  Accompanying Part I, this sequel delineates a validation of the recently
proposed AI for Regularisation in radio-interferometric Imaging (AIRI)
algorithm on observations from the Australian Square Kilometre Array Pathfinder
(ASKAP). The monochromatic AIRI-ASKAP images showcased in this work are formed
using the same parallelised and automated imaging framework described in Part
I: ``uSARA validated on ASKAP data''. Using a Plug-and-Play approach, AIRI
differs from uSARA by substituting a trained denoising deep neural network
(DNN) for the proximal operator in the regularisation step of the
forward-backward algorithm during deconvolution. We build a trained shelf of
DNN denoisers which target the estimated image-dynamic-ranges of our selected
data. Furthermore, we quantify variations of AIRI reconstructions when
selecting the nearest DNN on the shelf versus using a universal DNN with the
highest dynamic range, opening the door to a more complete framework that not
only delivers image estimation but also quantifies epistemic model uncertainty.
We continue our comparative analysis of source structure, diffuse flux
measurements, and spectral index maps of selected target sources as imaged by
AIRI and the algorithms in Part I -- uSARA and WSClean. Overall we see an
improvement over uSARA and WSClean in the reconstruction of diffuse components
in AIRI images. The scientific potential delivered by AIRI is evident in
further imaging precision, more accurate spectral index maps, and a significant
acceleration in deconvolution time, whereby AIRI is four times faster than its
sub-iterative sparsity-based counterpart uSARA.
