Driving sustainable transformation with AI-enabled telcos

Generative AI has created a buzz for new products that can solve business challenges. Telcos that are ready to integrate AI into their operations, infrastructure and services can be at the forefront of their own and others sustainable transformation.


As the telecoms industry has seen growth stagnate in recent years, operators are facing immense pressure to transform its business model, looking for key areas where they can cut costs and create new revenue streams.  However, as priorities shift in the face of a climate crisis, telcos, like other enterprises, must seek this growth in a sustainable way.

AI will undoubtedly be an important tool for telcos to reach this vision and is already having an impact across the industry. In our latest report, on the role of AI in transforming the future of work, we explored the ways in which it will catalyse the fourth industrial revolution, and result in significant societal, cultural and environmental changes.

Find out more in our full-length report: The Future of Work: How AI can help telcos keep up.

In previous articles we have explored the role that AI can play in enabling key telco use cases; specifically looking at how AI can play a role in the rollout of 5G. AI is also beginning to impact customer experience and consumer telecoms services, such as chat bots, managing connected home devices and AI-enhanced customer care.

This article will look to address how AI-enabled telcos can be at the forefront of sustainable transformation. We will explore how telcos can utilise AI to drive sustainability practices and accelerate their move to net zero, as they undergo the transition away from old business models to new agile techco models. We will also explore how AI is being integrated into broader telco services, enabling enterprises to operate more efficiently & sustainably, and offering telcos greater credibility.

There are a number of reasons why telcos must keep sustainability front of mind:


The role of AI in telcos’ sustainable network transformation

Network operations is an area with substantial potential for emission reductions as networks are by far the most energy-intensive part of a service provider’s business – the RAN alone consumes between 70-80% of a telco’s total electricity usage. Although the scope of wider sustainability goes beyond network energy consumption, the impact that AI can have on the network is currently far greater than other priorities like waste, water and establishing a circular economy. These, more structural problems, will likely need to be addressed through clear strategic and organisational changes. This section will cover AI’s impact on network operations, resilience, sleeper modes and network analytics.

AI-driven automation is an important part of telco network transformations.  Self-optimised networks, for example, can automatically tune capacity to current or predicted demand. This reduces the amount of work that is done by network teams manually monitoring metrics. With advanced diagnostics and AI-driven proactive repair, network teams can also undertake more maintenance remotely or enable self-healing capabilities for more routine tasks. This reduces the number of visits to sites required which, in turn, reduces Scope 1 emissions for telcos.

AI and automation can also play a vital role in optimising use of network resources and in reducing the equipment and energy required to deliver resilience. AI and closed-loop automation reduces the need for “buffers” and permanent redundancy that are traditionally designed into networks to safeguard against equipment failure. Sleep modes are another important example of where AI and automation could have a large, lasting impact on total energy consumption. These sleeper functions may operate at multiple levels: from the macro cell site right down to the underlying silicon (e.g. CPU core).

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With all of these examples, the challenge comes from reducing consumption without reducing the QoS for network users, with some telcos such as Telefonica already demonstrating an ability to do this. ML-optimised QoS scores can be integrated alongside sustainability initiatives to help inform network engineer teams of longer-term deployment and repair decisions. This means that AI can leverage customer impact scores and sustainability with various weighting to make longer-term decisions on creating the most efficient and service effective network deployments. For example, this might determine where small-cells are deployed to provide coverage within key metrics related to QoS & sustainability.

Finally, reporting through AI & automation can help with tracking the use of carbon emissions for the service provider and its customers. While better reporting does not necessarily equate to reducing emissions, data capture and methodology for Scope 3 reporting is an important part of the creating a transparent view of total emissions. The introduction of sustainability trackers and calculators enable service providers to get a holistic view of their carbon output and identify areas of the business that need to be improved.

Telcos looking to push forward in their cloud native journeys will quickly realise that AI & automation is an imperative to realise the investment made in cloudifying the network. The most advanced ‘techcos’ must push vendors to integrate AI features into network infrastructure while ML models should be utilised for larger, strategic and organisational decisions. AI-enabled telcos will be able to achieve the greatest CapEx & OpEx savings in the shortest amount of time, thereby reducing their total energy consumption. This narrative can be used effectively to differentiate themselves as a sustainable, low-energy service provider to consumers.


Enabling sustainable transformation for the enterprise

Optimising telco services with AI will have knock-on sustainability benefits for the enterprise customers that consume these services.

Beyond the core mobile network, edge computing services offer an opportunity for telcos to monetise their 5G infrastructure and create new enterprise use cases. However, it’s estimated that data centres will account for an estimated 5-10% of energy consumption by 2030 and this could be exceeded if edge networks develop faster. Improvements to HVAC systems and immersion cooling systems will become important to the sustainability of these deployments, particularly systems that are controlled by AI to maintain balance. These changes will help enterprises with on-premise edge deployments or private networks to control their Scope 2 emissions.


Platforms will also play an important role in enabling AI to push sustainable outcomes. Most edge-to-cloud platforms are integral to a service provider’s/hyperscaler’s edge infrastructure across network and on-premises. They provide fundamental monitoring, reporting, resource management & orchestration amongst other important functions. Nodes across the cloud, network, and premise must be orchestrated to maintain traffic optimisation but must also consider the sustainability of each node. AI is already being utilised by telcos to create closed-loop optimisation of workloads, so that the right type of compute (e.g. GPU vs CPU) is leveraged and done so in the most effective way. This can be extended to the enterprise IoT space that now spans across industries such as manufacturing, healthcare and public city environments, all seeking improved sustainable outcomes. The propagation of IoT has created many devices that may only require remote and sporadic monitoring, without constant feedback. AI-enabled platforms will help to ensure these low-energy devices can run with low amounts of data and communication. These devices could utilise mobile initiated connection only (MICO) modes- where parameters are set by AI-enabled platforms, and only connect to gateways and networks as needed. Finally, these platforms will help to put CPUs’ multi-core processors into sleep mode instantaneously. This reduces energy usage, particularly in relation to more volatile workloads in smaller footprint data centres.


AI: Sustainability’s double-edged sword?

AI & ML are powerful tools that will undoubtedly drive digital and sustainable transformation in the telecoms space. The more accustomed telcos become towards AI use cases and more importantly, orientate their business strategy towards cloud-native and DevOps practices, the more likely they will succeed in their sustainability journey. Earlier this year, Vodafone announced $500m OpEx and CapEx savings over the past three years from taking this approach, hiring aggressively towards software engineers. They have enabled a host of automation processes & digital twins to guide and action their decision making around network maintenance and strategy. AI will create opportunities for telcos to drastically reshape network maintenance & expansion, giving decision makers a clearer picture of the state of the network while automating away meaningless human input and wasted repairs. AI will also enable telcos to facilitate more sustainable outcomes in edge compute and IoT environments, where end-to-end orchestration will become important as the web of devices becomes more complex. That being said, generative AI has faced criticism for its intensive energy usage, something that will only get more intensive as more Large Language Model (LLM) parameters are included for GPT-4, GPT-5 etc. It’s important that telcos, in the midst of AI adoption, think about their transition towards renewable energy sources as an important pillar towards their overall transformation efforts.

Read more in our latest article “AI: Sustainability friend of foe?

Thomas Quinn


Thomas Quinn


Thomas Quinn is a consultant at STL Partners.

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