Analogical reasoning is central to strategy because it offers a basis for decision making in uncertain and data sparse contexts. Its effectiveness as a process depends not only on retrieving candidate analogies but on correctly matching them to the focal problem because a poorly chosen analogy can mislead decision makers and produce costly errors of commission. We investigate how humans and large language models (LLMs) perform at analogical reasoning through an exploratory study that extends classic analogical transfer designs by introducing multiple source analogs and target problems. Our results reveal a tradeoff: Humans in our sample frequently overlooked valid analogies (low recall) but rarely misapplied them (high precision); LLMs, in contrast, did not miss valid analogies (high recall) but often surfaced spurious, even if internally coherent matches (low precision). These findings suggest a complementary division of labor: LLMs might serve as expansive retrieval engines, generating a broad set of candidate analogies, whereas humans adjudicate their contextual fit through superior causal matching. This highlights a possible pathway for artificial intelligence (AI)–human collaboration in strategy making while underscoring the risks of over-reliance on AI-generated analogies until these models can improve their performance at matching analogies to problems.