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Adam Ronthal
VP Analyst
Gartner
Speaker Topics
- Agentic Analytics and AI
Speaker Bio
Adam Ronthal is a Research VP in Gartner's Data and Analytics group covering data management with a primary focus on database management systems, technologies and strategies. Mr. Ronthal's areas of specialization include data ecosystems, analytic and operational databases, cloud, financial governance for cloud and FinOps for D&A. Mr. Ronthal joined Gartner in January 2015 and is based in Toronto, Canada.
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Sessions
Monday, March 08, 2027 02:30 PM - 03:00 PM EST
The Psychology of AI and LLMs
Adam Ronthal,
VP Analyst, Gartner
This session explores the psychological aspects of how AI and LLMs learn, solve problems and interact with humans. Drawing on parallels to traditional psychological fields of study (cognitive, developmental and social), we explore real-world examples of what happened, and what kinds of guardrails might have prevented poor outcomes.
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Tuesday, March 09, 2027 04:00 PM - 04:30 PM EST
Managing Cost and Value for Data Agents
Adam Ronthal,
VP Analyst, Gartner
This session explores tactics for optimizing cost and value for data agents, focusing on data agent taxonomy, token-based pricing models, cost-impacting characteristics, and value assessment to enhance ROI in data and analytics operations. We further link data agents to a broader discussion on agentic cost optimization across the data and analytics landscape.
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Wednesday, March 10, 2027 10:30 AM - 11:00 AM EST
Panel Discussion: Evaluating Your Strategic "Data Management Platform" Choices
Adam Ronthal,
VP Analyst, Gartner
Peter Majko,
Sr Director Analyst, Gartner
Mark Beyer,
Distinguished VP Analyst, Gartner
Robert Thanaraj,
Sr Director Analyst, Gartner
D&A leaders are on a journey to renovate their data management infrastructure for AI initiatives — reevaluating their data management platform. The key question for success is: How should I evaluate my data management platform and which one should I select for future AI use cases? Join this session to ask our expert panel questions like: How to evaluate the various categories of data management platforms? Is there a decision framework to rate and score these data platforms? Is this a rip and replace, or are there other ways to complement?
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