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Can Artificial Intelligence Help In Hiring Talent? Darwin Ecosystem Says Yes!

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Talent, one of a company’s most significant assets, remains one of the hardest assets to acquire and retain. The interview process for new hires is wrought with challenges that minimize its effectiveness including unconscious bias, inconsistent interview protocols and limited knowledge of the job requirements. Even if a company hires the right individuals, success isn’t guaranteed. Managers struggle to understand why certain teams work and others fail. What are the characteristics of top performing individuals and a high performing team? How can you replicate the success of one group or individual across an organization? Moreover, how do you motivate a person to do their best work? These are critical questions that managers must address to build well run organizations.

Hiring the wrong individual or placing a good candidate on the wrong team holds dire consequences for organizations, including lost productivity and diminished workplace morale. According to the State of the American Workplace report by Gallup Inc., 51 percent of American workers are "not engaged" in their work and an additional 16 percent are "actively disengaged" in their work . This dissatisfaction adds up to real money for corporations with Gallup estimating that an actively disengaged employee costs their organization $3,400 for every $10,000 of salary, or 34 percent. Furthermore, in hot job markets, such as the technology sector, employers are also spending heavily on additional benefits such as meals, transportation and fitness facilities. Even with these perks, turnover is high. Voluntary turnover, when employee separation is not initiated by the employer, was reported at 17% in 2017, according to research by Mercer. The rate is even higher in certain industries and certain areas of the U.S. and Canada.  For example, Hay Group reported a median turnover rate of 67 percent for part-time retail employees. 
Companies need a new strategy to turn the tide, and artificial intelligence (AI) technology may be just the tool businesses need to make progress. To understand how artificial intelligence (AI) technology could resolve many of these issues, I spoke with Darwin Ecosystem's CEO, Thierry Hubert. The company, based in Dallas Texas, is part of a growing list of AI startups that are attempting to tackle more nuanced analytical challenges. There’s no more nuanced challenge than hiring talent and managing team dynamics.
 
The untapped role of personality in hiring

In March, Darwin Ecosystems launched its Projected Personality Interpreter (PPI) at IBM’s Think 2018 conference to tackle this type of problem. It's one of the solutions based on the Darwin Analytics Engine (DAE) comprised of functions such as neural networks, streaming analytics and correlation services. The PPI fits into the category of pattern recognition and detection services. The DAE APIs can also help companies leverage third-party data from the Internet and connects to IBM Watson Cognitive services.

Employers can leverage the PPI service to understand if a job applicant has the right mix of personality traits for the role or to work within a specific team. While many interviews focus on communications skills and previous work history, the PPI measures personality traits such as curiosity, susceptibility to stress and self-discipline levels. In a world that focuses on interviewing for the “ hard skills” of a job such as technical proficiency and metrics, it’s been difficult for companies to define interview protocols that uncover skills such as a sense of ethics, cautiousness and cooperation levels.

Hubert said one of the goals of the PPI is to help an organization understand the shared personality attributes of top performers, assigning the right team roles to the right people and help supervisors discover unconscious biases that may impact their performance. It does this using pattern detection, a branch of machine learning, which allows companies to recognize patterns and regularities in unstructured data such as speech, images and Internet data.

Approximately 80 percent of a company's data is unstructured. Part of the pitch and appeal of Darwin Ecosystem’s solutions are to help a company gain insight from unstructured data without the need to hire dozens of data scientists to build and train complex models. Pattern detection, a branch of machine learning, allows companies to recognize patterns and regularities in unstructured data from speech, images and Internet data. This type of solution was previously unavailable with conventional analytics.

Currently, businesses that range from healthcare to law enforcement are testing this technology. For example, police departments are using this technology to identify the best talent for law enforcement positions. Darwin Ecosystem, working in partnership with the Police Exam Solutions (PES) team built a solution that analyzes natural language responses from a recruit’s written essay exam. The test asks open-ended questions such as “Tell us how you might improve your hometown” and “Describe your favorite vacation.” The PPI analysis supplies insight on 52 different personality traits and provides a score on how well the candidate answers in each category match to the ideal characteristics of a police officer. According to Darwin Ecosystem, the system can identify trends and personality traits that would have otherwise gone unnoticed.

As noted earlier, job dissatisfaction is high in many organizations. Just as companies look to predict customer churn, AI can also assist organizations in predicting employee attrition and suggesting actions to improve the workplace. In high transition areas, such as customer service agents and retail employees, improving the workplace environment can lead to improvements in customer satisfaction as well.

Darwin Ecosystem’s PPI doesn't attempt to replace the standard interview and management processes. However, PPI is an intriguing option for augmenting a company’s existing procedures to improve results. For the skeptics in the room, I admit this approach might not be perfect. However, the haphazard method organizations have towards talent acquisition is poor at best. Additionally, very few organizations attempt to predict employee attrition. Any improvement in the process stands to offer considerable gains in the culture and productivity of an organization.

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