Artificial Intelligence (AI) for Telecom Professionals

1. Training Introduction

The telecommunications industry is undergoing rapid transformation driven by Artificial Intelligence (AI). AI is now central to network optimization, customer experience, fault prediction, traffic management, cybersecurity, and business intelligence across 4G, 5G, IoT, and emerging networks.

This programme provides telecom professionals with a clear, structured understanding of AI concepts and their real-world application in telecom environments. Participants will learn how AI enhances network performance, reduces operational costs, and enables data-driven decision-making—without requiring advanced programming backgrounds.

 

2. Training Objective

By the end of this programme, participants will be able to:

1. Understand core AI, machine learning and data analytics concepts.
2. Identify AI use cases across telecom networks and operations.
3. Apply AI concepts to network planning, optimization and maintenance.
4. Understand AI-driven approaches to customer experience and revenue assurance.
5. Evaluate AI solutions for 5G, IoT and next-generation networks.
6. Recognize ethical, security and governance considerations in AI adoption.
7. Support AI-driven decision-making within telecom organizations.

 

3. Targeted Group

This programme is suitable for:

• Telecom engineers and network planners
• Network operations and performance teams
• ICT and data professionals in telecom
• Telecom managers and technical decision-makers
• Regulatory and policy professionals
• Graduate students in telecommunications, ICT, or data-related fields

 

4. Course Duration

• Total Duration: 2 Weeks
• Weekly Commitment: 16 Hours

Total Learning Hours: ~40–45 hours

 

5. Training Methodology

The programme adopts a practical and applied learning approach:

• Instructor-led conceptual sessions
• Telecom-focused AI case studies
• Visual demonstrations and simulations
• Group discussions and scenario analysis
• Hands-on exercises using sample telecom data (conceptual level)
• Capstone project focused on AI use-case design

Assessment includes quizzes, assignments, participation, and a final capstone project.

 

6. Course Modules & Content

Module 1 — Introduction to AI & Data in Telecommunications

• What is AI, machine learning, and data analytics
• AI vs traditional telecom optimization methods
• Data types in telecom networks
• Overview of AI-driven telecom transformation

Activity: Identify AI opportunities in a telecom value chain

 

Module 2 — Telecom Data & Analytics Fundamentals

• Telecom data sources (RAN, core, OSS/BSS, customer data)
• Data quality, preprocessing, and visualization
• Key performance indicators (KPIs)
• Role of big data in telecom

Exercise: Interpret sample telecom performance dashboards

 

Module 3 — Machine Learning Concepts for Telecom

• Supervised, unsupervised, and reinforcement learning (conceptual)
• Pattern recognition and prediction
• Model training, validation, and limitations
• Interpreting AI outputs for telecom decisions

Workshop: Match ML techniques to telecom use cases

 

Module 4 — AI for Network Planning & Optimization

• AI in radio network planning and optimization
• Traffic forecasting and capacity planning
• Energy efficiency and resource optimization
• AI-assisted 4G/5G network tuning

Exercise: Design an AI-driven network optimization scenario

 

Module 5 — AI for Network Operations & Maintenance

• Predictive maintenance and fault detection
• Anomaly detection in network performance
• Self-organizing networks (SON) concepts
• AI-driven automation in NOC environments

Workshop: Simulate predictive fault management use case

 

Module 6 — AI for Customer Experience & Business Operations

• AI in customer behavior analysis
• Churn prediction and service quality improvement
• AI-driven service personalization
• Revenue assurance and fraud detection concepts

Activity: Analyze AI use cases for customer experience improvement

 

Module 7 — AI for 5G, IoT & Emerging Networks

• AI in 5G network slicing and orchestration
• AI support for massive IoT deployments
• Edge AI and real-time decision-making
• Future trends: AI-native networks

Exercise: Map AI applications to 5G and IoT scenarios

 

Module 8 — Capstone Project: AI Use Case for Telecom

• Identify a telecom challenge
• Propose an AI-driven solution
• Define data requirements, expected benefits, and risks
• Present solution design and business impact

Deliverable: Capstone project report and presentation

 

7. Expected Outcomes

Participants completing this programme will:

✔ Understand AI and machine learning concepts in a telecom context
✔ Identify and evaluate AI use cases across telecom operations
✔ Apply AI thinking to network planning, optimization, and maintenance
✔ Support data-driven operational and business decisions
✔ Understand AI’s role in 5G and IoT ecosystems
✔ Recognize ethical, governance, and security considerations
✔ Communicate AI concepts effectively to technical and non-technical stakeholders

 

8. Certificate of Completion

Participants who:

• Attend at least 80% of sessions
• Complete all module assignments and assessments
• Submit and present the capstone project

will receive a Certificate of Completion issued by:

FOTADE Training, Research and Resource Development Centre

Certificate Includes:

• Participant’s Full Name
• Programme Title: AI for Telecom Professionals
• Programme Duration and Completion Date
• Summary of Competencies Acquired
• Official Seal and Signature of the Programme Director