Session Chair: Jerry Hogan - Solara Technical

This session will cover the future of connectivity, leveraging AI and automation for secure, reliable, and scalable networks. For over a decade, Verizon has been strategically integrating AI into its operations focusing on enhancing internal efficiencies, elevating customer experiences, and optimizing the network infrastructure. Take a deep dive into how Network Operations is using AI to support fault and performance management, forecast and optimize resource utilization, and mitigate risks across Verizon’s network. 

Session Chair: David Curran - Frontier Communications

As broadband networks rapidly digitize, operators must equip their teams with the data science and analytics skills needed to turn vast data streams into actionable insights. This session explores how big data, AI, and machine learning (ML) are transforming network performance, workforce development, and customer experience. Learn practical strategies to build internal data pipelines, close skill gaps, and future-proof your organization.

Session Chair: Francis Alueta - Altafiber/Hawaiian Telcom


Explore how automation, AI and Network-as-a-Service (NaaS) empower organizations to enhance agility, reliability, and security. Learn how AI-driven observability improves network operations and edge decision-making accelerates service deployments from months to minutes. Explore the benefits of NaaS and discover strategies for integration automation, AI, and cloud to enhance your network’s agility.

Session Chair: Jim Pelegris - SENKO Advanced Components

Technological innovations in power and energy storage solutions allow operators to improve resilience in networks ranging from RAN to edge data centers, FTTx to Small Cells. Panelists will address questions such as: How do different network topologies cope with a fallible power grid? What solution best fits each application? How do we handle remote sites in distributed networks? How can we meet sustainability challenges and more? 

Session Chair:William Kurtz - Norvado Inc.


While there’s significant "buzz" about AI/ML solving all network evolution woes, few people understand how AI works, and what questions to ask when presented with a problem that a vendor says can be "easily" solved with AI. This session will help leaders and decision-makers clarify terminology, determine AI/ML strengths and weaknesses and gain key insights to separate AI’s promises from its real potential. 

Session Chair: Peter Fretty - Endeavor Business Media

Learn the critical applications of AI and ML across the network by diving into the classification of network use cases, the evolution of AI and ML, and the path to network foundation models. You’ll also learn the role of AI and ML in driving network efficiency and performance, addressing challenges like the volume of devices, complex dynamics, varying traffic, and the need for near real-time management.

Attendee Learnings:
•    Real-life network use cases
•    Path(s) to network foundation model(s)
•    AI and ML can drive network efficiencies.

Session Chair:William Kurtz - Norvado Inc.


Gen AI adoption is expected to grow from 19% to 48% within two years. CSPs are projected to increase their Gen AI spending by 6X within two years. Attend this session to learn how Gen AI can be used for network monitoring, network operation management, predictive maintenance, cybersecurity, and fraud mitigation.

Session Chair: Hayden Beeson - Endeavor Business Media

The adoption of AI is on the rise, with AI in telecommunications expected to grow at a CAGR of 28.2% from 2023 to 2030. Whether you're deploying new fiber networks or maintaining existing ones, AI and automation are becoming essential tools in breaking down data silos, integrating third-party data, keeping as-builts updated, and expediting field data capture. You will learn about integrating diverse information streams into a comprehensive and reliable dataset; migrating data between systems without compromising integrity or security and using AI to enhance data capture and reduce manual workload.