LLMs in Business, AI agents, RAG : CIOs and decision-makers hear about nothing else. But there's a chasm between promises and reality. Most generative AI projects never make it to production.
Why?
Because the problem isn't the model. It's the data. Outdated, poorly classified documents, scattered across messaging apps and drives. The result: inaccurate answers, hallucinations, and POCs that never scale.
The rule is simple: 80% data and governance, 20% AI.
Without structured, reliable, and governed data, even the best LLM on the market won't produce anything sustainable. Garbage in, garbage out.
Making the right choices isn't about selecting "the best model." It's about understanding the difference between LLMs and AI agents. It's about choosing based on your business use cases. It's about arbitrating between cloud, on-premise, confidentiality, and sovereignty. Based on concrete criteria, not promises.
Key takeaways
The Problem : Companies want to deploy LLMs and AI agents but get lost amidst technical jargon, model choices, and sovereignty concerns.
The Solution : Understand the difference between LLMs (language engines) and AI agents (autonomous systems), map model capabilities to your business use cases, and decide between cloud and on-premise based on your actual constraints.
Tangible Benefits : With well-structured data (via an EDMS, for example), you can achieve +30% relevance in AI responses, +35% quality on complex corpora, and -40% latency.
The Method : Data first, then use case analysis, then choosing the right LLM. Not the other way around.
Key Consideration : Not all "open" models are created equal. Open source ≠ open weight, with direct impacts on reversibility and governance.
LLM or AI agent: what's the difference for your projects?
An LLM generates text. An agent acts autonomously. The difference is significant when you're scoping a project.
An LLM (Large Language Model) is a language engine capable of understanding and generating text. It answers questions, summarizes documents, and extracts information. It does nothing more than manipulate language.
An AI agent is an autonomous system driven by one or more LLMs. It pursues a defined objective, plans actions, interacts with tools (APIs, databases, applications), and makes decisions. Simply put: an agent is a workflow driven by an LLM.
Anatomy of an Agent :
- Brain : One or more LLMs for reasoning and decision-making
- Memory : Stores past interactions for contextual continuity
- Tools : Access to APIs, web search, databases, business applications
- Planner : Breaks down complex tasks into actionable steps
- Action Loop : Perception → reflection → action cycle
Comparative Asset #1: LLM vs. AI Agent
Your Decision : If you need a one-off answer (summary, extraction), an LLM is sufficient. If you want to automate an end-to-end process (mail processing → classification → workflow dispatch), you need an agent.
LLM Capabilities: identifying the right model for your business needs
There's no such thing as a "universal" LLM. Each model excels in specific capabilities. The right LLM for your business is one that addresses a real business need.
The 6 main capabilities of LLMs (early 2025) :
- Text Generation : Automatic report writing, summary generation, Q&A responses based on documentation
- Text Manipulation : Structured information extraction, comparative analysis, automatic classification
- Code : Script generation and analysis, development assistance
- Vision : OCR (paper mail digitization with data extraction), image analysis, responses based on charts
- Tool Calling : Interaction with APIs, databases, business applications
- Reasoning : Complex problem solving, logical task decomposition
Specifically, for document management (DMS) :
Comparative Factor #2: LLM Capabilities × DMS Use Cases Matrix
These capabilities also apply outside of DMS: contract analysis (legal), patient data extraction (healthcare), ticket categorization (customer support).
Your Decision : List your time-consuming and error-prone tasks. Map them to LLM capabilities. Choose the model that excels at your priority use case, not the one that promises to do everything.
You can have the best model in the world, but if you feed it dirty data, you'll get dirty decisions.
Cloud or On-premise: Balancing Performance and Data Control
Cloud or on-premise? The real question isn't technical; it's strategic. It depends on your privacy policy, budget, and internal skills. No ideology, just pragmatism.
Comparative Factor #3: Cloud vs. On-premise
Key Questions for Your Decision :
- Do your documents contain sensitive data (healthcare, finance, defense)?
- What is your annual budget for AI (usage vs. infrastructure)?
- Do you have in-house MLOps/DevOps expertise or are you ready to recruit for it?
- What is your tolerance for vendor lock-in with a cloud provider?
Your Decision : If absolute confidentiality and total control are critical, and you have the budget and skills, go with on-premise. Otherwise, cloud models offer the best speed/performance/cost ratio to get started.
Open source vs open weight: what the "open" labels conceal
Not all "open" models are created equal. Behind the labels lie differences that directly impact your ability to govern your models.
Open source : Code + architecture + model weights + methods + training data. Everything is accessible and modifiable. You can audit, fine-tune, redistribute.
Open weight : Code + model weights, but potentially without the rest. You have the pre-trained model, but not necessarily the data or the details of the training method.
Impact on reversibility and governance :
- Open source : Full reversibility. If the publisher disappears or changes strategy, you retain control. Simplified governance (full audit possible).
- Open weight : Partial reversibility. You can use the model, but not necessarily reproduce or deeply improve it.
Risk example: jailbreaking
Jailbreaking involves bypassing a model's security barriers to make it produce unauthorized responses (illegal content, sensitive data, amplified biases). An open weight model without complete documentation on its training makes it harder to identify and correct these vulnerabilities.
Players to watch :
- Closed-source leaders : ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google)
- Open Source Leaders : Meta (Llama), Mistral, Qwen (Alibaba), DeepSeek
- Sovereign Hopes : Mistral, OpenLLM (Lucie), OpenEuroLLM
Your Decision : If sovereignty and risk management are priorities, opt for models that are truly open source. Check the complete documentation (code + weights + data + methods) before committing.
From Usage to Production: The Approach to Integrating an LLM Without Issues
Moving from a compelling POC to a functional deployment: here's the data-first approach to get there.
Golden Rule: 80% data and governance, 20% AI. If you inject disorder into your system, you will get amplified disorder. Garbage in, garbage out.
DMS as the Foundation for RAG and AI Agent Use Cases :
- Centralization : All your strategic documents in one place
- Structuring : Reliable metadata, consistent indexing, version management
- Governance : Access rights, traceability, GDPR/ISO 27001 compliance
Results measured on structured EDM corpus (Efalia/Wikit partnership): +30% relevance in AI responses thanks to structured metadata, +35% quality on complex corpora with vectors + metadata, -40% latency thanks to optimized document fragmentation.
Conclusion: data first, usage first, informed decision
LLMs are not "magic." But when properly framed, they are powerful.
Remember three principles:
- Clarify your concepts : LLM or agent? Cloud or on-premise? Open source or open weight?
- Start with your business use cases : What concrete problem are you solving? What task are you automating?
- Structure your data first : 80% data and governance, 20% AI. Without reliable data, the best model in the world will be useless.
ECM and AI are the perfect match: centralization, structuring, governance. This foundation allows you to build efficient and sovereign agents.
👉 Do you want to structure your data to prepare for your AI applications? Contact us for a document maturity audit or download our guide "Data First: Preparing Your ECM for AI".
