A decision aid only works if used within the decision-making process. As computational power and data storage become more accessible across industries, reliance on intelligent decision aids, those with embedded decision-making technologies, will become more necessary to address the increase in volume, velocity, and variety of data that is now available for use to make decisions. We analyzed over 4900 transactions provided by an organization utilizing an intelligent decision aid as part of their business processes. Using multilevel regression analysis, this dissertation evaluates whether or not differences in the complexity of embedded agents used within intelligent decision aids influence Decision Aid Reliance and whether Decision Aid Complexity moderates the relationships proposed in the Theory of Technology Dominance; Task Experience, Cognitive Fit or User Familiarity. We found that the complexity of a decision aid’s embedded agent is negatively associated with Decision Aid Reliance and negatively moderates the relationship between Decision Aid Complexity and Task Experience. Our results provide additional empirical evidence to support and extend Arnold & Sutton’s (1998) Theory of Technology Dominance.