The ideal enterprise environment is one in which employees are treated fairly and respectfully, practices that typically pay dividends by inspiring productivity and company loyalty.
But ideals are elusive in the real world, as employees tend to bring their human flaws and complications into the enterprise. One of the most damaging of these flaws is bias, the practice of discriminating against another person based on things such as gender, race and religion.
There are, however, other forms of bias that are less blatant, yet still damaging — particularly when they impact the process of assessing employee performance. Forbes contributor Bernard Marr writes:
“One is known as contrast bias, meaning an assessor is inclined to compare an individual’s performance to his peers, rather than to defined standards of achievement. Another is recency bias – where actions in the recent past are given more weight, perhaps unfairly, than actions which happened further back in time (but still within the period where performance is being assessed).”
A possible solution, Marr says, is artificial intelligence (AI).
Being unburdened by fatigue and fallacies in logic gives machines a distinct advantage when conducting performance analyses. Further, by harnessing AI to conduct ongoing performance evaluations, enterprises can avoid the pitfalls inherent in that traditional process both employees and employers hate: the annual performance review.
AI, he writes, “won’t treat the job of performance reviews as something to do ‘when I’ve got time.’ Unlike many human managers, it won’t put off assessments until the last minute.” (Marr is being generous; most human managers procrastinate about performance reviews.)
Remember, one of the most effective ways to motivate employees is to establish a clear connection between performance and rewards. That clarity dissolves when reviews are conducted just once a year. Artificial intelligence provides an immediacy that’s sadly missing in most enterprises today.
“AI-driven assessment can happen in real-time (with systems monitoring targets, quotas and how these are affected by people’s connections), incentives and praise for good performance can be dished out immediately,” Marr writes. “If targets are not being met or performance standards are slipping, then intervention can take place before the problem grows and becomes unmanageable.”
That sounds a lot better — for the organization and the employees — than waiting around for a year to address a performance problem. And while many enterprise employees may balk at the notion of being continually assessed by an AI program, others may welcome a regular, bias-free process.
What do you think about AI-based performance assessments?