In the “The Global CIO Point of View” survey conducted by ServiceNow, it is revealed that the Chief Information Officers (CIO) in Singapore are slower in adopting machine learning than the others in Asia Pacific, North America and Europe.
Three key areas were identified as barriers to adoption and maturation of automated decision making in their organisation.
- Outdated processes and insufficient data quality as a substantial barrier to adoption.
- Lack of skills to manage and maintain smart machines, and a lack of budget for new skills
- Lack of budget allocated for new technology in their organisation.
Duncan Egan, VP Marketing, APJ, ServiceNow 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.“
He added: “To realise the full potential of machine learning technology, CIOs need to elevate their role to a transformational leader who influences how organisations design business processes, organise data and hire and train talent.”
Some CIOs in Singapore also revealed in the study the benefits of early adoption, such as profitability growth, more time for product and service development and possible improvement in productivity, talent retention and recruitment.
As Singapore gears towards becoming a Smart Nation, 28 percent of the Singapore CIOs surveyed are optimistic that more counterparts will join them in coming years, investing in machine learning.
To help CIOs who have yet to come onboard the machine learning journey, ServiceNow has come up with some recommendations for you.
Five Steps to Achieve Value from Machine Learning
ServiceNow recommends how CIOs can jump-start their journey to digital transformation with machine learning:
- Build the foundation and improve data quality. One of the top barriers to machine learning adoption is the quality of data. If machines make decisions based on poor data, the results will not provide value and could increase risk. CIOs must utilise technologies that will simplify data maintenance and the transition to machine learning.
- Prioritise based on value realisation. When building a roadmap, focus on those services that are most commonly used, as automating these services will deliver the greatest business benefits. At a high level, where are the most unstructured work patterns that would benefit from automation? Commit to re-engineering services and processes as part of this transformation, and not simply lifting and shifting current processes into a new model.
- Build an exceptional customer experience. A core benefit of increasing the speed and accuracy of decision-making lies in creating an exceptional internal and external customer experience. When creating a roadmap to implement machine learning capabilities, imagine the ideal customer experience and prioritise investment against those goals.
- Attract new skills and double down on culture. CIOs must identify the roles of the future and anticipate how employees will engage with machines—and start hiring and training in advance. CIOs must build a culture that embraces a new working model and skills. That means establishing guidelines for executives, engineers, and front-line workers about their work with machines and the future of human-machine collaboration.
- Measure and report. The benefits of machine learning may be clear to CIOs, but other C-level executives and corporate boards often need to be educated on its value. CIOs must set expectations, develop success metrics before implementation, and build a sound business case to acquire and maintain the requisite funding. CIOs should also consider building automated benchmarks against peers in their industry and other companies that are of similar size.