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Data-Driven System Modernization
System modernization, particularly in data infrastructure, is crucial for leveraging AI and driving business transformation, but internal barriers like lack of skills pose challenges.
Themes
Timeframe
near-term
Categories
Subcategories
Impact areas
Detailed Analysis
The increasing importance of data necessitates system modernization within TMT organizations. Effective data infrastructure is essential for unlocking the full potential of AI and driving meaningful business transformation. However, internal barriers hinder these modernization efforts. "Al certainly plays a role in helping CIOs and telcos modernize their legacy code. You can reduce your operational cost of running the network and billing systems, modernize your code and take that freed-up capital and reinvest it into innovation," Shah explains. This highlights the opportunity for AI to streamline operations and free up resources for innovation, directly addressing the challenge of outdated systems.
Context Signals
32% of TMT respondents consider their cloud transformation "very mature" (Publicis Sapient survey)
Telcos burdened by outdated billing systems and cumbersome code
Reliance on legacy BSS and OSS systems
Edge
Leveraging AI for automated code refactoring and modernization
Developing data lakes and warehouses for advanced analytics
Implementing cloud-native architectures for scalability and flexibility
