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Telecommunications

Telcos are turning to AI to cope with volatile shifts in customer demand and to optimise supply. But current machine learning systems, which are unable to anticipate demand shocks or supply-chain disruptions, are failing to unlock real efficiency savings and growth. 

Causal AI identifies the cause-and-effect relationships that govern supply-and-demand dynamics in telecommunications, enabling providers to harness the true value of AI.

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Ambition for telecommunications knows no bounds

Telecoms stand at the crossroads of the information revolution but are handicapped by ageing infrastructure at the same time as surging demand. The can turn to Machine Learning to stay on top of their problems, but current AI techniques often fail to realise ROI:

  • Even state-of-the-art AutoML picks up may spurious correlations in data, undermining the accuracy of its demand predictions in the face of changing consumer behaviours
  • Non-causal ML techniques fail to translate predictions and models into actionable maintenance decisions

Solutions

Data-driven approaches enable telecommunications providers to make more informed investment decisions. Whether it’s acquiring new towers or upgrading a network, enterprises need to understand the causal drivers of demand and the macro-economic environment at scale. Causal AI also enables businesses to evaluate and model competitor actions before they actually take place in order to optimize their decision making.

Efficient predictive maintenance can only be achieved with root cause analysis. Existing correlation-based predictive maintenance models often fail to distinguish cause-and-effect relationships from spurious correlations. At the same time they fail to adapt to changing conditions in the environment. causaLens autonomously discovers the causal relationships in real time, enabling businesses to optimize the maintenance of their systems.

Optimizing network traffic in real time requires the ability to process huge amounts of data in real time, continuously adapting to new data as well as flexible deployment options. Causal AI addresses these challenges and is also able to optimize for a specific KPI of the network like inference time, energy consumption or utilisation from raw data.

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