Role of AI in Performance Management
In the previous blogs we have discussed the “Role of AI in Recruitment” and the “Role of AI in Learning and Management”.
PMS or Performance Management System is a process of tracking and monitoring performance of an individual or set of individuals as a team or department or as an organization. Different organizations have different processes. These objectives are always aligned with the objectives of the organization.
The traditional process generally includes 4 steps starting as below
1. Objective setting
2. Self evaluation
3. Manager evaluation
4. Discussion and sign off
Limitations of this process are that since the entire process is manual, there might be errors or flaws in the process. This process is lengthy and spread over the entire year. Hence often people remember the recent achievements and rate the individuals on that. Also, people do not take this activity seriously many a times. Generally, people in organizations are always busy and mark this activity as a tick mark entry and hence the feedback is always not relevant or mostly biased. This entire process fails to capture the potential of the individual.
The new age or the millennial work force wants feedback frequently. They do not wait till the annual appraisal happens and the ratings are communicated. If feedback is not provided then they proactively ask for that.
Hence AI in performance management system plays a great role. The data driven appraisal or review system helps in maintaining transparency and avoids any sort of misunderstanding or doubts. It also helps in avoiding bias, “Halo effect” and “Stereotypes”.
There are several other benefits of AI in performance management.
Tools for performance management using AI can collect data from different sources which will help managers to draw insights. Since this is totally data driven, there is less or no space for biases. These are any time available real-time feedback.
Any objective comes with the timeline to achieve, similarly during objective setting, the timeline and the percentage of activity is set to achieve. AI-driven performance management can help us in monitoring these objectives and keep sharing the real-time feedback with amount of activity yet to achieve. If the employee's performance is ahead of time allotted then AI can recommend rewards. If the performance is not on track or losing time then AI in performance monitoring can notify the individual about the same and can also suggest topics for increasing knowledge or productivity. Similarly, if the employee needs improvement in some area, then the AI can suggest learning. We have discussed “Role of AI in Learning and Management” and its impact in my previous blog.
AI can unveil the potential of an employee. It can tell us in future if the employee will perform well or not. If not, then AI can also help us in succession planning as well. These AI powered tools can alert us quickly about the trends and challenges in the organization by analyzing the patterns of large amount of data collected from various sources. With this technology in hand, we can get the dashboards with the details of employees’ performances, potentials and trends.
Ex: A software engineer who’s all the information is already with AI tool collected from various sources, with sensors around at workplace, using image processing; one can get to know the mood. Tools can tell us whether he is happy working on a particular project. If he is stuck at some place what websites does, he refers to. It can also predict If the engineer successfully delivers a project will he be able to perform with the same pace, knowledge and interest in the next project. It can also predict which engineer would fit best for which project considering the skills and attributes.
Limitations of AI in performance management system:
- The best algorithms may be deployed to automate the performance management system but the employee would like to hear the last word from the reporting manager.
- The data collected may not be sufficient to predict an employee as the tenure of the employee in the organization may not be too long.
- It would be unclear as to what kind of data should be considered for this process.
- Data collected from other employees cannot be used in this case as every individual is different and skills are different. A set of employees (of same experience and skills) data can only be used in this case which makes it further difficult to predict about an individual.
In the next blog we will discuss about “Ai in employee engagement and recognition”
