Ai+ Training

AI is everywhere—yet its transformative promise remains constrained by opacity, fragmented architectures, and reactive risk management. This session introduces a new AI engineering framework designed to restore trust, accelerate adoption, and deliver measurable outcomes across industries including healthcare, logistics, finance, and government.

Grounded in explainability, governance, and seamless integration, this framework enables practitioners to design AI systems that are transparent from the outset, proactively mitigate risks, and deploy with minimal technical debt. Attendees will explore foundational models spanning prediction, cognition, optimization, generative AI, agentic AI, and responsible AI—each orchestrated to support real-world use cases and operational scale.

Participants will learn how to:

•         Architect AI systems with built-in explainability and governance

•         Align semantic models with business, regulatory, and operational goals

•         Simplify integration patterns to reduce deployment friction

•         Operationalize agentic AI and coworker orchestration for enterprise impact

This is not a theoretical exercise. The framework has already delivered billions in cost savings, improved safety and compliance outcomes, and enhanced customer service across Fortune 500 and public sector deployments. Whether you're a data scientist, ML engineer, product leader, or policymaker, this session will equip you with actionable design patterns and strategic insights to elevate your AI practice—and build systems that are not only intelligent, but trusted, scalable, and aligned with human outcomes.