The Role of AI in HR Data Analytics : A beginner’s Guide

1. Introduction

Some experts have coined artificial intelligence (AI) as the ‘internet of our time’ because of its potential to disrupt the world as we know it. Just like the internet, AI has come to stay and that is why many forward-thinking companies have decided to develop and adopt AI software in their various activities. Undoubtedly, they don’t want to be left behind in this technology that has taken the world by storm. For most though, early adoption has been because of how efficient AI technology has made business processes. Companies like Google and Meta (formerly Facebook) installed this feature in their various platforms almost without delay. It is clear then that companies that do not take advantage of AI will not be able to compete even more so, maintain the competitive edge in their industries especially if their counterparts decide to embrace this technology. When it comes to implementation, the goal is not to have AI run all company operations but to simply have its help in solving one problem in the company. So why not start by solving a data problem in the HR department? This is data that is readily available and could have been collected over the years. Here is how to go about it:

2. Step 1: Implementing AI in HR by Identifying a Problem

Start by identifying the problem and asking how AI can help solve it. There are various problems and pain points in HR because of the direct dealings with human beings who happen to be complex creatures. The success of any business hinges on employees who are the key contributors. Employees can suddenly leave a company, start to underperform, become dissatisfied, or even worse, the wrong one is hired. That is where AI comes in to help. It helps:

· Predict which employees might leave (Attrition).

· Improve hiring decisions by removing biases (Recruitment).

· Understand what affects employee performance (Productivity).

· Analyse employee satisfaction surveys (Engagement and Satisfaction)

3. Step 2: Preparing HR Data for Machine Learning

The first thing to do is to collect the right HR data which should ideally be gathered over a period of time. This could be 2 years or more, if possible. This is to ensure accuracy and patterns in the data set. Here are examples of such data;

· Employee demographics such as age, department, salary scale, years of service, marital status etc.

· Performance Review Scores

· Attendance Records

· Training History

· Exit Interview Feedback

Fortunately, in HR, most of this data is always readily available as this is collected when employees are being onboarded into the company as well as over the course of the employee’s stay in the company. This data should be clean, complete and organized in an Excel or Google sheet. These tools can also be used for basic or preliminary analysis because they are simple and easy to learn. Tools like Power BI and Tableau can be used for dashboards and patterns. Other tools like Python can be used if the company has a person who is an expert in data analysis. Ultimately, clean data should be the starting point and is the most important step because the AI tool learns from this data and gets better over time. This means that over time, the machine will generate answers based on what it is learning. That is why the ROI when it comes to the use of AI is there, over time.

4. Step 3: AI Tools for HR Analytics, In-House or Purchased

It’s important to use beginner-friendly AI platforms especially if there are no in-house AI experts. There are AI platforms that can help with text analysis like sentiment from surveys and others for learning and testing models. Other platforms are either no code or low code making them user-friendly for beginners. Start with one pilot project by picking one simple problem like employee attrition. Use historical data to train a basic model and test its predictions. Interpret results and take action needed to make the change. For example, AI can show that long hours or lack of training leads to employee turnover. So, it is up to HR decision-makers to solve those issues. AI can only give insights into the data but can’t make the decisions. Human beings do that. Even better, easier and faster is purchasing the AI software. There are companies that can help in the implementation process and tailor your AI needs to your company goals.

5. Step 4: Keep Improving

Keep gathering more data and improve the quality of the data. Also important is learning from mistakes and upgrading to better tools as needed ensuring that you are automating your HR processes with AI. Understanding why AI software was sought in the first place will help in maintaining the drive when an obstacle is encountered.

6. Conclusion

In the end, companies that fail to embrace and utilize the power of information that is contained in AI to their competitive advantage will lose to their competitors. The investment in technology does not mean that the human touch is being replaced but it is simply an investment in efficiency. The key is not to give up because the end result will be an efficient, proactive and responsive HR department adept at making decisions that solve people problems.