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7/3/25, 11:21 AM Inside Red Hat’s Telecom Strategy: AI-Enabled, Open, Secure, and Built for What’s Next - Telecom Review Middle East

Inside Red Hat’s Telecom Strategy: AI-Enabled,

Open, Secure, and Built for What’s Next

Posted on July 2, 2025 7 min Read

During Red Hat’s Telco Day in Dubai recently, Telecom Review was invited to explore the
technologies and strategies driving the sector’s agility, efficiency, and innovation with a
specific focus on artificial intelligence (AI).

In line with this, Fran Heeran, VP of Global Telecommunications Business at Red Hat, discussed
the evolving role of AI in telecom; why open source is key for interoperability and security; the
importance of trust, choice, and consistency in multi-cloud and edge environments; and how Red Hat
is building an open, flexible, collaborative, adaptable, and future-ready telco ecosystem.

How is the telco AI transformation reshaping traditional network architectures and operations
at scale?

The telecom industry has always embraced innovation cycles, and AI is no exception. However,
practical AI applications are now emerging, moving beyond theory into real-world deployments,
especially in network operations and customer experience (CX)

AI use cases today mainly fall into two categories: internal network applications and customer-facing
enhancements. The most immediate, visible impact is seen in customer interactions, where AI-
driven tools are improving response times, scaling services, and increasing customer satisfaction, but
beyond customer service, AI is being integrated into many areas of the network lifecycle, including
network design and network operations. Generative AI (GenAI) is playing an increasingly valuable role
in building next-generation networks, particularly as they grow more complex and are distributed
through advancements like 5G, 5G-Advanced, and (in the future) 6G. AI is helping operators manage
this complexity and optimize design processes efficiently.

Once networks are operational, AI is then applied to proactive management, moving beyond basic
machine learning (ML) to predictive operations. By analyzing past trends and anticipating future issues
(combined with the ability to take operational actions), AI enhances network resilience and efficiency.

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