Artificially Intelligent Human Resource - Part 3
AI in Performance Management System (PMS)
Many organizations are in the process of overhauling their PMS. They are doing away with rigidities of performance ratings and going in the direction of providing real-time feedback to employees, instead. Also, they are moving from away from the annual PMS cycle to a half year or in some cases quarterly PMS cycle. AI in PMS can be effectively used to leverage some of these trends.
a) Real-Time Feedback: There are machines available in the market which can create a feedback write-up based on the keyword triggers. This helps the Managers to save time and make the process of giving and receiving feedback more lightweight. Also, some companies are able to convert written qualitative feedback into numeric performance rating with the help of Natural Language Programming and Deep Learning algorithms. This implies that Managers over the year have to only give honest feedback to employees and it will get converted into performance rating on its own at the end of the performance cycle. How cool can that be!
b) Goals Setting & Evaluation: Can employees set their goals and get evaluated in shorter loops and not on an annual basis? AI in PMS can help shorten the loop of goals setting and evaluation to match it with the rhythm of the business.
AI in Employee Engagement
Most of the organizations do annual employee engagement surveys and corresponding annual action planning. However, this once in a year activity is not very fruitful in the current dynamic context. It’s important to have a check on the real-time pulse of the employees. AI in employee engagement can help us address some of these challenges.
a) Making Sense of Employee Footprints: Employees leave a trail of data points in their day-to-day working in the organization. Examples include their in-time & out-time, the sentiment of their e-mails, leave patterns etc. AI can be effectively used to identify deviations from their usual patterns and draw out inferences on their overall engagement. For example, Vibe is an algorithm that analyzes keywords and emojis sent among employees on Slack to gauge whether a team is feeling happy, stressed, disappointed or irritated. Keen also provides a real-time snapshot of employee engagement by searching employees’ anonymized emails to uncover word patterns and contextualize them as positive or negative emotions.
b) Engagement Surveys: Many companies are deploying Bots to continuously engage with employees to understand their concerns and other pain areas. These Bots then share the employees’ concerns in a controlled environment with those who can solve for them. Thus annual engagement surveys and action planning is giving way to real time engagement and action planning. Amber by Infeedo is one innovative company that is trying to solve this problem using AI & ML algorithms.
AI in Rewards & Recognition (R&R)
Science of motivation says that employees appreciate rewards & recognition the most when the time lag between the act of performance and rewards is minimum, the quantum and form of reward is valuable to the employee and when the reward is made public. AI in R&R can deliver on all the above expectations of employees.
a) Compensation Structure: AI in R&R can help the organizations to provide personalized compensation to employees keeping in mind their unique requirements and life stage. Advanced machines are capable of recommending the personalized compensation structure based on the employee’s persona.
b) Incentive Programs: Unfortunately many incentive programs do not yield favorable results, in fact, some even demotivate the employees. Organizational data can be effectively leveraged using AI & ML to accurately predict the success of various incentive programs. After a few iterations the machine becomes reasonably intelligent to predict the kind of incentive program which will have the highest probability of success in the given business context.
c) Employee Recognition: Recognition platforms powered by AI & ML are used to provide more social experience of recognition to employees. Also these platforms trigger the employees to appreciate one another based on the anonymized analysis of various employee interactions on e-mails, Slack etc.