There's a line in EY's latest US AI Pulse Survey that captures the central problem of the agentic AI moment better than any statistic. "Agentic coding is like having 500 junior coders at your disposal," said Traci Gusher, EY Americas AI and Data Leader, "and leaders must come to terms with the capabilities and the drawbacks of this abundance when their minds are primed for scarcity." Then she names the catch: the cost of pursuing a good idea has fallen dramatically, but so has the cost of a bad idea.
That's the bind finance leaders are walking into. AI can now produce work at a volume and speed that would have been unthinkable a year ago. But volume isn't value, and speed isn't accuracy. Five hundred junior coders producing output all day is only an asset if someone qualified is checking that output, catching the bad ideas before they ship, and taking responsibility for what's kept. The abundance AI creates is real. So is the review burden it generates, and that burden lands on people.
The survey data shows how quickly this abundance is arriving and how unprepared many organizations are to govern it. Among senior leaders whose organizations are investing in AI, 92% say they face barriers to developing in-house, AI-built software. The barriers aren't trivial: 31% cite concerns about the accuracy or reliability of AI-built work, 31% cite a lack of internal talent or skills to do it properly, and 34% cite the risk of shadow IT, where AI-built tools slip outside any oversight and become unmanaged.
Put those numbers next to the "500 junior coders" framing, and the picture sharpens. Organizations can now generate output faster than they can review it, and a meaningful share of them lack the people to close that gap. The cost of a bad idea hasn't just fallen; it's been multiplied across every AI-generated output that no qualified person has actually examined. In finance, a bad idea that ships unreviewed isn't a minor embarrassment. A flawed model, a mistaken treatment, an unvalidated forecast can drive a real and expensive decision.
This is why the review layer matters more, not less, as AI capability grows. Someone has to be the check on the 500 junior coders, and in finance that someone needs genuine expertise. Reviewing AI-generated financial work isn't a matter of skimming for obvious errors. It requires a US GAAP-certified accountant who can tell whether a treatment is defensible, trace the logic of an AI-assisted model, and distinguish a good idea from a plausible-looking bad one. Without that layer, abundance is just risk produced at scale.
Consider what "checking the work" actually demands in a finance function. It means validating AI-generated outputs against accounting standards, catching subtle errors that look plausible on the surface, and knowing when an output that seems reasonable is actually wrong. That's not clerical review; it's professional judgment applied by someone with real technical grounding.
The EY data underscores how scarce that grounding is right now. When 31% of leaders say they lack the internal talent to build and manage AI work safely, they're describing exactly this shortage: not enough qualified people to serve as the check on AI's abundance. A US GAAP-certified accountant who can review AI-assisted work with authority is precisely the profile in shortest supply and highest demand, because they're the mechanism that converts AI's raw output into work a company can actually trust and act on.
The practical problem is speed. AI produces output immediately, but hiring the qualified reviewer to check it traditionally takes months. That mismatch is the gap where risk accumulates. Every week the review role sits unfilled is a week of AI output going out with no qualified person standing behind it, which is why the ability to hire an accountant fast has become a real operational concern rather than a nice-to-have.
If AI gives you 500 junior coders, the strategic priority is getting the qualified reviewer in place fast enough to supervise them. A conventional local search that takes two quarters is badly out of step with how quickly AI abundance arrives. Finance leaders who need to close that gap are widening where they look for the review talent.
Drawing from a pre-vetted global pool is the most direct way to do it. When candidates have already been screened for US GAAP proficiency, technical depth, and demonstrated judgment, you can hire an accountant fast because the vetting is done before they reach you. You get a US GAAP-certified accountant ready to be the check on your AI-generated work in days rather than months, closing the gap between AI's output and the oversight it requires. EY's survey makes the abundance vivid: 500 junior coders, ready to work, with no inherent sense of when they're wrong. The finance leaders who benefit from that abundance rather than getting burned by it will be the ones who staff the human check quickly, and the fastest way to do that is to hire an accountant fast from a pool of proven professionals.
It's a framing from EY's AI Pulse Survey by Traci Gusher, EY Americas AI and Data Leader. Agentic AI can produce work at enormous volume and speed, like having 500 junior coders available. But as she notes, the cost of a good idea has fallen and so has the cost of a bad idea, meaning that abundance requires qualified people to review the output.
Because volume isn't value and speed isn't accuracy. AI can generate more financial work than a team can review, and EY found 31% of leaders cite accuracy concerns and 31% lack the internal talent to manage AI work. In finance, an unreviewed flawed model or forecast can drive an expensive decision.
Reviewing AI-generated financial work means validating outputs against accounting standards, catching plausible-looking errors, and knowing when a reasonable-seeming output is wrong. That requires professional judgment and technical grounding. A US GAAP-certified accountant can tell whether a treatment is defensible in a way general review cannot.
AI produces output immediately, but traditional hiring takes months, leaving a gap where risk accumulates. Working with a pre-vetted global talent pool lets companies hire an accountant fast, since candidates are already screened for US GAAP proficiency and judgment before they're presented.
EY found 92% of investing organizations face barriers, including accuracy and reliability concerns (31%), lack of internal talent or skills (31%), and shadow IT risk (34%). Together, these point to a shortage of qualified people to review and govern AI's output, which is the real constraint.