A lack of process discipline and data quality issues are among the most common barriers faced by operators and other companies in the adoption of AI technologies, Deborah Battaglia, SVP of customer experience with insurance company Assurant, told Mobile World Live.

In an interview, the expert noted “for AI to be successful, the work and tasks must be clear, and the augmentation or automation must be applied to a well-suited subset of the process and technology”.

She said companies often have not clearly defined the inputs, outputs or process steps of a task, which limits their ability to implement AI to augment or enhance it.

“Taking the time to map out, understand, document and streamline the workflow not only enables AI to be effectively adopted, but it also reduces costs and improves quality as waste, redundancy and/or errors can be removed prior to implementing AI,” Battaglia commented.

Regarding data quality, Battaglia noted that AI is heavily reliant on being fed quality information to operate in an optimised manner and return the best possible results.

“Companies with fragmented, legacy or siloed data should invest in data quality clean-up efforts, such as master data management, governance and consolidation”, she said.

The good news is these obstacles, while challenging, “are not insurmountable and companies can apply an iterative mindset toward adoption using smaller use cases to test, learn and scale rapidly”, Battaglia said.

In terms of which applications of AI provide the greatest opportunities for mobile operators, Battaglia said creating greater efficiencies is the easiest place for companies to get started, such as using AI to reduce tasks and simplify processes.

“Creating efficiencies is about augmenting the customer experience with AI to provide a better, faster, and more successful interaction,” she said. “This means eliminating the need for customers to perform mundane tasks and reducing friction.”