DALLAS -- Automation Everywhere -- If telcos are to make significant strides in automation in 2018, reduce costs and increase efficiencies in their network operations, carriers must overcome barriers like engineers' skills gaps, and develop agile company cultures that are "adaptable to change," says Heavy Reading Analyst James Crawshaw.
Crawshaw kicked off Light Reading's Automation Everywhere event this morning with a look back at the evolution of automation, which began by automating exchange equipment to electromechanical switching, to electronic switching of software management systems, and finally to the actualization of SDN and NFV.
Crawshaw defines automation according to McKinsey's framework of intelligent process automation which includes three categories -- robotic process automation (RPA), machine learning (ML) and natural language processing. Mastering both RPA and ML is key for telcos to achieve success on their automation journeys.
Robotic process automation
Robotic process automation has garnered the most traction in the financial services industry, but is gaining momentum in the telecom industry as a way to automate mundane tasks, "eliminating the need for manual, error-prone, duplication of data entry across different systems," explains Crawshaw. RPA ties together different software systems to perform tasks such as back office activities, customer support or IT support.
"It's a way of trying to automate a lot of disparate processes without having to do some big software rollout or software development -- you can use these relatively inexpensive tools to automate," he says. "AT&T has adopted RPA quite extensively, Deutsche Telecom is using it, Telefónica has a good case study. It's a useful tool for telco's broad back-office processes -- not so much on network automation per se, but automating things like customer contact centers."
AT&T Inc. (NYSE: T), for example, has trained over 2,000 employees to write bots that have been used in the migration of customer accounts from DirectTV to AT&T, completing requirement documents for Ethernet services, automating sales order entries and reconciling revenue against assets and inventory, says Crawshaw.*
Automation will not only be central to reducing manual tasks such as customer support, but also in managing 5G networks where machine learning will also play an important role to addressing the influx of data on operators' networks, explains Crawshaw. A subset of artificial intelligence (AI), machine learning is the aspect of AI drawing the most interest from both academia and industry.
"One of drawbacks of applying ML to networking … is there isn't this theory of networking in the way we can theorize how the brain or eye works -- we don't know what is the correct way a network should work, there's nothing you can model per say."
While machine learning isn't a new concept, Crawshaw says it only made significant strides about ten years ago due to academic breakthroughs, and with ongoing improvements in compute capacity. Machine learning has also become a more cost-efficient endeavor: the telecom industry has access to more massive data sets, and data analysis can be performed using cloud-based systems like AWS.
Next page: Use case for machine learning