How Algorithms Are Changing The Way HR Works

Thanks to technology, the business world continues to evolve faster and faster. In this world of constant innovation, the most successful organizations are those that can keep up with or even get ahead of the technological trends. Just think of Google or Apple.

Of course, creating an innovative company is impossible without the right workforce. Hiring the right employees for the right roles at the right time is critical, and it requires good resource management to do it. That’s why many corporations are leaning more and more on HR data and algorithms to help with their decision-making.

In fact, 65% of HR leaders say AI and workforce analytics can improve performance across their organization. As more HR professionals leverage AI for HR solutions, we’re starting to see a shift in the landscape.

In this article, we lift the lid on how some of those algorithms are changing the face of HR, and the kind of systems you can expect to see in your workplace within the next decade.

HR, Google, and People Operations

The rise of algorithm-driven HR can be traced back to Google. They were one of the first major companies to consider how to incorporate AI in human resource management. The company started by not calling it ‘HR’ in the first place. It referred to HR as “people operations”, which used data and people analytics for more accurate people management decisions.

With the dawn of a new AI era, it’s no surprise we’re seeing more instances of data-driven HR. According to a Slack report, the use of AI in US workplaces is up 24% from 2023. Around 80% of those integrating AI have seen a boost in productivity, effectively reducing the “work of work.”

From an HR standpoint, AI is already embedded across human capital management (HCM) solutions, which encompass core HR, payroll, onboarding, recruiting, people analytics and more.

Google’s business success, and the growing use of AI in HR, demonstrate the real benefits of adopting data-driven HR practices. What’s more important than ever is fostering a culture of continuous learning in the workplace. Implementing algorithms can also provide insights into how to improve employee satisfaction.

How Are Algorithms and Workforce AI Impacting HR?

Driven by organizations like Google, algorithms are now being used across marketing and customer service in the form of conversational AI, and in many HR departments. You can compare it to how an organization uses Google Analytics to improve website performance, except that HR data focuses on employees and candidates.

One of the beauties of data-driven HR, or people analytics, is its flexibility. Algorithms can be adapted to measure specific elements which can help organizations address their most pressing HR concerns.

A SHRM report found that 82% of organizations use people analytics for employee retention and turnover, while 71% use it for the recruitment process. Other HR functions such as employee engagement (59%), benefits (58%) and performance management (58%) also benefit from people analytics.

In short, AI and algorithms are changing the scope of HR in several ways. But with AI laws in the US on the horizon, this also brings compliance and best practice into question.

For employers using AI, this includes:

• Conducting regular audits.
• Acquiring consent from employees to use AI.
• Creating and complying with HR policies.
• Training HR and managers.
• Ensuring vendor compliance.

Is Algorithm-Based Hiring the New Normal?

Any HR professional will tell you that their field isn’t only about hiring and firing. The fact remains, however, that overseeing hiring and onboarding is still key to HR. It’s also an area in which algorithms have a considerable influence.

The impact of hiring algorithms on HR has been widely tested and analyzed. A variety of different studies, including one by Harvard Business Review, have concluded that recruitment driven by data and algorithms leads to higher quality hires for companies.

The reasons for this, while diverse, boil down to a couple of major factors. One of the main benefits of HR data-driven decision making is the breadth of data sources available to algorithms. The second is the removal of human biases.

Algorithm hiring tools can collect and process a huge volume of data. They can also analyze information from CVs, publicly available information, and responses to assessments. This allows them to build a comprehensive picture of any and every candidate.

This data identifies qualities that make for a successful employee. Hiring teams can then look for those qualities within the skills and personality of candidates and choose people who are best suited for the job. Not only does this make for a better hire, but it also allows hiring teams to make decisions much faster than before.

The fairness of data-driven hiring also prevents human error from getting in the way of hiring the right candidates. Proponents of hiring algorithms argue that the algorithms can remove human biases—biases that color decision-making—from the equation.

At best, biases can mean making the wrong hiring decision, and at worst, they can derail the hiring process completely. In theory, people analytics and algorithms would remove that subjectivity.

With that said, AI is capable of its own biases right at the data collection stage. For instance, if the data isn’t representative or diverse to begin with, the AI algorithm may generate biased results. For AI models trained on data based on human choices, biases are inherent to their training to an extent. For this reason, datasets must be assessed for bias.

The average person on the street probably wouldn’t like to have their CV screened by an algorithm, though. A study conducted by the Pew Research Center found that 71% of US workers wouldn’t want to apply for a job where AI was involved in making the final decision, and 15% think algorithms would do a worse job than a human.

