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Current approaches to adaptive game AI typically require numerous trials to learn effective behavior (i.e., game adaptation is not rapid). In addition, game developers are concerned that applying adaptive game AI may result in uncontrollable and unpredictable behavior (i.e., game adaptation is not reliable). These characteristics hamper the incorporation of adaptive game AI in commercially available video games. In this paper, we discuss an alternative to these current approaches. Our alternative approach to adaptive game AI has as its goal adapting rapidly and reliably to game circumstances. Our approach can be classified in the area of case-based adaptive game AI. In the approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource-intensive learning) to evoke effective behavior in a controlled manner in online play. We performed experiments that test case-based adaptive game AI on three different maps in a commercial real-time strategy (RTS) game. From our results, we may conclude that case-based adaptive game AI provides a strong basis for effectively adapting game AI in video games.