AI in HR
What is AI in HR?
AI in HR refers to the use of artificial intelligence (AI) in human resources (HR) to improve the workplace experience, increase productivity and efficiency, and streamline operations. The AI technology includes Machine Learning (ML), Natural Language Processing (NLP), Predictive Analytics, Large Language Models (LLM, for example, Generative Pre-trained Transformers (GPT)), and more. Some end models are built by combining two or more models in layers.
Common application areas are employee experience, employee engagement, culture development, decision-making, change and transformation, recruitment, retention, learning and development, people analytics, employee well-being, diversity and inclusion, onboarding, administration, and performance management.
The field of AI in HR is quickly evolving with increasing access to new technologies and AI adoption. Given AI’s broad possibilities, the European Parliament adopted the EU AI Act in March 2024 to “ensure better conditions for the development and use of this innovative technology.”
AI in HR is broad, and we will explain the concept specifically in relation to employee surveys.
How AI impacts HR when used in employee surveys
AI in HR should solve people or organizational challenges and further the work of achieving strategic goals. Whether it is increasing productivity, uncovering insights, or providing recommended actions, AI in employee surveys can add a lot of value.
The analysis and insights AI generates in a modern employee survey enable HR to move away from the traditional administrative work of understanding the employee experience and the organization’s overall people status. Using your employee survey, AI gives you an automatic summary of the people side of the organization, brings out relevant insights, predicts future outcomes based on past patterns, and provides improvement recommendations.
Instead of working traditionally without AI, HR can place their attention on acting on the measurement results. The focus shifts to ensuring the organizational frameworks and foundations empower people to perform, helping leaders lead, and supporting employees to work smarter. Not only HR but also leaders, managers, and employees can work more productively and efficiently and focus on more complex tasks and strategic work.
“Where we see a higher engagement today, more people respond to the survey, and the measurement results increase. It is due to a collaboration that is not just the manager’s responsibility but the result of an active workforce.”
Anders Westerholm
HR Director at Ambea
How to use AI in HR through your employee survey
In employee surveys, AI in HR can be used to impact several areas, for example:
Employee experience: AI lets you understand your employees’ experience across their work, career development, work environment, and overall employee lifecycle. With an inclusive employee survey process, your employees can co-develop their experiences, increasing the positive impact.
Employee engagement: AI helps employees engage in the right conversations, highlighting what needs to change and demonstrating what works well. When you ask employees for their feedback, they feel listened to and are more likely to increase their work engagement.
Employee well-being: A key to building an environment that promotes well-being is changing what is preventing it. AI not only provides insights into the current employee well-being status and behavioral patterns but also lets you predict sick leave and recommends how to prevent it.
Workplace safety: An employee survey is favorably used to focus employees on workplace safety and uncover problems around it. AI helps to bring out insights that might otherwise be difficult to find.
Decision-making: AI produces enhanced analysis and insights into the people side of the organization and their trends, providing a more accurate basis for decision-making.
Organizational culture: AI in your employee survey not only lets you know what to build your organizational culture on, but as you measure it regularly, AI helps you ensure that it serves as the implementation enabler you intend it to be.
Self-leadership: In an inclusive employee survey process, employees can study the measurement results and reflect on what they can do to improve their own and the team’s performance. AI provides personalized analysis, insights, and recommendations that enable employees to gain a deeper understanding of what they can do to improve their own and their team’s measurement results.
Change and transformation: Regular employee surveys with AI let your people adopt a continuous improvement mindset. When an AI-powered employee survey is used to track a transformation initiative, you know what works well and where to make changes along the way for the initiative to be successful from a people perspective.
Retention: AI not only helps you understand the employee experience and what people prefer with your brand. It also lets you predict employee turnover and recommend changes to prevent the turnover from happening based on the employee survey answers.
Performance management: A regular and open feedback process with AI uncovering hurdles and providing recommendations builds on a culture of continuous improvement. The manager and employee can have a continuous progress conversation, either as opposed to or to add to the traditional yearly performance meeting.
Learning and development: Measuring development places employee, leader, and organizational focus on it. AI in your employee survey uncovers what works well and might prevent your people from developing.
People analytics: Understanding the employee experience of your organization’s key focus areas adds another layer to your people analytics. Employee surveys are easily integrated with HR master data and, with the use of AI, paint a much clearer and more in-depth picture of the people side of the organization. AI lifts relevant insights for what works well and what might be in the way of performance, summarizes the current status, and provides improvement recommendations on individual, team, leader, and organizational levels.
Diversity and inclusion: When you ask everyone for confidential feedback on how they experience inclusion, AI analyzes and provides insights into how you can best improve the work environment.
Onboarding: In onboarding surveys, AI uncovers how your new hires experience their work, introductions, early challenges, and what they need to be more productive and perform better and earlier in their role.
Administration: AI in your employee survey lets you remove a lot of administrative work, such as summarizing free-text comments, analyzing the survey responses, providing recommended actions, and preserving confidentiality.
Examples of AI in HR
Clear data visualization and quality AI models enable you to understand the big picture and examine the details of how your people perceive their work and work environment. You gain an aggregated team- or segmentation-based view and can never see each individual’s information.
