Large language models (LLMs) have shown massive growth in reasoning, summarization, and natural language understanding tasks. OpenAI’s GPT-4, for instance, scored 86.4% on the MMLU benchmark, surpassing the average human baseline of 89.8% across professional and academic tasks [1]. However, LLMs is limited in enterprise deployment because of their inability to access or manipulate structured operational data.
According to McKinsey’s 2023 global AI survey, 55% of enterprises identified integration complexity as a primary barrier to production-scale AI implementation, particularly when models must interact with real-time data, APIs, or enterprise systems [2]. Forrester 2024 report said that 64% of IT decision-makers reported delays in LLM deployments due to the absence of standardized model-to-application interfaces [3]. In environments governed by regulatory constraints, such as healthcare or finance, integration risks also raise compliance concerns. Cisco’s Enterprise Security Report (2023) said that over 41% of AI-enabled systems lack structured authorization layers which increases the chances of privilege escalation in loosely integrated model environments [4].