And yet, a study by Ford found that 69% of Millennials and Gen Z predict they’ll trust AI career advice within the next five years. This demonstrates people’s belief in AI when it can prove beneficial, but a wariness of AI when it has the potential to harm them.

There’s one final way in which algorithms and AI have impacted recruitment. That’s by analyzing and reassessing the hiring process itself. Part of Google’s early research into data-driven HR focused on the optimal length of the hiring process.

The company found that four interviews were optimal for hiring, dubbed the “Rule of Four”. Further assessment of candidates gave little additional value. Thus, Google recommended the shortening of what were often far longer hiring processes, a decision that saved both time and money.

Do Algorithms in HR Simplify Workforce Planning?

Algorithms also shape HR when it comes to predictive modeling and analytics. Predictive modeling software uses algorithms to find patterns in large volumes of data, allowing people to more accurately predict future trends.

In HR specifically, the data we’re looking at is information about an organization’s workforce. HR professionals and executives can use algorithms to identify factors that make for successful employees and influence employee retention.

Discovering what qualities make successful employees at your company can help your recruiting team find and attract better candidates for your organization. Meanwhile, identifying reasons for turnover and attrition is vital to workforce planning. Predictive modeling in this area can help firms answer the following questions:

• Who is at risk of leaving the company?
• What is it that persuades employees to go?
• What can we do to retain our best employees?

Answers to these questions help HR departments get ahead of any problems and take preemptive action to retain the staff they might otherwise lose. Such predictive modeling in HR can also help identify skills shortages and leadership needs, aiding firms in mitigating the risk of a resources gap.

In short, algorithms in this area allow for predictive workforce planning. Firms can identify potential issues or other patterns within their workforce, and they can do so before problems come to fruition. That allows them more time to find solutions.

HR Predictive modeling and analytics are particularly important to firms experiencing rapid growth. These are the businesses at most risk of workforce problems. When a company grows at pace, it’s more difficult to ensure that the workforce keeps up. Algorithms help these firms stay ahead of the curve.

People Analytics and Employee Satisfaction

It’s no secret that employee satisfaction and retention contribute to business success. It’s how firms retain their edge over competitors and another of the major areas where algorithms are remodeling HR.

Low retention and high turnover can significantly affect a company’s bottom line. As mentioned earlier, people analytics and algorithms can provide an insight into the employee experience. In this context, AI can help HR professionals to understand their employee’s needs better.

How is this done, exactly?

• For starters, AI platforms can analyze plenty of data on a large scale. This data can allow talent managers to gain insights into job satisfaction, career growth opportunities, work-life balance and other contributing factors to employee attrition.

• AI technologies can also support feedback and performance management systems. These can then produce objective evaluations of employee performance and allow managers to provide personalized coaching and development plans.

• Machine learning models in particular can tailor training, benefits and career opportunities to individual preferences to aid talent development. This can optimize team composition based on personality and skill compatibility, improving job satisfaction and collaboration.

That’s not to say human interaction should take a backseat. HR professionals that work alongside algorithms and people analytics can enhance the employee experience while preserving the human element in the workplace.

Striking the right balance is key to harnessing the power of algorithms while fostering a sense of community. And as organizations grow and change, it’s crucial to adjust algorithms based on real-time feedback and results too.

Concerns Of Algorithm-Driven HR Strategies

The path to innovation is rarely a smooth one. All paradigm-changing developments come with some complications, and that much is true of data-driven HR and algorithms as well.

Ethical Implications

Experts in the HR field have highlighted some moral and legal concerns, including unintended bias and questions of privacy. As we’ve covered, it’s possible for unconscious bias to seep into Ai models through the data scientists and developers responsible for their training.

Inherent Discrimination

Data-driven HR helps to remove human bias from recruitment, as mentioned earlier. However, poorly implemented algorithms could actually reinforce discrimination if they are designed to focus on the wrong character traits or qualities in applicants. In extreme circumstances, this can lead to certain groups being overlooked completely and thus an unfair hiring process.

Privacy and Data Protection

The collection and use of employee data are what raises privacy concerns. Companies often rely on outside firms to collect and process that data. Because the security of personal information is very important to many people, some employees might have issues with sharing their data with such third-party companies.

The Future of HR Is What You Make It

The concerns surrounding data-driven HR shouldn’t be minimized. However, that doesn’t mean algorithms in HR aren’t useful and valuable when used properly.

The success of Google, as one of the first to use data-driven HR, is compelling by itself. Add to that the studies which have provided evidence of its efficacy, and it seems data-driven artificial intelligence in HR is here to stay. By considering how HR algorithms could fit within your organization, you can propel your organization into the future of HR and use the best tools and strategies to help your people at the same time.