Multidisciplinary Team Architecture in AI Centers of Excellence: Integrating Data Science, Business Strategy, and Governance Expertise
Main article
Abstract
As organizations accelerate artificial intelligence (AI) adoption, the AI Center of Excellence (CoE) has emerged as a pivotal organizational mechanism for centralizing expertise, standardizing practices, and scaling AI initiatives across enterprise functions. A defining characteristic of effective AI CoEs is their reliance on multidisciplinary teams comprising data scientists, machine learning engineers, business strategists, governance specialists, and domain experts. However, empirical evidence documenting the precise composition, integration patterns, and performance implications of such teams remains sparse. This study addresses this gap through a mixed-methods investigation combining a quantitative survey of 186 organizations across twelve industries with semi-structured interviews of 34 senior AI leaders. The findings reveal that higher levels of disciplinary diversity within AI CoEs are positively associated with project success rates (r = 0.68, p < 0.001) and stakeholder satisfaction (r = 0.71, p < 0.001). Organizations employing hub-and-spoke or hybrid operating models demonstrate superior cross-functional integration relative to centralized structures. Governance maturity, assessed through a five-level capability framework, emerges as the strongest single predictor of AI deployment scalability. The study further identifies four archetypal team configurations and maps their alignment with organizational AI maturity stages. These results substantiate the claim that the AI CoE is typically composed of multidisciplinary experts, including data scientists, engineers, business strategists, and governance specialists, who collectively guide the organization’s AI agenda, and offer practical frameworks for designing effective CoE team architectures.
