Artificially Intelligent HR
HR is generally not known to be the front runner for adopting new technologies. However HR can’t remain isolated from the digital revolution which is gripping the overall business. Outside of the organization technologies powered by Artificial Intelligence, Machine Learning and Deep Learning are already impacting our lives in a big way. From getting our personalized playlist of songs on Spotify each morning to getting hotel recommendations by Expedia just when we want to plan our travel, technology is spoiling us for choice, personalization and convenience. Virtual assistants like Alexa and Siri are making interaction with technology as easy as interaction with humans. HR needs to adapt to this new world order where technology and people need to coexist hand-to-hand. Most of these technologies are driven by the recent advances made in the areas of artificial intelligence, machine learning and deep learning. Even though these terms have got a lot of media attention in the last 4-5 years, unfortunately not many people truly understand what they mean. Let us spend some time to discuss these in simple words.
The word Artificial Intelligence (AI) was coined by John McCarthy in 1956. It is the form of computer science used to create intelligent machines that can recognize human voice, visual objects, can learn, plan and solve problems like humans. It exists at broad three levels. At the first level, it is called Artificial Narrow Intelligence (ANI), which is used to program a machine to do specific pre-defined tasks. Examples of ANI include some simple automation tools such as ERP systems, payroll automation etc. At the second level there exists Artificial General Intelligence (AGI), which is used to make machines do more human like functions such as learn from experiences, adapt to new situations, understand abstract concepts and identify patterns. Most of the experiments in the area of Artificial Intelligence are happening at this level. Examples of AGI include Alexa, Google Translate and recommendation engines used by other e-commerce companies. At the most evolved level is the Artificial Super Intelligence (ASI), which is used to program a machine such that it has the level of intelligence far exceeding that of humans. Not much advancement has been made at this level.
Machine Learning (ML) is a subset of Artificial Intelligence. It is a form of computer science where a machine is trained to learn in a particular way from the past data and patterns. This learning can be of three types. First is called Supervised Learning, in which a machine uses the labeled data and associated features to learn. For example if we define a car with a given set of features such as 4 wheels, front, side & back dimensions etc. a machine next time will be able to differentiate between a car and a motorbike. Second is called Unsupervised Learning, in which there is no labeling of data and corresponding features. Machine is made to learn in a way such that it creates its own set of features to understand the concept. In the same example if we feed millions of images of different cars to the machine, gradually machine will be able to understand different features associated with a car on its own and consequently will be able to differentiate between a car and a motorbike. Last type of learning is called Reinforced Learning, in which the machine is given human feedback to make it learn on its own.
Generally in machine learning volumes of data is given to the machine to learn using a specific learning algorithm. Once the machine is trained it can start to solve problems.
Deep Learning (DL) is a classic use case of unsupervised learning which is inspired by the functionality of our brain cells called neurons. It uses the concept called Artificial Neural Networks to learn on its own. In the case of Machine Learning we have to manually give out the features of the data sets for the machine to learn, whereas in case of Deep Learning the machine automatically finds out the features which are most important for classification – from low level features to high level features. Deep Learning algorithms require huge data sets to be trained and are heavily dependent on high end computing.
Until recently, the primary benefits of HR technology were to improve efficiency and drive cost-savings by automating repetitive tasks. Today, above technologies can enable HR teams to solve critical business challenges, drive exponential performance improvements and even impact larger business outcomes and profitability. AI is fueling HR’s transition from administrative to strategic to mission critical. It can help transform HR in the following ways:
1. Personalization of HR
One size fits all approach of HR is fast becoming obsolete. Now AI in HR presents us an amazing opportunity to personalize HR keeping in mind unique requirements of employees. If our business is ready to treat each customer uniquely why can’t HR treat each employee uniquely?
2. Best in Class Employee Experience
The narrative in HR is fast shifting from employee engagement to employee experience. AI in HR can provide state of the art employee experience across the employee lifecycle,from onboarding to exit. Intent here is to make all the employee transactions extremely seamless and delightful for employees. For example is it possible to provide a virtual assistant, like Siri, to employees who can help them to do jobs like blocking meeting rooms, fixing up meeting, applying for leaves etc.? The answer is probably a ‘YES’.
3. Data based Decision Making
There is huge amount of data about each employee available in the organization. Natural Language Generation (NLG), powered by AI, has the ability to transform data into data-driven text automatically, which makes it a valuable asset for HR teams across industries. Also can we draw insights from the available data and make predictions about employee attrition, retention, engagement level, learning needs etc.?
In a nutshell, there is a huge opportunity for HR to leverage the power of AI, ML & DL to solve for some of the most pressing people challenges in the areas of Talent Acquisition, Learning and Career Development, Performance Management, Employee Engagement and Rewards & Recognition. There are other multiple areas also where AI, ML & DL can add significant value such as HR operations, employer branding, succession planning, culture change, manpower planning etc. HR can no longer afford to be a laggard in adopting these transformational technologies, neither can HR afford to isolate itself from rest of the business. AI, ML & DL have immense potential to transform HR into an ‘Artificially Intelligent HR’.