What challenges did Telecom Argentina face before AI implementation?

Streamline IT operations and customer incident management. Replace on-premises models and create an AI framework for AIOps. Reduce misunderstandings and shorten response times for internal and external customers.

What was the impact of the AI solution?

Processes shortened from days to hours. Test cycles shortened from weeks to hours. Processes 3.8 TB of data every day. Improved infrastructure availability and expected NPS/customer satisfaction gains.

Source

GCPEvidence: Low40/100

Telecom Argentina speeds up technical incident resolution using Google Cloud tools

Use case typeIT operationsUpdated Jun 13, 2026

Telecom Argentina, a connectivity and technology provider serving more than 30 million customers in Argentina, is implementing an AI initiative focused on customer experience and IT operations. The company built an AIOps MVP from scratch in Google Cloud to improve network assurance, QA, and customer service. The solution processes 3.8 TB of data per day and supports a predictive model for customer service and incident management.

Organization: Telecom Argentina
Industry: Tech & Comms
Location: Argentina
Published: May 2026

Reported outcomes

Strategic outcomes

New product / capabilityBuilt an AIOps incident management capabilitySpeed & agilityShortened incident resolution processesSpeed & agilityAccelerated model testing cyclesScale & capacityProcesses large daily data volumes

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Telecom Argentina
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1AIOps
2Predictive Maintenance
3Customer Service Automation

Streamline IT operations and customer incident management.
Replace on-premises models and create an AI framework for AIOps.
Reduce misunderstandings and shorten response times for internal and external customers.

Built an AIOps minimum viable product in Google Cloud over about nine months.
Used BigQuery for in-house information processing and Google Cloud AI/ML capabilities for predictive modeling.
Developed an ML model to provide assessment metrics for customer claims and ran multiple simulations to improve the model iteratively.

Technologies

BigQuery Vertex AI Google Compute Engine

Processes shortened from days to hours.
Test cycles shortened from weeks to hours.
Processes 3.8 TB of data every day.
Improved infrastructure availability and expected NPS/customer satisfaction gains.

Architecture

The article describes an AIOps architecture built from scratch in Google Cloud with BigQuery for in-house information processing and Google Cloud AI/ML capabilities for predictive modeling. It mentions processing 3.8 TB of data per day, a data warehouse for model training, and virtual machines used for simulations and testing.

Sources & evidence1

Evidence: Low40/100Evidence strength