This paper briefly summarizes some valuable properties of ART architectures that are advantageous in engineering applications, and outlines some areas of likely future progress, together with their motivations. Some of ART's advantages, such as its stability, biological plausibility, and responsiveness to the stability-plasticity dilemma, are well-described in the literature. This paper's focus will be on the advantages of scalability, speed, configurability, potential for parallelization, and ability to interpret the results. A valuable new area of innovation will be the application of ART to more generalized data structures such as trees and grammars. Continued progress on distributed representations would be valuable because of increased data representation capability, both in terms of system capacity and template complexity. Another valuable area of progress would be removal of the dichotomy between match-based and error-based learning.