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…Global Logic-

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

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

3. Knowledge management system-“Dr.Koogle”, helping 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- Collaborative filtering and Clustering algorithms

Clustering algorithms and collaborative filtering are techniques that organizations use enhance personalization while ensuring fairness in a RnR AI driven Programs. So what do these terms mean. Lets understand them in simple terms.

Collaborative Filtering: is a recommendation technique used in real time recommender engine within the AI model. It filters recommendations or predicts preferences basis the likes and dislikes of similar individuals. If the AI has to take 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 recognitions from other people, the AI can notice patterns and nudge the managers to acknowledge that consistent performer or based on past preferences and likings of similar employees AI can recommend the rewards. If a group of employees appreciated receiving gift cards, the system might recommend similar rewards to employees with corresponding behaviours.

Another general example to understands this better are the recommendation one gets on OTT platforms. the recommender engine predicts the preference basis past user behaviour or behaviour of similar users.

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

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

A similar example in general can be a organizing or clustering bunch of online documents into different folders. Like, Academic records or Employment records. Each folder contains documents that are similar to each other than to those in other folder.

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 like voice recognition, gesture recognition, facial expression recognition with technology and gauge it via AI model can help address many aspects, especially around communications.

Monica from BI world sheds light on this point that makes it easier to comprehend the integration of emotions and technology.

“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 humans interaction and emotional support. As organizations communicate compensation increments, the process eats aways the bandwidth of the managers, specifically in organization with many employees. Incorporating emotional intelligence and the typical corporate guidelines around the communication into the AI model  and train the model to handle and deliver personalized conversation with the team, rather than the manager sitting in person in real time, can save a lot of managers significant time. 

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

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

Decision made by managers are not always logical. Basis a survey (BI World), 77% of managerial decisions are based on emotions rather than logic. In the world of AI, which is data driven and data dependant, it is important to understand, 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. Some considerations when deploying AI in recognition platforms to avoid bias.

Simple biases like recency bias or affinity bias can creep in if the AI model thinks one favourable decision is fit for all, which would be incorrect. The importance human intervention, also talked about in our previous blog, keeping human 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 the be biased or wrong. A human emotional intelligence plays an important role, 

A transparent and unbiased process and real time feedback to address potential errors is essential. Users must understand why AI has generated or suggested a particular advice. For this organization must ensure that AI driven decisions are backed by explanation 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 maintain their effectiveness and functionality. “Being relevant all the time is something that applies to AI as well”

Conclusion

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