Artificial intelligence in Human Resources: Benefits, examples and trends
With 76% of HR leaders predicting that AI will become essential to organizational success, the proliferation of HCM, HRIS, and human resources tools leveraging large language models, predictive analytics, and machine learning should come as no surprise.
With the potential to dramatically increase efficiency, productivity, and accuracy across multiple HR processes, AI stands to revolutionize how organizations manage people operations. Understanding the nuances of specific technologies and their applications, however, is key to choosing the right tools for your business.
How AI is used in Human Resources: Examples & applications
From increasing productivity through automation to streamlining compliance with enhanced document management, today’s HR professionals use AI to save time and cost with sophisticated HRIS and HCM solutions.
Recruitment
One of the first applications of machine learning in human resources, modern recruiting has evolved into an AI powerhouse. From sourcing talent and screening resumes to engaging with candidates and predicting headcount, generative and predictive tools have completely changed how organizations compete for talent.
- Job posting. Given the right prompts, generative AI tools like ChatGPT can assist in drafting accurate, informative job descriptions.
- Sourcing. Algorithms can search databases and social networks to identify suitable candidates with best-fit qualifications.
- Screening. An AI-powered applicant tracking system (ATS) can parse resumes and applications to surface ideal candidates faster.
- Candidate engagement. Chatbots and automated email sequences help busy recruiters connect with prospects quickly without sacrificing a personalized touch.
Administrative HR tasks
Offloading repetitive tasks to tireless machines gives your HR professionals time and energy to focus on substantive work that moves your organization forward.
- Data management. Organizations that accumulate large quantities of data can struggle to maintain it properly. Automating the data cleansing and updating process ensures that everyone makes decisions on the basis of correct information.
- Scheduling. Rather than manually working out where to assign employees across shifts and locations, teams can leverage AI-driven pattern recognition tools to develop efficient, cost-effective schedules.
- Document retention. Automating the collection and retention of important documents like Form W-9 or 1099 helps your organization stay compliant and eliminates paper waste.
- Payroll. AI-driven payroll and HCM solutions can remove the stress of calculating payroll correctly and transferring employees who shift locations.
Employee performance management
Machine learning might not seem the most obvious tool for guiding, developing, and nurturing your employees, but AI can provide significant support in the form of automated learning experiences that adapt to employee performance. For example, AI may suggest additional learning activities or automatically enroll employees in related follow-up training based on a team's aggregate score in a training module.
Benefits administration
Predictive AI tools can crunch the numbers on benefits usage and recommend adjustments to your organization’s offering. On the employee side, automated enrollment removes the risk of missing annual deadlines.
Onboarding and offboarding
Saying ‘hello’ to a new employee and ‘goodbye’ to a departing one both entail a fair amount of paperwork. AI can smooth the experience by helping ensure everyone receives essential communication and documents as quickly as possible, enhancing the employee experience.
- Benefits enrollment. Automated benefits enrollment removes the need to chase new joiners for important paperwork while they’re acclimating to new processes.
- Document collection. Custom onboarding workflows can provide and collect key tax documents, including Form I-9.
- Inquiries. An always-available chatbot trained on company policies and procedures can guide new joiners to valuable resources and answer common questions.
- Endpoint protection. Automated communications about equipment retrieval and remote shutdowns protect your organization’s intellectual property when a remote employee leaves the company.
Benefits of using AI in HR
For organizations struggling to make the most of limited time and budget resources, AI can fill some important gaps and provide much-needed support to your dedicated employees.
Time efficiency
Many rote or manual HR tasks that take humans hours to complete can be done by machines in minutes if not seconds. Instead of asking a talent specialist to sift through 500 responses to an open job description, tap an algorithm to help with the initial screening and free up valuable time to debate the merits of the most qualified candidates.
Reduced costs
Using AI allows HR and other professionals to leverage their creativity and strategic intelligence in ways that grow your business—all while reducing the amount of time required to complete manual tasks. Leveraging machines’ powerful pattern recognition and analytic capabilities can give HR leaders deep insight into how and where to deploy resources for maximum effect.
