Auditoria.AI is wagering that corporate finance is on the cusp of a “Golden Age of Transformation,” and the company’s new 2025 State of AI Automation in the Finance Office Report is an effort to outline both the tailwinds behind that thesis and the clutter that remains a drag on teams’ productivity. The sixth annual survey, which is based on responses from over 250 finance and accounting professionals, found that generative AI has eclipsed previous machine learning and rules-based automation to become the biggest influencing technology trend in finance this year. Approximately one-quarter (25.9 percent) of respondents placed generative AI at the top of their list, while autonomous agents, which Auditoria characterizes as digital teammates that can act on behalf of staff in finance, entered the study as the third most impactful trend at 16.5 percent.
Traditional RPA, which for years was the poster child of back–office automation, fell to 5.6 percent. The report’s adoption numbers suggest finance teams are making progress past the experiment phase, but not nearly quickly enough to get rid of the largest operational bottleneck. Basic automation usage is at a record 47.5 percent, but less than one in ten respondents say they have moved past early–stage use. Auditoria contends that gap is why manual work remains rooted in core operations. Accounts Receivable has taken over from Accounts Payable as the most manual area, at 21.7 percent of respondents, while Financial Reporting and Analysis climbed to 19.3 percent, more than double its level of two years ago. The biggest hurdle remains depressingly consistent: extracting data from documents is again the number one challenge, at 19.5 percent, highlighting how much unstructured invoices, remittance advice, and emailed attachments continue to set the tempo of teams’ work.
Email overload and coordination costs are now the other major offenders. Shared inbox traffic shows no signs of abating, with 72.2 percent of finance teams receiving between 100 and 1,000 emails per week. Nearly half of teams (48.2 percent) said they get 100 to 500 emails weekly, while another 29.3 percent said inboxes were closer to 500 to 1,000. That number seems to be reshaping where time is spent. Respondents say the biggest time sucks in their day are no longer task execution but coordination, with data gathering (20.9 percent) and stakeholder communication (20.3 percent) together accounting for more than 40 percent of daily workload. The most frequently cited daily pain points cluster around responsiveness, inaccurate or bad data, and sheer email volume, with approval delays also starting to trend upward as a recurrent slowdown.
Auditoria CEO Rohit Gupta frames the results as evidence that AI is shifting from hype to material impact, but he also stresses that the technology’s returns are predicated on clearing certain bottlenecks. In his analysis, generative AI and autonomous agents are both on course to become copilots for more complex decisions and autopilots for routine workflows, yet finance teams are still investing too much time in data hunting and cleaning up messy inputs. The survey reflects that friction: 72.1 percent of finance professionals say they are satisfied with their work, including 35.4 percent who say they are very satisfied, but 21.3 percent also acknowledge that they spend too much time looking for data. The take from Auditoria’s vantage point is that finance roles are incrementally moving from transaction processing toward more AI augmented advisory work, while the operational stack underneath them is still playing catch-up.
Auditoria, which offers agentic AI tools that target AP, AR, GL, and FP&A, is using the report to double down on its pitch that automation is no longer table stakes and that the next phase is autonomous, goal directed systems that reduce inbox triage, speed up collections, and surfacing real time insights. Whether the broader market moves on that trajectory quickly will depend on an enterprise truth that always holds for finance: transformation efforts are less about models and more about integration, data quality, and the tedious work of turning documents and email into clean, trustworthy inputs. Auditoria’s report argues that the teams that address those fundamentals first will be the ones who actually unlock the speed and strategic upside promised by today’s wave of AI.
