The Benefits of Combining Traditional Employee Feedback with AI and Machine Learning
Performance reviews, one-to-one meetings, and employee surveys have long been the go-to methods for obtaining employee feedback. And while these traditional methods will always have their place, the fact that we tend to hold performance reviews, or send employee feedback surveys bi-annually, begs the question: how relevant are the insights? And can you truly rely on these insights, when they can often be subjective to an individual's perception and prone to biases and even lead to unfair treatment?
This is why AI-driven feedback analysis has become an increasingly popular tool for HR professionals and organisations looking to use employee feedback to drive innovation, enhance the overall employee experience, and drive positive business results, giving them a competitive advantage.
By using machine learning and natural language processing (NLP) algorithms, HR can analyse employee data in a way that was not possible before. It goes beyond collecting and analysing simple quantitative data - formal feedback, such as employee satisfaction levels. It can instead focus on the quality of feedback, the sentiment expressed by employees, and any potential patterns or trends in their behaviour providing more constructive feedback.
Here are some of the ways that AI and machine learning is augmenting traditional feedback methods:
Sentiment Analysis
Performance reviews, one-to-one's, and employee surveys alone are great for getting a high-level overview of an employee is feelings. But with AI-driven sentiment analysis, HR professionals can get a much more granular look at how individuals feel in real-time and take action accordingly.
Text Analysis
By leveraging natural language processing (NLP) algorithms to look at employee feedback from a range of sources, such as surveys, one-to-one meeting transcripts, performance review scripts, and even internal communication channels, AI can detect patterns in human language used that reveal how people are feeling and what they want from their roles.
For instance, text data analysis in employee feedback may identify that an employee has used words and phrases that might suggest lower levels of engagement, such as "tired" or "stressed". With this information, HR professionals can address the issue and develop tailored feedback strategies to ensure a more constructive workplace environment, and make sure employees are engaged.
Voice Data Analysis
Voice data analysis in employee feedback takes this text analysis process a step further by using machine learning algorithms to analyse audio data and gain insights into the emotion and sentiment of employee feedback.
By leveraging AI-driven voice data analysis technology, HR professionals can detect subtle nuances in language that traditional methods cannot pick up. For instance, they may be able to tell if an employee feels frustrated or overwhelmed by simply analysing the tone of voice.
But these Natural Language Processing (NLP) algorithms can go beyond simple sentiment analysis by extracting contextual information from feedback. They can identify key themes, topics, and patterns within the text, providing valuable insights into the underlying issues or areas of strength and weakness. And while you could achieve this with a team of expert data analysts, AI-driven feedback analysis systems can provide these insights in a fraction of the time and cost - embodying a truly data driven approach. All you need to do is search for the right feedback technologies for your business and ensure that the operatives are trained to use them effectively.
Real-Time Feedback and Continuous Learning
Traditional feedback management methods often need a time lag between data collection and actionable insights. Take, for example, employee surveys. When insights are obtained and analysed, employee feedback may no longer be relevant.
However, with the introduction of AI and machine learning feedback technologies, employee feedback can provide near real-time insights into how employees feel and what they need from their roles. This greater accuracy means HR professionals and managers can quickly act on these insights and create more effective feedback strategies that address any issues or concerns promptly.
This provides the opportunity for continuous learning and development, as feedback can be gathered on an ongoing basis rather than relying on bi-annual surveys or reviews. It lets you understand the employee's journey in more detail and act accordingly.
And don't worry about the old myths that employees suffer survey fatigue and resist completing these surveys due to lack of time. These myths only reign true when the surveys sent aren't relevant to them or their job role and fail to address the questions that matter. But with AI and machine learning in your arsenal, tailored feedback solutions can be created for employees, ensuring that surveys are relevant and timely and providing HR teams with real-time data on engagement levels.
A company that is doing this well is Genpact. With their AI chatbot, Amber, Genpact sending employees surveys at every milestone of their career at the company, they can provide timely and relevant feedback that allows them to create more effective strategies for creating a positive employee experience.
But it doesn't stop there. Based on the employees' responses in context of the previous conversation with Amber, the chatbot can adjust the following survey and provide more personalised feedback that can help HR teams create even better strategies.
Bias Mitigation
Then we have to think about bias. If we think back to traditional methods of employee feedback, these can be subject to biases, both conscious and unconscious, which can influence evaluations of outcomes and hinder objective decision-making.
However, modern AI-driven feedback methods can help to mitigate this by focusing on objective criteria and eliminating subjective factors. AI and machine learning algorithms provide a holistic view of employee performance by analysing a broad range of data, such as performance metrics, feedback history, and other relevant indicators. This eliminates the potential for human bias and provides a more accurate and objective view of how employees are faring, which can help create personalised feedback strategies.
To do this successfully, though, HR professionals in themselves need to have the skills to understand the technology and how to interpret these insights, as well as be aware of their own biases. Therefore, upskilling in artificial intelligence, machine learning, and modern feedback methods is handy.
Creating More Personalised Feedback Strategies
By leveraging AI-driven modern feedback methods such as text and voice data analysis, HR leaders and people managers can gain invaluable insights into employee sentiment and engagement levels. This allows for creating more tailored strategies that consider individual needs and objectives, ensuring employees feel supported in their roles.
At the same time, these feedback technologies also serve to provide an opportunity for continuous learning as they enable organisations to collect data on an ongoing basis, allowing HR teams to stay ahead of the curve and create more effective feedback strategies that address any issues or concerns on time.
So, if you want to ensure your organisation is leveraging modern feedback methods and creating personalised experiences for employees, make sure you’re upskilling yourself in areas such as AI, machine learning, and modern feedback methods. Doing so will help you create personalised feedback strategies that focus on the objective and mitigate potential bias, providing a more meaningful employee experience.
Looking to take the next step in understanding how modern feedback methods can shape your organisation? Check out myHRfuture Academy, and learn the skills of the future of employee feedback.
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