Artificial Intelligence Workgroup
Thursday, June 18, 2026
1:00pm – 2:00pm

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AI Architecture: How It Really Works

Event Description
This talk offers a practical, end-to-end view of how modern AI systems actually work beneath the surface, moving beyond hype to explain the full architecture that turns data into useful products. It traces the stack from data pipelines, storage, feature engineering, and model training through inference, cloud infrastructure, APIs, and user interfaces, showing how each layer introduces its own design tradeoffs, costs, and operational challenges. It also contrasts predictive and generative AI, highlights why transformers and large language models changed the field, and examines the growing importance of retrieval, orchestration, monitoring, security, governance, and feedback loops in real-world deployments. For NYS Forum, the talk makes the case that successful AI is not just about choosing a powerful model, but about building reliable, efficient, and trustworthy systems that connect technical architecture with business outcomes.

Speaker:
Kay Apperson, AI Capability Leader - Kyndryl

Kay Apperson, PhD, is an AI Capability Leader at Kyndryl, where she drives the design and delivery of enterprise AI solutions that create measurable business impact including enabling a $1.2B capital optimization through predictive analytics and delivering $10M+ in annual efficiency gains through AI and cloud transformation initiatives.

Based in Silicon Valley, Kay is an internationally recognized, award-winning data scientist and trusted advisor to Fortune 500 executives, known for orchestrating cross-functional teams delivering generative AI and advanced analytics solutions across industries including healthcare, insurance, manufacturing, and retail.

Prior to Kyndryl, she held an academic role at Harvard Medical School as an Assistant in Computer Science and served for a decade at Microsoft in senior roles such as Principal Technology Strategist and Chief AI Architect. Kay is also a speaker, mentor, and advocate for ethical and inclusive AI, holding a Ph.D. in Computer Engineering and Computer Science.

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