5 ways firms can use employee data to drive learning & productivity
Big Data, Personalization, Artificial Intelligence has permeated into our day to day experiences. AI today plays a role right from movie recommendations when we are watching Netflix or suggestions on the choice of books on Amazon Kindle or help gig workers with assignments or connect advisors to start-ups.
Imagine if the same power of data could be unleashed on employee training, productivity and team composition, then employee engagement can be off the charts.
Sources of employee data under consideration
The format data set includes work products, CRM and emails sent out by an employee, appraisal ratings, any assessments including psychometric or leadership training; The informal inputs include workplace interactions i.e. working hours, leave absence and work teams, etc.
- Learning is Personal: In the future, work will be project-based and skills will be more personalized with an individual assessment of past work to predict future fitment and assessment, the learning platforms will cover not just employees but also free-lancers and contractors to ensure organizational success. Rather than skills, the ability to drive learnability is the goal of these analytics.
- Gamification to simplify interviewing: In order to reduce human bias, firms such as Pymetrics and Montage provide assessments which are text-based interviewing, gaming or a first-level video interview which are assessed by machine learning algorithms to build a wider pool of viable candidates. This will help employees compete for internal job postings or new roles and also brings more objective datasets to the table.
- Team building cannot be left to chance: In an increasingly globalized organization, bringing the right set of individuals and role fitment brings a significant competitive advantage. IBM is already piloting AI to determine the right role, right team, and the probability of success based on expertise, diversity of knowledge and social connections to enhance sales effectiveness.
- Time and Motion Studies are back: In the 1950s, time and motion studies were in vogue to understand how the process could be made more efficient. Now with RFID technologies, employees can be tracked in terms of time spent at the desk as compared to meeting rooms, time taken at lunch breaks or meetings to assess the efficiency and even which employees tend to collaborate vs loners. A startup in Boston was able to achieve 11% improvement in productivity by discovering having a larger table of 8 programmers were more effective than programmers sitting alone or groups of 4 as the knowledge transfer was higher.
- Wellness & Personal Coaching: In the future, Voice-based assistants will become mainstream and allow employees to focus on the creative aspects of the job while taking over the repeated tasks. Universities such as MIT and Stanford are also working on chatbots that can assess if employees are under stress and suggest solutions to alleviate mental stress. Chatbots based on your past queries could become a confidant like JARVIS or SIRI but also actively recommend trainings or wellness suggestions and help employees to be a better version of themselves.
A word of caution
- Firms need to guard against biases: As managers and employees will start trusting AI recommendations, it is imperative the inputs used are free of biases. A leading e-commerce portal realized the AI algorithm for performance management was skewed towards white males in the mid-30s since the previous promotions had shown a similar trend. So programming and selection of the right algorithms will be key
- Right use of data: While technology provides a platform to accelerate employee productivity, it is essential data privacy & permissions are respected as it is a very slippery slope for misguided surveillance and tracking the wrong metrics. Discretion and adherence to laws need to be paramount.
Given the right safeguards and employee opt-in, data can be unleashed to drive personal productivity and better outcomes for the firm.