Today’s business landscape is rapidly evolving—and the key to staying on top lies in attracting and retaining top talent. Cutting-edge technology will continue to be essential as organizations seek out new ways to optimize human resources (HR) operations, talent acquisition, and employee engagement.
Machine learning (ML) and generative artificial intelligence (AI) have already begun to revolutionize critical HR functions. They are being used to support data-driven business decisions, enabling HR professionals to streamline processes and create more personalized experiences for employees.
In this post, we will explore the short and long-term applications of ML and generative AI within the HR space. We will examine the current impact and future transformations within talent HR operations, talent acquisition, and employee engagement.
Transforming Talent HR Operations
New technology has already begun to transform talent operations. In addition to improving efficiency, there are several ways ML and AI can impact overall business goals, including increased productivity and revenue growth.
1. Automating recruitment processes
A recent study from the Society of Human Resources Management (SHRM) showed that nearly 25% of organizations are now using AI to support various HR functions, including recruitment and hiring. Among those, 42% were employers with 5,000 or more employees. Only 16% of employers with 100 or fewer employees surveyed used AI.
There are multiple ways advanced technology can help streamline and improve HR processes.
AI-powered resume screening and candidate shortlisting accelerate recruitment processes, reducing the time and resources necessary to move candidates from application to interview. In fact, the SHRM study found that among employers using AI tools in their recruiting processes, 53% stated the time to fill open roles was somewhat better, while 16% found it much better.
Here are some of the current key players in the space, as well as the customers who use them:
Each of these companies offers unique solutions to common recruiting challenges and they have all been used by big brand names.
2. Predictive analytics for talent management
Predictive analytics is the process of using past and present data to forecast future events. And according to the 2022 SkyWest Technology survey, in just the last three years, the use of predictive analytics in the workforce has grown by nearly 50%. Predictive analytics help businesses mitigate risk by making more informed, data-driven decisions. With more data available than ever before, businesses can use ML models to support decisions that affect their most essential asset—their people.
ML algorithms have the ability to identify patterns in employee data, leading to new insights on performance, retention, and engagement. This data can be used to take quick, actionable steps to retain and develop current talent, leading to higher productivity and revenue growth.
Additionally, ML models can analyze historical employee data to predict attrition. Armed with more comprehensive data, HR professionals can be proactive in addressing trending issues in a particular location, role, or department. Addressing these issues early can have an incredible impact on business goals and profits.
Arya searches the internet for candidates' social media profiles. The platform is looking for any “red flags,” such as extreme political content or unsavory posts about previous employers to predict any potential problems. The system can also make predictions regarding how likely an employee is to leave the company. These are just two of the many ways predictive analytics are currently being used.
3. Intelligent performance management
The 2022 SHRM study also showed that 1 in 5 businesses plan to use or increase their use of AI tools in performance management over the next five years. AI tools can be used to assess productivity, compare current and previous output, and track engagement, among other things.
For example, Microsoft’s Workplace Analytics enables employers to monitor employee data, including time spent on certain tasks like meetings, writing emails, and time working after they’ve left the office. And Domino’s DOM Pizza Checker is used to monitor how employees make pizzas. The system determines whether sauces and toppings are appropriately distributed, among other things. If a pizza isn’t up to standards, the employee can be alerted to start over.
Integrating AI into performance review processes helps eliminate human bias and potential errors, leading to more accurate assessments. Additionally, performance appraisal systems driven by AI allow for real-time feedback. HR professionals can use more timely feedback to easily identify skill gaps and offer training to address specific issues more quickly. Algorithms that analyze performance metrics can also suggest new ways to strategically improve individual and team productivity.
For example, Indian startup, Datoin, tracks data to improve the accountability of sales teams. Sales executives can see how they’re performing compared to business objectives and so can their managers. Managers are alerted when their sales team members fall short of targets. They can quickly step in to identify key issues and help train or coach the employee in a timely manner.
4. Revolutionizing Talent Acquisition
In today’s job market, talent acquisition must be strategic and efficient. Candidates are turned off by long timelines and unclear expectations. Technology can help employers acquire the right candidates from the start with enhanced sourcing processes. ML and AI can improve various aspects of the interview stage as well, ensuring candidates feel comfortable, confident, and positive about their experience.
5. Enhanced Candidate Sourcing
With hundreds of job seekers applying to every job, it can be difficult for recruiters to wade through so much applicant information. ML algorithms and natural language processing (NLP) techniques enable recruiters to analyze both job descriptions and resumes to determine the strongest candidates. New technology can also identify and scan social media profiles for more accurate candidate matching. Once an applicant is further along in the process, AI-powered chatbots can manage the first round of candidate screenings.
As AI is integrated into sourcing, recruiters can spend less time sifting through resumes and searching online for strong candidates. Instead, they can dedicate more time to scheduling interviews, nurturing candidates through the interview process, and identifying recruitment trends to continue improving processes.
6. Bias Reduction in Hiring
Any company seeking top talent knows the importance of bias-free hiring practices. Human bias can affect everything from recruitment efforts and promotions to company retention rates. But it can be difficult to eliminate all biases when humans are involved.
