Executives

    Carrying fiduciary and oversight responsibility for AI risk, investment, and regulatory exposure

What Will You Learn in the Program?

The curriculum spans four modules over six months, progressing from AI strategy and literacy through financial and technical evaluation to portfolio management and governance. The program culminates in an on-campus immersion at Texas McCombs

MODULE 01: STRATEGIC DIAGNOSIS AND AI LITERACY

Week 1: Building an AI Strategy Mindset

● Define the CAIO mandate and explain how it differs from the CTO and CIO roles ● Learn to distinguish a genuine AI strategy from buzzword-driven initiatives ● Identify where AI can create a meaningful competitive advantage and where it is unlikely to deliver value ● Develop a structured approach to evaluating AI opportunities based on strategic business priorities ● Frame AI investments and technical debt as executive-level risk conversations

Week 2: Creative Thinking and Executive Problem Solving

● Understand the importance of clearly defining business problems before investing in AI solutions ● Learn techniques to identify the underlying problem amidst diverse stakeholder perspectives ● Generate high-impact AI use cases through structured brainwriting sessions ● Evaluate whether a business problem is well suited for AI or requires alternative approaches ● Use shuttle diplomacy to advance AI initiatives through resistant organizations

Week 3: AI and Machine Learning Foundations

● Develop a practical understanding of AI and machine learning concepts without requiring coding expertise ● Learn how machine learning models work and the principles behind their decision-making processes ● Understand the differences between Generative AI and traditional predictive machine learning models ● Apply reversible versus irreversible decision design to manage AI risk based on the type of AI deployed ● Build the foundational AI literacy needed to make informed strategic and business decisions

Week 4: Learning Break

Week 5: AI Technology Landscape: Waves, Disruption, and Strategic Integration

● Understand that not every AI capability requires the same level of strategic urgency ● Learn to distinguish between experimental AI capabilities and those that are becoming industry standards ● Assess where an AI capability sits on the maturity curve to inform strategic decision-making ● Critically evaluate AI vendor pitches and solution proposals using governance failure cases ● Differentiate between AI as a standalone product and AI as a capability that enhances existing products and services

Week 6: AI Capabilities, Pitfalls, Use Cases, and Business Impact

● Understand that AI capability claims are often easier to make than to verify ● Recognize the gap between AI performance in demonstrations and reliable deployment in production environments ● Develop the ability to distinguish genuine AI capabilities from marketing claims ● Learn to set realistic ROI expectations for AI initiatives based on practical business outcomes ● Build the judgment to determine when an AI initiative should continue to receive investment and when it should be discontinued

MODULE 02: FINANCIAL VALUATION AND TECHNICAL ARCHITECTURE

Week 7: Measuring AI Value: ROI, Build vs. Buy vs. Partner

● Understand how to evaluate AI investments across the full solution lifecycle, beyond initial deployment ● Learn to assess the true ROI of AI initiatives by accounting for operational, governance, and maintenance costs ● Develop a structured framework for evaluating the financial viability of AI investments ● Compare the strategic trade-offs of building AI capabilities in-house, purchasing commercial solutions, or partnering with external providers ● Build the ability to make informed AI investment decisions that align with business objectives and long-term value creation

Week 8: Learning Break

Week 9: AI Enterprise Architecture

● Understand the key infrastructure decisions that determine the scalability and reliability of AI applications ● Develop familiarity with enterprise AI architecture, including data infrastructure, model selection, and integration approaches ● Learn how architectural choices impact the performance and scalability of AI solutions in real-world environments ● Build the ability to evaluate technical proposals and assess whether they are designed to support enterprise-scale AI deployment ● Gain the technical fluency required to make informed decisions on AI architecture without needing to build the systems yourself

Week 10: Agentic AI for the Enterprise

● Understand how Agentic AI is evolving from experimental demonstrations to enterprise deployments ● Learn how AI systems that perform multiple actions introduce new risks beyond traditional chatbot applications ● Develop the ability to evaluate Agentic AI proposals by assessing key design choices and implementation considerations ● Identify potential risks associated with AI systems operating with reduced human oversight ● Understand the governance frameworks and controls required before deploying Agentic AI solutions in production environments

MODULE 03: MANAGING THE AI PORTFOLIO: FROM PROTOTYPE TO PRODUCTION SCALE

Week 11: Decision Analysis for Management

● Understand how AI initiatives involve uncertainty and require structured decision-making approaches ● Learn to differentiate between sound decisions and favorable outcomes influenced by chance ● Apply the Decision Quality framework and the data value test to structure AI investment decisions ● Learn when an AI decision should be treated as reversible and when it requires long-term commitment ● Identify and overcome sunk-cost thinking that can prevent organizations from discontinuing underperforming AI initiatives

