Survey findings: 5 steps to achieve value from machine learning

Machine Learning — the new poster child for boosted productivity

Gabriela Motroc

© Shutterstock / Aleutie

Machine Learning is a catalyst for digital transformation, a new survey reveals. This survey is not meant to point out the obvious but to give solutions. Read on to find out the five steps to achieve value from machine learning.

Machine learning is the hottest tech trend these days and it’s showing up in all sorts of places in tech. Although it is deemed a high-security risk, many companies feel the pressure to invest in such a strategy (before fully understanding what they are aiming to achieve).

Service management software provider ServiceNow Inc. has surveyed 500 CIOs and has concluded that “many businesses are preparing for the widespread adoption of machine learning to automate decision-making.”  This survey explores the strategies CIOs are adopting to realize value from machine learning and the competitive advantage for organizations that are advancing to decision automation.

Key findings

Almost 90 percent of the CIOs surveyed said that greater automation will increase the accuracy and speed of decisions and over half of them said that machine learning is one of the focus of their digitization efforts. Almost 70 percent believe that decisions made by machines will be more accurate than those made by humans.

CIOs who are at the forefront of adopting machine learning recognize the need for process and talent changes, but many cite challenges. Roughly 40 percent have redefined job descriptions to focus on work with intelligent machines, and 27% have hired employees with new skill sets. However, over 50 percent believe that data quality and outdated processes are substantial barriers to adoption. The lack of budget for new skills and the lack of skills to manage smart machines are also cited as challenges.

SEE ALSO: Top 5 open-source tools for machine learning


ServiceNow Survey. Source:

Although current spending on machine learning still trails spending in other areas of modern computing, the impact of machine learning is set to grow as CIOs plan to increase investment in the technology. Furthermore, most CIOs expect payoffs in the next three years.

5 steps to achieve value from Machine Learning

Getting value from machine-learning investments will require substantial planning and disciplined followthrough—all while adjusting to rapid, ongoing changes in technology.

According to the survey, these five steps will ease the transition.

Build the foundation and improve data quality. As mentioned above, one of the top barriers to machine learning adoption is the quality of data because if machines make decisions based on poor data, the results will not provide value and could increase risk. Therefore, CIOs must utilize technologies that will simplify data maintenance and the transition to machine learning.

Prioritize based on value realization. Ask yourself the following questions:

At a high level, where are the most unstructured work patterns that would benefit from automation?

What would be the productivity gains from increased automation?

Where are the customer pain points?

Last but not least, commit to re-engineering services and processes as part of this transformation, and not simply lifting and shifting current processes into a new model.

SEE ALSO: Top 5 machine learning libraries for Java

Build an exceptional 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. Their role is to build a culture that embraces a new working model and skills. That means establishing guidelines for executives, engineers, and frontline workers about their work with machines and the future of human-machine collaboration.

Measure and report.  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.

If you want to learn more about the different types of ML tasks, the most suitable programming language for ML, misconceptions and best practices, read this interview

Gabriela Motroc
Gabriela Motroc was editor of and JAX Magazine. Before working at Software & Support Media Group, she studied International Communication Management at the Hague University of Applied Sciences.

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