Artificial intelligence (AI) has become integral to 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 Technology Officer at Bi Worldwide, and Monica Singh, the APAC Total Rewards Lead at Global Logic, delve into some interesting insights on AI-assisted R&R in our latest webinar, "The Role of AI in Employee Rewards and Recognition," in collaboration by Bi Worldwide.
This blog presents excerpts from the webinar and highlights how AI is transforming the rewards and recognition landscape by harmonizing cutting-edge technology. 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 have a positive perception regarding its possibilities for improving performance and spurring innovation. As employees get used to AI, it can be introduced as a tool to improve recognition solutions while preserving their authenticity.
He rightly pointed out, “Keeping the human element is pivotal, as we are not working towards an automated recognition experience. Recognition is a deeply personal connection that benefits both the receiver and the giver.”
He also highlighted a few elements that must be considered for AI-assisted recognition.
Recognition must be timely.
AI can alert managers and potential employees when someone can be recognized. However, it’s the human who makes the final decision and decides whether it’s really time or too early.
Recognition must be as specific as possible.
Sometimes, managers and employees need to be detail-oriented when writing recognition. This can help by alerting them to be more specific about what they recognize. People can take the prompt and use creativity to create a more detailed message.
Making recognition personalized.
Personal recognition is one that individuals take very well, and AI can help managers by informing them how their employees like to be recognized. This does not necessarily mean telling them what to do; instead, it means using their emotional intelligence to create recognition that is more personalized and meaningful.
Recognition must be comparable to the effort.
Employees don't feel much value if an award feels small compared to the effort put forth. AI may suggest appropriate awards based on the goal's type of achievement and size. 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. At the same time, we must understand the various professional and ethical safeguards organizations need. While there is the magic that AI can create, problems like biases, wrong timing, hallucinations, etc., can also crop up. It's important to include necessary guardrails.
Principles and recommendations for organizations
Prasad recommended 3 principles that can be used as a guardrail by organizations when using AI-assisted recognitions:
AI twining Approach
No AI is out by itself. A human should always be who manages and owns AI in any workflow, advisor, or solution. AI twining is an approach that is like an 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. This includes identifying and monitoring the decisions made by AI, ensuring they are made correctly, and eliminating any biases by automatically monitoring them from the backend. The complete elimination of biases is not feasible, but the speed and efficiency of acknowledging them reflect the success of AI programs.
Feedback Mechanism or Attentive AI
Attentive AI is a design principle that requires continuous feedback from 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 tenets 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 an organization's 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
The AI model operates on a vast set of data. The availability of sufficient, robust, and unbiased data is fundamental to designing an AI model
Validation and Work Policy
AI models can be developed in-house or bought from hyperscales in the market. One can leverage real-time validation with internal organizational policies, but with an 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
When launching a new reward or recognition program, it is essential to assess the relevance of the existing AI model. Determine if the existing model will be relevant to 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 improving the AI model. Reinforcement learning helps the AI model to learn, improve, and evolve at a fast-paced basis with the corrective feedback provided.
Data Security
Last but not least is data security. Data security is crucial for any organization, especially one that handles sensitive PII (Personal Identifiable information). When designing or using external AI models, organizations must do their due diligence to ensure 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, workers, and the workplace. AI-driven employee rewards and recognition programs are a 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 outcomes and develop recognition practices that will remain relevant in changing times.
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