Telcos are turning to AI to optimize core operations and improve commercial outcomes. Correlation-based machine learning systems are failing to unlock real efficiency savings or growth — decision-makers do not trust them. Causal AI augments human intelligence, enabling providers to harness the true value of AI.
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.
Data-driven approaches allow Telcos to optimize advertising ROI increasing customer retention, conversion & profitability. causaLens enables marketing teams to autonomously discover the causal drivers of marketing performance. Distinguishing between statistical correlations and true causal drivers translates to vastly improved models and, as a result, optimized marketing spend and product recommendations. It also allows business users to automatically answer what-if questions and assess market interventions that happened before without the traditional, costly trial-and-error process
