How To Use AI in HR Analytics: Your 2025 Go-To Guide
Modern HR practices rely on data. AI is a pivotal tool that unlocks in-depth ways to use HR data.
AI in HR is now the norm in the field of Human Resources. In fact, 60% of C-suite level executives and HR decision-makers believe their HR departments will integrate more AI and automation into their functions and processes within five years, as doing so is expected to enhance HR practices and support organizational competitiveness and achievement.
To help you gain a better understanding of how to use AI in HR analytics, this article discusses why it matters, how to apply it to your organization’s HR function, and real-life company examples of AI in HR analytics to inspire your own approach.
Contents
AI in HR analytics: HR’s role and impact
AI in HR analytics: Abbreviations to know
Why HR analytics is important in Human Resources
Real-life AI in HR analytics examples
How to use AI in HR analytics: 5 steps
The future of AI in HR analytics
AI in HR analytics: HR’s role and impact
AI is changing HR analytics by improving data collection, processing, and decision-making — this streamlines HR processes, helping HR teams work faster and smarter.
Some key ways in which AI impacts HR analytics include:
Recruitment and hiring
Recruiters save vast amounts of time with AI tools for HR. AI-driven applicant tracking systems (ATS) scan and evaluate résumés and cover letters to assess candidates’ skills and qualifications rapidly and objectively. It also uses chatbots to schedule interviews, update candidates, and compare current data with past trends to predict candidate performance.
Employee experience
AI is changing how employees interact with HR by simplifying access to services. Chatbots assist with onboarding, answer frequently asked questions, and offer instant access to policies and benefits. AI tools can also analyze employee feedback to highlight areas for improvement.
Learning and development
AI in HR analytics can transform an organization’s approach to training and developing employees. It can channel data on employee skills and career aspirations to recommend training initiatives and help create personalized development plans. In addition, AI-enabled learning platforms can adapt training to different skill sets and accommodate various learning styles.
Retention and turnover prediction
Companies using AI in HR can analyze employee turnover rates and data on retention plans for deeper insight into why employees resign. AI models can also analyze workforce trends to predict areas of potential employee attrition. HR teams can then use this information to improve recruitment, onboarding, and retention strategies to attract and retain top talent.
Upskilling and reskilling
AI can help detect skills gaps to inform organizations where employees require upskilling and reskilling to fulfill future roles. It can also identify any employees who are already well-positioned for these roles. With AI-driven personalized training, you can meet the organization’s skills needs and support employees in their career growth.
Abbreviations to know with AI in HR analytics
Before we dive further into the topic, let’s get familiar with some of the most common AI-related terms, acronyms, and abbreviations, as well as what they stand for and what they mean:
- AI (Artificial Intelligence): Computer and machine technology programmed to perform tasks by mimicking human learning, decision-making, and problem-solving.
- Algorithm: A set of clear, step-by-step instructions a computer follows in order to complete a specific task or solve a certain problem.
- Machine Learning (ML): A type of AI that uses algorithms to improve with more data input. It also spots patterns and makes predictions based on what they learn.
- Generative AI (GenAI): AI that uses sophisticated algorithms and ML models to create content (e.g., text, images, audio, and video) based on user input known as prompts.
- Natural Language Processing (NLP): Technology that uses computer science, linguistics, and ML to help computers understand and use human language.
- Chatbot: A program that uses NLP and ML to understand and respond to user queries. It mimics human conversation and communicates through text or voice.
Why HR analytics is important in Human Resources
HR analytics uses data and statistics to analyze and understand workforce trends, helping HR to improve decision-making. For example, tracking relevant metrics helps to evaluate the success of training programs, enhance the hiring process, or address DEIB issues.
Some of its key benefits are:
- Better understanding of employee engagement and retention
- Evidence-based decision-making
- Predicting future needs
- Evaluating HR initiatives
- Improving recruitment, training, and performance
- Efficient resource allocation
- Targeted HR strategies.
Master the application of AI in HR analytics
Learn to master the different aspects of applying Artificial Intelligence to HR so you can predict future needs, reduce bias, and improve recruiting processes and resource allocation.
AIHR’s Artificial Intelligence for HR Certificate Program will equip you with future-ready AI skills to streamline workflows, make more informed decisions, and ensure more time for strategic projects that support HR and business success.
Real-life AI in HR analytics examples
Here’s a brief look at a few companies that benefit from successful AI use cases in HR analytics:
IBM
IBM has implemented AI to transform its HR strategies. According to Chief HR Officer Nickle LaMoreaux, the company is applying AI within three broad categories — recommendations, assistants, and agents. This means AI suggests learning paths and budget plans, chatbots answer HR questions, and automated tools help with tasks like managing promotions.
PepsiCo
PepsiCo’s AI talent acquisition tool, Hired Score, scans candidate profiles in the ATS, candidate relationship management system, and HRIS to compile a list of suitable candidates. It then ranks them based on job fit and offers insights to improve hiring decisions. Hiring managers can also access a hub to retrieve other relevant information, such as similar past roles.
Walmart
Walmart’s GenAI-powered desktop and mobile app, My Assistant, answers questions and helps with tasks like drafting documents, freeing up campus employees to focus on core work. It’s also part of Walmart’s Me@Campus app, which allows employees to manage their careers and training, get financial well-being support, book conference rooms, and more.
