AI-driven networks are reshaping industries. From automation to security, find out how advanced technologies are defining the next decade of enterprise connectivity.
Ali, please share a bit about your journey at Nokia and your role as Head of Technology for Nokia Mobile Network North America’s Enterprise business.
I have a long history in the telecommunications industry, working for a leading RAN vendor for 20 years after earning my PhD in Wireless Communications in 1996. My roles included Head of Network Design and Optimization, Head of Mobile Broadband Strategy for North America, CTO of Central Europe, and Head of Media Strategy in North America. I decided to broaden my skills and focus on consulting with enterprises. Recognizing that AI and machine learning (ML) are set to transform our industry, I reskilled in Big Data and the fundamentals of AI/ML.
I consulted on private wireless networks for a major cable operator using Citizens Broadband Radio Service (CBRS) and partnered with a leading IP/MPLS vendor on Software Defined WAN (SDWAN). These experiences emphasized the importance of enterprise solutions, especially since capital expenditures (Capex) for our 5G customers were expected to plateau until we find the next groundbreaking application—similar to how voice and text drove 2G and smartphones drove 4G. I believe the next big application for 5G will emerge from enterprises.
I joined Nokia in 2019 as the Head of Network Cognitive Services, focusing on applying AI and ML algorithms to network planning and optimization. I supported utility customers in their network activities, which led me to my current role as Head of Technology for Nokia Mobile Networks Enterprise in North America. What excites me about this position is the daily discovery of new applications that will benefit from 5G technology, and our work to demonstrate their value to enterprises.
With the increasing integration of AI and ML in networks, how do you envision the role of AI shaping the networks of 2030?
The network of 2030 will harness powerful AI and machine learning capabilities to enable intelligent management and orchestration of operations. This will allow networks to respond autonomously and in real time to changing needs and events.
Many network functions have progressed from simple algorithms to advanced automated processes and intelligent agents designed to optimize performance, efficiency, and security. This automation will facilitate interactions between network AI models and those in external applications and devices.
Key priorities for these future networks will include improving efficiency, sustainability, security, and the overall user experience. To achieve network-wide AI, a robust data strategy will be essential, focusing on data ownership, quality, and lifecycle management.
let us be on the side of change or change will move us to the side!
You mention “network-wide, real-time performance optimization” as a key feature of the future. Could you elaborate on how AI will enable this level of optimization?
With the advancements brought by 5G, the Radio Access Network (RAN) is evolving significantly in terms of virtualization, incorporating concepts like Centralized RAN (CRAN), Virtualized RAN (VRAN), and Open RAN (ORAN). A key component of this evolution is the RAN Intelligent Controller (RIC), a new virtualized technology that enhances programmability and allows for Self-Optimizing Networks (SON).
The RIC offers several real-time capabilities, including:
- Advanced Traffic Steering: This feature dynamically optimizes how traffic is distributed within the RAN, improving resource utilization, spectrum efficiency, and overall device performance. This leads to a better user experience.
- Anomaly Detection: This system quickly identifies and classifies unusual behavior patterns, enabling proactive issue identification. It reduces reliance on human intervention, speeds up problem detection, and can automatically trigger network healing processes, enhancing the overall availability of the RAN.
Customer and service experience are becoming more crucial. How do you foresee AI-driven analytics and assurance transforming the way businesses manage user experiences in the network of 2030?
Customer care is incredibly important to us at Nokia, and we are always striving to enhance customer satisfaction in every way possible. AI-driven analytics and assurance will most definitely play a transformative role in how businesses manage user experiences by 2030. For example, at Nokia we use our Nokia AI Digital Assistant to make life easier for telecom engineers and our network operations center (NOC) personnel. This AI chatbot is tailored specifically for the telecom industry and is trained on a vast array of technical documentation. It offers a user-friendly interface that allows quick access to essential tools, documents, and data, ultimately saving time and minimizing manual effort.
Here are some of the key features that make our AI Digital Assistant so effective:
- Complex Query Handling: It quickly tackles complex questions, providing instant access to the information users need.
- Interactive Conversations: Powered by advanced language models from Nokia Bell Labs and OpenAI, it engages users in meaningful dialogue.
- Enhanced Troubleshooting: It offers visual support to help diagnose and resolve issues more effectively.
By integrating this AI chatbot, we’re not just improving customer experience; we’re also boosting operational efficiency.
Looking ahead to 2030, I see networks becoming even more intelligent, incorporating extensive AI and machine learning capabilities. This will include:
- Network-Wide AI Management: Networks will be able to respond autonomously and in real time to emerging needs and events.
- Automation: There will be seamless interactions between network AI models and those in external applications and devices.
In this future landscape, I’m excited about how these advancements will revolutionize user experiences, making them smoother and more responsive than ever.
Security and privacy are top priorities for enterprises today. How will automated AI solutions enhance these areas while maintaining the network’s efficiency and sustainability?
Security and privacy are top priorities for enterprises today, and automated AI solutions are significantly enhancing these areas while also maintaining network efficiency and sustainability. AI is revolutionizing the security landscape by providing enhanced threat detection, which allows for rapid identification and response to potential threats. This capability greatly improves the protection of digital assets. Additionally, AI systems have a dynamic defense mechanism that learns and adapts in real time, creating a more resilient digital environment. By continuously monitoring network traffic, these AI solutions can detect and respond to threats as they emerge, significantly reducing the risk of breaches and ensuring the reliability of mobile and cloud services.
