Suprema of random processes appear naturally in a plethora of disciplines, and Talagrand's majorizing theorem yields a geometric interpretation for them: for a centered Gaussian random process $(X_t)_{t \in T},$ $\mathbb{E}[\sup_{t \in T}{X_t}]$ is comparable to the $γ_2$-functional of $T,$ a quantity that depends solely on the space $(T,d),$ where $d$ denotes the pseudometric $d(u,v)=\sqrt{\mathbb{E}[(X_u-X_v)^2]}.$ Despite the explicit definition of this functional, an infimum over admissible sequences, this tool tends to be used exclusively as a means to bound the expectation of the supremum of a random process by that of another. This work considers the $γ_2$-functional as a proxy for the quantity of interest by constructing admissible sequences that are close to being optimal, and aims to provide a promising avenue towards understanding expectations of suprema of Gaussian random processes.