Summary
Leading diverse human-robot or human-AI teams is slowly becoming the new normal. As automation and AI become integral to many industries, leaders must learn how to manage teams that include humans and AI/robots. Although there is plenty of training on how to use the technology, however, no one is teaching leaders how to lead the technology, thus creating skills gaps and workforce adaptability issues. As organisations face digital disruption, leaders must integrate these digital innovations into their operations and inspire their teams to become ‘technologically savvy’. However, despite the pressing need to better understand how to cultivate cohesive team dynamics amidst human and AI entities, research in this field is limited, which is problematic as leaders lack evidence-based solutions to deal with these novel challenges. Therefore, this project aims to unlock the potential of human-AI collaboration by: 1) uncovering the essential leadership qualities needed to effectively lead human-AI teams; 2) optimizing the design of virtual teams, where humans and AI work symbiotically; 3) pioneering the use of the newest AI for accurately assessing individual traits through natural language analysis; 4) investigating how feedback acceptance varies when it comes from human or AI sources. This research integrates the scientific fields of leadership, organisational psychology and IT, which pushes the boundaries of traditional research domains. Moreover, it is built on the newest technological trends and employs cutting-edge deep neural network technologies offering a state-of-the-art exploration of the subject. All in all, by achieving these objectives, this project will reshape the future of work, empowering organizations to harness the full potential of human/AI teams, enhance leadership capabilities, and optimize team dynamics in this ‘brave new world’ of workplace digitalisation.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101154870 |
| Start date: | 01-10-2024 |
| End date: | 30-09-2026 |
| Total budget - Public funding: | - 176 773,00 Euro |
Cordis data
Original description
Leading diverse human-robot or human-AI teams is slowly becoming the new normal. As automation and AI become integral to many industries, leaders must learn how to manage teams that include humans and AI/robots. Although there is plenty of training on how to use the technology, however, no one is teaching leaders how to lead the technology, thus creating skills gaps and workforce adaptability issues. As organisations face digital disruption, leaders must integrate these digital innovations into their operations and inspire their teams to become ‘technologically savvy’. However, despite the pressing need to better understand how to cultivate cohesive team dynamics amidst human and AI entities, research in this field is limited, which is problematic as leaders lack evidence-based solutions to deal with these novel challenges. Therefore, this project aims to unlock the potential of human-AI collaboration by: 1) uncovering the essential leadership qualities needed to effectively lead human-AI teams; 2) optimizing the design of virtual teams, where humans and AI work symbiotically; 3) pioneering the use of the newest AI for accurately assessing individual traits through natural language analysis; 4) investigating how feedback acceptance varies when it comes from human or AI sources. This research integrates the scientific fields of leadership, organisational psychology and IT, which pushes the boundaries of traditional research domains. Moreover, it is built on the newest technological trends and employs cutting-edge deep neural network technologies offering a state-of-the-art exploration of the subject. All in all, by achieving these objectives, this project will reshape the future of work, empowering organizations to harness the full potential of human/AI teams, enhance leadership capabilities, and optimize team dynamics in this ‘brave new world’ of workplace digitalisation.Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
29-09-2024
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