Why Most AI Projects Fail | The S.O.N.G. Framework for Enterprise AI Agents

Most AI initiatives never make it beyond the proof-of-concept stage. Organizations invest heavily in AI strategy, pilots, and infrastructure, yet struggle to deliver measurable business outcomes once AI reaches production. In this session, Tushar Puri introduces the S.O.N.G. Framework, a practical approach for designing enterprise AI systems that move beyond demos and deliver reliable business value. You'll learn why successful AI adoption depends on much more than choosing the right model or building an impressive proof of concept. What you'll learn: Why most enterprise AI projects fail after the POC stage The four production gaps captured by the S.O.N.G. Framework Signal: Delivering the right context at the right moment Orchestration: Executing work across enterprise systems and workflows Normalization: Making inconsistent enterprise data usable for AI Governance: Ensuring secure, compliant, and auditable AI decisions Why AI agents require workflow execution, not just data access How to design AI systems that adapt to changing business conditions The engineering patterns needed for enterprise-grade AI agents, including cross-checking, policy validation, and human-in-the-loop approvals Whether you're an enterprise architect, CTO, CIO, engineering leader, product leader, or AI practitioner, this session provides a practical blueprint for moving from AI experimentation to production-ready AI systems. Topics covered: Enterprise AI AI Agents Agentic AI Production AI AI Architecture AI Governance Workflow Orchestration Enterprise Automation MCP FHIR Large Language Models (LLMs) Enterprise Software