Better structured processes
Automating repetitive tasks like payroll, onboarding, or shift scheduling means consistent, timely performance with a reduced risk of error. Depending on the specific type of tool, an AI-powered solution may even suggest specific process improvements based on its analysis of your existing workflows.
The challenges of AI in HR
Like most business decisions, integrating AI into your human resources comes with challenges as well as benefits. From legal risks associated with data privacy and bias in hiring to potential clashes with internal missions and values, HR leaders must carefully consider the implications of adding machine learning to the mix.
Data security risks
Machine learning tools improve performance by training with new data, and many of the most popular solutions rely on user-provided information to meet that need. But keep in mind that disclosing sensitive employee information to third-party solutions using generative AI runs the risk of violating laws designed to protect confidentiality. Before sharing company or employee data with a tool or platform that relies on artificial intelligence, review applicable data privacy laws, as well as company policies and procedures.
Loss of human expertise
AI can outperform humans in areas like pattern recognition, data analysis, and computation. When it comes to making complex, context-dependent decisions and exercising moral judgment, however, machines fall demonstrably short.
Over-reliance on AI in any part of your organization can lead to strategic missteps and lost opportunities if you consistently sacrifice innovation in favor of efficiency.
Ethical issues
The scope of how, where, and when businesses can leverage AI in their operations remains an open question in many jurisdictions. Technology continues to advance rapidly while governments and other regulators rush to keep up with new developments. Staying informed, let alone compliant, can pose unique challenges, particularly for larger enterprises in sensitive industries operating in multiple locations.
The future of AI in HR
As large language models, automations, and predictive tools continue to develop, they’re sure to find new use cases and applications in people operations.
Contrary to doomsaying predictions, industry experts reject the notion that machines can replace humans altogether. Instead, analysts predict a future where AI supports HR professionals in becoming more data-driven and dynamic. With the ability to analyze and interpret larger and more complex data sets and to automate time-consuming tasks, HR professionals of the future will prioritize strategic development and planning activities.
Enhance HR performance with Rippling’s Talent Signal
Early feedback is critical to any new hire’s success. But delivering high-quality feedback is a time-consuming challenge. Talent Signal is an AI-powered performance management tool from Rippling that reviews the first 90 days of a new employee’s work product and provides feedback on how to improve it. It provides an independent view of how a new employee is ramping, and gives managers concrete insights they can use to coach them.
The magic of Talent Signal lies in its ability to reason directly from the work product. It uses AI to review work, from specific lines of code to customer interactions, then generates a performance signal based on actual contributions. The model processes all of this work product data to gauge how well the employee is acclimating to their new role. It shares specific examples with the manager to allow for scrutiny of its conclusions, and offers recommendations for how to better support the employee.
See how integrating AI into performance management can save time, reduce manual work, and offer data-driven decision-making.
Learn more about Talent Signal
See RipplingFrequently asked questions
Can I integrate AI tools with Rippling?
Yes, you can integrate AI tools with Rippling. Rippling supports over 600 native integrations, many of which leverage AI to support human resources, finance, business operations, and other functions.
What AI tools can be integrated with Rippling’s platform?
AI-powered tools that integrate natively with Rippling include Algolia, Asana, Datadog, Sisense, Freshdesk, Fullstory, and many more across multiple HR modules. Native integrations mean your apps receive comprehensive, up-to-date information when providing you with analytics and recommendations.
Is AI going to replace HR?
No, AI will not replace HR or HR professionals. As much as machines may surpass humans in certain areas, they struggle with skills essential to the HR function, such as emotional intelligence, critical thinking, and strategic planning. Rather than replace HR leaders, AI will likely continue to play a supporting role, taking over repetitive tasks that distract from strategic planning and problem-solving.
How does AI help HR teams in decision-making?
AI can assist with HR decision-making in numerous ways:
- Parsing resumes during recruitment and predicting job performance based on historical data.
- Reviewing and aggregating performance data to identify trends and provide personalized feedback and recommendations.
- Analyzing turnover, hiring, and business growth to enable better resource allocation and workforce planning.
This blog is based on information available to Rippling as of August 28, 2024.
Disclaimer: Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.