<p>Despite significant advances in our understanding of human hearing and assistive hearing technologies, the benefits of hearing loss compensation continue to vary widely across individuals. A central challenge is the complexity and heterogeneity of hearing loss, whose perceptual consequences often extend far beyond reduced sensitivity. Conventional strategies rely on signal processing algorithms—such as spatial filtering, noise reduction, and dynamic range compression—to improve audibility and enhance target signals. These components are usually optimized in isolation, yet their combined effects may interfere with one another and therefore do not necessarily yield an overall benefit. More recently, machine learning techniques have been used to further improve the performance of individual components. To tailor compensation strategies to specific acoustic environments or listeners, various steering mechanisms have also been proposed, guided by acoustic cues, audiovisual input, or listener attention. While these approaches show promise, a consistent and objective computational target for optimization has yet to be established. As an alternative, auditory model-based strategies, increasingly combined with machine learning, have emerged. These approaches aim to minimize the discrepancy between simulated auditory representations of normal and impaired hearing, thereby providing a physiologically motivated optimization goal. Although both categories of strategies offer considerable potential, achieving effective compensation under real-time, real-world conditions remains a major challenge. This paper reviews opportunities and limitations of these approaches for individualized hearing aid compensation.</p>