At Rogers, incoming calls to the contact centre are profiled by a machine-learning system that takes into account various types of data such as a sentiment analysis of the call and social media posts to gauge how likely that customer is to churn. The results are presented to the agent, who can then take steps to pre-empt the service being cancelled.
Apart from any other benefits, this approach has halved the number of customer complaints.
So the company uses a machine-learning system to screen out only those claims that really do need to be examined by a person. As a result, the average processing time has been reduced to 60 seconds.
Closer to home, the Sydney-based Black Dog Institute (which is dedicated to understanding, preventing and treating mental illness) is exploring the use of machine learning to determine which patterns of treatments work for different segments of society.
SAS's strategy is therefore less about creating AI-specific tools, and more about "infusing AI into our tools and platform" to make it easier to reach the results you want, said Frost.
Even smaller organisations are seeing value in this approach, so you don't have to be a multinational to benefit, he observed.
SAS's task is to deliver the technology in affordable ways, and this may include the provision of APIs that systems integrators can use in conjunction with services running on public clouds such as Amazon Web Services and Google Cloud Platform.
The company is investing in AI and other growth areas, taking on key staff, developing tools, and building centres of excellence for consultative projects with customers that use SAS's and other companies' technologies.
"The future for us looks very bright," said Frost.