As a new decade arrives, audiences hear often about the fallout from increasing automation and the rise of Artificial Intelligence. Dog-like robots show up on social media feeds and are widely labeled as "terrifying." Presidential candidate Andrew Yang warns that the collapse of manufacturing jobs and the coming loss of the retail sector portend a future in which human labor is obsolete. In all, the narrative is clear: technology is changing the way we live, and the endgame may not be in our favor.
We're faced with twin realities: on the one hand, unemployment is at historic lows. On the other, the rate of American adults employed full-time is only 63%, a rate that has been flat over the last half-decade, and workers are quitting jobs in rising numbers. So while jobs may be available, worker satisfaction does not possess the same rosy outlook. It's an open question what role technology will play over the coming decade in how we move from prospect to paycheck. As Harvard Business Review offers: "When it comes to the workplace, in many ways, AI is still in its infancy."
Artificial Intelligence has gradually entered American daily life, in ways both obvious and covert. Purchase an item online and you're then offered suggestions for what else you might like, for example. Those ideas aren't coming from Aunt Nancy anymore -- they're the result of an algorithm that tracks your purchasing habits. It's all the stuff that once required human judgment. Now it's lines of code.
So what does this mean for the decision-making that determines who gets a job offer? What about which roles a new hire is assigned to? Or which coworkers are paired together, and on which shifts? Should we attempt to rid this process of our human hunches? The one-two punch of resume keywords and managerial intuition has produced weak results. Turnover is high, with the problem plaguing both entry-level jobs and salaried positions. Workers just aren't sticking around, and when they do, many aren't happy in their roles.
While broader sociopolitical factors play a role in the modern employee's often blase response to the workday, it's worth considering how our turn-of-the-century hiring practices may have contributed to the high turnover and low satisfaction trends that plagued the first two decades. This begs the question: is hiring broken? Are common practices for placing new hires into tasks and teams also chronically falling short?
As experts have cautioned, the model on which AI "makes it decisions" needs to be free of bias. If we get the foundation wrong, then the AI will perpetuate bias. Having a sound, research-based model is therefore vital. When it comes to using personality as a predictor, twentieth-century research methods and theory-building are a deep well from which we can draw. There's no reason that the introduction of technology must mean reinventing the wheel. AI that is informed by conventional behavioral science mitigates the potential for bias and will therefore have the most staying power.
Further, employers would be wise to recognize the psychology of AI interactions. This is an emerging field of critical focus. While research reported last summer suggests that the American worker has a favorable view of AI in the workplace, notes of caution emerged from this dataset: younger workers are more wary of AI's influence than older ones, and part-time staffers are more fearful of being replaced by technological innovation than are full-time ones. Combine this with cautions such as that of analyst Gerry Crispin, who asserts that tools to evaluate candidates must be understood by the job seeker and appreciated as relevant. The more we believe the technology around us operates from a black box, unknowable and potentially sinister, the more likely we are to shy away from it -- and this can undercut the benefits we could obtain from the tech as individuals.
This points to another goal for HR technology. The models need to give something back, not just to managers, but to the individuals. It's part of the ethos of letting people have access to their own data. In the case of assessment data, while that information can serve as a smart, science-based guideline to inform hiring and managerial decisions, the individual can and should see direct benefit as well. The data should broaden personal insights, not coldly implement cutoffs and move on. Quick data capture tools with mobile-first functionality can allow companies to offer takeaways to individuals with no additional investment. That means that the entire applicant pool walks away with growth-promoting information. Further, with research suggesting that employee engagement rises when the workforce has access to learning and development opportunities, it's clear that the most high-value HR tech will deliver qualitative content beyond its hiring and management criteria. Tools that can both inform hiring decisions and then turn around and promote on-the-job personal growth and career development will emerge as leaders.
In all, Artificial Intelligence and growing applications of technology can bring considerable benefit to both hiring managers and job seekers. The key is to approach implementation with an understanding of how to leverage decades-old knowledge, create a culture of transparency, and allow the technology to provide a mirror back to the individual that inspires growth. Without this, AI risks being a trend awaiting a backlash. When implemented well, gains from AI-mediated technology are both deep and wide, with everyone from first-time job seekers to high-volume hiring managers among the long-term beneficiaries.
