AI Training for Telecommunications & Connectivity

How can telecoms teams use AI to improve network reliability and customer service?

Excentra AI Academy can tailor AI training courses for telecoms professionals around the work of network, customer operations and revenue assurance teams. A network engineer might use an AI alert to investigate repeated service interruptions. A contact centre manager might check whether an AI agent uses the correct tariff rules when explaining an unexpected charge. Each decision depends on what the system can see and how staff check its output.

Course examples can reflect the work of mobile and fixed-line operators, broadband providers and connectivity companies. Participants can practise interpreting network and customer data, setting limits on automated actions and deciding when a case needs specialist review.

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In short

AI training courses for the telecommunications sector help professionals evaluate AI in network operations, customer service and revenue assurance. A service assurance team might compare an AI-generated fault diagnosis with alarms and recent network changes. A fraud analyst might check whether unusual traffic warrants investigation. Course exercises can show participants what evidence to check and where to hand the decision to the responsible team.

Where AI is used in Telecommunications & Connectivity

01

Customer service agents

Answer routine billing and service enquiries from approved sources. Pass unresolved cases to staff with the information already collected, and review repeat contacts to assess whether issues stay resolved.

02

Personalisation and churn

Analyse usage and service patterns to identify customers who may leave and assess suitable retention offers, using data within applicable privacy rules and customer preferences.

03

Fraud and revenue assurance

Flag unusual account, device or traffic patterns that may indicate subscription fraud, SIM-box activity or roaming abuse. Analysts review alerts against account and network records.

04

Network monitoring and service assurance

Correlate alarms, performance data and recent network changes to investigate service degradation and prioritise field work.

05

Workflow automation

Use AI to classify provisioning requests, identify incomplete orders and route billing or reconciliation exceptions for review.

06

Governance and security of AI

Define which customer records and network systems an AI tool can access, what actions it can take and who approves changes to those permissions.

AI Product Development for Business Leaders

From AI Opportunities and Product Strategy to Development, Governance & Scalable Business Value

5 days · Live online · In-house available

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Artificial Intelligence (AI) for Leaders and Managers

Leading organizational transformation through AI-powered decision-making and innovation.

5 days · Classroom or live online · In-house available

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Start here in any sector

Thirty Excentra courses apply whatever the industry. These four are where most organisations begin.

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Generative AI

Turn Ideas and Information into Valuable Work with Generative AI

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AI Leadership, Strategy & Transformation

Set Direction, Prioritise Investment and Lead Organisational AI Adoption

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AI Governance, Risk & Responsible AI

Establish Accountability, Assess Risk and Strengthen Oversight of AI Use

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AI for Business Functions

Connect AI Applications with the Responsibilities of Your Profession

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Who attends

Executives and business unit heads: Executives and business unit heads who select AI initiatives and assess their impact on service performance, operating costs and customer outcomes.
Customer operations and marketing: Customer operations and marketing professionals who oversee contact centre workflows, service communications, personalisation and retention.
Network, engineering and IT: Network, engineering and IT professionals who investigate faults, maintain service levels and assess how AI tools fit into existing systems.
Risk, fraud, security and compliance: Risk, fraud, security and compliance professionals who review alerts, investigate revenue leakage and oversee customer data and system access.

Standards the courses draw on

These references provide context for AI use in telecoms networks, information security and customer data governance.

  • ITU and GSMA guidance. ITU-T recommendations address AI-based network automation and fault management. The GSMA Responsible AI Maturity Roadmap helps telecoms operators assess their AI governance practices.
  • ISO/IEC 27001 and ISA/IEC 62443. ISO/IEC 27001 sets requirements for information security management systems. The ISA/IEC 62443 series addresses the security of industrial automation and control systems where relevant to operational technology.
  • EU AI Act and data protection law. The EU AI Act illustrates a risk-based approach to AI regulation. The GDPR provides an example of obligations for processing personal data where it applies.
  • ISO/IEC 42001 and the NIST AI Risk Management Framework. References for establishing an AI management system and assessing and managing AI risks. 

In-house training for telecommunications & connectivity teams

In-house training for telecoms teams: Excentra AI Academy can tailor an AI training course to your team’s responsibilities. A network and customer operations group might examine how an outage alert becomes a customer update. A revenue assurance team might review a simulated traffic anomaly. Exercises can use approved sample records or synthetic data that reflects your workflows.

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What AI skills do telecom professionals need?

Telecoms professionals need to understand what data an AI system uses, how to check its output and when to escalate an issue. Network teams should be able to compare a suspected fault with service and change records. Customer operations teams need to assess response quality and handovers. Fraud analysts need to distinguish suspicious activity from legitimate changes in usage.

How is AI used in telecom fraud detection?

Telecoms operators can use AI to flag unusual traffic or account activity and identify links between accounts, SIMs or devices that merit investigation. For example, a sharp increase in calls may warrant a check for SIM-box activity. Analysts review the pattern alongside legitimate usage and other account or network evidence before deciding how to respond.

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Frequently asked questions

Yes. Excentra can adapt course examples to the work your teams do, such as monitoring service quality, reviewing a provisioning backlog or investigating unusual usage. The learning objectives can reflect the decisions participants make and the information they use to make them.

Teams can learn to define which billing and service enquiries an AI agent can handle, what information it should pass to an adviser and how to assess the quality of its responses. A course exercise can use repeat contacts and escalations to assess whether a billing issue was resolved.

Participants can learn to interpret alerts, compare them with account and traffic records and identify findings that warrant investigation. Exercises can also explore how confirmed cases and false positives help analysts evaluate an alert rule or model.

Network engineers can learn to evaluate AI-generated fault indications against alarms, service performance and recent changes. For example, they might compare a rise in dropped calls with network events and maintenance records before deciding where to investigate.

Prerequisites depend on the course. Reviewing agent responses, assessing fraud alerts and interpreting network data can be practised without writing code. Courses involving model development or integration with network systems may require technical experience.

Find the AI training your telecommunications & connectivity team needs

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