Artificial intelligence (AI) has become an integral part of the modern work environment. Leveraging AI in the world of work has reformed how organizations work and deliver results. With the AI and HR bond taking shape, we today discuss the role of AI in employee Rewards and Recognition. Prasad Poosarla, the Chief Ttechnology Officer at Bi Worldwide and Monica Singh APAC Total Rewards Lead at Global Logic, delve into some interesting insights on AI assisted RnR in the latest webinar “name”.
This blog presents some excerpts from the webinar and highlight how AI is transforming the rewards and recognition landscape by harmonizing cutting edge technology with irreplaceable human touch, fostering truly meaningful experiences. We will also understand the guardrails that organizations must include against the problems that crop up with AI.
Embracing AI
AI is here to stay. As prasad rightly said, people are now using AI very frequently in their workspace and hold a positive perception regarding the possibilities that AI holds for improving performance and spurring innovation. As employees are getting used to AI there is an opportunity to introduce AI as tool to improve recognition solutions preserving its authenticity.
He rightly pointed, “keeping the human element is pivotal as we are not working towards an automated recognition experience. Recognition is a deeply personal connection that benefits not only the receiver, but also the giver”.
He also highlighted a few elements that must be considered for AI assisted recognition.
Recognition must be timely:
AI can be used to alert managers and potential employees when someone could be recognized. However, it’s the human who takes the final decision and decides whether it’s really time or is it too early that the recognition is coming.
Recognition must be as specific as possible:
Sometimes manager and employees need to be details oriented when writing recognition.AI can help by alerting to be more specific about what they recognize. People can take the prompt and use their creativity to create a more detailed message.
Making Recognition personalized:
Personal recognition is the one which is taken very well by an individual and AI can help managers by informing them how their employees like to be recognized. Not necessarily telling what is to be done and using their emotional intelligence to create recognition that is more personalized and meaningful.
Recognition must be comparable to the effort:
If an award feels small in comparison to the effort put forth, employees don't feel much value. AI may suggest appropriate awards based on the type of achievement in size of the goal. But often this is subjective the manager must ultimately determine what recognition, and reward should feel meaningful to the recipient
The AI Guardrails
The AI roadmap is extremely exciting, with many developments to look forward to. But at the same time, we need to understand various guardrails- professional and ethical, that organizations need. While there is magic that AI can create, problems like biases, wrong timing, hallucination etc. can also crop up, it's important to include necessary guardrails.
Principles and recommendations for organizations
Prasad recommended 3 principles which can be used as a guardrail by organizations when using AI assisted recognitions:
AI twining Approach
There is no AI that is out by itself. There should always be a human whose manning and owning n AI in any workflow, in any advisor, any solution. AI twining approach- it is like AI twin that needs to be a human to an AI.
Explainable AI
It’s important for a human to oversee the entire life cycle of AI operations. Identifying and monitoring the decisions given by AI and ensuring they are made correctly and eliminating any biases by automatically monitoring them from backend. Complete elimination of biases is not feasible, but speed and efficiency in acknowledging them reflects the success of AI program.
Feedback Mechanism or Attentive AI
Attentive AI is a design principle where you take continuous feedback from the humans. The participants who are recipients of the decisions of AI can respond back to the AI model whether they like it don't like it, what issues they have, and keep using that to keep fine tuning, the decision making of the model.
So, above are the 3 simple tenet that can be used continuously to eliminate biases as much as possible from AI supported programs.
Key Design Principles for AI driven RNR
Recognition works best when it reflects organizations unique culture. Yet many AI tools risk applying one size fits all solutions. So, what design principles must organizations prioritize when building or choosing AI led RNR platforms to ensure alignment with their culture and goals?:
Data is Key
Ai model operates on vast set of data. Availability of sufficient, robust and unbiased data is the fundamental of designing and AI model
Validation and Work Policy
AI models can be developed in house or can be bought from hyperscale’s in the market. One can leverage real time validation with internal organizational policies, but with outside model, validations must be considered in detail because what works for one organization may not suit the requirements of other organizations. Hence organizations must ensure that all policies validate against the model (built or bought) before it is implemented.
Model Relevance
It is essential to assess the relevance of the existing AI model when launching a new reward or a recognition program. Determine if the existing model will be relevant for the new program. If not, reevaluate and potentially revise the existing model to align with the new program objectives.
Reinforcement Learning
Real time feedback is very important to improve the AI Model. Reinforcement learning helps the AI model to learn, improve and evolve at a fast-paced basis the corrective feedbacks provided.
Data Security
Last but not the least, is data security. Data security is very crucial for any organization., organizations handle sensitive PII (Personal Identifiable Information. When designing or using external AI models organizations must do the due diligence that data security is not compromised, and sensitive information does not leave the internal network.
Conclusion
To wrap it up, AI is rapidly revolutionizing work, worker and workplace. AI driven employee rewards and recognition program is witness to the integration of technology and humans. The balance between automation and personal connections drives engagement and motivation. With experts recommended guardrails and design principles, organizations can create meaningful personalized outcome and evolve recognition practices that evolve and remain relevant with changing times.
Also Read
Blending AI Techniques with Emotional Intelligence for Meaningful Employee RNR
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