Column

Hiring in a world of intelligent machines

Now more than ever, managers should hire for curiosity and a commitment to lifelong learning

skills for the future of automation

I began my career as a mechanical engineer working on a manufacturing floor. In just five years, I witnessed a major shift in the way work was completed. While these changes were beneficial—productivity, customer satisfaction, and employee happiness all improved—they also came with a few unanticipated side effects.

As processes were automated, manual tasks eliminated, and overall efficiency increased, the definition of an excellent employee changed. Why? As automation made product assembly easier and faster, giving employees extra time to devise creative solutions to production issues, critical thinking and creativity became increasingly important.

Manufacturing is an obvious place to introduce new systems and technologies, but even workers whose skills machines cannot easily master will likely see their jobs change as smart technology further permeates the workplace. A 2018 report from the World Economic Forum (WEF) predicted that by the end of 2022, machines will begin handling tasks that require reasoning and decision making. This comports with our belief at ServiceNow regarding the imminence of AI-powered prescriptive models.

The WEF report also predicts that AI and machine learning could create a net of more than 50 million new professional roles by the end of next year. What those roles will be or how people’s jobs will evolve based on the introduction of automated counterparts is anyone’s guess.

One thing seems assured: tomorrow’s jobs will be different from today’s. That being said, how can organizations attract the right talent for the next three, five, or even 10 years?

[Take the quiz: What skills will matter most in 2030?]

I believe they must shift their focus from hiring hard skill sets to identifying and developing “continuous learners.” Here’s how hiring managers can get started.

Focus on skills, not degrees

In my career, I’ve encountered three types of professionals. Let’s take software developers as an example. The first type received their bachelor’s degree in computer science, found a job, and have done the same work since graduation. The second type also earned a CS degree but has continued to learn and master new skills. The third type did not receive a degree at all, but learned on their own through platforms like Coursera or Udemy.

Which type makes the best job candidate? We can all agree the second developer is preferable to the first, but I’d argue that it’s actually the third type who is best positioned for our future economy. The self-taught developer has demonstrated an ability and desire to continually learn, which provides resilience in an era when human workers must adapt to increasingly intelligent machines.

This scenario extends to a variety of other positions and careers (just look at my own jump from mechanical engineering to technology). It underscores the point that while higher education will always be important, college degrees are now just the beginning of a professional education.

Think beyond experience

Unfortunately, recruiters often disqualify continuous learners because they don’t check conventional boxes for experience and education. In the future, I think recruiting will be less about finding the perfect fit for the current role and more about identifying candidates who are curious enough to adapt for the next one.

When I switched from mechanical engineering to technology, many recruiters and hiring managers doubted that I had the appropriate skills. However, my analytic skills and problem-solving abilities turned out to be valuable across job functions. That’s why companies should look beyond a narrow definition of experience to identify the skills gained via that experience and think about how they might apply to a shifting work environment.

Careers and job descriptions are no longer static.

Because I’m passionate about data science, I think machine learning can play a significant role here. Today, many companies are automating at least part of the hiring process, especially when it comes to pre-screening applicants. This can save time and effort, but can also go quite wrong. We’ve all heard the horror stories about screening algorithms that unintentionally hinder workforce diversity efforts by favoring applicants who fit the profile of existing employees.

Similar concerns apply to automating other parts of the recruiting and hiring process. For example, if an applicant tracking system is trained solely on data about developers with computer science degrees, it will exclude self-trained developers from consideration.

Careers and job descriptions are no longer static. That means organizations must identify individuals who are comfortable with our new reality of continual change.

I’m a living example. From mechanical engineering to teaching math to consulting on analytics solutions to my current job as a ServiceNow evangelist, I’ve always maintained my desire to learn. Curiosity will never become obsolete, which is why organizations should seek continuous learners who can fill roles yet to be created.