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

Talking about models is interesting. But in a real company the question isn’t which one answers a prompt better. The question is: How do I integrate AI without breaking my current systems, exposing sensitive data, or taking on unnecessary regulatory risk? That’s where the serious conversation starts.

1. The real architecture: AI doesn’t replace, it layers on top

In large companies — telecom, banking, energy, retail — AI doesn’t replace core systems. It integrates as a set of coordinated layers, each with a clear role:

Sequential architecture flow:

• User (NOC, Marketing, Product, Call Center)
• Internal application (CRM, OSS, ERP, Portal)
• AI orchestration layer (controls, enriches and protects data)
• AI provider (OpenAI, Anthropic, Gemini)
• Internal systems (Jira, CMDB, Data warehouse, Logs)

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

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

The key isn’t the model. It’s that orchestration layer.

2. How it integrates by area (simple examples)

📡 Operations (Telecom / NOC)

Imagine an operations center with thousands of alarms per minute.

Before: Engineer reviews logs manually. Cross-checks history. Forms hypotheses.

With AI integrated: Logs and historical incidents are indexed. A button appears on the dashboard: “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 experience, the most effective companies don’t try to replace their existing systems. They build intelligent layers on top, making operations more efficient without risking what already works. That detail is fundamental.

What I imagine, and how I see things evolving, is that soon, with a simple search or chat, AI will be able to find and fix specific issues by looking at logs, with context, and integrating with Jira, Kibana, Grafana, Teams or any other platform. Full visibility, simplified flows and total integration: that’s the future I see for operations.

📈 Marketing

The CRM already exists. The churn model already exists (churn is the probability that a customer will leave — something marketing monitors to avoid losses). In my experience, not everyone understands the concept, but it’s key: knowing who is about to leave lets you act before losing them.

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

Typical flow:

• The system detects at-risk customer.
• AI generates: personalized offer, risk explanation, optimized email.
• The responsible person validates. The model doesn’t decide discounts. It suggests text.

📞 Call Center

The agent handles the call. While they talk:

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

The agent doesn’t “use a chatbot”. They see recommendations inside their usual system. Successful AI is invisible.

3. Integration with legacy systems (the uncomfortable part)

This is where the real challenge is. In many companies there are:

• 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 giving direct access:

• Historical tickets are indexed
• Manuals are indexed
• Logs are indexed
• An isolated vector base is created

The model queries structured information. It doesn’t make changes in production.

4. What happens when a user makes a technical query?

I’ll separate how it should be (ideal) and how it really is today:

How I’d like it to be (ideal vision)

If I work in deployment and need to know about a rollout, a single query should be enough, for example: “Give me the summary for the production deployment of change CRQXXXXXX.”

The AI agent should take the question, query all relevant systems (dictionary, CRM, Jira, CMDB, logs), check scope, components, risks, operators, and deliver a clear summary and suggest category. All in one step, integrated and simple.

Visually, the ideal flow would be:

1. User (Internal portal) ↓
2. Corporate AI Gateway ↓
3. Query to internal systems (Jira, Confluence, CMDB, logs) ↓
4. AI model (if applicable) ↓
5. Validated response with references

How it really is today (current reality)

Currently, most companies don’t have this level of integration. The process is usually manual and fragmented:

• The user must know who to ask or which system to search.
• They query Jira, CMDB, Confluence, or ask an operator for help.
• They gather information from several systems and tickets.
• Often, the information is incomplete or outdated.
• Summary and validation depend on human experience and judgment.

In mature companies, some steps are automated (e.g. dashboards that cross ticket and log data), but rarely is the whole flow automatic and AI-validated. The trend is to move toward that ideal, but today it remains an integration and data quality challenge.

Does the query go to the provider’s datacenter?

It depends. In most implementations, the 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, the critical part is resolved internally and the model only drafts. The difference is in the system design.

5. Security architecture (what you don’t see)

Serious implementations include:

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

Security doesn’t depend on the model. It depends on the design.

6. 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 long documents, good performance on compliance and legal. Widely used in massive document analysis.

Google Gemini (Vertex AI)

Advantages: Natural integration with BigQuery, unified ML + GenAI ecosystem, strong multimodal. Ideal for companies already operating on GCP.

7. Who is responsible if AI fails?

This is the critical question. In enterprise contracts:

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

In practice, visually:

1. If AI suggests wrong and a human executes → Internal responsibility
2. If the service goes down due to SLA → Provider’s limited contractual responsibility
3. If there’s a data leak due to misconfiguration → Company responsibility

AI doesn’t transfer responsibility. It redistributes it.

8. How companies organize to govern it

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”, which is when employees use AI tools without control or supervision, generating security, data and compliance risks.

9. 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 in order yet

AI amplifies processes. If the process is weak, the error scales.

10. The real strategic difference

The model is 20% of success. Integration is 80%.

The companies that will win aren’t the ones that choose the most advanced model. They’re the ones that:

• Design better architecture
• Integrate AI without friction for the user
• Keep human responsibility
• Don’t lose operational control

Conclusion

Anthropic, Gemini and OpenAI compete on capability. But in the real enterprise, the real competition is in:

• Ecosystem
• Security
• Integration
• Governance
• Contractual trust

The advantage isn’t just in using AI, but in integrating it responsibly and carefully.

I have high expectations for what can be achieved today by integrating AI in large corporations, in networks, telecommunications and critical systems. What I’m seeing is a revolution similar to when the internet appeared: we’re barely understanding the potential, and every week new tools and features emerge.

If we didn’t destroy ourselves before through politics or human self-interest, AI will surely change how we evolve. At this pace, I have no doubt that in 10 years internal systems and the way companies operate will be completely different. It has to be; there’s no other way.

What I’m still not clear on is how large and mid-size corporations will organize to face all these changes. Is management ready to stop making decisions only by instinct or conviction? More data also means more pressure not to make mistakes. It will be interesting to see how all this evolves.

For those of us working in technology and telecommunications, this is just beginning.

---

✍️ Claudio from ViaMind

Dare to imagine, create and transform.

---

• Previous
  🤖 How AI Cuts Incident Time and Speeds Up Delivery in 2026

---