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CIO Global
Point of View

The New Agenda for Transformative Leadership

Improve speed, accuracy, and growth by combining machine learning with new business processes and skillsets. Our survey of 500 Chief Information Officers across 11 countries identifies strategies to succeed.

Explore the Takeaways

Develop Strategy

Machine learning has arrived in the enterprise—and investment is set to rise sharply. Chief Information Officers say the technology will deliver competitive advantage, but most are in the early stages of adoption, and success requires equal focus on talent and process. The opportunity to become an industry leader is significant.

Machine learning has arrived

CIO Priorities

Automation ranks among the top 5 business success strategies by 500 CIOs

05 Digitizing Business Processes
04 Increasing Speed to Market
03 Exploring Emerging Technology
02 Automating Routine Processes
01 Innovation

Investment in machine learning will increase sharply

2020 investment plans 2017 investment
0% 0%

Organizations are adapting for machine learning

0% 0%
Not adapting
Adapting
0% 0%
Adapting
Not adapting
0% 0%
Adapting
Not adapting
0% 0%
Adapting
Not adapting

CIOs identify processes and people as top barriers

Barriers to machine learning adoption

Humans remain central to business operations

State of decision automation across business functions

0% 0% 1%
Largely automated
Not automated
Requires substantial human intervention
0% 0% 1%
Largely automated
Not automated
Requires substantial human intervention
0% 0% 1%
Largely automated
Not automated
Requires substantial human intervention
0% 0% 0%
Not automated
Largely automated
Requires substantial human intervention

Realize Value

By modernizing the way organizations work, Chief Information Officers have the opportunity to drive business-changing economics. More than half of CIOs say they are already advancing beyond the automation of routine tasks to the automation of complex decisions. The results will be greater efficiency, accuracy, and speed.

Machine learning will improve decision making

Percentage of CIOs who expect substantial results

0%
0%
0%
0%
0%
Accuracy of Decisions Speed of Decisions Top-line Growth Competitiveness Reducing Risk

Machine learning is becoming more sophisticated

CIOs rank top three important capabilities of machine learning

Simplest Capability
Most Complex Capability
68

Automation of Repetitive Tasks

40

Recognize data patterns

32

Establish links between events

31

Making Predictions

32

Supervised learning

18

Making basic decisions

54

Making complex decisions

Machines can enable humans to focus on strategic work

2017
0%
routine decision-making takes up a meaningful amount of human time
2020
0%
machine-made decisions will be increasingly accurate

Machine learning will improve quality of decisions

More than 4 out of 5 CIOs say machine-made decisions will improve the speed and accuracy of business

Compete to Win

An elite group of Chief Information Officers, who comprise just 10% of the survey sample, are ahead of peers in spending, automating and making organizational changes. These "First Movers" provide a useful guide for how CIOs must adjust people, process, and technology to lead the competition.

Meet the first movers

0% 0%
Expect decision automation to support top-line growth
Others
First Movers
0% 0% 0% 0%
Changed job descriptions to focus on work with machines
Set plans for workforce size and role changes
Others
First Movers
0% 0% 0% 0% 0% 0%
Developed a roadmap for future process changes
Developed methods of monitoring mistakes made by machines
Implementing policies for ensuring data accuracy
Others
First Movers
0% 0% 0% 0% 0% 0%
Focusing on innovation
Focusing on automating routine processes
Focusing digitizing business processes
Others
First Movers
0% 0% 0% 0% 0% 0%
Analytics
Cloud
IoT
Others
First Movers

Survey Methodology

Learn More About Our Process
Back

CIO Survey Methodology

Global Reach

Oxford Economics led a survey of 500 CIOs from 25 industries in 11 countries through 20-minute Computer-Assisted Telephone Interviews (CATI), sourced from survey panels and independent research.

