Biography
Catherine Ganduri is a public-sector technology professional, Senior Systems Analyst, and AI practitioner with experience across enterprise systems, responsible AI adoption, business analysis, data analytics, and digital transformation. She currently works with a Texas state agency(the agency name is intentionally withheld for privacy and confidentiality reasons.)
Her professional background includes enterprise workflow modernization, reporting automation, platform development, governance-focused system processes, and AI research. Catherine’s work focuses on helping government and highly regulated organizations move from technology experimentation to trusted, measurable, and scalable operational adoption.
She holds a Master’s degree in Business Analytics from The University of Texas at Dallas and also an active researcher and speaker with interests in generative AI, AI governance, enterprise inference systems, responsible automation, and public-sector digital transformation.
Abstract
Title: Responsible AI for Public Sector Operations
Abstract: Artificial intelligence is rapidly reshaping how governments, enterprises, and highly regulated organizations deliver services, manage operations, and support decision-making. Yet many organizations still struggle to move beyond isolated AI pilots because of legacy systems, fragmented data, security concerns, governance gaps, and uncertainty around human oversight.
In this keynote, I will present a practical framework for moving from AI experimentation to responsible, measurable, and trusted AI adoption. The session will explore how organizations can identify high-value AI use cases, build governance into implementation, establish data readiness, design human-AI workflows, and manage risks such as privacy, bias, explainability, cybersecurity, and compliance.
Drawing from public-sector modernization patterns and publicly available government AI initiatives, the presentation will highlight how responsible AI requires more than model deployment. It requires operating models, cross-functional governance, human review, trust metrics, and clear pathways to scale.
Attendees will gain practical insights on how to transform AI from a promising pilot into a governed enterprise capability that improves operations, strengthens accountability, and earns public trust.