On the sustainability front, network operators are increasingly focused on implementing sustainable practices due to rising energy consumption and carbon emissions. Recent climate challenges have further highlighted the need for sustainable networks. As data and traffic grow from advancements in AI and digital transformation, sustainability becomes even more critical. AI-driven predictive analytics can optimize network power based on traffic patterns, and self-regulating networks can lower energy usage by identifying anomalies. AI-based energy management systems help reduce both energy consumption and operational costs, paving the way for more autonomous and sustainable network operations. In this way, AI not only strengthens security and privacy but also supports the broader goals of efficiency and environmental responsibility.
Network-wide AI management and orchestration seem pivotal for future operations. Could you discuss how this will facilitate interactions between AI models within the network and those in external applications and devices?
This is a summary of the above discussion
Network-wide AI management and orchestration are becoming essential for the future of telecommunications, facilitating seamless interactions between AI models within the network and those in external applications and devices. By leveraging AI and machine learning (AI/ML), the industry is advancing toward greater automation and optimization of network operations, enhancing efficiency, agility, and resilience.
One significant aspect of this approach is automation combined with predictive management. AI/ML enables automated orchestration, allowing predictive analytics to anticipate issues before they arise. This proactive management helps adjust resources—like bandwidth and power—based on real-time usage patterns, optimizing network performance while reducing operational costs.
Dynamic resource allocation is another crucial feature. With AI/ML-driven orchestration, networks can allocate resources in real time to adapt to fluctuating demands. This capability is particularly beneficial in complex environments characterized by 5G, IoT, and edge computing, ensuring better utilization of network resources and minimizing downtime.
Sustainability is also a key focus. By regulating power and energy consumption based on traffic trends, AI/ML contributes to energy efficiency, which can significantly lower carbon footprints. This aligns with the industry’s growing emphasis on sustainable practices in response to climate challenges.
Furthermore, AI/ML models enhance network reliability through self-healing and anomaly detection. These systems can autonomously identify and resolve issues without human intervention, reducing the need for manual troubleshooting and ensuring a more resilient network.
Comprehensive end-to-end orchestration is essential for coordinating across various network layers and domains, ensuring seamless cooperation between core, access, and edge networks. AI/ML enhances this by optimizing the flow of data and services across the entire network infrastructure.
Ultimately, the integration of AI/ML in network orchestration is poised to enhance the user experience. By continuously optimizing network latency, throughput, and service quality, the telecommunications industry is moving toward creating automated, energy-efficient, and resilient networks capable of managing themselves in a complex and dynamic environment.
You’ve mentioned the concept of “Network Digital Twins.” Could you explain how sandboxing and simulations using these digital twins will benefit network design and operations in the coming decade?
Network Digital Twins is set to transform network design and operations in the coming decade. These digital twins provide a real-time view of network operations, enabling organizations to monitor performance and predict maintenance needs before issues arise. This proactive approach enhances efficiency and productivity by minimizing disruptions.
Currently, Network Digital Twins offers several critical capabilities. It allows users to visualize the network from the perspective of various industrial devices, ensuring that service-level agreements (SLAs) are met. In cases of sub-optimal performance, the digital twin can recommend corrective actions, and it enables testing of new scenarios without affecting the live environment.
Looking ahead, the benefits of Network Digital Twins will only grow, particularly as industries embrace digital transformation and the principles of Industry 4.0. By facilitating sandboxing and simulations, these tools will allow organizations to explore various configurations and scenarios, optimizing network design and operational strategies. Ultimately, Network Digital Twins will play a crucial role in ensuring that networks are resilient, efficient, and capable of adapting to future demands.
Finally, generative AI is being applied in many sectors. How do you see Gen AI-based virtual assistants improving the work of coders, customers, and network operations teams in the future?
Generative AI is poised to significantly enhance the work of coders, customers, and network operations teams across various industries. As organizations increasingly integrate generative AI into their operations, we can expect a shift toward fully autonomous networks that can sense, think, and act independently, thereby reducing the need for human intervention. This advancement includes closed-loop automation, which empowers networks to autonomously detect, predict, and respond to issues, ultimately improving reliability and efficiency.
Generative AI also introduces a range of powerful capabilities that can transform network operations. For instance, it can create dynamic data, summarize unstructured information, and generate tailored recommendations for complex tasks. These functionalities will allow teams to make more informed decisions and streamline workflows. Additionally, generative AI enables intent-based orchestration, allowing networks to align with specific business outcomes and self-adjust to meet organizational goals.
Finally, when combined with tools like network digital twins and AIOps, generative AI supports the pre-validation of network actions, minimizing risks and ensuring smoother operations. As these technologies continue to evolve, they will not only improve operational efficiency but also enhance customer experiences, making networks more responsive and adaptive to changing needs.
Ali Shah
Head of Technology, Enterprise for Nokia Mobile Networks in North America
Dr. Ali Shah is the Head of Technology, Enterprise for Nokia Mobile Networks in North America. He joined Nokia in 2019 as the Head of Network Cognitive Services. Ali has experience in Mobile Network Strategy & Solutions, Network Planning and Optimization for Mobile Network Operators and Enterprise customers. Prior to Nokia he held several consulting/leadership positions with Tier 1 vendors and Operators.
Dr Ali Shah’ interests are around the Application of 5G, IoT and AI/ML to solve Enterprise challenges around security, automation, opex reduction and improve the resiliency/reliability.
