Note From the Editor
AI@Work Magazine
Artificial intelligence, and large language models in particular, is changing how work gets done. As code generation becomes cheaper and analysis faster, the organizational bottleneck has shifted from execution to decision-making: What do we build next? How do we use AI to move faster? How do we structure teams for sustained success when the landscape changes weekly?
There is no shortage of technical guides and opinions on generative AI. There is a shortage of field reports showing how AI is applied to create real business value. When the work itself no longer teaches the business, the knowledge of those who have done it matters more. Technology changes; the objective of business does not. That objective is delivering sustainable value to customers, and AI, however powerful, is a tool in service of it.
Research on enterprise AI adoption points to a common problem: despite heavy investment, measurable ROI remains elusive. We launched this editorial platform under the UC Davis GSM banner to close that gap. Through first-person insights and field reports from practitioners, we highlight how decision-makers navigate AI integration across three themes:
- Team Enablement: How to structure, develop and prepare teams for AI, including how to rebuild the understanding that execution no longer supplies.
- Capability Expansion: How AI unlocks new capabilities, speeds up operations and solves problems that were previously out of reach.
- Human-in-the-Loop Judgment: Where automated systems falter, and where human understanding and strategic insight remain indispensable.
Each quarterly edition brings together three to five practical articles on real-world AI applications and business outcomes. We are as interested in what failed as in what worked.
We invite you to engage with these perspectives, share feedback and recommend future contributors. We look forward to building a thoughtful, practitioner-led conversation together.
Thank you,
Ashwin Kumar Suresh
UC Davis MSBA 19