Position: Avoid Overstretching LLMs for every Enterprise Task

A new research position argues that enterprises should stop treating large language models as monolithic solutions for all tasks and instead use them primarily for structured data extraction within modular architectures. The paper contends that LLMs have inherent capacity limits for enterprise knowledge needs and proposes delegating computation and storage to specialized components like knowledge bases and symbolic systems for better reliability and cost efficiency.

The research challenges the prevailing enterprise approach of deploying LLMs as universal problem-solvers, presenting a fundamental architectural critique grounded in information theory. The authors demonstrate that finite-capacity neural models cannot adequately capture the breadth of domain-specific knowledge enterprises require, leading to inefficiency, unreliability, and opacity in production systems. This matters because enterprises have already invested significantly in LLM deployments and fine-tuning strategies based on the assumption that scaling and distillation would solve their problems.

The broader context reflects growing frustration with LLM limitations in real-world enterprise settings. Production deployments reveal persistent hallucination issues, difficulty maintaining consistent knowledge across updates, and prohibitive inference costs at scale. The paper positions modular AI architecture—separating language understanding interfaces from dedicated knowledge retrieval and symbolic computation layers—as the sustainable path forward. This architectural shift mirrors earlier industry transitions, such as separating databases from application logic.

The implications extend across multiple stakeholder groups. Enterprise software vendors face pressure to redesign platforms around hybrid architectures rather than pure LLM approaches. Development teams must evaluate whether their current LLM strategies align with deterministic task requirements or if modular alternatives prove more cost-effective. Cloud infrastructure providers may see shifting demand patterns away from continuous LLM inference toward episodic language-based interfacing with specialized backends.

Looking ahead, the validation of this theoretical position depends on whether real-world deployments demonstrate superior performance with modular approaches. The enterprise AI market will likely fragment into LLM-centric solutions for creative tasks and hybrid architectures for deterministic workflows, establishing clearer use-case boundaries.

• →LLMs should serve as structured data extraction interfaces rather than universal enterprise engines due to inherent capacity limitations
• →Modular architectures delegating knowledge and computation to specialized components improve reliability and maintainability versus monolithic models
• →Enterprise workloads require deterministic, cost-efficient solutions misaligned with current LLM deployment strategies
• →Theoretical analysis demonstrates finite-capacity models cannot capture breadth of knowledge needed for enterprise task performance
• →Hybrid AI systems combining language models with knowledge bases and symbolic procedures offer sustainable enterprise foundation