Position: Avoid Overstretching LLMs for every Enterprise Task
Enterprise workloads are dominated by deterministic, structured, and knowledge-dependent tasks operating under strict cost, latency, and reliability constraints. While these are often addressed through large language model (LLM) deployment or distillation into smaller models, we argue this is inefficient, unreliable, and misaligned with enterprise task structures. Instead, AI systems should treat language models as interfaces rather than monolithic engines, externalizing knowledge and computation into dedicated components for greater reliability, scalability, and transparency. Our theoretical evidences show that finite-capacity models cannot fully capture the breadth of knowledge required...
| Authors | Kuldeep Singh, Anson Bastos, Isaiah Onando Mulang' |
|---|---|
| Primary category | cs.AI |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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