An Organization Journey to Making AI in HR a Reality

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Industries, companies and functions thrive on their ability to innovate or be early adopters of innovations in their space. Today, Artificial Intelligence is probably the most debated and researched phenomenon among all that influence the Human Resource space or even Humans as a species. Leaving the larger debate about humanoids taking over human’s jobs for another day and another time the scope to improve efficiency & experience in HR with AI is boundless.

Having said that, there is a wide gap between what HR teams believe they can do with AI and how confident they feel about leveraging it. Deloitte found a significant “readiness gap” in a survey where 72% of respondents believe AI is important but only 31% said they felt ready to address it in their industry

In the hype and excitement about the game-changing possibilities of AI, there’s little thought given to what it really takes to apply and adopt them. It of course starts with understanding what it can really deliver but success lies in executing a digitalization journey that can lead the organizations to these results. For a region that has recently picked up steam on the entire HR digitalisation drive only in the last decade, the journey Asia Pacific enterprises in particular will involve substantial effort & preparation.

The Unlimited Possibilities of AI in HR

1- Time to think and not just do:  

First and most common form of AI we know is Robotics Process Automation (RPA) — essentially the use of “software bots” to automate operational tasks that are manual and repetitive in nature. This allows the HR department to do away with mundane tasks, giving them the scope to reinvent and invest their efforts towards driving more strategic initiatives which directly impact talent.

2- A step ahead in the game with prediction:

Not only can organizations make data-backed decisions and reduce the dependency on intuitions, but AI can be about making the right predictions about talent. Predicting employee attrition, identifying top performers at flight risk, suggesting the most suitable candidate for a role, and identifying successors for a critical role – AI can make these human decisions much more smarter and bias-free.

3- Achieving greater heights of efficiency:

AI is already driving efficiency for HR teams in areas like talent acquisition through CV ranking, parsing and bot based screening. But, the scope to impact efficiency and productivity positively will extend to even employees. The next expense claim need not be filled by an employee if AI learns to parse and AI can become the manager’s personal assistant to record performance feedback or find the right reports and emails relevant for the performance conversation.

4- Employee experience at the heart:

The combination of NPL ( natural process language) & ML (Machine learning) is starting to give reality to the popular belief that zero interface in the best interface. Commendable advancements in the field of voicebots and other conversational forms of AI are now ensuring great employee experience making technology adoption seamless.

On a current day, organizations are able to reap the benefits of the new age cutting edge technology and automate most of their manual mundane tasks using RPA. Various functions like payroll management, data entry, offer letter generation and offboarding tasks have been automated to a large extent. There is also a significant increase in the use of AI in the recruitment process to make it more transparent and address the bias that creeps in.

However, in terms of leveraging the real potential of AI to drive data-backed decisions, predictions and enhance the overall employee experience there is a wide gap in terms of the readiness that organisations need to address.

Traversing this wide gap to become AI-ready

From skills to the data sets that organizations need to build, enabling AI is definitely not an instant deal. The starting point can be traced back to an activity as basic as converting the physical HR documents to digital or in mandating your employee to apply that leave on the HRMS and not through an email message.

The Data Gap:

A vast majority of the AI based technology is functional and accurate only when supplied with exhaustive sets of data. Certain AI based applications like video analytics may work on universal datasets across organizations which make the algorithms robust enough sooner but to apply AI for an organizational context, data becomes the foundation step.

However, a common learning from our interaction with many large enterprises is the lack of digitized employee information. The completeness of a simple employee profile in a large enterprise with a HRMS implemented for last 2 years or more is still as low as ~50%. For instance, an ecommerce or CRM industry that is pioneering prediction through AI has an extremely rich  customer profile data along with the transactional data as well.

While employee profile information is the starting point, organizations should really invest in driving adoption of the digital systems they introduce to convert real world conversations and context into digital. The correlation between feedback session with manager and employee disengagement can only be used for prediction of attrition if that performance checkin is done on the system and the feedback is available for sentiment analysis. 

Disintegrated and erroneous data spread across multiple systems only adds to the existing struggle. One cannot interpret the impact a hiring source can have on the longevity of employee tenure if your organization has talent acquisition data isolated on a stand alone system. Organizations thus should focus on choosing a system of record that is all-encompassing and can become a single source of truth about the employees.

The Skillset Gap:

Substantial investments need to be made in training the business leaders to convert a business problem into a data problem. Only when the problem statement can be clearly communicated with the entire context can an AI expert formulate and arrive at the solution. Similarly, another simpler but significant problem that also needs to be addressed from a skill standpoint is the employee or manager’s ability to leverage the new age technologies.

This aside, there is a deeper and more specialized skillset that organizations will need to establish from scratch. While solutions in the market may promise an off-the-shelf AI capability, these need to be tailored to the context of the organization with the help of an internal data science or analytics specialist. Organizations should crack the problem to finding such talent and nurturing them inhouse.

Though HR leaders acknowledge the fact that AI is going to revolutionize workforce interactions and call it out as one of the most important HR priorities, progress is yet to materialize in leveraging the technology. To be AI-ready and attain that competitive advantage, now is the right time for HR leaders to start driving a culture of digitalisation through both data and skilling.


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