Building intelligent networks: How telcos can take advantage of autonomous networks

While telecoms networks were largely dependent on manual systems and processes before they had software-driven rules and automation built in, today, new innovations are set to drive tangible business benefits across the telecoms industry. Today, by leveraging machine learning (ML) and data and analytics, networks have the autonomy to take certain low risk, rules-based actions. However, telco networks look set to evolve once more, as the promise of a ‘self-serving’, ‘self-fulfilling’, and ‘self-assuring’, or autonomous networks draws closer.

The increasing demand for improved customer experience is placing pressure on telcos and their network resources. Despite investments in 5G and fiber broadband improving the reach and quality that networks provide, the increasing number of mobiles and devices connecting to networks, and the subsequent rise in volume of data means that telco networks are now more complex than ever. Essentially, the management of networks is increasingly going beyond the reach of manual operations. This is where autonomous networks can drive tangible business value for telcos, providing a springboard to improved customer service, as well as more efficient and more sustainable networks.

Investment in autonomous networks is on the rise

According to the latest research by the Capgemini Research Institute, telcos are expected to invest $87 million on average in autonomous networks over the next five years. [KA1] 

Today, according to TM Forum’s taxonomy of autonomous networks, the majority (84%) of telcos have either a level-1 or level-2 autonomous network. We know from our research that this is set to increase, with 61% of telcos aiming for at least level-3 autonomy over the next five years. Currently, Europe leads the way in overall network maturity, with over half (51%) of telcos at level 2 network autonomy, although North America has the greatest proportion of telcos at level 3, at 14%. Despite the clear eagerness showcased by telcos for greater network autonomy, the majority of the use cases are still at proof-of-concept stage. Of those surveyed, adaptive/dynamic network policies for changing conditions are the most popular use case at proof-of-concept stage (46%), followed slice optimization and SLA assurance in RAN/ORAN (40%).

Subverting the challenges

Despite the innovative use cases that autonomous networks can deliver, there remains considerable barriers to adoption. Chief among these is cultural issues, with just over half (51%) of telcos citing that employees don’t have the right mindset to undertake such a shift. Although, when you consider that just 17% of telcos have a well-defined autonomous networks transformation strategy, and fewer than one in five organizations have appointed a dedicated leader, it’s no surprise that cultural issues persist. However, the barriers to adoption aren’t limited to just cultural issues. In fact, we found that issues around technological maturity is slowing down telcos’ autonomous network journeys. Our survey found that 48% of telcos flagged technology integration was a noteworthy issue. Additionally, 33% and 25% of telcos flagged that technology maturity and the lack of skills among the internal workforce as significant barriers to adoption.

Gen AI and sustainability benefits front of mind

Over the past year, generative AI has risen from emerging technology status to the center of the boardroom conversation. All industries alike are assessing their operations to understand where they can integrate the technology, and telecoms is no different. According to our survey, three in five telcos are exploring generative AI for autonomous networks, and one in ten has implemented generative AI for networks at partial scale. Generative AI strikes that crucial balance for telcos as a cost reducer, and efficiency driver. We know from our research that the most popular use cases are complex event processing and dynamic bandwidth and path selection. On a more granular level, generative AI can assist telcos with translation, fraud resolution and model training.

However, it’s not just generative AI-related benefits that telcos are reaping and in fact, those telcos that are moving faster on their autonomous network journeys, are realizing the benefits. In fact, in just the past two years, telcos have on average achieved a 20% improvement in operational efficiency and 18% reduction in OPEX through autonomous networks. The survey also finds that telcos are expected to invest $87 million in autonomous networks over the next five years, but that this would amount to $150 million – $300 million in OPEX savings. And the benefits aren’t limited to simply cost savings, with the sustainability benefits front of mind for many telcos.

Today, it’s crucial that businesses have sustainability built into their core. Energy accounts for 30-40% of telco OPEX, with the Radio Access Network (RAN) accounting for 80% of network energy consumption. And those who transition to a higher level of autonomous network can expect a reduction of somewhere between 7.5%-15% reduction in their networks carbon emission. For instance, Telefónica Group has successfully reduced its energy consumption by 7.2% between 215 and 2022. While this initial number may seem low, when you consider that their network traffic has increased seven-fold over the same period, they have reduced their overall emissions by 51% over the period. As generative AI continues to cement itself as a key innovation across the telecoms landscape, we’re going to see these results improve much quicker.  

Accelerating the transition

As aforementioned, just 17% of telcos have a comprehensive autonomous networks strategy in place. Those who have this strategy in place can expect to realize the benefits of autonomous networks much sooner. With this in mind, I wanted to take a moment to explain what a comprehensive autonomous network strategy consists of.

• Strategy & roadmap: Here it’s critical that telcos establish the business case early on, so that they can secure the necessary finance and build a strategy that simultaneously resonates at both a global and local level.
• People: With innovations comes the need for new skillsets. Telcos should work to bridge the skills gap in areas such as AI by upskilling and reskilling the current workforce. As I mentioned earlier, the cultural shift presents one of the biggest barriers. By reorganizing systems, processes and tools, telcos can guide their organizations through to a new, more efficient operating model.
• Technology: Technology integration issues were high on the agenda of telco executives. To combat this, they should ensure they have an end-to-end view of their data landscape and leverage the cloud for virtualization where possible. Atop of this, telcos should invest time into establishing robust data-governance and data-management frameworks.
• Pace of transformation: As always, the pace of transformation depends on the maturity of the technology. For instance, beginner telcos should consider which network domains and use cases to prioritize, whereas those midway through their journey should double down on investment and focus on scaling.
• Innovation: Open and disaggregated networks open the door to new innovative use cases. Telcos should experiment with emerging technologies such as generative AI, metaverse and digital twins to ensure they enhance network efficiency.

The operating model of networks is going through a generational shift, from one managed by human operators, to an autonomous one whereby AI and data take center stage. While this shift requires significant investment, telcos should welcome it with open arms. Autonomous networks provide strike that all important balance of reducing costs, increasing efficiency, and contributing to a more sustainable future.

We look forward to meeting you at the Capgemini booth (2K21) in Hall 2 at MWC Barcelona from February 26th to February 29th