Deloitte's accounting blog takes a balanced look at generative AI in the finance function: it can accelerate first drafts, summarize complex material, automate first reviews and flag risks, but it also introduces accuracy and control questions that need to be assessed deliberately rather than discovered in production.
That matches what we see in bookkeeping automation generally. Machine learning is genuinely good at the repetitive middle: reading invoices, proposing ledger codes, matching payments. Used carelessly, it automates mistakes at scale; a model trained on three years of miscoded costs will continue the tradition faster than any bookkeeper could. Our approach is to combine third party accounting software with our own tooling and keep judgment in the loop: clear coding rules as the baseline, machine suggestions where the rules run out, and human review concentrated on exceptions. The measure of success is not the share of invoices booked untouched, but whether month-end reporting gets faster and more reliable at the same time.