Feedstock physical properties determine not only downstream flow behavior, but also downstream process yields. Enzymatic treatment of pretreated feedstocks is greatly dependent on upstream feedstock physical properties and choice of pre-processing Technologies. Currently available enzyme assays have been developed to study biomass slurries at low concentrations of ≤1% w/w. At commercially relevant biomass concentrations of ≥15% w/w, pretreated feedstocks have sludge-like properties, where low free water restricts movement of unattached enzymes. This work is an account of the various steps taken to develop a method that helps identify the time needed for solid-like biomass slurries transition into liquid-like states during enzymatic hydrolysis. A pre-processing technology that enables feedstocks in achieving this transition sooner will greatly benefit enzyme kinetics and thereby overall process economics. Through this in situ rheological properties determining method, we compared a model feedstock, Avicel ® PH101 cellulose, with acid pretreated corn stover. Novozymes Cellic ® CTec2 (80 mg protein/g glucan) can reduce 25% (w/w) Avicel from solid-like to liquid-like state in 5.5 h, as the phase angles rise beyond 45 • at this time. The same slurry needed 5.3 h to achieve liquid-like state with Megazyme endoglucanase (40 mg protein/g glucan). After 10.8 h, CTec2 slurry reached a phase angle of 89 • or complete liquid-like state but Megazyme slurry peaked only to 64.7 • , possibly due to inhibition by cello-oligomers. Acid pretreated corn stover at 30% (w/w) with a CTec2 protein loading of 80 mg/g glucan exhibited a solid-like to liquid-like transition at 37.8 h, which reflects the combined inhibition of low water activity and presence of lignin. The acid pretreated slurry also never achieved complete liquid-like state due to the presence of biomass residue. This method is applicable in several scenarios comparing varying combinations of pre-processing technologies, feedstock types, pretreatment chemistries, and enzymes. Using this method, we can generate a process chain with optimal flow behavior at commercially-relevant conditions.