The article discusses the systemic limitations of large language models (LLMs) in enterprise environments, highlighting issues like rising compute costs, latency challenges, and explainability gaps. It argues for a modular approach that integrates small language models (SLMs) and retrieval-augmented generation (RAG) to distribute intelligence effectively across specialized components.
This modular architecture allows enterprises to manage AI capabilities more sustainably by scaling horizontally with focused agents that adhere to specific governance protocols. RAG enhances reasoning accuracy by grounding outputs in verifiable information, fostering accountability and clarity. The author emphasizes that while LLMs are impressive, they do not align well with enterprise control frameworks, which heightens risks of unpredictable outputs.
The article concludes that adopting modular AI could help organizations integrate intelligence into existing systems, promoting responsible growth that is adaptable to business needs. It suggests that a semantic layer could facilitate this transition by managing how AI agents access data and validate decisions. As enterprises move toward this architecture, the focus shifts from disruptive innovation to maintaining operational continuity.
👉 Pročitaj original: CIO Magazine
