Cadient Aims To Help Hire Great Employees With A Bias-Free Process

Cadient Talent CEO Jim Buchanan

In the hourly labor market, securing the right fit for employees can make all the difference. 

Cadient Talent CEO Jim Buchanan, who has worked in software and talent acquisition for years before becoming Cadient’s CEO last fall, still believes talent acquisition is the most important thing you can do in management. Getting the right person in the first place is the biggest opportunity to get a return on investment, he said.

So it makes sense he is at the helm of Morrisville-based Cadient Talent. Cadient Talent acquired Massachusetts-based Kronos Talent Acquisition in 2019, enabling them access to years of hourly worker talent acquisition history data, specifically 17 years of data on which hires end up being good employees, Buchanan said.

Hourly workers in restaurant, retail, construction and other industries typically have high turnover, so Cadient Talent is on a mission to launch a machine-learning algorithm by the end of this year that can evaluate candidates against high-performing employees in their clients’ businesses.

“It’s a very exciting proposition that we have for our clients, and it can really add significant value,” Buchanan said. “The outcome that you’re going to get from this is that you’re going to make better-quality hires, which is going to have a direct impact on your bottom line. It’s going to reduce turnover.”

Early signs from Cadient Talent’s proof of concept are encouraging, Buchanan said.

While many other recruitment tech companies are focusing on automating the recruitment process, they often don’t zero in on whether the candidate will truly be a good hire or not beyond meeting pre-set qualifications. But Cadient Talent’s process will ultimately give you a better employee, Buchanan said.

“The data is like the rocket fuel for your machine-learning algorithm,” Buchanan said. 

Cadient Talent has 17 years of rocket fuel from Kronos. This is the data that shows the full picture of whether candidates with certain characteristics became good employees.

“That’s really the secret sauce,” Buchanan said.

Cadient already attracts job applicants for more than 100 clients with constant “evergreen” job postings, an employee referral solution, an applicant tracking system and qualification reviews and assessments, utilizing a price-per-employee-per-month business model. The machine-learning algorithm is just an extension of the process, allowing Cadient Talent to deliver candidate recommendations among otherwise qualified applicants. 

Removing bias from hiring

But the machine-learning algorithm can do more than just help companies pick the best employees. It also has the potential to entirely remove bias in the hiring process, Buchanan said. 

Race, gender and age may end up playing a role in decisions made by even the most well-intentioned hiring managers, but the Cadient Talent algorithm completely ignores those details and finds the best employees without the implicit or explicit biases humans may make when selecting candidates.

“Our vision is to revolutionize the way the world makes hiring decisions,” Buchanan said. “That is a very bold vision. We intentionally chose the word ‘revolutionize’ because it is broken right now. The hiring process for local managers is broken.”

Covid-19’s effect on Cadient Talent has varied. Some clients like gyms are still closed in many areas, but other clients including Costco saw an overwhelming increase in applicants when the pandemic first hit, Buchanan said. He said the pandemic has also created an environment where many businesses are not choosing to implement new systems.

Ultimately, Buchanan compares Cadient Talent’s proposition to the early chess match between world chess champion Gary Kasparov and IBM’s supercomputer Deep Blue, which showed the early extent of how much machine learning technology can rival a human being in decision-making.

“I don’t have to beat a chess champion,” Buchanan said. “I just have to beat that local manager. I have to make a better decision than that person would have made.”

About Suzanne Blake 362 Articles
Suzanne profiles startups and innovation for GrepBeat. Before working at GrepBeat, Suzanne attended UNC Chapel Hill, obtaining a degree in journalism and political science. Previously, she wrote for CNBC, QSR Magazine, FSR Magazine and The Daily Tar Heel.