TRENDING
• Fewer than half (40%) of CIOs have
redefined job descriptions to focus
on work with intelligent machines,
41% cite a lack of skills to manage
intelligent machines and about half
(47%) say they lack budget for new
skills development.
• CIOs cite data quality (51%) and
outdated processes (48%) as
substantial barriers to adoption.
• Fewer than half (45%) have
developed methods for monitoring
mistakes made by machines.
“Machine learning allows enterprises
to digitise in ways that were not
possible before,” Bedi said. “To realise
the full potential of machine learning
technology, CIOs must elevate their
role to transformational leaders who
influence how our organisations design
business processes, leverage data, and
hire and train talent.”
First mover CIO advantages –
delivering results today
A select group of CIOs surveyed
(fewer than 10%) are running ahead
of their peers in the use of machine
learning. These ‘first movers’ provide a
model for how CIOs can better utilise
machine learning:
• Almost 90% of first movers expect
decision automation to support top-
line growth vs 67% of others.
• Roughly 80% have developed
methods to monitor machine-made
mistakes vs 41% of others.
• More than three-quarters have
redesigned job descriptions to focus
on work with machines compared
with 35% of others.
• More than 70% have developed a
road map for future business process
changes compared with just 33%
of others.
“First mover CIOs who combine machine
learning with new business processes
and skill sets will better support their
enterprise growth,” Bedi said. “They
report higher levels of maturity in the
use of leading platforms, which allows
them to concentrate on innovation,
such as automating complex decision-
making, which immediately impacts the
bottom line.”
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INTELLIGENTCIO
“To realise the
full potential of
machine learning
technology, CIOs
must elevate
their role to
transformational
leaders.”
2.
Financial services leads,
healthcare industry lag
The survey uncovered viewpoints from
CIOs in the financial services and
healthcare sectors. Of note:
3.
• CIOs from financial services are
more likely to say their company
is moving from the automation of
simple decisions to the automation of
increasingly complex decisions (68%
vs 52% of others). They are more likely
to have made organisational changes
to accommodate digital labour,
including redefining job descriptions
to focus on work with machines (62%
vs 36%), developing a road map for
future process changes (52% vs 35%),
and recruiting employees with new skill
sets (42% vs 25%).
• CIOs in the healthcare industry
remain cautious. They are less likely
to use machine learning across the
organisation and less likely to say the
technology will have a positive impact
on top-line growth, competitiveness,
or reducing risk. They are less likely
to expect value from decision
automation in a number of functional
areas, including security (70% vs
80%), operations (46% vs 58%), risk
and compliance (36% vs 58%).
Five steps to achieve value from
machine learning
ServiceNow recommends how CIOs
can jump-start their journey to digital
transformation with machine learning:
1. Build the foundation and improve
data quality. One of the top barriers
4.
5.
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 road map, 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 road map 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 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. n
www.intelligentcio.com