Why do AI pilot programs often fail in enterprises?

AI pilot programs often fail because of fragmented enterprise architectures that prevent AI models from accessing a complete and unified view of business context, not due to a lack of model intelligence.

What is the 'Franken-stack' and how does it impact AI?

A 'Franken-stack' refers to a fragmented IT architecture with disconnected point solutions and brittle APIs. This hinders AI by preventing it from accessing a single source of truth, leading to operational pitfalls and failed initiatives.

How can a platform-native architecture improve AI reliability and security?

A platform-native architecture provides AI with a unified data source and eliminates integration complexities. It also enhances security by keeping sensitive data resident on a single platform, reducing the risk associated with numerous API connections.

Home / Technology / AI's Context Crisis: Why Your Pilot Programs Fail

AI's Context Crisis: Why Your Pilot Programs Fail

4 Feb

•

Summary

AI fails due to lack of context, not intelligence.
Fragmented IT architectures prevent AI from accessing truth.
Platform-native approach offers AI reliable data and security.

AI's Context Crisis: Why Your Pilot Programs Fail

The initial excitement surrounding Generative and Agentic AI has given way to practical challenges. CIOs and technical leaders are observing that even simple AI automation pilot programs are not yielding the expected outcomes. The root cause of these failures is not the AI models' intelligence, but rather the lack of contextual data within enterprise systems.

Modern enterprises often operate with a 'Franken-stack' of disconnected point solutions and brittle APIs, hindering AI's ability to access a unified source of truth. This fragmentation is particularly detrimental for services-centric organizations where context spans sales, delivery, customer success, and finance. Unlike humans, AI cannot intuitively bridge information gaps between siloed systems.

To overcome these limitations, the focus is shifting from model selection to data location. A platform-native architecture, built on a common data model, eliminates integration complexities and provides the single source of truth necessary for reliable AI. This approach also enhances security by keeping sensitive data resident on a unified platform, reducing the attack surface associated with numerous API connections.