Adopting Large Language Models (LLMs) in enterprise settings can be compared to learning to drive. Initially, interactions feel manual, requiring precise instructions for every action. As organisations gain experience and the technology matures, LLMs evolve into collaborative partners capable of handling complex workflows under human supervision.
Understanding this evolution is crucial to developing a strategy that delivers measurable value at each stage without sacrificing oversight. Ushur has put together a critical guide to this pipeline.
Stages of LLM usage
Stage 1: Foundational LLMs (Manual driving)
At the outset, using LLMs is akin to driving a manual car—every instruction must be spelled out. Simple prompts such as “Summarise this” or “Draft an email marketing campaign” offer immediate, transactional productivity gains but provide no memory or context for follow-ups. The human operator remains in control, guiding the model step by step.
Stage 2: Enhanced LLMs (Driver-assist features)
The second stage introduces assistive capabilities that enhance both reliability and performance. This often involves combining Chain-of-Thought (CoT) reasoning with Retrieval-Augmented Generation (RAG).
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Chain-of-Thought (CoT): This allows the LLM to process tasks step by step, breaking down complex queries into logical sequences. This capability is essential for multi-step workflows.
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Retrieval-Augmented Generation (RAG): RAG ensures answers are accurate and grounded in verifiable information. By retrieving relevant data from trusted sources such as internal wikis or document libraries, the LLM can provide factual responses while citing sources—critical for trust and compliance.
Stage 3: Multi-capability systems (The professional crew)
The most advanced stage involves orchestrating multiple specialised tools within a single system. Often referred to as Multi-Agent Systems (MAS), these setups divide labour across specialised capabilities:
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Specialised tools: One module may query customer data, another retrieves policy details via RAG, and a third performs calculations.
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Parallel processing: Multiple tasks run simultaneously, speeding up complex operations such as onboarding or claims processing.
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Modularity and extensibility: New agents, APIs, or integrations can be added without redesigning workflows.
This collaborative “digital crew” approach is more powerful and adaptable than relying on a single AI, particularly for multi-step enterprise processes.
Essential capabilities for enterprise-grade systems
Effective multi-capability AI systems must offer control, visibility, and trustworthiness. Enterprises should prioritise solutions with:
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Structured & observable workflows: Clearly defined, loggable task sequences prevent the AI from deviating and provide a full audit trail.
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Human-in-the-loop (HITL) control: Managers can approve actions or correct errors at critical points, with the option to rewind and retry steps.
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Centralised state & context management: Maintaining shared context ensures coherent workflows across multiple stages and sessions.
Real-world applications
When properly orchestrated, multi-capability systems can transform key business functions:
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Customer experience (CX): Systems identify intent, retrieve relevant data via RAG, draft personalised responses, and escalate to humans if needed.
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Insurance claims processing: AI collects incident details, retrieves policy documents, analyses claims, and proposes actions for human approval.
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Healthcare plan support: Systems verify coverage, locate providers, and escalate complex cases to human coordinators seamlessly.
A practical roadmap for adoption
Phase 1: Solve a pressing business problem
Identify a manual, slow, or error-prone process. Choose a partner that offers visibility, control, and extensibility.
Phase 2: Expand and integrate capabilities
Once initial solutions prove effective, connect them to more data sources and scale features like RAG and CoT.
Phase 3: Introduce collaborative workflows
Orchestrate multiple capabilities to manage end-to-end processes, initially under human supervision to build trust.
Phase 4: Mature toward a digital workforce (Year 2+)
Gradually reduce oversight for routine tasks, establishing “digital workers” that handle entire business functions while humans provide strategic judgment.
Looking forward
The future of enterprise AI is not fully autonomous systems but hybrid human-AI teams. By focusing on specific problems and prioritising control and transparency, businesses can unlock significant productivity gains while maintaining oversight, creating a powerful collaborative workforce.
Read the full blog from Ushur here.
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