As AI rapidly advances, new application areas develop continuously. Today, there are several types of AI applications in an employee survey, for example:
Free-text comment analytics
In Populum, most quantitative questions encourage the user to suggest improvement recommendations by typing free-text comments. Analyzing them manually would take a long time and is not an option in large organizations. The Populum Comment Analytics is an AI model that automatically analyzes free-text comments and organizes them into themes and topics. The AI model is a Large Language Model (LLM) with additional layers of Natural Language Processing (NLP), allowing it to recognize the comments’ sentiments, analyze comments written in any language, and extract keywords and topic identification. You can sort based on time and dive into different organizational segments to, for example, learn the feedback of your new hires or employees in a specific country or department.
Sick-leave prediction and prevention
When people approach the risk of going on sick leave, their employee survey feedback often changes. To pick up on this and predict the segments most at risk and what measurement area has the most impact on preventing sick leave, we built an AI model that detects this. In collaboration with a large organization, the model was built, trained, and tested using actual employee surveys and sick leave data. The final version is used as the basis for sick leave prediction and prevention. The AI model uses the feedback collected in each new survey, combines it with historical data, and updates the risk status and what measurement area you should improve to reduce the risk of the segment going on sick leave.
Employee turnover prediction and prevention
Similar to the sick leave model, which predicts people at risk of sick leave, our AI model for employee turnover predicts the risks of employees resigning. This AI model was built, trained, and tested using actual data. It enables you to learn what segments are most at risk and what measurement area has the biggest impact on preventing turnover.
Intelligent comment summary
In the Populum free-text comment analytics model, you can quickly summarize the status using a GPT model. It uses the measurement results in combination with your HR master data. You can filter based on the HR master data segments you have mapped to the employee survey.
Generative recommendations
Based on the survey measurement results, our GPT model provides recommendations based on your current people status, allowing you to quickly determine the best path forward. You can choose to generate the summary in different lengths and tones to fit them for their use, whether it is in a team discussion, leadership meeting, internal communications, or a section for the annual report.
Sensitive comment identification
While an inclusive and transparent employee survey process reduces the number of non-confidential or inappropriate words and comments and focuses people on constructive feedback, some might still be found. To protect everyone’s confidentiality and ensure proper language and content are used, we built an AI model that detects sensitive words. The model catches names and content that includes swear words and inappropriate language and tone. The comments containing these are hidden until HR sorts through them and decides which should be included in the measurement results.
How to implement AI in HR using your employee survey
A modern employee survey and AI is a perfect match. With lots of data collected over time, an employee survey lets you effortlessly use AI in HR to gain valuable and relevant insights about the people side of the organization. The key is to ensure the individual’s confidentiality and use high-quality AI models. If you lack confidentiality, you will break employee trust, and their feedback will not be as authentic. Without quality AI models, the insights will not be as relevant.
Combining confidential employee survey data with HR master data uncovers insights about your people’s perception of various areas of the organization and provides tailored improvement recommendations. You can view trends, learn how changes in the organization impact people’s perceptions, and understand the status and current themes and sentiments.
- Define objectives: Decide what you want to use your employee survey for.
- Set the strategy for how you want to involve your people: The more transparency and involvement you provide, the more people take ownership and engage in their work, and AI can influence your organization on a bigger scale.
- Evaluate software providers: Learn what different employee survey tools can do and what they cannot. Remember that an integration with your HR system makes the ongoing data transfer faster and smoother. Ensure that the survey provider handles data confidentially.
- Run a pilot: Do a first test run on a selected group, get their feedback, and adjust as needed.
- Train super users: Often, HR manages the employee survey, and super users need to learn the tool to help others.
- Communicate internally: Ensure you communicate how the employee survey will work, its purpose, and what is expected of everyone. Highlight the benefits it will bring each employee and explain the confidentiality. Explain the managers’ expectations and how they will act on the measurement results.
- Monitor and adjust: Run the employee survey and learn how you can make changes as needed to ensure a high response rate and make it easy to leverage AI for acting on the measurement results.
Pros and cons of AI in HR
It is important to know the pros and cons of using AI in HR. There are several things to consider when evaluating whether to use AI in HR and when choosing the employee survey, the most important being:
Pros
Enhanced data analytics and predictions: AI can analyze a lot of data, and your employee survey is a perfect source. Not only can AI uncover patterns and insights into your current employee experience, but when trained correctly, AI can provide expected future patterns based on historical data.
Real-time analysis: With real-time analysis, HR can understand the current situation and act promptly.
Remove human bias: When AI models are trained with real, quality data, they analyze new data based on a more objective understanding than what a human can do. Humans always bring a certain level of unconscious bias as everyone views reality based on their understanding of it, which is largely shaped by their experience.
Personalized insights: AI can deliver personalized insights and recommendations to individuals, teams, HR, managers, and senior leaders without compromising confidentiality
Time and cost-effectiveness: AI in your employee survey provides automatic and immediate analysis without the need for HR to spend administrative time or analyze the measurement results.
Cons
When there are low-quality models: When an AI is trained with bad data, the result will be a low-quality AI model. It is essential to choose the right survey provider to ensure your data is analyzed using the market’s highest-quality AI models.
Software providers who do not care: Some irresponsible software providers may not care about your security, ethical, or privacy considerations. Choosing the right partner is key. At Populum, your data and data security are our highest priority and we do not compromise on confidential information.
When there is a lack of confidentiality: While AI models need individual IDs to provide personalized insights and recommendations, the survey provider must ensure the security of the IDs. Without the proper set-up for confidentiality, trust and authenticity are compromised, and the data will not reflect employees’ reality as closely.