Blending AI Techniques with Emotional Intelligence for Meaningful Employee RnR

Continuing from our previous blog, with expert insights to understand AI assistance in employee rewards and recognitions, it is noteworthy that AI-driven innovations in RnR influence engagement and retention.

 To understand this, we delve into some real-time industry insights as shared by Monica Singh from BI Worldwide:

1. Metaverse experience. It's an onboarding visual AI experience for new joiners

2. Virtual reward programs, where the employee is rewarded based on their overall efforts and contribution to companywide projects, apart from their immediate responsibilities and projects.

3. The knowledge management system "Dr. Koogle" helps employees get real-time information and answers to questions even in their native language. The questions don’t have to be very precise; the AI tool understands the intent behind their questions and provides responses aligned with what they're looking for

Techniques Driving RnR Programs

Clustering algorithms and collaborative filtering are techniques that organizations use to enhance personalization while ensuring fairness in RnR AI-driven programs. So, what do these terms mean? Let's understand them in simple terms.

Collaborative filtering is a recommendation technique used in real-time recommender engines within the AI model. It filters recommendations or predicts preferences based on the likes and dislikes of similar individuals. If the AI has to make a decision, it tracks similarities among people who have taken similar actions or supported a decision and generates personalized recommendations

For example, if an employee constantly receives recognition from other people, the AI can notice patterns and nudge the managers to acknowledge that consistent performer. Alternatively, based on past preferences and likings of similar employees, the AI can recommend rewards. If a group of employees appreciated receiving gift cards, the system might recommend similar rewards to employees with corresponding behaviors.

Another general example of how to better understand this is the recommendations on OTT platforms. The recommender engine predicts the preference based on past user behavior or the behavior of similar users.

Clustering algorithms are more of a data analysis technique than a data science technique helping at the back end. The data load is segregated into clusters with similar items matching certain levels of parameters and identifying patterns and groups of target audience that would benefit most from a specific reward program that the organization plans to roll out.

For example, an AI model can group employees on parameters like meeting sales targets, high customer satisfaction rates, or showcasing leadership qualities. This will help organizations identify the performers who can be rewarded and recognized.

A similar example, in general, can be organizing or clustering a bunch of online documents into different folders, like academic records or employment records. Each folder contains documents similar to each other than those in other folders.

Integration of Emotion with Technology

AI is great at speed and scale, but meaningful recognition also depends heavily on emotional nuance and authenticity. From a technology perspective, how can organizations integrate AI's efficiency with human emotional intelligence to create meaningful and authentic recognition experiences?

Emotional intelligence can play an important role in total rewards or HR functions. Blending human emotions, such as voice recognition, gesture recognition, and facial expression recognition, with technology and gauging them via an AI model can help address many aspects, especially communications.

Monica from BI Worldwide illuminates this point, making comprehending the integration of emotions and technology easier.

“By embedding emotional intelligence into an AI model, we can ensure meaningful recognition experiences while reducing the dependency on human interaction for routine tasks”.

Communicating compensation increment outcomes is an area where we are heavily dependent on human interaction and emotional support. As organizations communicate compensation increments, the process eats away the bandwidth of the managers, specifically in an organization with many employees. Incorporating emotional intelligence and the typical corporate guidelines around communication into the AI model and training the model to handle and deliver personalized conversations with the team rather than the manager sitting in person in real-time can save a lot of managers significant time. 

A prominent example of voice recognition in our daily lives is the use of voice search on our phones, or Alexa and Siri.

Preventing Biases in Employee Recognition and Rewards Distribution: How to Achieve it?

When AI models are trained alone on past data, mistakes are inevitable. Organizations or individuals implementing an AI model must establish critical governance mechanisms or principles to achieve the desired outcomes.

AI bias is a growing concern. Fairness and inclusivity must remain central to R and R program designs. Here are some considerations when deploying AI in recognition platforms to avoid bias.

Simple biases like recency or affinity bias can creep in if the AI model thinks one favorable decision fits all, which would be incorrect. The importance of human intervention, also talked about in our previous blog, is keeping humans in loop for consistently controlling and monitoring and not letting the AI act on its own. This process for monitoring is called ‘Observable AI,’ where the human is continuously gauging AI’s actions, decisions, flows, and paths to prevent it from repeating a behavior because there can be different personas and different moods of a person, and the outcomes would then be biased or wrong. Human emotional intelligence plays an important role. 

A transparent and unbiased process and real-time feedback to address potential errors are essential. Users must understand why AI has generated or suggested particular advice. This organization must ensure that AI-driven decisions are backed by explanations to support those decisions. 

Timely reviewing the AI models is also important. As times change and requirements evolve, keeping the models adaptable and robust is critical to maintaining their effectiveness and functionality. “Being relevant all the time is something that applies to AI as well.”

Conclusion

In conclusion, the views and opinions expressed by the industry leaders illustrate the successful implementation of AI models in the RnR space. Leveraging various techniques, embedding emotional intelligence within the AI model, and practicing transparency to prevent biases ensures the recognition program remains meaningful and authentic. By embracing and harnessing the power of AI, companies can foster motivated and engaged employees aligned with the companies’ culture and goals.