"World of DaaS" The LM Brief: Why Many AI Projects Fail
Nov 21, 2025
The podcast dives into why many AI projects stumble, highlighting that the real roadblock lies in data pipelines rather than the models. It discusses findings that about 30% of projects fail due to poor data quality and unclear business value. The host explores the security risks from unchecked AI usage and sheds light on the complexities of integration that can lead to spiraling costs. Solutions like managed streaming platforms are presented as a way to centralize governance and ensure proactive security while restoring cost predictability.
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Infrastructure, Not Models, Is The Bottleneck
- Many generative AI projects fail after proof-of-concept because infrastructure, not models, is the core issue.
- Weak data management causes poor data quality, inadequate risk controls, and no clear business value.
Uncontrolled AI Usage Is A Major Security Risk
- Security risk from uncontrolled AI usage, not model capability, keeps executives awake.
- Employees often share sensitive data with generative tools, creating major compliance exposure.
Point-To-Point Integrations Scale Poorly
- Fragile point-to-point integrations create exponential technical debt as agents scale.
- Adding agents multiplies connections and maintenance until the integration cost exceeds agent value.