Unilever
Unilever operates in 190 countries and processes about 1.8 million job applications every year. To better handle this, it implemented an AI-powered online recruitment platform. It also uses AI to assess candidates through games and video interviews and has saved about 70,000 HR hours by automating screening.
How to use AI in HR analytics: 5 steps
Here are five steps to take when applying AI in HR analytics:
Step 1: Ensure data privacy and compliance
Consider ethical factors, such as data privacy, regulatory compliance, and security measures. Obtain consent, collect only necessary data, encrypt sensitive information, and maintain secure data storage. Use generative AI prompts to pinpoint what data is worth collecting.
For example, if you want to reduce employee turnover, a prompt you could use might be: “Besides salary and tenure, what other types of data could be useful in predicting employee turnover?” AI tools can suggest factors and insights to help you build stronger data sets right from the start.
Step 2: Train HR teams on AI usage
Offer comprehensive AI training to help HR teams use AI effectively, ethically, and under legal compliance. Also, provide hands-on training sessions that cover how AI works, what it can and can’t do, how to read data outputs, and when to step in.
It’s important to understand AI’s limitations and the importance of human oversight in applying AI to HR analytics. AI should guide decisions, not make them. For example, recruiters should always review AI-driven recommendations and factor in the context a machine might miss.
Step 3: Use AI to complement, not replace, humans
AI can only reflect the data it’s trained on, and it lacks empathy. So, use AI to save time and deliver faster services but still remain responsible for innovation and decision-making.
For example, an AI tool might suggest a candidate based on historical hiring patterns, but a hiring manager should still assess fit based on team dynamics and potential. Remember that AI can boost productivity, but cannot replace personal judgment.
Step 4: Use AI for continuous improvement
Assess current processes to determine AI’s positive potential. For instance, AI tools can automate tasks, provide metrics analysis, and support data-driven decision-making. Start by identifying bottlenecks — if you’re losing great candidates, use AI to analyze drop-off rates and identify common traits.
Based on the information you obtain, you can adjust your process. Also, track which AI predictions were accurate and which were not. This helps you improve the model over time and get closer to your hiring goals.
Step 5: Monitor and minimize AI bias
AI can’t grasp cultural and societal dynamics and needs training on diverse datasets and ethical guidelines. Conduct periodic checks to monitor data and algorithms for biases so you can mitigate them quickly. Look for patterns — if it favors certain demographics, adjust the algorithm and data inputs.
Finally, create a feedback loop. Allow recruiters to flag questionable AI suggestions so your team can investigate and fix them. This helps you keep the system aligned with organizational culture and values.
HR tip: Minimizing bias and maximizing security
When AI systems are trained on biased historical data, they can perpetuate or amplify biases, posing significant legal risks and negatively impacting workforce diversity. Additionally, the ‘black box’ nature of many AI systems — which have limited understanding or transparency of how decisions or recommendations are made — can lead to compliance and ethical issues.
AIHR’s Psychometrics Assessments Expert, Annelise Pretorius, strongly recommends implementing explainable AI (XAI) systems in HR analytics to ensure transparency and mitigate these risks. The sensitive and personal nature of HR data is also a significant factor to consider when using AI in HR analytics.
“To address concerns related to data protection, privacy, and information security, it’s essential to implement strict data encryption and access controls, as well as conduct regular audits of AI systems. Ethical AI implementation in HR also requires appropriate consent mechanisms that give employees control over their personal data, ensuring meaningful human oversight of algorithmic decisions,” says Annelise.
Generative AI in people analytics
Generative AI can use existing data to revolutionize people analytics with new content, patterns, and insights. Here are some examples of what GenAI can do in this regard:
- Summarize candidate profiles
- Generate reports from simple prompts
- Compile employee feedback
- Draft recruitment emails
- Identify potential leaders by comparing traits
- Model future scenarios based on current data.
The future of AI in HR analytics
Going forward, AI will continue to shape HR with further automation and more advanced support tools. Some upcoming trends in the area of AI in HR analytics include:
Increased HR process automation
AI will handle more administrative tasks with software robots that can interact with digital systems to enter and extract data, submit forms, and move files. More sophisticated chatbots and virtual assistants will also offer employees instant, more personalized support.
AI-driven DEIB initiatives
AI can help eliminate bias in hiring when more advanced systems can recognize and remove biased language from recruitment materials, such as job posts. It can also aid in constructing unbiased dataset frameworks and maintaining algorithmic fairness and transparency.
Predictive HR analytics
Advanced algorithms and the ability to analyze HR data as it’s produced will allow for more precise HR decision-making and strategies in areas such as workforce and succession planning, sourcing and recruitment, talent acquisition, and talent management.
AI-powered mental health and wellbeing tools
There will likely be more emphasis on personalized employee support, which will lead to systems being able to integrate data from workplace communications, wellness platforms, and even wearable devices to provide customized recommendations for mental health solutions.
Integration with HR platforms
As AI-enabled features continue to develop, you can expect to see further integration with HR software solutions. You may have even more HR platforms to choose from to help streamline processes, support data-driven decision-making, and foster employee engagement.
To sum up
AI is profoundly impacting HR analytics by providing HR professionals with more strategic ways to use data. This data-driven decision-making will empower you to refine and optimize processes, thereby offering a higher level of service to employees and your organization.
Adopting AI in HR analytics requires careful consideration, transparency, and maintaining compliance and ethical standards. HR professionals must embrace AI and learn how to apply it successfully while balancing the irreplaceable elements of human judgment, input, and interaction.
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