Analog compute-in-memory (ACiM) systems show promise for energy-efficient AI inference, but their performance is hindered by variations in conductance, resulting in reduced accuracy. This work investigates the impact of mean error, which represents the discrepancy between actual conductance values and their intended targets from the measured distribution of 256 kb analog resistive switching cells, on the accuracy of neural network models. We propose opposing mean error compensation (OMEC), a technique that mitigates these errors without necessitating alterations to the memory device. Through simulations, we illustrate that adjusting weight targets can lead to a remarkable improvement in the inference accuracy, elevating it from a mere 12.59% to an impressive 90.65%, without modifying the memory device.