Three ways AI can drive forward telecommunications
With a sharp focus on technology, the telecommunications industry has been going through its own digital transformation journey for years.
The industry has successfully automated a number of processes and if combined with artificial intelligence (AI) and machine learning (ML), can benefit from ML feedback loops which can improve automated processes more.
On top of this, the industry by nature generates a large volume of datasets, and if stored properly and fed into AI/ML systems the telecommunications industry would benefit from a number of benefits as they unlock new opportunities for growth.
As the key driver for 5G technology (a key technology for driving forward AI and automation), telcos need to be prepared as other businesses come to them to support their own digital transformation. Because of this, AI has become central to the telcos’ digital transformation as it aids the delivery of superior performance in the short and long term.
Using AI to reduce 5G costs for telcos
Working in a capital-intensive industry means telcos are always on the lookout for solutions that can control operating expenses as well as looking for ways to allocate financial investments now so they can profitably manage and operate the next generation of 5G IoT networks.
To help with the rollout of 5G, AI can automate a lot of this process, particularly with monitoring and managing the costs of the networks. Through data analysis, AI can reduce costs by proactively boosting network efficiencies and performance without human intervention. The technology also provides telcos with new opportunities for applications and services such as customer service virtual assistants, intelligent CRM systems and real-time cyber security.
Achieving real-time insights with AI
Powered by complex algorithms that require vast amounts of high-quality data to operate effectively, AI is perfectly placed to support the telecommunications industry as it has access to a large amount of data itself. However, much of this data is inconsistent or fragmented because of outdated methods, which for many can stall an AI project before it begins.
Although this should not stop telcos from AI implementation, telcos should clean and label historic data sets as well as identify gaps where the data is not sufficient for AI/ML. To do this, the industry needs to improve and advance real-time data capture processes and determine which data sets are fed into the ML models. As a result, telcos can automate decisions quickly, and with a higher degree of confidence.
AI and efficient working in telecommunications
As AI continues to develop at pace, telcos need to look for ways to work in an agile and iterative way as a way to innovate and cultivate new solutions for the industry. To find these new ways of working, telcos need to forge strong collaborations with analytics vendors.
Additionally, telcos need to ensure their data is cleaned and labelled to be used as a valuable asset, particularly as teams look to select specific data sets for training and tuning the AI models. To execute this properly, it is key to introduce new capabilities into production as soon as possible and, a strong data processing and analytics framework is needed to enable large-scale experiments and high reliability in production use.
