AI Strategy in Telecom: Building an Actionable Roadmap
Course Overview
This course is designed for technical executives and senior leaders in the telecom sector who need to make informed strategic decisions around the adoption and integration of Artificial Intelligence (AI) technologies. From foundational concepts to practical deployments, the seminar bridges the gap between strategic vision and technical implementation.
It emphasizes real-world use cases, best practices in AI lifecycle management, deployment models (edge/cloud), ethical considerations, and insights into how leading operators and vendors are using AI to achieve network intelligence, network autonomy, and monetization.
Target Audience
- CTOs, VPs, Heads of Network / IT / Strategy / Innovation
- Senior Network Architects and Operations Leaders
- AI Transformation Leads, Technical Program Managers
- Executives in charge of Digital Transformation, Automation, or Data Strategy
- Heads of OSS/BSS, Planning, CX Operations, and Digital Services
- Regulators and Government Leaders involved in AI policy-making for telecom
Duration & Training Format
Instructor-led Training
- Classroom: 3 days
- LIVE Virtual: 21 hours
*Note:
- A minimum of 8 or more participants is required for a Classroom session to commence.
- A minimum of 6 or more participants is required for a LIVE Virtual session to commence.
- LIVE Virtual courses can be conducted for 5 hours or 7 hours daily. Please note that the number of training days will be extended if you opt for 5 hours daily.
Upcoming Course Dates
There are no upcoming course dates currently scheduled for this course. If you are keen on attending this course, please register your interest via our course enquiry form for us to open a Classroom/LIVE Virtual class schedule for this course.
Course Objectives
At the end of this course, participants will be able to:
- Understand how AI is reshaping the telecom value chain, from infrastructure to services
- Identify and prioritize high-impact AI use cases across RAN, Core, Transport, and CX
- Analyze successful AI implementations by leading vendors and operators
- Gain insights into AI-driven network intelligence, automation, and customer experience enhancement
- Build clarity on the technological landscape, platforms, and frameworks enabling AI in telecom
- Build an organisational and talent plan for AI driven operations
- Quantify ROI using a TCO/NPV toolkit and craft a phased investment roadmap towards 2030
- Align AI opportunities with business outcomes, regulatory context (IMDA, PDPA, EU AI Act), and operational KPIs
- Build a forward-looking view on next-gen AI architectures, platforms, agentic integration and tooling, risks and security
Key Benefits
- Executive understanding of AI’s impact on networks, operations, services, and business models
- Confidence in selecting the right AI strategies and platforms for the organisation
- Insight into industry-proven frameworks, such as TMForum, AI-RAN Alliance, O-RAN Alliance AI use cases, and more
- Preparation to lead cross-functional AI initiatives in alignment with enterprise goals
- Deep strategic understanding of emerging trends: Agentic AI, GenAI, telco LLMs, AI security, AI regulation (e.g., EU AI Act)
Course Outline
Strategic Foundations and Technology Enablers
- AI in Telecom: Strategic Imperatives
- AI as a Business Enabler: Efficiency, Intelligence, Monetization
- How Telcos are Transforming: Global Benchmarks from Tier-1 CSPs
- Map TM Forum AN Levels to Business KPIs – Why L4 ≠just more Automation, it’s Closed-Loop Intent
- AI’s Alignment with BSS/OSS, Orchestration, and Customer Lifecycle
- Importance of Data as a Key Competitive Advantage
- Telco AI Use Cases: Network-Centric Applications
- Examples of ML, AI, GenAI, Agent AI, GraphAI, Graph RAG, Agentic RAG
- RAN: AI/ML for RAN, GenAI-assisted SMO, rApps, Dynamic Spectrum, Anomaly Detection, GenAI NOC, etc.
- Core: Traffic Prediction, Slice Management, Latency Optimization, etc.
- Transport and Edge: Energy-Aware Routing, Self-Healing Topologies, etc.
- AI Best Practices and Deployment Models
- The Telco-Grade AI/ML Lifecycle: Data Ingestion, Labeling, Training, Inference at Scale (Data Contracts and AIOps Pipelines for L4)
- Centralized, Distributed, and Federated AI in telecom
- Data Management Best Practices (Lineage, Cleaning, ETL) for High Quality AI
- MLOps at Telco-Grade Scale
- Deployment (On-prem, Cloud, Hybrid) Best Practices
- Telecom Agentic AI – Architecture, Tooling, Risks, Best Practices
- Vendor/Platform Comparison (Top Tier Vendors)
- Enabling Infrastructure for Autonomous Networks
- Standards and Frameworks: 3GPP, TMF, ETSI, ORAN
- Enabling Platforms (e.g., AIOps, SMO, ORAN-AI)
- Role of Real-Time Data Lakes, AI Fabrics, and Edge Compute
- Integration of Agents, Models, and Feedback Systems
Roadmap to Autonomy and Organizational Readiness
- 2030 Vision – Path to L4+ Autonomy
- Closed Loop Intents
- Agentic AI
- Network Digital Twins
- NeuroSymbolic Systems
- Self-Driven Operations towards AN L4
- Maturity Milestones
- AI-Driven Operations, CX, Personalization and Monetization
- Network Operations: Dark NOC and Agentic AI Operations
- Predictive Customer Churn, NPS Optimization
- AI-Powered Support: NLP, LLMs, Conversational Agents
- Service Design and Targeting using Behavioral AI
- Building the AI Business Case: ROI Toolkit
- TCO Template
- OpEx Savings Levers
- Revenue-Uplift Scenarios
- Sustainable Metrics (How to measure the value of AI use cases, Agentic AI – early days)
- Operating Model Transformation
-
- Organizational Design:
- Autonomous Networks
- Skills Pipeline and Talent Development
- Build vs Buy: Strategic Sourcing in AI
- Vendor Governance
- Overview – Responsible AI in Telco
- Explainability Toolkits, Model-Risk Scoring, AI Red/Blue-Teaming
- Privacy Engineering and Telco Data Localization
- Impact of Regulations (e.g. IMDA Singapore, EU AI Act and Data Privacy Laws)
- AI Auditability and Regulatory Alignment Frameworks
- Q&A / Open Discussion
Note: A Certificate of Completion will only be issued upon achieving at least 75% attendance for the course.
Pre-requisites
- Strategic or technical leadership in telecom domains (Network, IT, Strategy, Operations)
- Familiarity with 4G/5G network architecture and digital transformation programs
- Interest in AI/ML applicability for automation, analytics, service assurance, and innovation
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