The survey also found that 28% of CIOs in
Singapore are making some investments in machine learning currently, and this
number is expected to grow within the next few years as Singapore gears towards
a Smart Nation.
ServiceNow recently released the results of its “The
Global CIO Point of View”, and found that the adoption of machine learning
is on the rise in the enterprise. However, it was revealed that Singaporean
respondents are lagging behind their peers in Asia-Pacific, North America and
Europe in adopting machine learning.
The report surveyed 500 CIOs from around the world with almost 10% from
Singapore, to uncover the competitive benefits of adopting machine learning and
hear how these leaders are accommodating digital labour, including creating new
jobs that focus on work with intelligent machines.
A third (32%) of CIOs in Singapore surveyed said their organisation are using
machine learning in some or all parts of their business, compared to
counterparts in Australia (59%) and New Zealand (49%). 3 key areas were
identified as barriers to adoption and maturation of automated decision making
in their organisation:
70% of CIOs in Singapore cite outdated processes
and insufficient data quality (65%) as a substantial barrier to adoption.
35% cite the lack of skills to manage and
maintain smart machines, and a lack of budget for new skills (61%).
Almost 40% of respondents in Singapore feel that
there is a lack of budget allocated for new technology in their organisation.
More than half (52%) of CIOs in Singapore surveyed agree on
the ability of machine learning to make complex decisions that are imperative
to the success of their organisation, with 54% respondents citing that machine
learning as a strategic focus for their organisation. The enthusiasm for this
technology is driven by widely-held confidence by CIOs that greater automation through
machine learning will increase the accuracy (80%) and speed of decisions (87%).
Machine learning software promise to analyse and improve its own performance
without direct human intervention, giving it the ability to make increasingly
complex decisions as it learns:
87% of Singapore CIOs cite profitability growth
and top-line growth as the area that would benefit the most from decision
automation over the next 3 years.
59% said that product development and research
are automated to an extent but still requires substantial human intervention.
41% of CIOs expect decision automation brought about by machine learning to
allow more room for developing new products and services for the organisation.
76% said that routine decision making takes up a
meaningful amount of employee and executive time especially in departments like
Finance and Human Resources (57%). CIOs in Singapore expect decision automation
to contribute to their organisation’s employee productivity by 41%, and talent
recruitment and retention by 35%.
The survey also found that 28% of CIOs in Singapore are
making some investments in machine learning currently, and this number is
expected to grow
within the next few years as Singapore gears towards a Smart Nation. Half
of CIOs in Singapore say that they are making organised changes to processes or
leadership to prepare their organisation for machine learning adoption.
Machine learning is not just about the right technology – organisations must
train employees to work with machines and redefine their job scope to accommodate
the necessary skillsets, which are diverse across multiple disciplines from
engineering to data science, critical thinking to problem solving.
Organisations in Singapore have shown that they are willing to make such
changes to make rapid progress with machine learning:
17% of CIOs in Singapore have already set plans
for workforce size and role changes within their organisation.
More than half of CIOs in Singapore (52%) have
begun to redefine job descriptions to include a focus on work involving
intelligent machines – well ahead of Asia-Pacific peers in Australia (43%) and
New Zealand (27%).
Almost 40% of respondents said that they have
developed a roadmap for future process change.
Achieving Value from Machine Learning
ServiceNow recommends five steps on how CIOs can jumpstart their journey to
digital transformation with machine learning:
1) 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.
2) 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 the most unstructured work patterns
that could 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.
3) 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
service. When creating a roadmap to implement machine learning capabilities,
imagine the ideal customer experience and prioritise investment against those
4) 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
5) 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 prior to implementation, and build a
sound business case in order 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.
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