Why Enterprise AI Keeps Failing - And It Has Nothing to Do With the Model
About this session
Most enterprise AI deployments do not fail at the model layer. They fail underneath it.
Governance without accountability. Decision ownership that lives nowhere. Legacy architecture that cannot support a real-time pipeline. Change management that is checkbox training dressed up as transformation. The engineering team does the hard work. They select the right model, optimize for production, and deploy. Then the organization around them fails to absorb the system. These are not soft organizational problems. They are structural failures with specific, diagnosable signatures. It is not a model problem. It is a structural one. The Structural Intelligence Framework™ is a six-dimension diagnostic built from 30 years of running enterprise technology at scale, covering governance, decision ownership, architecture, change management, and cross-functional alignment. This session shows where organizations break down, why they score themselves falsely high, and what structural readiness actually looks like in production.
Speaker
Key takeaways
- A six-dimension diagnostic framework to assess whether your organization is structurally ready to deploy and sustain AI at scale
- The specific failure patterns that separate organizations that sustain AI in production from those that cycle through pilots indefinitely
- A self-assessment lens and language to communicate structural readiness to boards and non-technical leadership in a way that drives decisions rather than deferral