A survey of 500 Chief Information Officers (CIOs) from around the world
by ServiceNow (NYSE:NOW)
finds that machine learning has arrived in the enterprise making
material contributions to everyday work. To realize its full value,
technology leaders must find skilled talent to work side-by-side with
machines in addition to redesign their organizations and processes.
This press release features multimedia. View the full release here:
Global CIO Point of View,” ServiceNow surveyed CIOs in 11 countries
across 25 industries to uncover the competitive benefits of adopting
machine learning and hear how those leaders are driving results. IDC
estimates that investment in machine learning will nearly double by
2020,* and recent analysis shows that machine learning specialist are
among the fast growing roles in IT.**
Humans Work Side-by-Side with Smart Machines for Better Accuracy,
Speed and Growth of Business
The survey finds a growing sense of confidence among senior executives
that machine learning will lead to faster and more accurate decisions.
Machine learning software possesses the ability to analyze and improve
upon its own performance without direct human intervention, allowing
them to make increasingly complex decisions over time:
More than half (52%) of respondents say they are advancing beyond the
automation of routine tasks, such as security alerts, toward the
automation of complex decisions, such as how to respond to alerts.
87% said that they would get value from the accuracy of decisions. In
fact, 69% say decisions made by machine learning will be more accurate
than those made by humans.
57% said that routine decision making takes up a meaningful amount of
employee and executive time, so the potential value of automation is
high. CIOs expect this decision automation to contribute to their
organization’s top line growth (69%).
“We see three kinds of processes as targets for machine
learning—anything requiring rating, ranking or forecasting,” said Chris
Bedi, CIO at ServiceNow. “Everyday work such as the assignment of IT
tickets and prioritizing sales leads are already delivering results.
Machine learning has rapidly moved from hype to reality.”
Machine Learning Specialists Alone Won’t Help CIOs Succeed in Digital
Nearly three-quarters (72%) of CIOs surveyed said they are leading their
company’s digitalization efforts, and more than half (52%) agree that
machine learning plays a critical role. Nearly half (49%) of the CIOs
surveyed say their companies are using machine learning and 40% are
planning to adopt the technology.
But there are key talent, organization and process areas that must be
addressed in order for companies to take full advantage of machine
Only 27% of CIOs have hired employees with new skill sets to work with
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
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 digitize in ways that were not
possible before,” Bedi said. “To realize the full potential of machine
learning technology, CIOs must elevate their role to be transformational
leaders who influence how our organizations 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 utilize 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 roadmap for future business process
changes compared with just 33% of others.
“First-mover CIOs who combine machine learning with new business
processes and skillsets 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
Financial Services Leads, Healthcare Industry Lags
The survey uncovered viewpoints from CIOs in the financial services and
healthcare sectors. Of note:
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 organizational changes to accommodate digital
labor, including redefining job descriptions to focus on work with
machines (62% vs. 36%), developing a roadmap 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 organization 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 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 utilize technologies that will
simplify data maintenance and the transition to machine learning.
2) Prioritize based on value realization – 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.
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 experience. When creating a
roadmap to implement machine learning capabilities, imagine the ideal
customer experience and prioritize investment against those goals.
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 human-machine
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.
ServiceNow applies machine learning to four of the biggest
use cases that IT has today. Preventing outages, categorizing and
routing work, predicting future performance, and benchmarking
performance against peers are examples of everyday work ServiceNow
automates in leading enterprises.
ServiceNow commissioned Oxford Economics to survey 500 CIOs about
machine learning and automated decision-making. Respondents are based in
Austria, Australia, France, Germany, the Netherlands, New Zealand,
Singapore, Sweden, the United Kingdom and the United States, and
represent a broad range of B2B and B2C sectors. The survey was
administered via Computer-Assisted Telephone Interviews (CATI). Founded
in 1981 as a joint venture with Oxford University’s business college,
Oxford Economics specializes in evidence-based thought leadership,
forecasting, and economic impact analysis.
*Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending
Guide, IDC, October 2016. http://www.idc.com/getdoc.jsp?containerId=prUS41878616
Spending on artificial intelligence and machine learning is expected to
grow rapidly from less than $8 billion in 2016 to $47 billion by 2020,
according to IDC.
This: New #ServiceNow #CIO research: Beyond hype: Machine
Learning drives faster, more accurate business decisions http://bit.ly/2gk4Ha7
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