Many executives stepping into a newly created chief data officer role might start their time in the position with a data governance project or analytics software rollout. But for Steve Stine, who recently took on CDO responsibilities for telecommunications company AT&T, it’s all about AI-powered automation. “We believe that there are many capabilities that are […] 
Many executives stepping into a newly created chief data officer role might start their time in the position with a data governance project or analytics software rollout.
But for Steve Stine, who recently took on CDO responsibilities for telecommunications company AT&T, it’s all about AI-powered automation.
“We believe that there are many capabilities that are ready for prime time and some that will continue to evolve,” Stine said. “Bottom line is, yes, we’re a believer in AI.”
CDO responsibilities have traditionally focused primarily on data management issues. Things like how data gets stored and used as a corporate asset have dominated the time of typical CDOs. But AI is opening up new business opportunities and, at least for now, most enterprises lack a single executive or business unit responsible for overseeing how AI is implemented and used.
Stine is using this responsibility gap to make AI and automation a big part of his remit as AT&T’s CDO. The decision makes sense given his background. Prior to becoming AT&T’s first CDO, Stine worked on automation projects related to customer service in his role as senior vice president of technology planning and optimization.
Now he’s looking to apply AI and automation throughout the company. For example, he and his team are developing and will soon deploy chatbots to support field technicians. The techs will interact with the chatbots through a company-issued tablet, enabling them to retrieve billing information, make account changes, activate television set-top boxes and get answers to technical questions.
Previously, field techs had to do all this by calling into a live person manning a support line.
The platform is being built using natural language processing and machine learning models to improve performance over time. The project is currently in proof-of-concept testing, and Stine’s team expects to have it go live in 2018.
Stine’s team is also developing machine learning models to detect service interruptions using network data and to dispatch field technicians and give them optimal routes based on service requests, traffic and weather data.
“When you start adding those things together, it provides insights that we weren’t previously aware of,” Stine said.
The primary theme of all these projects is automation using machine learning and natural language processing to do jobs that were previously performed by people. Some see this trend toward automation in the workforce as a threat to jobs, but Stine sees it differently. He said that instead of taking people’s jobs away, automation at AT&T is enabling workers to skip the drudge work and focus on more interesting tasks.
Read the source article at TechTarget.