We study how automated investment rules affect saving behavior and investment outcomes using detailed data from a FinTech app designed to help retail investors access mutual funds. Users choose how to design these rules, which vary along dimensions such as frequency, amount, and triggering conditions. Using a randomized encouragement design, we show that automated rules causally increase average savings without crowding out manual contributions. We also show that automated rules reduce trend-chasing behavior: while manual deposits respond strongly to recent returns, automated ones do not, narrowing the gap between fund returns and realized investor returns. However, rule timing remains performance-sensitive—users tend to activate rules after periods of strong returns and suspend them during downturns, especially for equity funds. A survey deployed on the app user population reveals that adopters of automated investment rules are primarily motivated by a desire to avoid procrastination, reduce cognitive load, and simplify decision-making, while non-adopters cite preferences for flexibility and concerns about income volatility. Our findings highlight both the promises and the limitations of automation in improving individual financial outcomes.