The new Heavy Reading survey identifies how AI will reinvent next-gen networks.
Sponsored by Kyndryl
For business survival and growth, operators must stay competitive by differentiating themselves with leading-edge services and irrefutable customer experiences. Today, technological evolution is faster than in any previous generation, so operators must adapt their networks to be future-proof. Failure to prepare will limit choice and increase customer attrition.
Artificial intelligence (AI), especially the emerging generative AI (GenAI), is providing a boost to next-generation OSS/BSS, delivering greater insights than ever. This boost is enabling a new era of AI operations (AIOps) whereby operators can leverage machine learning (ML) and AI to automate processes, enhance the customer experience and operate with greater agility. Data consolidation, governance, flexible infrastructure and secure, mindful AI implementation will reinforce AIOps to support premium services and products.
This blog highlights findings from the Heavy Reading 2024 5G AIOps Operator Survey and investigates how operators will use AIOps for next-gen networks. The new survey provides the latest outlook on operator strategies for analytics, automation and AI. (To download a copy, click here.)
Key AI strategies
Operator workloads and compute requirements have significantly changed as digitalization, cloud technology and 5G spawn new processes and the need to increase efficiency. As a result, strategies must evolve to deliver and manage network infrastructure and services with greater agility, efficiency and adaptability — ensuring secure customer experiences.
In the figure below, a question from the Heavy Reading 5G AIOps Operator Survey asks what the most important network strategy is for organizations. More than a third of operators (38%) believe flexible and adaptable networks will have the highest importance in operator network strategies. Establishing adaptability from day one allows operators to support various applications and exploit distinct efficiency gains through optimal workload placement across edge, core or private clouds. Over a quarter (26%) will prioritize network capacity, reiterating the need for variable characteristics to provision and operate diverse services across industry, enterprise and consumer markets.
Automation security for the core to edge (17%) is lower in third place. Security attacks and breaches are frequent and sophisticated, with significant time and financial costs often incurred. AI-driven anomaly detection can quickly identify issues and enforce end-to-end protection. The lower scoring suggests operators are still developing strategies for this complex environment to ensure they can implement secure, automated protection.
Consolidation and management of hybrid environments (13%) is fourth, and ESG/sustainability (6%) surprisingly scores lowest. Operators are unlikely to place a lower importance on operational efficiency measures such as consolidation or ESG/sustainability, as each tends to have dedicated strategies. Heavy Reading believes this low scoring is due to votes being cast for other options.
What is the most important network strategy in your organization?
For operators to undergo large-scale AIOps integration as part of their next-gen platform, strategies must consider long-term data, consolidation, energy efficiency and risk mitigation. In particular, they should focus on the following:
Simplification: A major goal of AI is to streamline the complicated network landscape. But, before adding new AI processes, operators must consider practical steps, such as removing and consolidating older systems and determining cloud strategy (i.e., whether public, private or hybrid cloud offers efficiencies), to simplify their footprint.
Derisk AI: Gaining maximum benefit from AI will require governance, usage tracking and a full understanding of all processes. Building AI skills and knowledge among operational teams will be essential to derisking new processes. Such skills will allow responsible operation within expected parameters and help prevent the introduction of errors or security vulnerabilities.
Energy efficiency: Data center energy usage will likely rise as AI workloads infiltrate the entire network, adding additional compute infrastructure and cooling to accommodate model training and running. Operators should ensure they have detailed KPIs across their infrastructure and domains to monitor usage, estimate future resources and aid AI-driven energy efficiency techniques.
Use case priority: Operators have high expectations for AI across many use cases. Early priorities focus on enhancing customer experience and insights along with efficiency. Prioritizing use case integration will achieve greater overall efficiency and improve customer experience.
These are not small undertakings. To be successful, operators must draw support from the wider ecosystem on best practices as they begin AIOps integration.
The role of AIOps
AIOps is central to next-gen networks to ensure customer experience and future service adaptability. Data accuracy and rationalization, network adaptability and governance are key components of the AIOps system. Incorporating AIOps will necessitate a logical approach — operators must ensure high priority tasks, such as data quality and governance, are complete before processes utilize these assets.
For more information on this topic and the survey, check out this report: Heavy Reading's 2024 5G AIOps Operator Survey.
This blog is sponsored by Kyndryl.
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