LLMs Overstretched for Enterprise Tasks, Paper Argues
A recent paper on arXiv (2605.09365) contends that utilizing large language models for business applications is both inefficient and misaligned with the nature of tasks. Enterprise activities are characterized by their deterministic, structured, and knowledge-driven nature, functioning within stringent constraints related to cost, latency, and reliability. The authors advocate for the use of language models as interfaces instead of singular engines, proposing that knowledge and computation be managed by specialized components. Theoretical insights indicate that models with limited capacity cannot adequately encompass the breadth of enterprise knowledge, which hampers both efficiency and interpretability. The paper suggests that LLMs should mainly focus on structured extraction in deterministic processes, while dedicated modules manage computation and knowledge.
Key facts
- arXiv paper 2605.09365 argues against overstretching LLMs for enterprise tasks.
- Enterprise workloads are deterministic, structured, and knowledge-dependent.
- LLM deployment or distillation into smaller models is deemed inefficient and unreliable.
- AI systems should treat language models as interfaces, not monolithic engines.
- Knowledge and computation should be externalized into dedicated components.
- Finite-capacity models cannot fully capture enterprise knowledge breadth.
- LLMs should be used primarily for structured extraction in deterministic workflows.
- The paper provides theoretical evidence for inherent limits to efficiency and interpretability.
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- arXiv