Vectorization only works when you have a table stored in an optimized columnar format and compute an run a function over a column or to combine multiple columns.
The moment you throw in group bys or windows the data turns into rows that you read from a hash table or after a sort - at which point you lose all opportunities of vectorization.
Since group bys break vectorization, the other use case is for map or reduce (sums, counts) operations over the entire table. In absence of filters you can precompute these for each column.
Plain map or sum like operations in presence of a filter is the only real use case for vectorization in OLAP, if I'm not missing anything.
In that case you need to implement the vectorized operation to work across together with a mask, so that you don't include the filtered out values, and over compressed data, otherwise you're wasting time on bringing the data from disk closer to cpu.
Most general big data sql tasks will not gain significant improvement using vectorization, unless they specialize on map after filter, no group bys, operations, such as perhaps log processing.
Vectorization and other kinds of hardware acceleration is highly useful for small array data that fits into memory such as geo data, APL, numpy, tensors on TPU processing and similar stuff.