AI For Employee Engagement

Pravir Ishvarlal
October 12, 2022

AI For Employee Engagement

Employee engagement changes in response to an ever-changing world, as the workforce’s generations evolve, so do: the trends, tools, and strategies used to engage, keep and assess employee engagement levels. Engaged employees, without a doubt, work the hardest, stay in their jobs the longest, and make significant contributions to their company™s bottom line.

Younger generations are more interested in digital engagement tools that have the potential to change how they work and communicate with others; whereas, older generations still believe that face-to-face conversations are the most effective form of communication. Additionally, the pandemic and ongoing remote work agreements have brought about new challenges for Executive and HR teams. As a result of these disruptions, organisations have begun to prioritize their employee™s well-being by listening, adjusting, and ultimately improving their practices.

On the one hand, traditional employee engagement techniques, such as employee engagement surveys, office game lunches and financial incentives may cause more harm than good. On the other hand, revolutionary advancements in AI may give these traditional approaches to employee engagement a much-needed boost they need. Now, let™s talk about the two main AI tools for engaging employees, sentiment analysis and topic modelling.

Sentiment Analysis

As a manager, it™s essential to regularly determine your employee™s engagement levels to find out if: they are happy or unhappy, inspired or stagnant, and so on. When done well, employee surveys provide insightful data about a company’s staff as well as potential suggestions for ways to boost productivity and efficiency. However, when done poorly, employee surveys can lower morale and cause a company to deviate from its course.

Traditional employee surveys have several drawbacks, including the time required to: execute, analyse, and repeat them for maximum benefit. Although the process is simple, the time and labour required by a human to provide up-to-date and relevant insights can be challenging and costly. So, it should also be noted that employee surveys provide a snapshot of a moment that has already passed, placing companies in the awkward position of reacting to the past rather than planning for the future.

As a result, survey insights must help Human Resource teams identify problems early on and address them as quickly as possible. Just by taking immediate action, HR teams could start engaging employees more effectively, and it turns out that machine learning is good at these kinds of tasks. Machine Learning techniques automatically analyse employee feedback and quantify or characterise how employees feel about their company.

The basic idea behind sentiment analysis is to analyse any text to determine its emotional content and to assess if there is a language that indicates if employees are engaged or if they are disengaged. Each employee™s response from the several surveys can be labelled with the appropriate emotion, such as positive, negative, or neutral. The algorithms do the labour-intensive process of analysing the qualitative remarks and classifying them into distinct groups.

Lastly, other applications for sentiment analysis include monitoring emails across different groups, and times, also, creating employee engagement chatbots that proactively inquire about employees™ feelings toward their jobs on a more frequent basis. Indeed, these are only a few of the many uses for sentiment analysis that we can use to increase employee engagement. It also offers us greater power to try to understand the motivations behind these sentiments, which leads us to the second AI tool, topic modelling.

Topic Modeling

The goal of topic modelling is to identify the numerous “themes” inside a group of texts “ this forms part of the Machine Learning tools and application. To expand, the machine learning algorithm reads through the group of texts, searching for words or phrases that recur frequently, organises them, and then produces a summary that most accurately captures the content of the group of texts.

On the whole, the use of topic modelling enables HR teams: to organise, comprehend, and summarise numerous employee surveys all at once. Subsequently, Human Resource professionals can then easily identify underlying themes that are present and affecting the company culture, and make data-driven decisions more quickly.

Machine learning has created some intriguing possibilities when it comes to employee engagement, especially when it comes to figuring out new ways to measure how employees are feeling at work. With sentiment analysis, we have a technique to quickly assess the general amount of positivity in any type of content that employees are writing.

More specifically, by applying topic modelling, we can go much further than that and rapidly identify the most important themes that are most prevalent and observe how those are affecting the overall employee experience. As a result of AI, organisations are approaching employee engagement differently. HR professionals don’t need to spend hours examining and interpreting complex survey data, allowing them to invest in developing effective strategies to raise employee engagement and promote long-lasting relationships.

When people go to work, they shouldn™t have to leave their hearts at home. – Betty Bender