“We confidently expect continued strong growth in the use of predictive business analytics, including Artificial Intelligence (AI) and Machine Learning (ML),” says SAS, making predictions of its own about where the analytics industry is headed in the coming year.
The global analytics software development company begins by outlining the sectors where it sees AI and ML taking off in 2019.
“The industries with the skills and data to invest in leading-edge technology such as financial services and telecommunications will take the lead,” it says.
“The companies willing to redesign their business processes to take advantage of AI rather than simply accelerate existing processes will be the ones to take an early competitive lead in through AI and ML.
Locally, we will continue to see take-up of AI and ML to address very specific niche business problems across a wide variety of industries. The solutions that deliver real value will be the ones that have been deeply embedded in frontline operations.”
However, SAS recognises that there will be roadblock along the way.
“The biggest risk to the widespread adoption of AI and ML will be a lack of transparency and interpretability and the gradual fading of the hype around AI,” the company explains.
“This will happen more as projects that consumed a lot of resources are found to have failed to deliver on ‘the last mile’ of being implemented and more importantly adopted by users.
“As economies look to find growth in an increasingly tough commercial environment, tolerance for high-cost initiatives which deliver results that are interesting but not implementable will dry up. As a result, business leaders will invest in AI and ML projects with a clear path to becoming a living function of business as usual with long-term prospects, rather than once-off projects.”
But there is more than just RoI to be considered in the new world of automation and prediction.
“As regulation and privacy concerns continue to grow, there will be an increasing focus on ethical considerations associated with AI and ML,” SAS explains.
“As such, there will be pressure to ensure good data governance and management of the underlying data assets, as well as management of the algorithms supporting AI-based decisions.”
SAS also says that the data analysis skills gap across A/NZ may begin to close as students and universities alike see the opportunities the area offers.
“Australian and New Zealand universities are motivated by employer demand and support from technology vendors to enrol increasing numbers of analytics students.
“In parallel, students who might never have previously considered a career as a data scientist are strongly attracted by the salaries now being offered.”