AI Research in Action
Faculty shape how AI is changing business and society
At the UC Davis Graduate School of Management, AI research is not siloed from business practice.
Faculty are using advanced analytics, machine learning and decision science to tackle urgent questions in healthcare, marketing, hiring, public policy and management education.
That matters for students. They learn from faculty who are not simply commenting on AI from the sidelines, but actively producing new insights about how intelligent systems influence markets, organizations and society.
Applied, Interdisciplinary and Timely
This is research with immediate relevance: how AI can support better healthcare decisions, how algorithms shape incentives and consumer behavior, how hiring tools are changing labor markets and how educators can use custom-built AI tools to improve teaching and learning. Faculty are also exploring and advocating for AI regulation and how policy is impacting business.
A key advantage of UC Davis is that you'll be immersed in this broader, interdisciplinary university ecosystem on the cutting edge of AI discovery. You can gain access to ideas shaped not only by management scholarship, but by collaboration across healthcare, engineering, data science and public policy.
Scholarship that Informs Leadership
You want more than exposure to the latest tools. At UC Davis, you'll be learning in a place that understands the stakes. Our faculty brings both intellectual depth and real-world urgency to the AI conversation.
Hear directly from our faculty about their latest research
Incentive Architecture in Marketing
Read blog: Insights into Incentive Architecture in Marketing
Marketing Professor Olivier Rubel explores how technologies like AI are reshaping relationships and incentive architecture:
- How do firms create value when they must delegate decisions to other organizations whose interests may not perfectly align with their own?
- Who will be accountable when agentic AI systems will fail?
- How should performance contracts evolve when value creation is shared between humans and AI tools?
- How should firms design contracts, monitoring systems, teams and incentives to minimize agency costs and maximize value creation?
“Embracing AI is making me a better teacher. I now bring more technical topics—like dynamic programming—into the classroom and show students why they’re powerful and relevant. AI helps demystify these tools and plants the seeds for deeper learning. At the same time, I raise the bar where it matters.”
— Marketing Professor Olivier Rubel
Risks and Rewards of Gen AI in Creative Industries
Read blog: The Digital Undertow: How Technology Quietly Reshapes Our Social Worlds
Professors Greta Hsu and Beth Bechky, leading scholars in organizational behavior, have explored in-depth the dual nature of AI’s impact on creative industries like film, television, and advertising, looking at both the immediate, surface-level benefits, and the deeper, less visible consequences—the digital undertow. By understanding both sides, business leaders can better leverage AI for its potential while safeguarding the human elements of creativity and expertise that are essential to long-term success.
"Exploiting AI’s productivity and efficiency gains is crucial for staying competitive, but it is also important to foster environments where innovation can still thrive. By recognizing the potential tendency of generative AI to pull creators toward uniformity, leaders can encourage creative, out-of-the-box thinking that AI alone cannot provide."
— Professors Greta Hsu and Beth Bechky
When More Means Less: The AI Pricing Paradox
Read blog: When More Means Less: The AI Pricing Paradox
Distinguished Professor Hemant Bhargava, director of the Center for Analytics and Technology in Society at UC Davis, recently presented his research on the future of AI economics at Wharton Human-AI Research's Annual Business & Generative AI Conference. Bhargava's research shows that seat-based pricing dominated the software-as-a-service (SaaS) industry, and has quickly become popular for AI tools (e.g., ChatGPT’s $20/per-user-per-month model). But with AI’s promise to increase productivity and reduce headcount, seat-based pricing can create a paradox where firms that enjoy greater value and savings pay less; a paradox that does not bedevil traditional software.
"AI can replace human labor and drive significant productivity gains. Companies talk about big cost savings in areas such as coding, customer service, paralegal work and more. Here’s the paradox: with per-seat pricing, firms that reduce headcount the most—and thus gain the most value—end up paying less because they have fewer employees using the AI."
— Distinguished Professor Hemant Bhargava, Suran Chair in Technology Management
The Statistical Foundation Behind AI, Finance and Modern Business
Read blog: The Statistical Foundation Behind AI, Finance and Modern Business
As AI reshapes business, UC Davis Distinguished Professor (Emeritus) Chih-Ling Tsai’s new book “Covariance Analysis and Beyond” explores the statistical foundations quietly powering machine learning, data analytics and modern decision-making.
Book Quick Facts:
- Presents a novel covariance-driven approach to incorporate classical and modern techniques together.
- Encompasses cutting-edge skills like dimension reduction, banding, shrinking, penalizing, convolution and transformer.
- Covers covariance regression model, network model, machine learning and covariance matrix, and tensor covariance model.
“Artificial intelligence, big data, high-dimensional data, machine learning, network analysis, and tensor analysis have grown rapidly and let to pioneering research and real-world application in the last two decades. One of the key drivers of this rapid progress has been the role of the covariance matrix . . .”
— Distinguished Professor (Emeritus) Chih-Ling Tsai from “Covariance Analysis and Beyond”
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