GenAI solutions offer opportunities for challengers in telecom space
Generative AI is quickly moving from experimentation to real-world deployment in telecom network operations, according to a new study. Research from Analysys Mason suggests that operators are leveraging GenAI to drive automation, modernise networks, and enable new service and business models.
Recent years have seen the telecom sector come up against a mounting number of challenges. In a period of transition, operators have struggled to roll out 5G services, while rising costs and spiralling complaints have seen providers desperate to find ways of improving customer attraction and retention.
In response to these pressures, the largest names in the business have opted for consolidation. In Britain, for example, the merger of VodafoneThree reduced the number of major mobile network operators (MNOs) at the top of the market to just three behemoths. If smaller challengers hope to gain market share in that environment, they will need to find ways to outflank their larger opponents, with agility and responsiveness to customer needs. According to Analysys Mason, this is where AI – and particularly Generative AI (GenAI) tools are coming into play.
Founded in 1985, Analysys Mason is a management consultancy focused on telecoms, media and technology. Working with Google Cloud, Analysys Mason has developed its own GenAI maturity index, to evaluate how networks are adopting the tools. It explores operator priorities, implementation strategies, challenges, best practices and organisational shifts needed to support GenAI at scale – and finds that while much of the GenAI adoption in telecom networks is still in its early stages, a widening gap is already emerging between first-movers and laggards.
GenAI trailblasers are companies which Analysys Mason identifies as having clear C-level sponsorship and strong conviction in the return on investment (ROI) of GenAI. These firms are integrating it into their systems across multiple network domains, including advanced cross-domain use cases, and have established centres of excellence to embed GenAI and its necessary requirements across their organisations.
According to the study, only 14% of operators currently fit this description – and primarily they are based in the Asia-Pacific region. With North American and European markets increasingly dominated by unwieldly monopolies, this could present opportunities for Asia-Pacific-based operators to find inroads into new markets, offering agile services that outflank their competitors.
At present, it seems fewer operators outside the Asia-Pacific region would be equipped to mount such a challenge, or fend one off. While 34% of operators around the world are actively experimenting with GenAI, the majority of 52% simply remain GenAI explorers, running small, experimental pilots without a defined strategy or significant resource commitment.
This may be partially because it remains unclear exactly what the benefits of being a GenAI trailblazer in this field are. While Analysys Mason does not the usual list of broad items GenAI could help with – from cloud modernisation, to responsiveness to demand, and efficiencies emerging from reduced manual interventions in a network – the study also notes that no GenAI network use case has actually reached the mainstream commercial stage – where it is deployed across at least a third of all operators. This presents something of a leap into the unknown – something which may be keeping many operators in wait-and-see mode – but it is also an opportunity for firms which act now to get ahead of the curve, according to the researchers.
Looking ahead, Analysys Mason’s experts recommend a number of points for operators to shift gears with at their organisations. To overcome adoption challenges and accelerate progress, they note, operators prioritise use cases aligned with business goals that deliver near-term ROI and can scale over time – which can secure C-suite buy in. At the same time, they should strengthen internal capabilities through upskilling, and targeted hiring, and develop a unified data strategy with shared architecture. Central to all of this, though, is to obtain a strategic partner – such as a consulting firm – with deep AI expertise, to help accelerate adoption.
