Over the past few years, the use of technology has grown considerably and it has been the driving force helping organizations engage with their employees efficiently and improve productivity. Keeping up with this momentum in the HR tech industry, Kwench Global Technologies offers a platform that empowers organizations to engage effectively with their employees through social learning, recognition and collaboration features.
Prashant John, co-founder and executive director, Kwench Global Technologies, in an exclusive interview with ComputerWorld.in talks about the dawn of AI in the human resources technology space, and how companies are using it in the employee engagement, hiring, and productivity space.
How can AI be used by a company for competitive advantage in the HR space?
AI has been around for a long time. Off late, this whole AI landscape has exploded, which is why everybody is talking about it. Marketers have a lot of fault in that as they slap on AI to everything. There are different levels of AI and when an organization is going through a transformation—be it digital or even an overall transformation—they have multiple choices of what they need to do. There is a three-level transformation.
First is an IT transformation where they use technologies just to improve the processes the company has. Next, the company has process transformation where it transforms the entire process end-to-end. The last is the business model transformation like Meru looking to be Uber.
Where Kwench Global Technologies fits into things is: At the very least we may come into IT transformation, where you have processes, but you use our engagement models to do much better. Next level is embedding into a real-time conversation. Kwench engine is running in the background; depending on the conversation in the context may trigger it or Kwench can actually prompt you.
What challenges are the HR tech industry facing?
Adopting technology to take things to the next level is a paradigm shift. It involves massive transition and also sometimes a change in the way the company works. These are things on which HR head and CIO work together. Typically, with these kinds of paradigm shifts, the result is slow and changes don’t happen overnight. Sometimes the company is visionary, senses the change and are active about it. Sometimes the company has a strong legacy; a new CEO or CIO needs to come in, convince the leadership, and get the company screaming and shouting into the future.
How can AI improve the hiring process for companies?
Take for instance your resume screening process. You have a portal on which you specify 10 parameters you have, and in a particular format. This method isn’t prescriptive as then you can’t source from people with appropriate resumes and social media. But if you have a system which can take all kinds of fields, parse that data, figure out if your relevant search terms exist in the resume and flag them for you. This is the very minimal ML/automation kind of system.
A more intelligent system would be deep learning. Over a period of time, the algorithm will figure out what kind of resume would work for your organization. So the next time, the person looking for system scanning resume would avoid ‘dud’ people even if it fits all parameters.
How are companies adopting AI to their advantage in the space of employee engagement?
Today, HR is adopting strategies into employee lifecycle. An employee lifecycle starts when you zero-in on a potential employee, until the time he/she moves on to the next job or retires. Employee engagement starts even before the person becomes your employee. You have onboarding, appraisal automation, and feedback mechanism.
Now companies also have employee net promoter score. It defines: How engaged are you, what’s the probability that you recommend the company to your friends. This can be asked to the person explicitly, or derived from variables. Intelligent systems look at the way you are behaving over a period of time. When you get disengaged, there is a behavioral change in the way you work. This can be monitored with sentiment analysis at the very least. Sentiment analysis in the most primitive way would be pulse surveys every month instead of once a year or quarter.
Can you elaborate a bit more on sentimental analysis and its use cases?
So, the problem with something that happens once a year is relevance. A pulse survey done after appraisal, you would give a bad response after bad appraisal. But that necessarily doesn’t mean you don’t like the company or your job, it just is a momentary distraction. After every transaction that happens, you have an option to give your feedback on how you feel that day. Kwench, kind of does it on a daily basis. Every day you log onto the system, you get a question how do you feel today and a couple of smileys. In the background, we are able to build an emotional map of every employee to the team level.
For instance, consider a people manager who isn’t good. In that case, you see green everywhere and one orange developing. This is a very simplistic use case. At the deep learning level, you can figure out sentiment through conversations. If the conversations are getting aggressive, it is a symptom of impending stress. So then you can take action to reduce stress. These systems today can raise flags for intervention. Systems are evolving but right now they are a little discreet and in silos. That’s where the next generation collaboration platforms are enabling them to come together.