Anthropic vs Gemini vs OpenAI: How AI is Really Integrated in Companies (Architecture, Responsibility & Real Cases)

Introduction

Talking about models is interesting.

But in a real company, the question is not which model answers a prompt better.

The question is:

How do I integrate AI without breaking my current systems, exposing sensitive data, or taking unnecessary regulatory risks?

That’s where the serious conversation begins.

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1️⃣ Real Architecture: AI Doesn’t Replace, It Overlays

In large companies—telecom, banking, energy, retail—AI doesn’t replace the core systems. Instead, it is integrated as a series of coordinated layers, each with a clear role:

Sequential architecture flow:

1. User (NOC, Marketing, Product, Call Center)
2. Internal Application (CRM, OSS, ERP, Portal)
3. AI Orchestration Layer (controls, enriches, and secures data)
4. AI Provider (OpenAI, Anthropic, Gemini)
5. Internal Systems (Jira, CMDB, Data warehouse, Logs)

At no point does the AI model interact directly with the core systems. The orchestration layer always:

• Controls which data is sent
• Adds internal context
• Applies security rules
• Validates permissions
• Logs everything for audit

The key is not the model.

It’s the orchestration layer.

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2️⃣ Integration by Area (Simple Examples)

📡 Operations (Telecom / NOC)

Imagine an operations center with thousands of alarms per minute.

Before:

• Engineer manually reviews logs.
• Crosses historical data.
• Formulates hypotheses.

With integrated AI:

• Logs and historical incidents are indexed.
• A dashboard button appears: “Analyze pattern”.
• The system queries:
  • Related alarms
  • Affected topology
  • Similar past incidents
• The model summarizes and prioritizes.
• The engineer decides.

Visible benefit: From 40 minutes of analysis → 5 minutes.

In my view, the most effective companies don’t try to replace their existing systems. Instead, they build smart layers on top, making operations more efficient without risking what already works. That detail is fundamental.

What I imagine—and what I see coming—is that soon, with a simple search or chat, AI will be able to find and resolve precise issues by looking through logs, adding context, and integrating with tools like Jira, Kibana, Grafana, Teams, or any other platform. Full visibility, simplified workflows, and seamless integration: that’s the future I see for operations.

📈 Marketing

The CRM already exists.

The churn model already exists.

Generative AI doesn’t replace the predictive model. It complements it.

Typical flow:

• The system detects a customer at risk.
• AI generates:
  • Personalized offer
  • Risk explanation
  • Optimized email
• The manager validates.

The model doesn’t decide discounts. It suggests text.

📞 Call Center

The agent answers the call.

While talking:

• The system transcribes.
• AI suggests next best step.
• Automatically summarizes the conversation.
• Generates the structured ticket.

The agent doesn’t “use a chatbot”. They see recommendations within their usual system.

Successful AI is invisible.

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3️⃣ Integration with Legacy Systems (The Uncomfortable Part)

Here’s the real challenge.

Many companies have:

• Systems from 15–20 years ago
• Old APIs
• On-premise databases
• Monolithic architectures

You can’t connect a model directly to that.

So you do:

1️⃣ Intermediate APIs

Small services that:

• Translate formats
• Filter data
• Control permissions

2️⃣ Enterprise RAG

Instead of direct access:

• Index historical tickets
• Index manuals
• Index logs
• Create an isolated vector database

The model queries structured information.

It doesn’t execute changes in production.

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4️⃣ What Happens When a User Makes a Technical Query?

Real example.

You work in deployment and ask:

“I need the scope of deployment R-24.02, connected tickets, risks, and rollback procedure.”

The real flow in a mature company is:

User (Internal portal) ↓ Corporate AI Gateway ↓ Internal query (Jira, Confluence, CMDB, logs) ↓ AI model (if applicable) ↓ Validated response with references

Step by step:

1️⃣ The system validates your permissions. 2️⃣ Queries: - Associated tickets - Official documentation - Scope in CMDB - Similar historical incidents 3️⃣ Builds an “evidence package”. 4️⃣ The model summarizes and organizes it.