Company Size by Revenue

$500M–$1B

33% of respondents

$1B–$5B

33% of respondents

$5B+

33% of respondents

Explore the Data

Questions
Which global trends do you expect to have the greatest impact on your organization over the next three years?
Responses
Technology change
0%
Global economic conditions
0%
Shortages of skilled talent
0%
Business model disruption
0%
Data and information security threats
0%
Increasing regulatory requirements
0%
Labor market shifts
0%
Customer empowerment
0%
Increasing barriers to trade
0%
Questions
Which of the following do you see as most important to the success of your organization over the next three years?
Responses
Focusing on innovation
0%
Automating routine processes
0%
Exploring emerging technologies
0%
Increasing speed to market
0%
Digitizing business processes
0%
Increasing investment in digital skills and technology
0%
Risk management
0%
Increasing quality of talent recruitment and retention
0%
Increasing employee engagement
0%
Increasing brand recognition
0%
Entering new geographic markets
0%
Entering new industries
0%
M&A and/or divestiture
0%
Questions
To what extent are you investing in the following technologies today?
Responses
Industry-specific technologies
0%
Analytics
0%
Mobile
0%
Cloud
0%
Internet of Things
0%
Social/Collaboration
0%
Software-defined networking
0%
Big Data
0%
Process automation software
0%
Artificial Intelligence
0%
Machine learning
0%
Augmented Reality/Virtual Reality
0%
3D printing
0%
Robotics
0%
Blockchain
0%
Drones
0%
Questions
To what extent are you investing in the following technologies in three years?
Responses
Industry-specific technologies
0%
Analytics
0%
Big Data
0%
Cloud
0%
Internet of Things
0%
Social/Collaboration
0%
Software-defined networking
0%
Process automation software
0%
Mobile
0%
Machine learning
0%
Artificial Intelligence
0%
Augmented Reality/Virtual Reality
0%
3D printing
0%
Robotics
0%
Blockchain
0%
Drones
0%
Questions
How mature is your use of the following technologies (including assets deployed, worker skills, and integration into business operations)?
Responses
Industry-specific technologies
0%
Analytics
0%
Cloud
0%
Mobile
0%
Big Data
0%
Social/Collaboration
0%
Internet of Things
0%
Software-defined networking
0%
Process automation software
0%
Machine learning
0%
Augmented Reality/Virtual Reality
0%
Artificial Intelligence
0%
Robotics
0%
3D printing
0%
Blockchain
0%
Drones
0%
Questions
Which best describes your organization's use of machine learning?
Responses
We have no plans to use machine learning.
0%
We are in the research and planning phase of our deployment.
0%
We are piloting the technology.
0%
We are using machine learning in some areas of the business.
0%
We are using machine learning across the business.
0%
Questions
To what extent do you expect decision automation to deliver value in the following areas over the next three years?
Responses
Accuracy of decisions
0%
Speed of decisions
0%
Top-line growth
0%
Competitiveness
0%
Reducing risk
0%
Profitability growth
0%
Employee focuson strategic work
0%
Customer service
0%
Cost savings
0%
Employee productivity
0%
Talent recruitment and retention
0%
Developing new products and services
0%
Questions
To what extent do you agree with the following statements about your role?
Responses
My role is expanding to lead broader, organization-wide digitization efforts.
0%
I am increasingly partnering with other members of the C-suite to effectively manage organization-wide digitization efforts.
0%
I am increasingly focused on strategy, rather than operations.
0%
Machine learning is a strategic focus for me.
0%
Questions
Which capabilities of machine learning are most important to the success of your organization over the next three years?
Responses
Automation of repetitive tasks (e.g., by bots)
0%
Making complex decisions
0%
Process data and recognize patterns (e.g., dynamic threshold assignments, anomaly detection)
0%
Establishing links between events or actions (e.g., root cause analysis)
0%
Supervised learning
0%
Making predictions
0%
Making simple decisions
0%
Creating intelligence based on human-designed rules
0%
Providing text or voice support in place of virtual agents
0%
Interpreting natural language
0%
Questions
What is your primary method of developing your machine learning capabilities?
Responses
Through our regular IT development process
0%
By a specialized team within our organization
0%
Through mergers and acquisitions
0%
Through third parties (e.g., consulting, vendors)
0%
Other
0%
Questions
To what extent do you agree with the following statements?
Responses
Decisions made by machines will be increasingly accurate over time.
0%
Decisions made by machines will be more accurate than decisions made by humans.
0%
Routine decisions take up a meaningful amount of employee and executive time.
0%
We expect to move from the automation of simple decisions to the automation of increasingly complex decisions.
