Just 32 percent of organisations in Singapore currently tap machine learning, although 52 percent believe such tools' ability to make complex decisions is imperative to the success of their business.
A further 87 percent said greater automation brought about by machine learning would speed up decision-making process, while 80 percent said it would improve accuracy of such decisions, revealed a survey by ServiceNow. Conducted by Oxford Economics, the study polled 500 CIOs across 11 countries, including 91 from three Asia-Pacific markets: Singapore, Australia, and New Zealand. Ten percent of the global sample were from Singapore.
ServiceNow touted machine learning as software that analysed and improved its own performance without direct human intervention, enabling it to make increasingly complex decisions as it learned.
At 32 percent, Singapore companies were slower to catch on to machine learning, compared to 59 percent in Australia and 49 percent in New Zealand where organisations also were using these tools in their business.
Asked what they considered key barriers in adoption, 70 percent in Singapore pointed to outdated processes while 65 percent cited poor data quality. Another 61 percent said insufficient budget to hire new skills was a barrier, while 35 percent pointed to the lack of skills needed to manage and maintain machine learning systems.
In addition, 40 percent highlighted an overall lack of budget dedicated to acquiring new technology in their business.
The CIOs, though, recognised the impact of machine learning on business automation, with 87 percent in Singapore noting this could improve their company's profitability and top-line growth over the next three years.
Another 41 percent said decision automation, enabled by machine learning, would better facilitate the development of new products and services for their organisation.
Duncan Egan, ServiceNow's Asia-Pacific vice president of marketing, added that 63 percent of Singapore CIOs believed automation capabilities brought about by machine learning would be the most important success factor for their company in the next three years.
Some 87 percent and 30 percent cited security operations and operations management, respectively, as the two main areas of deployment for machine learning.
In addition, 52 percent in Singapore already were making changes to processes or leadership, such as re-defining job descriptions, to prepare their company in its adoption of machine learning. This was higher than their counterparts in Australia, where 43 percent were doing likewise, and 27 percent in New Zealand.
Singapore CIOs expected decision automation to boost their company's productivity by 41 percent and talent hiring and retention by 35 percent.
Egan noted that automation was particularly useful for any process or job that involved routing, ranking, and forecasting, including the delegation of IT help requests or sales lead assignment.
In security operations, for instance, automation could be used to trigger basic functions such as password resets or more complex processes related to remediation, triggering necessary actions in the even of an incident.
Machine learning also could be used to analyse historical patterns and identify chains of events that led to a system downtime. It then could predict a potential failure based on past events and trigger the necessary workflow to address this, he said.
"Machine learning allows enterprises to digitise in ways that were never before possible, but its adoption is an evolution that requires careful consideration and planning," Egan said. "The outcome of faster and more accurate decisions lies in creating an exceptional internal and external customer experience. That means thinking not in terms of individual interactions with customers, but the entire customer journey from beginning to end."