  Foundation Models (FMs) have revolutionized many areas of computing,
including Automated Planning and Scheduling (APS). For example, a recent study
found them useful for planning problems: plan generation, language translation,
model construction, multi-agent planning, interactive planning, heuristics
optimization, tool integration, and brain-inspired planning. Besides APS, there
are many seemingly related tasks involving the generation of a series of
actions with varying guarantees of their executability to achieve intended
goals, which we collectively call planning-like (PL) tasks like business
processes, programs, workflows, and guidelines, where researchers have
considered using FMs. However, previous works have primarily focused on
pre-trained, off-the-shelf FMs and optionally fine-tuned them. This paper
discusses the need for a comprehensive FM for PL tasks from scratch and
explores its design considerations. We argue that such an FM will open new and
efficient avenues for PL problem-solving, just like LLMs are creating for APS.