Beginning with job postings, AI-powered language models can support recruiters and hiring managers in creating unbiased job descriptions. This includes an emphasis on reducing any gendered language. ML algorithms can detect unconscious bias and mitigate the risk of such practices. Using technology in combination with expert recruiters can help promote more inclusive hiring practices.
Textio is one such company currently helping employers to hire and retain diverse teams. The platform uses NLP and text analytics to help employers identify and address any bias language in job descriptions and communications. Customers include McDonald’s, Novartis, and Spotify, among others.
HiredScore assists employers in sourcing and nurturing diverse candidates. The platform also offers masked screening to help mitigate any impact from hiring manager biases. Customers include Intel, Alcoa, and Phillips 66.
7. Augmented Interviews
Nothing can replace a face-to-face interview and the personal connection that occurs between a strong candidate and a recruiter or hiring manager. But technology can certainly enhance how a company analyzes candidate responses and body language. ML-powered interview analytics can help break down these factors and provide additional insight into an applicant’s fit for a particular role.
Having additional data points with which to analyze a future employee will help companies make the best hiring decisions. In addition, generative AI has the power to simulate realistic interview scenarios and assess candidate skills. These AI-powered virtual interviews can help eliminate the time recruiters spend on early-stage processes, providing them with more time to accurately assess late-stage candidates.
8. Empowering Employee Engagement
Employee engagement has always been important, but it will be one of the leading contributors to company success in today’s job market. Given the rise of flexible work, online jobs, and better work-life balance, today’s candidates aren’t willing to settle. The companies that dedicate time and resources to employee engagement will find better customer engagement as well—and in turn—better profits.
Phia, created by peopleHum, is an employee engagement chatbot that can help automate certain HR functions. Phia is able to conduct surveys, answer common employee questions, and access data from different sources. It can be integrated with popular platforms such as Slack and Microsoft Teams.
9. Personalized Learning and Development
ML algorithms have the ability to recognize an individual’s unique learning style and preferences. These algorithms can use data to predict results and identify skill gaps. They can then use a combination of this information to recommend personalized training programs for HR professionals to implement. For example, based on an employee’s knowledge of a particular topic, they may be able to receive more support in real time or skip through sections of training.
HR teams with limited resources can also use AI-powered chatbots and assistants to provide on-demand learning support. In addition, AI can assist learning and development teams in creating new content, and scheduling and distributing that content based on previous employee training. HR teams can oversee these processes but will ultimately have more time free to launch and support new initiatives.
10. Sentiment Analysis and Employee Well-being
It’s essential for organizations to remain aware of how employees are feeling about their employer and their role. ML models have the power to analyze collected information, such as employee feedback, sentiment, and engagement data to pinpoint areas of improvement.
Sentiment analysis combines the power of ML and NLP to automatically identify text as positive, negative, or neutral. It helps more easily identify emotions in language that the average human may miss. Employers can use this information to better understand how employees feel about a particular topic. HR professionals can then be more proactive in identifying challenges, making changes, and implementing new initiatives before it’s too late.
In Deloitte’s 2022 Mental Health in the Workplace report, 59% of workers surveyed reported experiencing symptoms of depression, while 49% reported experiencing symptoms of anxiety. AI provides new opportunities for employers to provide support. For employees who are hesitant to reach out for support, AI-powered chatbots can provide on-demand support for day-to-day stressors or provide information on additional resources.
11. Intelligent Employee Benefits
Much like employee development and mental health, employee benefits needs are quite personal. They can change over the course of the employee lifecycle for varying reasons, including new company offerings. It’s important for employees to fully understand their options if they’re to appreciate the value of their benefits.
In the past, the burden of explanation has been left to HR professionals. But ML algorithms can assist HR teams by analyzing employee preferences and providing recommended packages tailored to those preferences. Generative AI can be used to support benefits communications efforts, providing personalized content to keep employees engaged and to assist them in maximizing their benefits.
The Future of Technology in HR Operations
As businesses embrace the use of ML and generative AI technologies in talent HR operations, talent acquisition, and employee engagement, we will continue to see workforce management change and develop in new ways. Companies that are prepared to integrate ML and generative AI technologies will find countless opportunities to streamline processes, enhance the employee experience, and operate more efficiently. The potential to reduce time spent on low-value tasks will enable companies to dedicate more resources to initiatives that have a great impact on company growth.
It's an exciting time to serve in the HR space, but HR professionals and business leaders must work together to understand how these advancements impact their organizations. It is crucial to ensure that new technologies are utilized in a responsible and ethical manner to prevent unintentional outcomes—such as reinforcing discriminatory practices in hiring or breaching employee confidentiality. They must consider the ethical implications and ensure transparency to benefit from integrating new tools. And lastly, leaders must not forget that the human touch is an important part of the overall employee experience. While technological advancements can reduce redundant or time-consuming tasks, they cannot replace the human connection that is also an important part of each stage in the employee lifecycle—from recruitment to retention efforts.
Kern and Partners is committed to supporting the development of positive, productive, and innovative leaders. Our development programs and consulting services are designed to help managers and leaders tap into the natural strengths of their employees to build stronger, more engaged teams. We look forward to hearing from you. Please call me at 818-264-8480 or click here to schedule a call.