Week 12: Entrepreneurship and Intrapreneurship: Ideas to Prototype

● Learn a lean approach to evaluating and validating AI ideas before committing significant resources ● Understand how to test AI concepts with real customers and gather meaningful insights ● Develop the ability to prioritize AI initiatives based on potential value and investment readiness ● Learn how to move from an initial concept to a working prototype without requiring a large data team ● Build an entrepreneurial mindset to identify, develop, and scale promising AI opportunities

Week 13: Learning Break

Week 14: Managing Innovation Life Cycle

● Understand that AI initiatives require different evaluation criteria based on their objectives, timelines, and potential impact ● Learn to differentiate between near-term efficiency improvements and long-term investments in new business models ● Develop the ability to manage AI initiatives across near-term, emerging, and future innovation horizons ● Learn how funding approaches and success metrics should vary across different stages of the AI innovation lifecycle ● Build the ability to manage diverse teams and mindsets required to drive different types of AI initiatives

Week 15: Data Strategy, Governance & AI Readiness

● Understand the critical role of data quality and availability in determining AI initiative success ● Learn to assess an organization's data readiness for AI adoption ● Develop the ability to identify data challenges that can impact AI performance and scalability ● Design governance frameworks, ownership models, and quality standards to support trustworthy AI initiatives ● Apply DataOps principles to build reliable, scalable data pipelines and evaluate architecture options for different AI workload requirements

Week 16: Learning Break

Week 17: Scaling AI Projects

● Understand the challenges of transitioning AI solutions from successful pilots to production-scale deployments ● Apply a scaling readiness assessment across organizational, technical, and governance dimensions ● Set fairness, robustness, and explainability thresholds for production AI systems, benchmarked against the MLOps maturity model ● Identify potential risks that can impact AI systems when deployed at scale ● Build the ability to determine production readiness and support sustainable AI deployment

Week 18: Creating AI Value: AI Planning, Adoption and Governance

● Distinguish AI adoption from AI absorption as separate, independently measurable metrics ● Design AI governance structures, including federated AI management models ● Develop shadow AI governance policies to reduce the risks associated with unsanctioned AI ● Identify the root causes of AI adoption failure at the enterprise scale ● Map AI planning requirements to organizational readiness criteria

MODULE 04: BOARDROOM GOVERNANCE AND CHANGE LEADERSHIP

Week 19: Corporate Governance in the Age of AI

● Understand the evolving fiduciary responsibilities, regulatory landscape, and liability considerations associated with AI deployment ● Learn how boards can establish effective governance frameworks for responsible AI adoption ● Develop the ability to assess accountability, risk, and oversight requirements for AI systems ● Identify the key elements required for organizations to govern AI initiatives responsibly

Week 20: Leading AI-Era Organizational Change

● Understand why organizational adoption is critical to the success of AI initiatives beyond technical implementation ● Learn to identify AI-specific barriers to adoption, including job concerns, workarounds, and resistance to change ● Develop a structured approach to leading AI-driven organizational transformation ● Design adoption strategies that enable teams to effectively embrace and use AI solutions

Week 21: Learning Break

Week 22: Advocating for AI Projects

● Understand how to build compelling business cases that secure support and investment for AI initiatives ● Learn to tailor AI proposals based on stakeholder priorities and decision-making needs ● Develop the ability to address legal, compliance, and procurement challenges that can delay AI adoption ● Build the skills required to advocate for AI initiatives and drive them through organizational decision-making processes

Week 23: Crafting and Communicating for Success in the Boardroom

● Understand the importance of effective communication, framing, and delivery in board-level AI presentations ● Learn to structure compelling AI presentations that clearly communicate strategic value and business impact ● Develop the ability to create board-ready materials that serve as a comprehensive record of key decisions ● Build confidence in addressing challenging questions and communicating AI recommendations with credibility ● Strengthen the skills required to influence executive stakeholders and drive informed decision-making

Week 24: On-Campus Immersion

Note: The curriculum listed above is indicative and subject to updates as technology evolves.

Who are the Faculty Members for the Program?

Learn from world-class Texas McCombs faculty and industry experts.

  • Dr. Kumar Muthuraman

    Dr. Kumar Muthuraman

    Faculty Director, McCombs School of Business, The University of Texas at Austin

    Faculty Director, Center for Analytics and Transformative Technologies

    21+ years' experience in AI, ML, Deep Learning, and NLP.

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  • Gaylen Paulson

    Gaylen Paulson

    Associate Dean and Director, McCombs School of Business, The University of Texas at Austin

    Senior Lecturer, Department of Management

    25+ years of experience in executive education, negotiation strategy, and organizational communication

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  • Eric Bickel

    Eric Bickel

    Professor and Director, Graduate Program in Operations Research & Industrial Engineering, The University of Texas at Austin

    His research has been covered in WSJ, NYT, Sports Illustrated and more

    His research is regarded as the top approach to address climate change

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  • Jim Nelson

    Jim Nelson

    Lecturer, Management, The University of Texas at Austin

    25 years of experience in wholesale banking, fintech, and health care

    Co-founder and former chief technology officer of Simply Business

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  • Clint Tuttle

    Clint Tuttle

    Associate Professor of Instruction, The University of Texas at Austin

    Award-winning expert in MIS, SQL programming, and data management.

