Sep 23, 2026 | Leadership
Turning Disclosure Review From Manual Matching Into AI-Assisted Confidence
As audit teams face growing pressure on quality and capacity, Thomson Reuters is bringing AI directly into one of the most time-consuming parts of the engagement, writes Corey Wells.
Ask any audit team where their time disappears, and disclosure review is almost always on the list. Auditors have to confirm that every required disclosure is present, complete, and accurate – cross-referencing lengthy financial statements against checklists and standards that shift by client, by industry, by year. In Guided Assurance alone, a fully completed disclosure checklist can run 1,200 to 1,500 individual requirements. It’s exacting work, and it’s easy to see how fatigue creeps in by question 200.
The stakes are real. A missed disclosure isn’t just an inconvenience, it can surface as a PCAOB inspection finding, a peer review deficiency, or a restatement. Firms know this, which is exactly why so many roll forward last year’s checklist rather than start fresh, or burn valuable senior and manager time doing it all over again. Neither option solves the underlying problem: this is manual, judgment-heavy work at a scale that doesn’t favor manual review.
Bringing AI into the checklist itself
At Thomson Reuters, today we’re launching Disclosure Review, a new AI capability available to all Guided Assurance customers with the Disclosure module, at no additional cost.
Disclosure Review matches disclosure requirements against what’s actually present in the financial statements, marking each as identified or not found, and pairing every recommendation with plain-language rationale and citations back to the source. That last part matters as much as the matching itself. Disclosure requirements are often written in dense, technical standards language. When junior staff can see why something was marked a certain way, in accessible terms, they build real understanding of the requirement, not just trust in the output.
Where the AI and the auditor’s own judgment diverge, the discrepancy is flagged automatically. This means teams can focus their attention exactly where it’s needed instead of re-checking everything from scratch.
We’ve spent months validating this in an extended customer beta, and the results have been encouraging. Roughly 30% of eligible Disclosure module customers are already using it, generating close to 300 disclosure review analyses a month across mid-sized and large firms alike.
Meeting the market, with room to grow
Future capabilities will extend further into the harder judgment calls, like “no” and “not applicable” determinations, where auditor expertise matters most.
Reviewing disclosure requirements will likely never be anyone’s favorite part of the engagement. But it doesn’t have to be the part that consumes the most time or carries the most risk. That’s the shift we’re focused on – freeing up capacity so audit teams can spend more of their time on judgment and insight, not repetitive matching.
