Jyoti Phogat
Founder and AI Strategy Researcher | Trustworthy AI, Decision Intelligence and Agentic Systems
Ravenna, Ohio, United States
Actions
Jyoti Phogat is the founder and AI strategy researcher behind Lotus & Dorje and Student-LAD, an AI-supported platform designed to help students and institutions manage planning, decisions, documents, reminders, and workflows in secure, context-aware environments.
Her work focuses on translating AI strategy into governed and verifiable implementation. She is developing frameworks for strategic AI initiative selection, multi-level value measurement, agentic software delivery, continuous context evolution, and evidence-based verification. Her research examines how organizations can choose the appropriate level of AI autonomy while protecting sensitive information, maintaining human accountability, and connecting technical performance to measurable educational and operational outcomes.
Through the applied development and testing of Student-LAD, Jyoti identified the “false-success” problem: an AI system can communicate that an action was completed even though the authoritative application state remains unchanged. Her work turns this implementation challenge into practical guidance for education executives, project managers, technology leaders, developers, and AI governance teams.
Badges
Area of Expertise
Topics
From AI Strategy to Verified Action: A Leadership Framework for Trustworthy Educational AI
Educational institutions are moving from generative AI experimentation toward systems that can manage assignments, schedules, communications, documents, advising, and institutional workflows. Yet an AI assistant may confidently report that it completed an action even when the authoritative record was never updated.
This panel begins with a real failure identified during the development and testing of Student-LAD: an assistant reported that an overdue task had been completed, while the stored task remained unchanged. This gap between conversational confidence and operational truth exposes a critical leadership question: How can educational institutions verify that AI systems are reliable, secure, accountable, and connected to measurable outcomes?
Panelists will examine a practical leadership framework for evaluating AI initiatives, selecting the appropriate level of autonomy, and establishing evidence gates before AI reports success. The discussion will compare deterministic automation, AI copilots, single-agent systems, multi-agent solutions, and human-controlled workflows.
Participants will learn how Verified Completion Rate, False Success Rate, authoritative-state validation, privacy controls, and outcome-linked measurement can strengthen institutional AI governance. Examples will include student reminders, advising, course planning, document support, calendar scheduling, and administrative workflows.
Attendees will leave with a reusable decision checklist for determining whether AI should perform a task, how much autonomy it should receive, what evidence must prove completion, and what educational or operational outcome should justify the investment.
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top