Build high-performance teams with AI/ML Models

by Dr. Bikramjit Chaudhari, on Jan 30, 2023 5:27:57 PM

Estimated reading time: 2 mins

Key takeaways from the blog 

  • Use AI/ML models to assess the finer points related to hiring, attrition, and engagement.
  • Connect the similarities between these three factors to build accurate predictions.
  • Leverage the integrated framework across different scenarios to drive high performance. 

Build high-performance teams with AIML Models

The Human Resources (HR) department’s primary task revolves around hiring the best fits for their business requirements to create an environment of a truly engaged, high-performance team. However, the factor of attrition makes it a distant dream. Big Data Analytics and AI/ML models enable the HR department to realize this dream and make headways toward achieving the desired target. AI/ML helps to create data-driven interventions to build and enrich the human capital. 

How does AI/ML-powered integrated approach enables building high-performance teams?

Most HR personnel focus on competency scores while hiring candidates. However, high competency scores don’t always translate into employee engagement. Hence, hiring candidates that have high competency, lie in the low attrition risk group, and are highly motivated in their current first would be the ideal candidates for hire. These parameters are data-driven; hence gathering the right data points or building them up from scratch by using interventions, in case of gaps, is essential. 

Some of these points are gathered right at the time of hiring and some are gathered through periodic employee engagement surveys. The HR best practice is to find individual data points of candidates related to the key selection factors. These factors include the Right competency hire, Low attrition risk potential, and High engagement/motivation of candidates and then associate those data points with each other to derive a holistic score. AI/ML-powered Predictive Analytical models enable the HR to find the right subset, or optimal hires, out of these three overlapping universes in a fraction of the time required for the manual activity. 

Predictive Analytical models use different methods, such as Random Forest, KNN, and Linear Regression to build a holistic framework that cuts through all the data points related to the three different universes. The “intersection” arrived at between the three overlapping universes are the ideal candidates for hire. They are the right investment for building a high-performance team. It is important to note that the best parameters in either of the three universes don’t result in the perfect hires. It is always the  “intersection” of the three universes that produces the optimal results for hiring. This model offers results on an ongoing basis and can be leveraged across all domains.   

Data-driven interventions to enrich human capital_2

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The AI/ML-powered integrated approach is important for deriving insights and building engagement programs for employees. Maximizing the engagement scores not only controls attrition but also drives the proficiency of a high-performance team. In effect, the combination of data-driven hiring and engagement programs produces the optimal results to build high-performance teams. 


Simply put

AI/ML powered integrated approach enables HR teams to hire the best fits for job positions by calculating different data points. These data points broadly fall into three categories – right competency, low attrition risk potential, and high motivation/engagement in their profiles. The intersection of these three datasets offers the right candidates for building high-performance teams.

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Topics:Artificial Intelligence / Machine LearningDigital

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