Contact us

Artificial Intelligence

How artificial intelligence creates value for large enterprises: LLMs, RAG, MCP and the AWS, Azure and Google platforms

Large organizations no longer ask whether they should use artificial intelligence, but where the value is and how to capture it without getting stuck in pilots that never reach production. This article explains, in business terms, the four technical building blocks that make useful enterprise AI possible today —large language models (LLMs), retrieval-augmented generation (RAG), the Model Context Protocol (MCP) and agents— and how the three major clouds (AWS Bedrock, Microsoft Foundry and Google Cloud) package them for the enterprise.

1. LLMs: the engine, not the solution

A large language model (LLM) is an engine capable of understanding and producing text, code or summaries from instructions. On its own, an LLM knows the world up to its training date and does not know your company’s data: contracts, policies, customer histories, procedures. Using it “raw” produces plausible but unverifiable answers —the well-known risk of hallucination.

That is why, in practice, business value never comes from the model in isolation but from how it is connected to the organization’s knowledge and systems. That is where RAG and MCP come in.

2. RAG: connecting the model to company knowledge

Retrieval-Augmented Generation (RAG) is an architecture in which, before answering, the system searches the company’s authoritative sources (documents, knowledge bases, internal systems) and hands that context to the model. According to IBM, RAG connects the model with external knowledge bases in real time, incorporates up-to-date information and reduces hallucinations: if it finds nothing relevant, the system can say “I don’t know” instead of making something up.

For an executive, RAG means three things:

  • Traceable answers: every answer can cite the internal source it came from.
  • No retraining: the knowledge lives in your repositories; updating a document updates the answers.
  • Access control: the search respects each user’s permissions on each document.

Typical use cases with fast payback are customer service assistants, internal support (IT, HR, legal), bid and contract management, and “copilots” for sales teams that need the right information at the right time.

3. MCP: the standard that lets AI use the company’s tools

Answering questions is useful; acting is more valuable. The Model Context Protocol (MCP) is an open standard that defines how an application exposes tools and data to a language model in a consistent, scalable way. Instead of coding a different integration for each system (ERP, CRM, service desk, databases), the company publishes “MCP servers” and any compatible agent can use them.

Microsoft describes it exactly that way in the Foundry documentation: MCP is an open standard that defines how applications provide tools and contextual data to LLMs, enabling consistent, scalable integration of external tools into model workflows. In practice, this turns AI into a collaborator that checks inventory, creates a ticket, schedules a meeting or prepares a report, with governance and traceability.

4. Agents: from conversation to process

An agent combines an LLM, access to knowledge (RAG) and tools (MCP) to complete multi-step tasks under human supervision. It is the point where AI stops being a chat and starts to move business processes: classifying and answering requests, preparing reconciliations, monitoring risks, orchestrating approvals.

5. The platforms: what each cloud offers

AWS – Amazon Bedrock. Bedrock Knowledge Bases is a fully managed RAG service that connects generative AI with proprietary data (SharePoint, Confluence, Google Drive, S3, websites) and manages the vector store, embeddings and result re-ranking for you. It includes agentic retrieval for complex multi-step questions, GraphRAG to understand relationships between entities, and multimodal analysis of PDFs, presentations, audio and video.

Microsoft – Foundry (formerly Azure AI Foundry). The Foundry Agent Service connects agents to MCP servers by URL or from a catalog (for example, Azure DevOps), bundles tools —web search, code interpreter, Azure AI Search, OpenAPI, agent-to-agent connections— into a single MCP-compatible endpoint, and manages authentication with Microsoft Entra identities or OAuth instead of embedded credentials. It is the natural choice for organizations already standardized on Microsoft 365, Azure and Copilot.

Google Cloud – Gemini Enterprise Agent Platform (formerly Vertex AI). Google emphasizes grounding: anchoring model output to verifiable sources (Google Search, Google Maps or the company’s own data through Agent Search), which reduces hallucinations and provides auditability with links to the sources. The platform is designed to build, scale and govern agents grounded in enterprise data.

All three converge on the same idea: model + proprietary knowledge + tools + governance. The choice depends less on the technology and more on where your data, identities and teams already live.

6. Where the value is (and where it gets lost)

In our experience supporting organizations in Colombia and the United States, AI value is not lost in the model: it is lost in the Value Gap between the pilot and adoption. Four practices make the difference:

  1. Formulation: choose use cases with an explicit business case (hours, errors, cycle times, revenue), not novelty.
  2. Execution with governance: quality data, access control, continuous evaluation of answers and costs (AI FinOps).
  3. Adoption: redesign people’s work around the assistant; train; measure actual use.
  4. Measurement: business KPIs before and after; scale only what proves a return.

A well-formulated enterprise AI architecture —with RAG for knowledge, MCP for tools and supervised agents— can significantly reduce the operating costs of information-intensive processes. The right starting point is not “which model do we use”, but “which decision or process do we want to improve and how will we measure it“.

Where is the biggest AI value opportunity in your organization today? Let’s identify it together in a Value Discovery.

References

Back to the blog

You may also like