Introduction

A leading telecom provider serving millions of subscribers, faced significant challenges in maintaining network reliability and ensuring customer satisfaction. Frequent network outages, especially during peak usage times, led to prolonged downtime and a surge in customer complaints. The company’s traditional monitoring systems were reactive, addressing issues only after they occurred, which resulted in operational in efficiencies and delayed resolutions. The company was missing an AI OPS solution to validate the pre-launch network features. To overcome these challenges, Colate’s cocreate implemented AI Ops (Artificial Intelligence for IT Operations) to transition to a proactive and predictive operational model.

Problem Statement

The network operations struggled to keep pace with the vast volume of data generatedacross their complex infrastructure. Their existing tools lacked the capability to analyzethis data in real-time, leading to several critical issues:
‍

• Missing network validation digital space: The company was missing network validation system for digital space
• Frequent Network Outages: Particularly during high-traffic periods, outagesdisrupted services.
• Reactive Issue Resolution: Problems were addressed only after they occurred,causing prolonged downtime.
• Manual Processes: Routine tasks like configuration updates were prone tohuman error and inefficiencies.
• Customer Dissatisfaction: Service disruptions led to rising complaints anddeclining satisfaction.
• The company needed a solution to predict and prevent network issues, automaterepetitive tasks, and enhance overall efficiency.

Approach

Company adopted AI Ops to leverage artificial intelligence and machine learning for advanced network management. The partnership with Colate’s cocreate platforms tuns the network giant to rollout the network features faster in pre-launch phases. The key objectives were:
‍

• Predictive Maintenance: Analyze historical and real-time data to identify patternssignaling potential failures.
• Automated Resolution: Enable automatic alerts and corrective actions fordetected issues.
• Operational Efficiency: Automate routine maintenance to reduce errors and freestaff for strategic work.

Solution

Company partnered with Colate, AI Ops platform provider to integrate cutting-edge AI capabilities into their network management systems. Cocreate, by Colate, made the data integration, processing, and automation seamless. The implementation included:

1. Data Integration and Analysis

• Machine learning models were trained on historical data to detect patternsand anomalies linked to network failures.
• The AI Ops platform collected data from network logs, performancemetrics, customer feedback, and environmental sensors (e.g., cell towertemperatures).

1. Predictive Analytics

• The system monitored the network in real-time, predicting issues beforethey occurred. For instance, if a cell tower exhibited overheating orunusual traffic, the AI forecasted a potential failure within 24 hours.
• Alerts were automatically sent to the operations team, and in some cases,the system rerouted traffic to prevent outages.

1. Automation

• Routine tasks—such as software updates, configuration changes, and resource allocation—were automated, minimizing human error and ensuring consistency.
• The platform optimized network capacity based on demand, preventing bottlenecks during peak usage.

3. Security Enhancements

• AI-driven analysis of network traffic identified anomalies suggestingsecurity threats (e.g., DDoS attacks), enabling proactivecountermeasures.

Challenges During Implementation

Colate’s cocreate platform is a cutting edge solution to solve tomorrow problem. The idea behind inteligenet automation with AI, made it easy for Colate to integrate the solution during implementation. The deployment of AI Ops presented hurdles:

• Legacy System Integration: Connecting the platform to companies older infrastructure was complex and time-consuming.
• Data Quality: Ensuring data was clean and structured for AI analysis required significant preprocessing.
• Staff Resistance: Employees accustomed to traditional methods needed training to trust and adopt the AI system.
• These challenges were addressed through collaboration with the platform provider, investment in data management, and a phased rollout with comprehensive staff training.

Results

Post-implementation, Company achieved significant improvements:

• One stop solution: Colate’s cocreate platform become one-stop-solution for the team to validate the network featues faster and easier. It reduced the operation cost by 40% in capex and 80% cost reduction in time.
• Reduced Downtime: Network outages dropped by 50%, from 10 hours to 2 hours per month.
• Faster Resolutions: Mean time to resolution (MTTR) improved by 40% due to proactive issue detection.
• Higher Customer Satisfaction: Complaints fell by 30%, and satisfaction scores rose by 25%.
• Cost Savings: Automation cut maintenance costs by 20%, freeing staff for strategic tasks.
• Enhanced Security: Proactive threat detection bolstered network integrity. Additionally, the AI Ops platform provided insights into customer behavior and network performance, aiding Company in planning expansions and refining marketing strategies.

Conclusion

By embracing Colate’s cocreate, AI Ops platform , Company transformed its operationsfrom reactive to predictive, leveraging AI and machine learning to anticipate issues,automate tasks, and optimize resources. This shift improved network reliability,customer satisfaction, and operational efficiency while reducing costs. Despite initialintegration and adoption challenges, companies success highlights the transformativepotential of AI Ops in the telecom industry, offering a model for others to emulate.