The adoption of Large Language Models (LLMs) in enterprises is a gradual journey, often compared to learning to drive a car. Early-stage usage is manual and transactional, but as systems mature, they become collaborative tools capable of handling complex workflows under human supervision. Understanding this evolution is crucial for organisations looking to implement AI without losing control or compliance, according to Ushur.
At the initial stage, LLMs operate like a manual car: every instruction must be clearly articulated.
Commands such as “Summarise this” or “Draft an email campaign” deliver tangible productivity improvements for discrete tasks but lack memory or context for subsequent requests. While useful, these early systems require constant human guidance to remain effective.
The next stage introduces advanced features that improve reliability and intelligence. Key technologies include Chain-of-Thought (CoT) reasoning and Retrieval-Augmented Generation (RAG). CoT allows LLMs to break down queries step-by-step, mirroring human reasoning, while RAG ensures responses are fact-based by retrieving data from trusted sources. Together, these features help enterprises achieve more accurate outputs, critical in regulated environments like healthcare, insurance, and finance.
Mature organisations adopt multi-capability AI systems, sometimes called Multi-Agent Systems (MAS). These orchestrate specialised tools that work together on complex processes. Features include:
-
Specialised tools: Components perform distinct tasks, from querying CRM data to executing financial calculations.
-
Parallel processing: Multiple actions occur simultaneously, speeding up workflows like customer onboarding or insurance claims.
-
Modularity and extensibility: New agents and APIs can be integrated without redesigning workflows.
This framework allows AI to tackle multi-step business challenges while remaining flexible and controllable.
Essential capabilities for enterprise-grade systems
Effective enterprise AI requires structured workflows, human-in-the-loop (HITL) oversight, and centralised context management. Structured workflows ensure predictable task completion with full auditability, HITL provides human approval gates at critical steps, and centralised state management maintains continuity across sessions and agents.
Real-world examples in business
Enterprises are already leveraging these systems to transform operations:
-
Customer experience (CX): AI identifies intent, retrieves relevant data, drafts personalised responses, and escalates when needed.
-
Insurance claims: AI collects details, searches policies via RAG, analyses eligibility, and recommends actions to human adjusters.
-
Healthcare plan support: AI verifies benefits, locates providers, and escalates complex cases to human coordinators seamlessly.
A practical roadmap for adoption
Companies should start by addressing a pressing business problem, then gradually expand AI capabilities. Integration with additional data sources, the introduction of collaborative workflows, and eventual development of a “digital workforce” allows enterprises to scale while maintaining control. Over time, AI and humans collaborate intelligently, handling entire business functions with improved efficiency.
Looking forward
The future lies not in fully autonomous AI, but in hybrid workforces where AI manages tasks and humans provide judgment, ethics, and oversight. Prioritising control, transparency, and gradual adoption ensures businesses unlock productivity gains while maintaining compliance and strategic control.
Read the full blog from Ushur here.
Copyright © 2025 RegTech Analyst
