For companies to ride successfully on the waves of any technological change, performance management will have to evolve to put the right skills and talents in the right places at the right times.
A large part of HR management is capturing and analyzing performance feedback of the company employees. Given that 70% of companies need to align performance management with other talent decisions, AI and data analytics can create a unified source of truth for managers to extract valuable insights.
Traditionally, managers relied heavily on employees’ self-assessments, peer reviews, and their proven business outcomes to perform appraisals. But this has several hidden biases, tendencies and subjective assessment to it. Today, the managers and employees alike see the old annual-review system as too subjective, too bureaucratic, and too backward-looking, and not addressing the need of the hour. The new AI-driven systems, on the other hand, provide managers with a wide range of up-to-the-minute information like how long someone has been in their current job, to what skills they have that might be useful in the future.
Further, it has been found that human managers don’t possess skills matching that of AI to synchronize performance feedback based on historical data to future pathways. They simply can’t store and analyze such a vast set of information for 1000s of workers. This is when AI intervenes with its predictive capabilities to collect, store and process large data collected from various stakeholders to predict an employee’s probable career trajectory and determine appraisal figures.
Typically, historical data and general opinion have been two key factors to complete the appraisal process. But AI’s predictive appraisals approach takes this to the next level. It analyzes data to recommend future performance levels, allowing managers to compensate employees for what they are going to achieve instead of what they’ve already done.
Companies introducing AI in their system have often found that during performance reviews, human bias creeps in, which may be intentional or not, but AI will help managers remove this underlying issue by assessing employee performance based purely on performance metrics, and it will then make future predictions about employee performance. This is a great way to improve diversity at work and also build a trustful environment.
AI also enables immediate course correction with AI through prescriptive analytics. For example, in IBM, all the data showed that giving a certain group of employees a 10% raise would reduce their ‘flight risk’ by 90%. The attrition rate for managers who didn’t take that advice was twice as high as the ones who took it. Thus, AI recommendation indeed proved very helpful for IBM managers. Prescriptive AI also helps indicate performance gaps where L&D could assist in employee development. Through real-time analytics, AI could detect such gaps early in the review cycle, allowing managers to stay one step ahead and upskill an employee for a new or existing role.
- Posted Date: 18-MAR-2020