As businesses increasingly adopt edge computing for real-time processing and decision-making, the ability to run AI at the edge is becoming imperative.
With decades of experience in AI development and deployment, Dell Technologies is ideally positioned to reshape how enterprises harness its potential, writes Doug Woolley, GM and vice-president of Dell Technologies South Africa.
It is clear that edge computing has emerged as a strategic paradigm shift in the world of data processing. Unlike traditional centralised cloud computing, edge computing brings computation closer to the data source – whether it’s a fleet of cars, automated industrial machines, drone, or autonomous vehicle.
It’s like having a mini data centre right where the action happens. IDC reports worldwide spending on edge computing is expected to reach $232-billion in 2024, an increase of 15,4% over 2023; and by 2027 62% of enterprises’ data will be processed at the edge. With this, edge locations are becoming ideal for not only collecting and aggregating local data, but also as input for AI processes.
A further IDC forecast shows that enterprise spending (which includes GenAI software as well as related infrastructure hardware and IT/business services) is expected to more than double in 2024 and reach $151,1-billion in 2027 with a compound annual growth rate (CAGR) of 86,1% over the 2023-2027 forecast period.
AI at the edge is a transformative leap for industries, with the ability to offer unparalleled benefits such as real-time responsiveness, privacy compliance, cost efficiency, and edge autonomy.
The telecoms sector stands out as a prime beneficiary of this technological revolution. The South African telecommunications market is expected to show an annual growth rate of 2,41%, resulting in a market volume of $17,6-billion by 2028. There is growing interest in the telecoms industry on how best to leverage AI to generate business growth.
Here are some of the benefits telecoms providers can unlock by seamlessly integrating AI into their edge infrastructure:
- Latency reduction: By bringing data processing closer to its source, AI algorithms deployed at the edge can prioritise essential tasks such as real-time video streaming or gaming. This localised processing effectively slashes latency, ensuring a smoother and more responsive user experience.
- Network optimisation: AI algorithms deployed at the edge can help enhance network optimisation by efficiently analysing network traffic patterns, predicting congestion points and dynamically rerouting traffic. This approach not only enhances network performance but also ensures optimal utilisation of resources, resulting in improved reliability and seamless connectivity for users.
- Enhanced security: Through the integration of edge AI, telecoms networks can further use algorithms to protect their security infrastructure by swiftly detecting anomalies and neutralising potential threats at the source. By embracing enhanced zero-trust principles and processing data locally at the edge, telecoms providers can significantly enhance data privacy and security, ensuring robust protection for users.
- Energy efficiency: Sustainability and lowering energy costs are top of mind for almost all telecoms operators. With this, AI stands to play a pivotal role in network energy efficiency – especially at the edge in usually unstaffed locations. By leveraging AI and machine learning to gain insights into equipment usage patterns, operators can automate energy management processes and optimise energy consumption at edge locations.
- Predictive maintenance: By harnessing the natural language processing (NLP) powers of GenAI, telecoms operators can greatly improve network troubleshooting. AI can swiftly identify irregularities, slashing troubleshooting durations from days or hours down to mere minutes. This accelerated response not only minimises downtime but also results in substantial cost savings for operators, enabling them to allocate resources more efficiently and maintain optimal network performance for users.
- Diversified services and revenue opportunities: With AI-ready infrastructure at the edge, telecoms operators are also able to introduce novel and innovative services that address specific customer needs and preferences. By capitalising on their strategic positioning to deliver inferencing services at the edge, telecoms operators can offer rapid, personalised and contextually aware solutions to customers. This not only enhances user experiences but also opens avenues for additional monetisation, driving sustained growth and competitiveness in the telecommunications market.
As telecoms operators continue to embrace the edge ecosystem, the transformative potential of AI at the edge will not only be advantageous but imperative in navigating the future of modern networks.
AI is expected to redefine the telecoms landscape and permeate through the entire network stack, presenting unique opportunities for telecoms operators to unlock new possibilities. From intelligent automation to predictive analytics and personalised experiences, edge AI ushers in a new era of innovation, enabling operators to chart a course towards unparalleled growth in the digital age.
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