This chapter of the Great Resignation isn't over. In March, there were two jobs for every job seeker, the highest disparity in decades according to BLS. In that same month, more than 4.5 million people, or roughly 3 percent of the workforce, quit their jobs, a 15 percent increase over the same period the year before. Employees across levels and industries are still restless and seeking better or different opportunities. 

By now, the Great Resignation has boardroom attention. More than two of every three members surveyed recently by the National Association of Corporate Directors reported discussing this topic -- and its attendant performance risks -- at the board level. What's changed, and how should organizations hire in this age of candidate scarcity?

Legacy hiring models assume there are masses of poorly qualified candidates vying for your exclusive career opportunities and willing to slog through dehumanizing, frustrating, and poorly integrated bureaucratic processes. Companies invasively poke and prod candidates until they inevitably ghost them so that, ultimately, the hiring manager can hire someone they already knew about. This model was always ineffective, inefficient, inhumane, and biased against those who do not fit the status quo. Now that the torrent of candidates has atrophied to a trickle, its utter bankruptcy has become painfully evident. 

Our world has many intractable problems, but outdated hiring methods need not be one of them. The key is to leverage modern technology to humanize the process and reestablish hiring as an enticing and informative two-way exploration for the candidates and hiring managers while predicting job fit and minimizing bias. 

To begin, understand that in today's world, hiring is a conversation, not an inquisition. Interviews go both ways, so don't forget that you are not only questioning but also recruiting and always selling the merits of your organization. But be mindful of the difference between recruiting and selling -- you want to engage talented candidates, but you must also present a balanced view of your opportunities. "Realistic job previews" work because they are grounded in what one experiences in the job at hand, not because they present a one-sided, idealistic image of a job that doesn't exist. 

Second, be mindful of the limits of human decision making. We humans are bias machines, taking in a vast array of data and filtering it through myriad cognitive biases. One of the most powerful of these is the "similar to me" bias, in which we feel a strong draw towards people who mirror our own style, humor, background, and appearance. Fortunately, algorithms can help by systematically weighing information that is proven to predict job performance in a fair manner across all candidates. 

Third, don't remove valid, fair, scientifically developed interviews and assessments in the interest of eliminating hiring friction. Companies that are stretched for candidates take a short-term view, lowering their standards for the job, and hiring for speed over quality, while failing to value candidate attributes that might signal their apathy, lack of commitment, or poor fit for the role or culture.

More troubling still are companies that shorten or drop candidate screening and assessments, fearing applicant drop-out. In addition to there being no evidence of a tie between assessment length and abandonment, there's empirical reason to believe that an impatient candidate who chooses to skip steps in a process is one who may not display staying power at your company.  

Big data and A.I. are revolutionizing talent management. But for A.I. and algorithms to be effective, organizations must understand their own employee population's skills, attributes, and performance data. Too often, organizations store data but cannot access it for analysis, or worse, fail to track important performance indicators in the first place. Without data, A.I. is useless. Understanding your existing talent is key to building and retaining a stronger employee base. 

Leveraging "artificial intelligence" is neither a monster nor a magic wand. Headlines that claim A.I. is unsuitable for helping with decisions about people and those that say it is our salvation are equally wrong. Used incorrectly, algorithms can scale bias. Used appropriately, they can identify and weigh data points that predict hiring success and do so fairly. Savvy companies do not buy the hype that A.I. is all good or all bad, but recognize it as one part of a larger, scientifically developed, candidate-friendly hiring process. 

The Great Resignation isn't over, but organizations that need to hire must examine how modern their hiring practices are. Organizations can put themselves in a position to identify talent that will be a good fit for their openings and their organization. With a thoughtful, modern hiring approach, every organization can leverage tools and capabilities that allow high quality, fully validated candidates to experience an exploratory process, have the benefits of fair processes that mitigate bias, and attract the best fit to their employee population. In this way, they can rapidly identify candidates that are likely to be fit for roles and cultures where they can be productive and successful, transforming the great resignation into the great retention.