Moving beyond macros and excel, technology has now expanded its horizons to image recognition software. And as technological advancements move from desktop automation to robotic process automation and now artificial intelligence, the complexity increases. With this increasing complexity, its essential for both the end users and the service providers to be able to distinguish between these available solutions and be able to use them as per their business need.
The spectrum of Automation – RPA and AI
What started as desktop automation in terms of the technological solution for HR, first moved on to robot process automation and now has further moved on to artificial intelligence. Through these capabilities, HR has been able to improve productivity and enhance operational efficiency thus leading to cost optimization.
The advent of RPA allowed HR to move away from basic operational tasks. RPA basically took care of structured and repeatable tasks, took care of decisions based on pre-determined rules and helped HR access multiple systems to perform a task. It also streamlined the process of aggregating or updating information.
But now with the emergence of AI, the HR solutions have become more enhanced and have made the job of HR a lot easier. However, it has also brought in more complexities. Unfortunately, while looking for a tech solution for a talent challenge, HR often struggles to distinguish and choose the one which is more relevant and suitable. To avoid this ambiguity it’s important that they are able to distinguish between RPA and AI.
Here are a few key differences between the two, that every HR must know:
What HR should know about AI – definitional clarity
Further deep diving into the most emergent trend in HR tech space AI, we find certain terms like Machine Learning and Deep Learning going around. But what do these technical jargons really mean and how can HR leaders differentiate among them?
Here are a few pointers which can help you understand these jargons in a better way:
- Generic term used for different approaches
- ML & DL are subsets of AI
- Algorithms learn to find patterns in data and then use a model to recognize the patterns in new data.
- Typically requires some supervision and structure
- Uses multi-layered neural networks that can run and learn unsupervised on data/tasks with little structure
As soon as HR professionals are able to attain clarity regarding the problem they are trying to solve and the tech solution that would be most relevant for them, defining the transformation roadmap becomes easier. Being able to distinguish between AI and automation is just one of the steps in the entire transformation journey. What follows later is getting the core teams who will drive the change ready. All of these small steps together help a company move forward from where they are to where they wish to be.
(The article is based on the session, ‘Masterclass: A roadmap for leveraging AI & Automation for superior performance’ by Kashyap Kompella, CEO, rpa2ai Research on day one of TechHR Conference 2018.)