Industry Primer for Telecommunications: Technology, Innovation, and AI
1h 33mBeginner2026-07-27
Authors

Starweaver
Course details
This course provides a working knowledge of the technology stack behind telecommunications, from the systems that keep operators running to the AI use cases now shaping network performance, customer experience, and revenue.
It begins with the core systems and the shift from legacy stacks to cloud-native, API-enabled architectures. Then it looks at how cloudification and automation are extending across IT and network operations, together with unified data platforms that make AI possible at scale. Next comes AI, from network performance optimization and churn prediction to fraud detection and customer lifecycle management. Here the course examines where AI delivers and where it fails, and how governance keeps operators reliable under national infrastructure and regulatory scrutiny. It closes with the consulting opportunities across AI strategy, technology modernization, data, and automation.
It begins with the core systems and the shift from legacy stacks to cloud-native, API-enabled architectures. Then it looks at how cloudification and automation are extending across IT and network operations, together with unified data platforms that make AI possible at scale. Next comes AI, from network performance optimization and churn prediction to fraud detection and customer lifecycle management. Here the course examines where AI delivers and where it fails, and how governance keeps operators reliable under national infrastructure and regulatory scrutiny. It closes with the consulting opportunities across AI strategy, technology modernization, data, and automation.
Concepts
Introduction
- Getting started
Technology Landscape Overview
- Core systems and the industry tech stack, part 1
- Core systems and the industry tech stack, part 2
- Infrastructure and security considerations
Digital Transformation Trends
- Cloud, automation, and the role of data
- Transformation challenges and customer experience
AI and Advanced Analytics in the Industry
- Decision intelligence and AI maturity
- AI use cases and associated risks, part 1
- AI use cases and associated risks, part 2
Innovation and Emerging Technologies
- Emerging technologies and innovation models
- Platform shifts and ecosystem change
Technology Challenges and Risks
- Legacy constraints and data quality
- Security, compliance, and change management
Consulting Opportunities in Tech and AI
- Data strategy and automation initiatives, part 1
- Data strategy and automation initiatives, part 2
Conclusion
- Course wrap-up