A key observation regarding generative artificial intelligence (AI) highlights a fundamental hurdle: its outputs are not readily composable within traditional software programs. This insight, initially articulated by Ankur Patel, was recently endorsed by Martin Casado, a General Partner at Andreessen Horowitz, who stated, "Ankur nails it. One of the big lessons from gen AI is that the output is not usefully composable within traditional programs." This challenge poses a significant barrier to seamless enterprise integration.
The core of the problem lies in the inherent nature of generative AI outputs, which are often unstructured or semi-structured, such as free-form text, images, or code snippets. Traditional software applications, however, typically rely on highly structured data inputs and predefined formats. This mismatch means that the creative, often unpredictable, results from generative AI models do not easily fit into the rigid input-output mechanisms of existing enterprise systems, lacking standardized APIs or data schemas for straightforward integration.
Furthermore, the "black box" nature of many generative AI models contributes to this composability issue. Understanding how these models arrive at their conclusions can be difficult, leading to concerns about explainability and reliability, especially in regulated industries. The potential for models to produce inaccurate, biased, or nonsensical outputs, commonly referred to as "hallucinations," further complicates their direct integration, as human oversight is often required to validate and refine the AI-generated content before it can be used downstream.
Integrating generative AI into legacy systems presents additional complexities, as older infrastructures may lack the flexibility or modern APIs necessary for seamless connection. This necessitates significant investment in custom integration solutions or system overhauls, adding to the cost and complexity of deployment. Addressing these composability challenges is crucial for organizations aiming to fully leverage generative AI beyond standalone applications and embed it deeply into their operational workflows.