0%
My company is advancing beyond the automation of tasks to the automation of decisions.
0%
Questions
In which of the following areas would it be most valuable to automate decision-making?
Responses
Security operations (e.g., alerts and remediation)
0%
Customer management (e.g., targeted offers based on consumer behavior, fraud detection)
0%
Technology vendor management (e.g., selecting the right cloud vendor for the right time and circumstance)
0%
Operations management (e.g., detection of manufacturing defects, predictive maintenance)
0%
Finance (e.g., vendor payment authorization)
0%
Supply chain management (e.g., purchasing decisions)
0%
Call center management (e.g., routing based on urgency, customer value, etc.)
0%
Sales and marketing (e.g., prioritizing leads)
0%
Product development/R&D (e.g., investment priorities)
0%
Human resources (e.g., support for hiring decisions)
0%
Questions
To what extent do you expect decision automation to deliver value in the following functional areas over the next three years?
Responses
Security
0%
IT
0%
Strategy
0%
Operations
0%
Risk and compliance
0%
Finance
0%
Sales and marketing
0%
Procurement/Supply chain
0%
Product development/R&D
0%
Human resources
0%
Manufacturing
0%
Questions
To what extent do the following factors interfere with the adoption and maturation of automated decisions at your organization?
Responses
Insufficient quality of data
0%
Outdated processes
0%
Lack of budget for new skills
0%
Lack of budget for new technology
0%
Lack of human skills to manage and maintain smart machines
0%
Insufficient quantities of data
0%
Regulatory complexity or uncertainty
0%
Insufficient expertise or tools for data analysis
0%
Lack of complex decision-making ability by machines
0%
Questions
Which of the following best describes your approach to business process redesign in support of decision automation?
Responses
We have not begun to reconsider business process design for decision automation.
0%
We are in the research and planning stage of business process design.
0%
We have piloted new business processes.
0%
New business processes are in use in select areas of the organization.
0%
New business processes are in use across the organization.
0%
Questions
To what extent do you agree with the following statements about your IT organization?
Responses
We have a defined business process for managing IT assets.
0%
We are making changes to our IT structure to accommodate machine learning.
0%
We have business services mapped to our IT management database.
0%
We are making organizational changes to accommodate machine-driven business processes.
0%
Our management system for IT systems is maintained by an automated process.
0%
Questions
Which changes, if any, has your organization made to accommodate digital labor?
Responses
Set up programs to expand employee skill sets
0%
Redefined job descriptions to focus on work with machines
0%
Developed a roadmap for future process changes
0%
Recruited employees with new skill sets (e.g., business-process experts, data scientists)
0%
Set plans for workforce size and role changes
0%
Developed plans for compliance and labor issues
0%
We have not made any substantive changes to process and leadership
0%
Questions
Which changes do you plan to make over the next three years?
Responses
Set up programs to expand employee skill sets
0%
Developed plans for compliance and labor issues
0%
Recruited employees with new skill sets (e.g., business-process experts, data scientists)
0%
Redefined job descriptions to focus on work with machines
0%
Developed a roadmap for future process changes
0%
Set plans for workforce size and role changes
0%
We have not made any substantive changes to process and leadership
0%
Questions
Which changes, if any, has your organization made to improve risk management for machine learning?
Responses
Developed methods of monitoring mistakes made by machines
0%
Addressed the operational impact of mistakes made by machines in risk policies
0%
Developed policies for insuring the accuracy of data
0%
Addressed the legal risk of mistakes made by machines in risk policies
0%
Amended risk policies to account for digital labor
0%
We have not made any substantive changes to risk management for machine learning
0%
Questions
What changes, if any, will your organization make to improve risk management for machine learning in the next three years?
Responses
Developing policies for insuring the accuracy of data
0%
Developing methods of monitoring mistakes made by machines
0%
Amending risk policies to account for digital labor
0%
Addressing the operational impact of mistakes made by machines in risk policies
0%
Addressing the legal risk of mistakes made by machines in risk policies
0%
We are not considering any substantive changes to risk management for machine learning
0%

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