I do understand that you are working with is an isolate and thus what occurs in metagenomics you feel is over doing it, particularly if 16S is e.g. >99% similarity via Blast.
Before reading further you might check my answer history to understand the angle I am approaching this question from. I would see select given methods as the 'gold-standard' other methods are compared against, in addition to single versus multi-gene trees. There is good theoretical criteria for believing this. For example, bacteria are often subject to heavy %GC bias and this makes other methods error prone, because it introduces compositional bias (below).
Summary
Blast alone - thats a 'no' (theory section). The problem with any one approach is that errors can arise from many different sources stemming from both biology and methodology. Thus a combination of approaches is required. Therefore your maximum approach is a minimum standard.
I personally would go one step further than your question: full maximum likelihood and Bayesian phylogenetics with bootstraps in addition to Blast (minor), ANI and GGDC.
History
From tradition 16S is the major standard, but even then multi-gene phylogenies were required. Thus genome to genome distance calculation (GGDC) with ANI is essential at a minimum. 16S is back in fashion because its easy to perform a metagenomics study of species population dynamics.
Species delineation has always been problematic in bacteria and the rule of thumb was 5% divergence defined a species boundary.
Advice
I would use a multi-locus appproach under a full phylogenetics analysis, where a species boundary is defined by >5% divergence and bootstraps >80% with Bayes P values > 0.95.
Recombination is essentially ignored in taxonomy providing there isn't over reliance on a single gene tree, but using BLAST, ANI, GGDC as a method data-mining would justify a select sample size. The classification is then nailed with a phylogenetics analysis.
One of your methods (probably ANI) might be based machine learning/deep learning (its not GGDC) and the accuracy of misassigning a species under this method is high. Historically it was really high (80% accuracy), this might have improved to 90%. Its not like image recognition where the error rate is beyond human perception.
Theory A fundamental issue is that recombination in bacteria exists on a spectrum from clonality to panmixia. This makes single gene taxonomy risky, thus at theoretical
level would not believe 16S was a singularly criterion, but it never was. Your bacteria is unknown (to us) so we'd have to assume some level of recombination.
Compositional bias is well known for taxonomic misassignment. It is readily demonstrated in simulation. Thus if two unrelated bacteria demonstrate e.g. 75% GC, they are more likely to artifactually grouped together on e.g. a distance criterion.
