Compare generative Artificial Intelligence (AI) use cases for telecommunications and build an application strategy that appreciates risks and opportunities.
Preempt common challenges to generative AI usage, planning in key areas such as data management, cost, security, and implementation.
Assess market impact and long-term support for network automation.
Critical Questions Answered
Which generative AI strategies are gaining traction in telecommunications?
Should generative AI be developed for network management, or reserved for customer care and business operations?
What are the main components of a successful telco generative AI strategy—from selecting a model type to data best practices?
How does generative AI relate to traditional AI in advancing automation?
Research Highlights
Interpretation of market trends in generative AI as a function of risk.
Detailed breakdowns of the benefits and challenges of generative AI for the telco network.
Analysis of AI-based automation in three progressive stages.
Who Should Read This?
Planners within telco organizations forming a generative AI strategy.
Innovation leaders for Communication Service Providers (CSPs) who need to address the challenges of generative AI implementation.
Business executives for CSPs who need to calibrate generative AI plans with broader company culture and strategy.
Decision-makers for organizations that offer telco solutions for generative AI (e.g., platforms, data) to assess opportunities in the network.
Table of Contents
Executive Summary
Recommendations
Generative AI and Its Use
Use Cases: Applicability
Markets: Applications and Trends
Interpretation of Market Trends
Generative AI’s Telco Benefits
Operational Streamlining
Performance Improvements
Challenges
Misalignment with Business Strategy
Investing in Proprietary Models
Protecting Customer Data While Using Public Models
