Today, organizations across the globe are looking for ways to digitize their businesses through innovative technologies like artificial intelligence (AI) and machine learning. According to a recent study, 31 percent of enterprises expect to add AI and machine learning capabilities to their core operational functions in the next 12 months.
This year alone, organizations will invest $19.1 billion worldwide on cognitive and AI systems. Alongside this demand for new tech comes the demand for employees who have the skills necessary to manager these tools. Currently, the three most in-demand skills on Monster are machine learning, deep learning, and natural language processing.
While machine learning has increased innovation across enterprises, the technology has proven particularly useful in HR functions. A recent study found that 66 percent of CEOs believe cognitive computing can drive significant value in HR, and 50 percent of HR executives believe this technology could transform key dimensions of HR.
By providing comprehensive data-driven insights, machine learning is making its mark on modern hiring practices. Moreover, it’s helping HR professionals think more critically and creatively about what — and who — is best for their organizations.
It takes both manpower and machine learning to create a sustainable and successful HR team that can attract, develop, and retain the best talent in the industry. As enterprises continue implementing AI and machine learning, it’s critical that we take some time to understand the relationship between data-driven technology and human empathy.
Machine Learning Supports Workplace Diversity
Building a more inclusive company starts with a company’s job postings. Few professionals realize that unconscious bias can easily creep into job ads, directly affecting the types of candidates who apply to said jobs. Left unaddressed, unconscious bias can create inefficient hiring processes that lead over time to declining organizational efficiency and performance.
Machine learning and AI can help organizations eradicate unconscious bias in job ads by identifying certain words or phrases that may deter from applying for a job. It is reported that there are more than 25,000 potentially problematic phrases that can skew job ads in favor of men or women candidates. Considering how few women are represented in leadership roles in the business world at large, it is crucial for HR departments to leverage tech tools that can help more women get through initial screenings that may otherwise be biased against them.
Machine learning’s usefulness in combatting bias doesn’t stop with job ads. Once candidates apply, machine learning technologies can identify the potential best fits based only on factors relevant to the role. Once the interview process begins, HR professionals can focus on candidates’ skill sets and pick the right person for the job, regardless of factors such as race, gender, or disability status.
The long-term benefits of diversity are quite evident. According to Bersin by Deloitte, diverse companies have 2.3 times higher cash flow per employee than non-diverse companies. Thus, the digitalization of hiring practices is an opportunity to increase both diversity and ROI.
Man and Machine: HR Is Still a Human Function
As more and more employers use machine learning technologies to help find the right fits for their roles, many HR professionals fear they will soon become irrelevant to the hiring process. Despite this widespread panic, AI and humans are not contradictory forces. In fact, AI is bringing more humanity to businesses around the globe.
AI, automation, and machine learning capabilities empower humans to think more creatively and strategically. For example, automation can uncover new insights that would otherwise go unseen by detecting and analyzing hidden patterns in data. It is then up to humans to make the right decisions regarding these insights using judgment and strategic thinking.
While machine learning will continue to transform hiring, technology cannot replace the human capacities to reason and empathize. It’s comforting to know that humans ultimately have the say when it comes to who is the best fit for a particular role.
In addition, it is important to remember that HR’s value doesn’t end with the interview. HR must also support individual employees once they are onboarded. HR plays a key role in creating and fostering teams that produce results, as well as in helping employees overcome any specific challenges they may face at work. This role is unlikely to change any time soon.
The Future of Machine Learning and HR
Machine learning has already started to transform the HR function, but plenty of new developments are still on the horizon.
For example, chatbots will become more prominent as they become more sophisticated through machine learning and repeat encounters with job seekers. Over time, their ability to interact with and support both candidates and employees will become just as sophisticated as a human HR rep.
Natural language processing technology is also advancing. As it does, digital assistants will become more capable of responding to voice commands, making them more efficient alternatives to manual data entry. Both digital assistants and chatbots will free up time for HR professionals to focus on the human side of the job rather than on operational requests.
If companies want to attract, develop, and retain the best talent in their industries, it is imperative they embrace machine learning. Digitalization offers organizations the opportunity to incorporate technology into the daily lives and functions of their employees with ease. By utilizing machine learning, HR departments can spend their time coaching tomorrow’s leaders rather than fulfilling administrative tasks. As technology advances, it is likely that diversity, ROI, and overall innovation will continue to grow as well. It is in our hands to seize the opportunities digital transformation brings. Let’s start now.
Stefan Ries, chief human resources officer and labor relations director, is a member of the executive board of SAP SE.