Competition for machine learning professionals expected to grow
Machine learning is rapidly taking hold in a growing number of organizations, and hiring managers can expect to find themselves in fierce competition for workers with machine learning skills in 2018.
Those are among the findings of a new survey of 500 chief information officers from around the world by ServiceNow. The research found that “machine learning has arrived in the enterprise, making material contributions to everyday work.” That is driving increased competition to acquire machine learning talent.
“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,” the study notes.
For “The Global CIO Point of View,” ServiceNow surveyed CIOs in 11 countries across 25 industries to explore the competitive benefits of adopting machine learning and to learn how IT 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.
The survey finds a growing sense of confidence among senior executives that machine learning will lead to faster and more accurate decisions. Many respondents noted that 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.
Some 87 percent said that they would get value from the accuracy of decisions. Further, 69 percent say decisions made by machine learning will be more accurate than those made by humans.
Some 57 percent said routine decision making takes up a meaningful amount of employee and executive time, so the potential value of automation is high. More than two-thirds of CIOs (69 percent) expect this decision automation to contribute to their organization’s top line growth.
“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.”
Nearly three-quarters (72 percent) of CIOs surveyed said they are leading their company’s digitalization efforts, and more than half (52 percent) said machine learning plays a critical role. Nearly half (49 percent) of the CIOs surveyed say their companies are now 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, the study cautions:
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.”
Achieving value from machine learning
The study also provides five steps that CIOs can do to advance 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,” the study says.
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,” the study says.
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,” the study says.
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 collaboration,” the study says.
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,” the study concludes.