A survey of 500 CIOs from around the world by ServiceNow 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 redesigning their organizations and processes.
For The 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 percent) 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 percent said that they would get value from the accuracy of decisions. In fact, 69 percent say decisions made by machine learning will be more accurate than those made by humans.
- 57 percent 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 percent).
“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 transformation
Nearly three-quarters (72 percent) of CIOs surveyed said they are leading their company’s digitalization efforts, and more than half (52 percent) agree that machine learning plays a critical role. Nearly half (49 percent) of the CIOs surveyed say their companies are using machine learning and 40 percent 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 learning technology:
- Only 27 percent of CIOs have hired employees with new skill sets to work with intelligent machines.
- Fewer than half (40 percent) of CIOs have redefined job descriptions to focus on work with intelligent machines, 41 percent cite a lack of skills to manage intelligent machines and about half (47 percent) say they lack budget for new skills development.
- CIOs cite data quality (51 percent) and outdated processes (48 percent) as substantial barriers to adoption.
- Fewer than half (45 percent) 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 percent) 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 percent of first movers expect decision automation to support top-line growth vs. 67 percent of others.
- Roughly 80 percent have developed methods to monitor machine-made mistakes vs. 41 percent of others.
- More than three-quarters have redesigned job descriptions to focus on work with machines compared with 35 percent of others.
- More than 70 percent have developed a roadmap for future business process changes compared with just 33 percent 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 bottom line.”
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 percent, vs. 52 percent 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 percent vs. 36 percent), developing a roadmap for future process changes (52 percent vs. 35 percent), and recruiting employees with new skill sets (42 percent vs. 25 percent).
- 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 percent vs. 80 percent), operations (46 percent vs. 58 percent), risk and compliance (36 percent vs. 58 percent).
Five steps to achieve value from machine learning
ServiceNow recommends how CIOs can jump start their journey to digital transformation with machine learning:
- 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.
- 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.
- 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.
- 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.
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).