    Led IT capstone projects helping hundreds of industry clients.

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  • Luis Martins

    Luis Martins

    Professor, Management, Zlotnik Family Chair in Entrepreneurship, The University of Texas at Austin

    His research has appeared in top management journals and newspapers

    His research has also received several awards

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  • Jeff Mihm

    Jeff Mihm

    Lecturer, Department of Business, Government & Society, The University of Texas at Austin

    Former CEO with nearly 30 years of experience in business and law.

    Teaches courses in strategy, international business, and ethics.

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  • Melissa Murphy

    Melissa Murphy

    Associate Professor of Instruction, Management, The University of Texas at Austin

    Expert in business communication, negotiation, and entrepreneurship.

    Founder and chief communication coach of The Pitch Academy.

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  • Dr. Prashant Joshi

    Dr. Prashant Joshi

    Department of Information, Risk and Operations Management, UT Austin

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  • John Daly

    John Daly

    Professor, Management, The University of Texas at Austin

    He has published 100+ articles and chapters in scholarly publications

    He served as editor of two academic journals

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  • Cam Houser

    Cam Houser

    Founder, Actionworks; Lecturer, Innovation & AI, Texas Executive Education

    AI pioneer who built a GPT-2 edtech product before ChatGPT existed.

    Lecturer of Innovation & AI consulting for Apple, Dell, and more.

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  • Ben Bentzin

    Ben Bentzin

    Associate Professor of Instruction, Marketing, The University of Texas at Austin

    McCombs Teaching Fellow focused on integrating AI into the classroom.

    Former marketing chief for Dell's consumer & small business division.

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  • Abhay Samant

    Abhay Samant

    Chief Software Engineer at NI, Professor of Practice, The University of Texas at Austin

    Has 24 years of R&D, product strategy, business management experience

    His research interests are in ML and cognitive cyberphysical systems

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Who Are the Mentors for Weekly Live Sessions?

Mentors may include*

  • G Anthony Reina - Mentor

    G Anthony Reina linkin icon

    Head of Machine Learning, Stealth BioTech Startup
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  • Jeremy Samuelson - Mentor

    Jeremy Samuelson

    Executive VP, AI and Innovation, Integrated Quantum Technologies
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  • Sudhir Shandilya - Mentor

    Sudhir Shandilya linkin icon

    Director, Digital Strategy, Transformation, and Operations Sanofi
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  • Michael Lively - Mentor

    Michael Lively linkin icon

    Founder, QuantumAI
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  • Arup Das - Mentor

    Arup Das linkin icon

    Senior Director Innovation - AI-Data Products-Analytics, Faegre Drinker
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Note: The mentors listed above are indicative and subject to change based on availability and scheduling.

Earn Your Certificate Of Completion

Get the Chief AI Officer Program certificate from from the McCombs School of Business at The University of Texas at Austin upon successful completion of the program

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* Image for illustration only. Certificate subject to change.

Program Fee

The program fee is USD 19,000

Invest in your career

  • benifits-icon

    Identify the right business problems that will benefit from an AI solution, before investing resources

  • benifits-icon

    Understand how AI and ML work to evaluate technical claims, vendor pitches, and capability limits

  • benifits-icon

    Model AI ROI, including the hidden costs of governance, so your investment decisions hold up under scrutiny

  • benifits-icon

    Evaluate and approve technical decisions around models, data infrastructure, and agentic systems

Take the Next Step

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Apply to the program now!

Drive enterprise AI strategy

Application closes: 30th Sep 2026

Apply Now

Application closes: 30th Sep 2026

Speak with our Program Advisor for details

Schedule a call

Admission Process

The admission process is conducted on a rolling basis and will close once the requisite number of participants has been enrolled

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    APPLY

    Fill out an online application form

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    REVIEW

    A panel from Great Learning will review your application to evaluate your fit for the program

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    JOIN THE PROGRAM

    Following a final review, you will receive an offer of admission to join the upcoming program cohort

Limited seats available

Apply today and become an AI leader

Apply Now

Batch Start Date

  • Hybrid · January 2027

    Admission closing soon

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Delivered in Collaboration With:

The McCombs School of Business at The University of Texas at Austin is collaborating with Great Learning to deliver the Chief AI Officer Program. Great Learning is an ed-tech company that has empowered learners from over 170 countries to achieve positive career growth outcomes.

Chief AI Officer (CAIO) Program

Download Brochure
Apply Now

phone icon Application closes 30th Sep 2026

Got more questions? Talk to us

Connect with our advisors and get your queries resolved

Speak with our expert +17372719092 or email to caio.utaustin@mygreatlearning.com

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