CIO OPINION
A total of 500 CIOs were recently polled for
the annual Global CIO Point of View Survey,
and the findings reveal that businesses
are preparing for the widespread adoption
of this transformational technology to
automate decision making.
Nearly 90% are using machine learning in
some capacity, and most are still developing
strategies or piloting the technology.
However, the full potential of machine
learning remains largely unrealised. For most
organisations, many decisions still require
human input. Only 8% of respondents say
their use of machine learning is substantially
or highly developed, as opposed to 35% for
the Internet of Things or 65% for analytics.
Designing an organisational structure to
support data and analytics activities, an
effective technology infrastructure and
ensuring senior management is involved
are the three most significant challenges
to attaining data and analytics objectives
related to machine learning, according
to a McKinsey study. It goes on to claim
that organisations that can harness these
capabilities effectively will be able to
create significant value and differentiate
themselves, while others will find themselves
increasingly at a disadvantage.
Capturing greater value requires regional
organisations to invest in more than just
technology. It is also necessary to make
significant organisational and process
changes, including approaches to talent, IT
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management and risk management. Making
progress requires following five steps:
Improve data quality
Ensuring the quality of data is a common
obstacle to machine learning adoption.
Poor data leads to machines making poor
decisions, which can lead to increased
risk. CIOs need to consider implementing
solutions that simplify data maintenance
in order to accelerate the transition to
machine learning. The first step should be
to consolidate redundant legacy and on-
premise IT tools into a single data model.
Establish value realisation
Articulate the business value of all technology
goals, then determine how best to reach
those goals. This includes examining existing
processes to identify which unstructured work
patterns will benefit most from automation.
Determining where fragmented data ‘lives’
will enable you to identify how automation
will lead to gains in productivity.
Create the best possible
customer experience
Using machine learning for automation
will boost operational efficiency, but do not
overlook the ROI of accelerating decision
making (without sacrificing accuracy) to
improve the customer experience. Start by
envisioning the customer experience you
want to create, then prioritise investment
against those elements of business processes
that could most improve the customer
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