Davos- Switzerland: Misiek Piskorski, Professor of Digital Strategy, Analytics and Innovation and Dean of Asia and Oceania at IMD, focused during a session held in the UAE pavilion in Davos, Switzerland, on advanced talent management models.

The session, entitled “How to build the leaders for tomorrow” discussed talent management models using artificial intelligence AI that would help institutions in the public and private sectors improve the talent management process.

The session comes in light of the United Arab Emirates’ increasing interest in attracting and retaining talent in various fields and specializations in a way that supports the labor market and UAE national economy and raises the country’s competitiveness and as one of the leading countries in the region and the world in terms of talent competitiveness.

Piskorski stresses that attracting the right talents is a strategic challenge for companies, adding that talent identification requires strong models of why leaders engage in value adding behaviors across the domains of leading strategy, execution, stakeholders, people and self.

Piskorski noted that the best models trace these behaviors to three drivers: know-how, motivation, and situational judgment to engage in the right behaviors at the right time.

Behaviors are context dependent. Behavioral choices are driven by a complex mix of situation, environment, individual, and situational judgment. So, the demonstration of target behaviors in one context does not mean that the individual will make the same choices in a new role or organization that contains different processes, challenges, culture – and leaders.

People are becoming increasingly better at measuring how good leaders are across the three drivers of behaviors for the jobs that they are doing today. This allows us to identify their executive development opportunities for today.

However, talent management is all about predicting performance in the future in a role which the executive still does not have. Our current assessment tools still need substantial improvement to predict such future performance, and AI can help us with this.

A great deal of effort in talent management is, therefore, expended in identifying these elusive high-potential employees to manage their trajectory toward target leadership positions.

In the context of shifting role demands, it is key to develop robust talent pools to ensure the availability of the leadership talent the entity need in the medium to long term.

An inclusive approach that helps individuals own, harness, and amplify their talents to create value for the organization in new ways is not only a good talent strategy but also drives innovation and fuels dynamic capabilities that can support sustained competitive advantage.   

Using robust data from a range of valid assessment tools continues to be the gold standard and the pathway to optimizing prediction. In addition, technology-driven assessment tools such as game-based and AI-enabled adaptive assessment tools are providing a wealth of data that can provide new insights into the underlying drivers of performance. Advances in data science enable us to extract far more knowledge and understanding than previously possible.