As first seen on Eightfold.ai
Gone are the days of hiring on gut instinct. Evidence shows that this is almost always a bad way of making decisions and, invariably, leads to unconscious bias, which results in homogenous organizations where innovation stagnates.
But data-driven recruitment strategies can change all of that. By using advanced AI that can scour billions of data points in a hiring round, managers and recruiters can identify diverse, highly qualified candidates who are most likely to excel.
5 Indicators That Predict Candidate Success
Employers should look for certain characteristics and behaviors during the hiring process to help predict candidates’ likelihood of success. Here’s a list — by no means an exhaustive — of strong indicators that suggest a candidate will thrive.
1. Look at How Candidates Address Ambiguity
Leaders at nearly every organization will tell you that change is constant and rapid. So, organizations should hire people who are comfortable with change, explains Dynasti Hunt, MD of talent and equity at Third Sector.
Hunt, a member of the Forbes Human Resources Council, says this adaptability is an essential employee attribute that can predict candidate success. Those who are OK with ambiguity tend not to be affected by the unknown or not having the right answer immediately and so will be more able to weather change.
2. Isolate Skills from General Outcomes
An individual’s skills need to be distinct from general outcomes in the recruitment phase. Derek Thompson, a staff writer at The Atlantic, notes that skills are inherent, but outcomes are affected by many different variables.
Thompson uses baseball scouting to illustrate this point: Assessing a young pitcher on number of team wins does not necessarily indicate the capabilities of the pitcher. Other factors are in play — runs scored, the way the team fielded. “It’s far better for scouts to focus on metrics that the pitcher controls exclusively, like strikeouts and walks,” Thompson explains.
To bring it back to recruiting candidates, Thompson says identifying and analyzing metrics that can predict candidate success is essential.
3. Identify People Who Demonstrate Self-Awareness
Candidates can often perform well in interviews or at recruitment fairs, but without being self-aware they will falter.
Brie Rangel, VP of services at digital marketing experience agency IMPACT, says candidates who are not self-aware do not respond well to criticism or feedback. They tend to blame others for their shortcomings.
Rangel knows this from experience. She hired one candidate who performed well in interviews and at a careers fair but was not sufficiently skilled. Rangel decided to take a chance and train the candidate on the job. It was a lesson Rangel learned too late: The new hire lacked self-awareness and could not accept criticism, engage with feedback or understand their own responsibility for their performance.
Signs of self-awareness and, with it, the ability to learn and grow are good indicators that a candidate will succeed.
4. When Hiring Leaders, Look to the Future
Hiring excellent leaders is incredibly tricky. They key is to look to the future rather than rewarding the past, write Josh Bersin and Tomas Chamorro-Premuzic, an organizational psychologist.
Business is becoming more complex and uncertain, so companies need leaders to guide teams through these tumultuous periods. Yet supposed ideal candidates may well present a different profile from those who have been successful in the past, Bersin and Chamorro-Premuzic say. Tied to this, organizations should look to future leaders who deviate from the prevailing culture, as people who think and act differently from the norm are needed.
Selecting against your company’s prevailing culture may seem risky, but it often stamps out unwanted selection biases and also shifts the organizational mindset, thereby forcing decision-makers to consider new ideas. For instance, leaders who, on paper, may not seem ready to lead could be worthy of consideration. Analyze their ambition, reputation and passion for the business, Bersin and Chamorro-Premuzic argue. The least experienced might turn into the best leader.
5. Measure Wellbeing
There are several different methods companies use to gather data. These include internal and external social media, ERP systems, surveys and analyzing information from business communication. It’s the last that could help to reveal the mood of employees, Bersin explains in another article.
Mood is an important metric. By measuring it, employers can help promote positive emotions to improve employee wellbeing. Many organizations realize improved wellbeing results in lower attrition rates and improved recruitment drives. Bersin gives the example of a company that pinned smart badges to their engineers to track their moods. Their research showed that the “happiest” engineers were those with the most interpersonal engagement and physical activity.
It’s what an organization does with such data that matters. In the case Bersin cites, the company changed its management practices so that, for example, engineers would be more engaged with their peers.
How AI Can Identify Potential Candidate Success
By increasing the quality and number of data sources, AI can help to improve accuracy and fairness in recruiting and hiring. And it can also empower HR directors to identify character traits and behaviors in candidates who are most likely to succeed in the role.
It Can Help Predict the Skills Candidates Will Need
Exactly what skills employees need is in great flux. As many as 34 percent of employees say they’ve had to learn a “new-to-world” skill in the past three years, Mary Baker at Gartner explains. And 19 percent say that a skill they acquired as recently as a year ago is no longer relevant.
Additionally, 65 percent of those responsible for heading up learning and development feel more uncertain now than they did three years ago about the skills their organization needs. The result is that only 20 percent of employees have both the skills they need currently and those they’ll need in the future.
Therefore, hiring based on current skills is not an effective way to predict the likelihood of candidate success. What is needed, Baker argues, is increased and diverse inputs to identify skills and external market requirements.
Baker says AI-enabled analysis can make sense of the data to identify both the skills needed now and the skills that will be needed in the future, based on data variables such as geography, industry, function and role.
As AI predicts the necessary future skills and identifies candidates most able to meet those new demands, it will also determine those most equipped to navigate ambiguity and adapt to organizational changes.
It Can Identify Future Leaders
By accessing multiple data points, AI can predict candidate success with greater accuracy than humans ever could. AI can help recommend candidates who perform better, stay in roles longer and make better decisions.
The predictive powers of AI mean that not only will there be a wider talent network to choose from, but those identified candidates are likely to be better-matched to an organization’s current and future needs. These candidates will also be far more likely to be the types of future leaders a company will need.
It Can Select Candidates With Positive Wellbeing
Being able to understand and decipher candidates’ emotions during the hiring process is a valuable tool to employers. And AI has made this possible. Although emotion recognition technology is still in its nascent phase, it is already promising great advancements in recruitment. Intelligent algorithms can determine important emotions such as candidates’ levels of boredom, enthusiasm or honesty, for example.
But AI can also identify behaviors that put employees at risk of accidents, and lead to high stress and poor performance, Bersin says. By scouring all the existing employee data an organization has, AI could apply emotion-aware algorithms to candidates to help predict those most likely to exhibit problematic behaviors.
It Can Help Spot High Levels of Self-Awareness
Another way of talking about self-awareness is in terms of emotional intelligence. And only recently has AI been trained in emotional intelligence, says Jesus Mantas, global head of strategy and offerings at IBM Global Business Services.
Recent progress can be attributed to developments in neuroscience and advanced tools such as functional magnetic resonance imaging (fMRI).
One of the main reasons for this focus on AI becoming emotionally intelligent is to match machines and humans emotionally, Mantas explains, so both can work together in more productive and familiar ways.
However, as AI’s emotional intelligence blossoms, it will likely be able to identify self-aware characteristics in candidates through similar processes employed to help it interact with its human colleagues.
Finding the right candidates used to be considered an art rather than a science. Today, however, hard data gathered and analyzed by advanced AI is transforming recruitment into a reliable science that selects for candidates success, hiring employees who will help an organization thrive.