Date of Award


Document Type


Degree Name

Doctor of Business Administration (DBA)


Bisk College of Business

First Advisor

Abram L. J. Walton

Second Advisor

Charles E. Bryant

Third Advisor

Jignya M. Patel

Fourth Advisor

Thomas C. Eskridge


Artificial intelligence (AI) can impact future workforce business operations in extraordinary ways through human-machine teaming. A human-machine teaming revolution will unleash enormous change upon businesses by merging humans and AI. For years, scholars and mainstream thought leaders have argued that firms must embrace AI and human-machine teaming to advance employee performance and deliver a high-performance, cost-effective, comprehensive business strategy (Raisamo, Rakkolainen, Majaranta, Salminen, Rantala, & Farooq, 2019). The era of human inadequacy, human-only teams, and human performance ceilings is disappearing as AI rapidly augments work and teams (Ashley & Sahota, 2019). AI augmentation and human-machine teaming will drive tomorrow’s blended teams and organizations. AI technologies will augment human skills and senses (Ashley & Sahota, 2019; Raisamo et al., 2019). Peter Drucker, the renowned management consultant, and educator, memorably said, what is measured, improves (Drucker, 2002). The ability of AI to quantify decisions and actions with big data analytics and improve a firm’s outcome and ability to maximize success in the future (Duan, Edwards, & Dwivedi, 2019) is essential to firms. This research defines how high-functioning virtual team members (HFVTMs) (Hill, Demirjian, & Walton, 2023), augmented with AI, can become superteams. This study uses an exploratory sequential mixed methods analysis to define and establish HFVTMs as a proxy for superteams to determine how to augment low-performing, moderate-performing, and high-functioning virtual team members with AI at the fundamental job task level to increase ROI. Finally, this study establishes a process and framework to examine jobs to elucidate which job tasks require AI.


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