As a Gartner analyst, I help technology leaders turn AI investment into measurable business value. I advise CIOs, CTOs, and VPs of Engineering on AI adoption strategy, agentic AI implementation, and AI economics — cost, tokenomics, and ROI. Through hundreds of annual client conversations, I've built a clear picture of what separates AI programs that compound value from expensive experiments: cost governance, workload prioritization, and outcomes-based measurement.
My coverage spans the AI stack (agents and applications, orchestration and management, runtime, and inference platforms) with a focus on the layer where value is actually delivered: applications, their outcomes, and their costs:
* AI adoption strategy, roadmaps, and enablement
* Agentic AI in software engineering and the AI-native product development lifecycle (PDLC)
* AI economics: tokenomics, FinOps for AI, and cost-per-outcome measurement
* AI platforms: application development platforms, inference engines, and the neocloud landscape
* AI value and ROI frameworks
My path here runs through Silicon Valley. I earned my PhD at Stanford with a concentration in Digital Humanities — applying computational analysis and data visualization to large, messy, multilingual datasets — then spent just under a decade as faculty running funded research programs as a professor and research program manager (PMP). The through-line: I've spent my whole career at the interface between technical builders and the people who need technology to deliver results.
Software Engineering Technologies and AI Solutions
Software Engineering Leadership
Software Engineering Practices and Delivery
PhD, Stanford University
BA, University of Oxford
AI in the SDLC
AI agents / Agentic AI
AI ROI and Value
Developer Productivity
AI inference platforms