The adoption of agentic AI demands a rethinking of foundational data and information quality practices. This panel brings together academic and industry speakers who have jointly advanced three complementary frameworks shaping the future of trustworthy AI. First, Explainability-Driven Data Quality (EDDQ) uses machine-learning explainability weights to automate end-to-end data quality decisions—from cataloging and key-element identification to change management and issue resolution—linking data quality directly to model behavior. Second, Zero Trust Data Quality (ZTDQ) applies zero-trust principles to dynamic, autonomous AI systems, asserting that data quality must always be verified, never assumed, as systems and data evolve. Third, emerging “Semantics 2.0” approaches leverage new generalized semantic data engineering tools to enable scalable, context-aware meaning representation, interoperability, and user-centric data governance across domains. Together, these perspectives outline a unified, lead practice agenda that advances both data and information quality and semantics for the next era of intelligent, adaptive systems.

Speaker: James Short, Lead Scientist, San Diego Supercomputer Center, UC San Diego

Dr. Short is the Lead Scientist and founder of the SPARK AI Industry Consortium at the San Diego Supercomputer Center. He has taught technology and business strategy, management information systems, and entrepreneurship at the London Business School, Boston University, and the MIT Sloan School of Management. He also served as a Belfer Fellow at the JFK School of Government at Harvard University, focusing on state and local IT governance policies. His current work focuses on topics in AI productivity and workforce transformation, data risk, growth and valuation, and semantic data engineering. He holds S.B., S.M., and Ph.D. degrees from MIT.

Speaker: James Cooper, Professor of Law, California Western School of Law

James Cooper is Professor of Law at California Western.  Professor Cooper has served as Associate Dean, Experiential Learning, Director of International Legal Studies, and as Assistant Dean at the law school. Professor Cooper is a serial disruptor – first in the law reform movement in Latin America and later with governments and emerging technology companies. He directed Proyecto ACCESO, a prominent judicial technology transfer project funded by governments, foundations, and international agencies.

Speaker: James Massa, Senior Executive Director, JPMorganChase

James Massa leads AI-assisted software development and Data Quality Application Development for JPMorganChase Wealth Management. James has 8 patents, and James is the 2026 & 2024 winner of the FSTech Award for Best Financial Services IT Team.  He is also a repeat winner of the American Financial Technology Award for Best Compliance Initiative and the FF Banking Tech Award for Best Reg Tech. James is a keynote speaker at CDOIQ, CDO Magazine, and WAICF. He holds master’s degrees in Digital Design from Harvard University and in Finance from Baruch College. He is also an AWS Certified Solutions Architect and a former Agile adjunct instructor at NYU. James is also an industry fellow at UCSD.

Speaker: Suresh Mani, VP of Engineering and Chief AI Architect, Solix Technologies

As VP of Engineering and Chief AI Architect at Solix Technologies, Suresh Mani spearheads the development of next-generation, AI-ready data platforms that enable enterprises to transition into AI-native organizations. Bringing over two decades of experience across IT, product management, and AI, Suresh focuses on Data-Centric AI, Agentic AI, and multi-agent architectures to deliver secure, scalable, and domain-specific solutions across Pharma, Healthcare, eCommerce, Logistics, and Financial Services.