AI-Enabled Maintenance and Testing in Telecoms Image

AI-Enabled Maintenance and Testing in Telecoms

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Actionable Benefits

  • Determine key trends that are driving the need for ‘intelligent’ maintenance solutions.
  • Educate decision makers and product developers on the impact that AI, ML and the cloud has on telco maintenance solutions.
  • Identify core technologies that vendors can use to design and develop next-generation maintenance and testing tools.  

Critical Questions Answered

  • How are maintenance solutions evolving in light of cloud tools, software and DevOps methodologies?
  • What are the key drivers that call for tools that are technology- and location-agnostic and that traverse multiple clouds and telco domains?
  • How are some telco vendors implementing AI and ML in their maintenance and testing solutions?

Research Highlights

  • Summaries of key strands that vendors must consider as they seek to bring to market maintenance solutions that are more suitable to cloud-native implementation.
  • Analysis of main AI/ML functions and their applicability in aiding CSPs to establish autonomous and adaptable troubleshooting.
  • An overview of operational requirements that next-generation maintenance solutions should address.  

Who Should Read This?

  • Chief Technologists and other key decision makers for product design and development.
  • Innovation Leaders, Architects and Strategy Principals from CSPs who need to understand how cloud and software will impact maintenance in their operations.
  • CSPs, network equipment vendors and system integrators who need to understand the dynamics of AI and ML integration in their operations and processes.  

Table of Contents

Table of Contents

1. EXECUTIVE SUMMARY

2. RECOMMENDATIONS

3. TRENDS AND DRIVERS FOR NEXT-GENERATION MAINTENANCE AND TESTING TOOLS

3.1. Trends in RAN and Core Networks
3.2. Manual Processes and Legacy Equipment
3.3. A Need to Redesign, Retool, and Prepare for Service-Centric Operations

4. IMPACT OF AI, ML, AND THE CLOUD ON MAINTENANCE AND TESTING

4.1. Three Major Types of AI Functions
4.2. Optimum Ecosystem Dimensions for AI in Radio Networks
4.3. Evolution of Legacy Maintenance and Testing
4.4. Closed-Loop Maintenance
4.5. Hardware and Physical-Oriented Testing and Maintenance

5. CORE PILLARS FOR AI-ENABLED MAINTENANCE AND TESTING

5.1. A Common AI-Enabled Automation Platform
5.2. Location-Agnostic Maintenance and Testing
5.3. Maintenance and Testing for Service-Centric Operations
5.4. Other Key Pillars of Telco Maintenance and Testing

6. FORECASTS FOR AI-ENABLED MAINTENANCE TOOLS

7. VENDOR ECOSYSTEM

7.1. Amdocs
7.2. Cisco
7.3. Ericsson
7.4. Netcracker
7.5. Nokia
7.6. TEOCO
7.7. ZTE