The model doesn’t invent the scope.

It organizes it.

Does the query go to the provider’s datacenter?

It depends.

In most implementations, cloud is used (Azure/OpenAI, Gemini, Claude), but only after filtering and structuring data.

In more regulated environments, it can run in private environments.

In mature architectures, critical parts are resolved internally and the model only drafts.

The difference is in system design.

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5️⃣ Security Architecture (What You Don’t See)

Serious implementations include:

• Data anonymization before sending
• Data Loss Prevention policies
• Granular user control
• Complete logging of prompts and responses
• Versioning of productive prompts
• Automatic quality validation

Security doesn’t depend on the model.

It depends on design.

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6️⃣ Real Differences Between Providers

OpenAI (Azure OpenAI)

Advantages:

• Strong integration with Microsoft
• Active Directory
• Permission control
• Clear enterprise SLA
• Configurable data residency

Ideal for companies already Microsoft-first.

Anthropic (Claude)

Advantages:

• Strong focus on security and alignment
• Long context for extensive documents
• Good performance in compliance and legal

Widely used in massive document analysis.

Google Gemini (Vertex AI)

Advantages:

• Natural integration with BigQuery
• Unified ML + GenAI ecosystem
• Strong multimodal capabilities

Ideal for companies already operating in GCP.

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7️⃣ Who Is Responsible If AI Fails?

This is the critical question.

In enterprise contracts:

• The provider limits responsibility.
• The model is defined as “assistive”.
• The final decision is the client’s.

In practice, visually:

1️⃣ If AI suggests wrong and a human executes ↓ Internal responsibility

2️⃣ If the service fails by SLA ↓ Limited contractual responsibility of the provider

3️⃣ If there is a data leak due to misconfiguration ↓ Company responsibility

AI doesn’t transfer responsibility. It redistributes it.

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8️⃣ How Companies Organize to Govern AI

Mature companies create:

• AI Governance Board
• AI Platform Team
• Clear usage policies

Business teams don’t call OpenAI directly.

They consume controlled internal APIs.

This avoids the chaos of “Shadow AI”—when employees use AI tools without oversight or control, creating risks for security, data, and compliance.

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9️⃣ When Should You NOT Integrate AI?

• If you don’t have clear data governance
• If you can’t audit decisions
• If you don’t know who assumes the risk
• If your systems aren’t organized yet

AI amplifies processes.

If the process is weak, the error scales.

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🔟 The Real Strategic Difference

The model is 20% of success.

Integration is 80%.

The companies that will win are not those that choose the most advanced model.

They are those that:

• Design better architecture
• Integrate AI frictionlessly for the user
• Maintain human responsibility
• Don’t lose operational control

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Conclusion

Anthropic, Gemini, and OpenAI compete in capability.

But in real companies, the true competition is about:

• Ecosystem
• Security
• Integration
• Governance
• Contractual trust

The real advantage isn’t just about using AI—it’s about integrating it thoughtfully and responsibly.

I have high expectations for what can be achieved today by integrating AI into large corporations, networks, telecoms, and critical systems. What I’m witnessing feels like a revolution, much like when the internet first appeared: we’re just beginning to understand the possibilities, and every week brings new tools and features.

If we managed not to destroy ourselves before due to politics or human interests, AI will surely change the way we evolve. At this pace, I have no doubt that in 10 years, internal systems and the way companies operate will be completely different. They have to be—there’s no other path.

What I still don’t know is how big and medium-sized companies will organize themselves to face all these changes. Is management ready to stop making decisions based only on instinct or conviction? More data also means more pressure not to make mistakes.

It will be fascinating to see how all this evolves. For those of us working in technology and telecom, this is just getting started.

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✍️ Claudio from ViaMind “Dare to imagine, create, and transform.”

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