- Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
- Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
- Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves over the span of their careers.
Hiring managers in search of qualified job candidates who can scale with their growing businesses are facing a crisis today. They’re not finding nearly enough good candidates using job recruitment sites that are designed for employers’ convenience first and candidates last. Outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities.
Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed—and it’s clear that data-driven approaches to recruitment work best. Resumes and job recruitment sites force hiring managers to bet on the probability they’ll make a great hire instead of being completely certain they are using solid data.
Quit Playing the Probability Hiring Game and Know with Solid Data
Many hiring managers and recruiters are playing the probability hiring game. They’re betting that a new hire chosen using imprecise methods will work out. And like any bet, it is expensive when a wrong choice is made. There’s a 30% chance the new hire will not make it through one year.
Consider the software industry. When the median salary for a cloud computing professional is $146,350 and it takes at least 46 days to find them, losing just one recruited cloud computing professional can derail a project for months. The cost of lost revenue or billing opportunity, recruiting, and re-hire to fill that role will easily exceed six figured.
These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas than hiring the best possible candidates if they rely on systems that deliver at best a marginal probability of success.
Betting on solid data and personalization at scale, on the other hand, delivers real results. Hiring managers, recruiters, HR directors, and Chief Human Resource Officers (CHROs) are abandoning the probability hiring game for new, data-driven approaches. Now candidates get evaluated on their capabilities and innate strengths and how strong of a match they are to ideal candidates for specific roles.
Real data is also the best protection against conscious and unconscious biases in hiring decisions. AI- and machine learning-based approaches to talent management strip away any extraneous data that could lead to bias-driven hiring decisions.
A Data-Driven Approach to Finding Employees Who Can Scale
Personalization at scale is more than just a recruiting strategy—it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring, and growing talent that can support their growth goals and strategies.
For example, Eightfold’s approach makes it possible to scale personalized responses to specific candidates while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person for the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.
Eightfold is also pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills and strengths they need to fill open positions.
Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions, including data scientists, than are evident from just reviewing a resume—and with AI, I want to do it at scale,” Russell said.
Ashutosh Garg, Eightfold’s founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.” One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning and defining their learning roadmaps that lead to reinventing their skills, strengths, and marketability.
Machine learning algorithms capable of completing millions of pattern-matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs, and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst of every company’s ongoing growth.