| | NOVEMBER 2023MANAGEHRMAGAZINE.COM9learns from the patterns and relationships within the data to make predictions. These predictions are likely far more nuanced than our current approach, allowing for greater accuracy.With this capability in mind, my first thoughts go to talent. AI and machine learning solutions already exist that enable us to match candidates to job requirements. For example, some emerging assessment companies identify 50-60 traits that predict success in a given role and then apply a 15-minute assessment that provides stronger predictors of success than the traditional batteries of psychological tests. The generative AI solutions may take it further and allow us to consider other factors, such as customer feedback and financial performance, and apply this approach to all talent moves.Beyond talent, generative AIcould help to create more personalised employee experiences. For example, we could analyse what skills, traits, and knowledge lead to success in a given role. With this insight and knowledge about the candidate, we could automatically deploy onboarding and development material to target any gap areas they may have.Another area that could be turbo charged using generative AI is employee listening, i.e., measuring employees' experiences. It could open up how data is collected, moving beyond static surveys, integrating performance data and generating powerful insights. For instance, a chatbot could ask employees about their experiences in the moments that matter, deep diving where it makes sense, and then integrate other data sources, such as sentiment analysis, performance data, and employee behaviour data, such as turnover. This would allow us to predict likely outcomes for performance and turnover and implement interventions. Similarly, you could train the generative AI on the desired culture and share data on the current culture and the company strategy. The model would help to identify the gaps and potential interventions. Of course, operationalising this would require training and testing the model before it is cut loose and released to the enterprise. Whilst the potential of generative AI is exciting, it has its risks. Some of the answers provided by models such as Chat GPT and Bing Chat are incorrect. It would be best to weigh the risks and benefits in determining when a decision requires human verification.The role of HR would beto critically evaluate the outputs of models.The other risk that has received much attention is that these models may propagate systemic biases, given that they are trained using biased data. The creators of these models say that they are working to reduce biases through actions such as diversifying the training data and monitoring their responses. However, given the pervasive nature of these biases, they are challenging to eliminate. We would need to monitor and test for this.The role of HR will evolve as this technology is sharpened.It is a time for us to be open, curious, and experiment to see how we can amplify our capability in the AI age. It will allow us to play in more strategic spaces whilst leveraging our uniquely human skill set. Generative AI lets us look at a variety of textual data or written language, including transcribed verbal data. It learns from data patterns and relationships to make predictions by combining and analyzing different data
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