There are many suggestions in the animal cognition literature of similarities between the mental capacities of humans and other animals, for example in terms of planning, imitation and reasoning. In this line of research, the power of associative learning for producing flexible behaviour in non-human animals has been downplayed, or ignored. Interestingly, within artificial intelligence research associative learning has been shown capable of beating humans in chess. Thus associative learning can be capable of producing flexible behavior. One specific phenomenon in which associative learning often is ruled out as an explanation for animal behaviour is flexible planning. However, planning studies have been criticized and questions have been raised regarding both methodological validity and interpretations of results. Here, I explored what associative learning can do for planning. A previously published sequence learning model which combines Pavlovian and instrumental conditioning was used to simulate two planning studies, namely Mulcahy & Call 2006 ‘Apes save tools for future use.’ Science 312, 1038–1040 and Kabadayi & Osvath 2017 ‘Ravens parallel great apes in flexible planning for tool-use and bartering.’ Science 357, 202–204. Simulations show that behavior matching current definitions of flexible planning can emerge through associative learning. Through conditioned reinforcement, the learning model gives rise to planning behavior by learning that a behavior towards a current stimulus will produce high value food at a later stage; it can make decisions about future states not within current sensory scope. The simulations tracked key patterns both between and within studies. It is concluded that one cannot rule out that these studies of flexible planning in apes and corvids can be completely accounted for by associative learning. Future empirical studies of flexible planning in non-human animals can benefit from theoretical developments within artificial intelligence and animal learning.