We develop a scaling-up investment strategy for stochastic investments. The investment strategy of a firm depends in part on the assets it controls that pertain to different investment opportunities. In the presence of uncertainty about the true value of the firm's assets, interest lies in experimenting to learn about those assets. This learning enables the firm to choose the investments that best utilize its assets. The firm can learn about the value of its assets by deploying those assets in an investment and observing the noisy returns from the investment. Quantifying the uncertainty regarding the return of its assets and using Bayesian inference allows the firm to update its belief about the true value of its assets; hence, providing guidance for the firm for its current and future investments. The scale-up policy is obtained through Bayesian updating and sequential decision-making.