How AI can boost HRIS practices
As in the case with most segments of the IT spectrum, the arena of Human Resource Information Systems (HRIS) can also benefit significantly from the use of Artificial Intelligence (AI) and its accompanying tools. This will not only enhance the power of HRIS solutions but also help streamline HR processes, improve decision-making, and most importantly, enhance the overall employee experience.
Traditionally, HRIS solutions have been associated with its specialised components for the purposes of Sales and Marketing as well as the administrative conveniences especially in large organisations. These include areas (or Modules) such as Payroll, Performance Evaluation, Employee Onboarding, and so on, just to name a few. We shall look at how AI can enhance related HRIS functionality;
Employee Onboarding and Talent Acquisition
In organisations such as large Banks, each recruitment drive can generate as much as thousands of applicants. In situations such as this, AI can be of immense help as AI-powered algorithms scan and filter resumes as well as identifying the most qualified candidates based on specific criteria and keywords.
Furthermore, the use of Chatbots can handle initial candidate inquiries, schedule interviews, and even conduct preliminary assessments.
AI can also be used to automatically peruse big data and create personalised onboarding plans for new hires, ensuring a smoother integration into a Company.
Training & Development
AI can suggest relevant training and development programs based on an employee's role, skills, and career goals. Built-in Predictive Analytics can be used to analyse historical hiring patterns to predict future talent needs and identify potential sources of talent.
AI algorithms can be used to analyse employee feedback and engagement surveys to identify trends.. The results be used to assess areas for improvement and recommend personalised recognition and rewards for employees based on their contributions and performance.
AI can suggest relevant learning materials, courses, and resources to help employees develop their skills.
AI driven analytics can provide insights into employee performance, helping managers make data-driven decisions about promotions, increments, job scoping, or coaching. With regard to coaching, AI-powered tools can offer real-time feedback and coaching suggestions for managers during performance reviews.
AI can be used to personalise learning paths based on an employee's strengths and weaknesses assesses during this phase.
AI-powered chatbots can handle common HR queries, such as leave requests, policy inquiries, balances, attendance discrepancies, and benefits questions.
AI based predictive analytics can enhance self-service portals and recommendations for employees to find information easily.
AI can assist predictive analysis to forecast workforce trends, such as turnover rates, identifying potential issues before they become significant problems.
Today, workforce diversity has become a key matrix of good governance and AI can help HR analyse diversity metrics and suggest strategies for improving diversity and inclusion in the workplace.
AI-powered chatbots can assist and support employees' mental health and well-being. AI based algorithms can help identify employees with high-potential and help HR prepare for future leadership transitions.
Compliance Management and Security
AI can assist in monitoring HR data for security breaches and compliance violations whilst maintaining comprehensive audit trails for HR processes to ensure compliance with regulations.
Whilst the above is not an exhaustive list of the uses of AI in HRIS, it is safe to say that the use of AI Integration with HRIS can save time, reduce errors, improve decision-making, and enhance the employee experience. However, AI should be used as a tool to aid the above processes and not as a replacement of the physical (human) aspect of HRIS management. Ensuring ethical practices and ensuring transparency and fairness in such AI-driven HRIS processes is very important. Regular monitoring and feedback loops are also crucial to fine-tune AI algorithms for optimal results.