How the AI Gold Rush Has the Potential to Take the Ceiling off The Network's Potential

It may feel to many like Artificial Intelligence (AI) has just arrived because people are now using it to create everything from art (through Dall-E) to poems (through ChatGPT). But anyone in business or technology knows that AI has been in use for some time now. Whether it’s automated accounting software or robotic manufacturing lines, McKinsey points out that, as of last year, the adoption of AI technologies in one area of a typical business has more than doubled since 2017, with the proportion of organizations using it hovering between 50 and 60 percent in that time. 

The rapid uptake of the technology among consumers, particularly Generative AI and Large Language Models, is making enterprises, including Communication Service Providers (CSPs), take a deeper look at use cases across the board. This rapid consumerization of AI technology is akin to the great cloud rush in years gone by.

Harnessing the fuel of AI

The key to AI is data – it’s the fuel that makes the AI engine hum. And when it comes to the telecommunications industry, perhaps more than any other, each network is saturated with this fuel, just waiting for it to be refined.

The data generated by today’s networks is vast and extremely valuable in terms of training machines to make use of it by creating insights and taking actions. By taking the data generated in the daily operation of communications networks, it’s possible to identify patterns and form effective policies to guide the machine’s decision-making skills when new situations arise.

And analyzing that data is also easier than ever, thanks to AI. Traditionally, data could be collected by machines, but the analysis and implementation of new policies would need to be handled by humans. AI has, therefore, entered the space as an enabler, which can evaluate data without human intervention and then determine the correct action before implementing it within the workflow.

Building the case for AI

The possibilities for CSPs are both clear and limitless. Take network bandwidth management as a specific example. Today’s dynamic network infrastructure contains millions of devices, and service providers are increasingly aiming to ensure that each of them are connected at all times, and receiving the services end-customers are paying for. AI provides deep network insights in real time, which can help service providers properly allocate bandwidth depending on demand, thereby ensuring that the path from the data center to the user is established and maintained.

AI can also thrive in conducting management and maintenance operations. Self-healing networks are envisioned to be the next step in intelligent networking, enabling the network to completely repair (and potentially even reconstruct) itself or reroute in a matter of minutes, should a failure occur. Using real-time data analysis, AI will compress decision-making timelines by orders of magnitude, minimizing or even eliminating disruptions from damaged cables or attempted network intrusions to save service providers significant downtime and revenue losses.

All told, AI will save significant time and resources for CSPs, as data collection and analysis can all be automated – and with intelligent decision making, engineers can be freed from routine network maintenance tasks to deal with more challenging core issues affecting the business. In addition, it has the potential to vastly improve security through proactive network monitoring using historical data to spot anomalies on network services and signs of intruder connections, thereby identifying a threat and conducting self-healing to protect the network and preserve functionality.

Implementing AI

While AI will prove critical to the networks of tomorrow, implementing it will require care and consideration. Each network requires tailored deployments and solutions and the ability to scale. Optimizing the network ahead of deployment is required to achieve an enhanced experience for the user, with minimal disruptions due to network failures and improved customer support services.

In addition, visibility and control will be critical both to the management of the AI itself and the health of the network. Using advanced analytics and machine learning capabilities will allow service providers to easily identify potential areas of risks in their network so that they can proactively take action and maintain service delivery.

By leveraging data insights and applying analytics through intelligent automation platforms that leverage AI, network providers can more easily evolve their networks to be faster, smarter, and governed by data-driven business policies that ensure profitability by providing a superior customer experience.

As the world consumes more bandwidth and adds new devices to the network, more data emerges, and more potential possibilities and use cases will surface, as will new technologies. In this communication utopia, an AI-driven network will continue to scale and identify these new opportunities as – or before – they arise.

Kailem Anderson is Vice President, Global Products & Delivery at Blue Planet.

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