"AI-powered audit software" as a blanket claim is not a checkable statement, because it could describe anything from a genuinely useful model that extracts fields from scanned invoices to a feature that was deterministic rule-based matching before the term became marketable and has had nothing actually changed except the label; the only way to evaluate the claim is to ask the vendor to name, feature by feature, which specific capabilities use a model and which don't, and then test the model-based ones for exactly what they were built to do rather than accepting that the whole product is uniformly 'AI.'
Why the phrase alone tells a buyer nothing
Every audit software vendor in the market today describes itself as AI-powered, including vendors whose underlying matching and computation logic hasn't changed in years and vendors who have genuinely rebuilt a feature around a model in the last year. The phrase has become a category label rather than a specific claim, which means it carries no information until it's broken down.
The real capabilities hiding under the label, and what each is
| What's claimed | What it usually means underneath | Question to ask |
|---|---|---|
| AI-powered reconciliation | Could be genuine fuzzy matching by a model, or could be a rule-based matcher relabelled | "Walk me through exactly how a specific match decision was made on this record" |
| AI document extraction | Usually genuinely model-based — pulling fields from invoices and receipts | "What happens when the model extracts a field wrong — is it flagged, and how is it corrected?" |
| AI risk assessment | Often a drafting aid that proposes language for a person to review, sometimes marketed as if it reaches a conclusion | "Does the system state a risk conclusion, or does it draft a suggestion the auditor accepts or rejects?" |
| AI-powered insights / anomaly detection | Varies widely — could be genuinely useful pattern flagging, could be a simple threshold rule relabelled | "What's the underlying method, and can you show a false positive it produced recently?" |
The questions that separate a real capability from a label
- 01Ask the vendor to name the specific feature and the specific model behind itA vendor that can't say whether it's using a general-purpose language model, a custom-trained model, or no model at all for a given feature likely hasn't thought through the claim either.
- 02Ask what the feature does when it's wrongA genuine model-based feature has a visible failure mode — it flags uncertainty, or produces an output a person is clearly meant to check. A relabelled rule-based feature has no new failure mode to describe at all, which is itself informative.
- 03Ask to see the feature on the firm's own messy data, not the demo dataA model-based extraction feature behaves very differently on a scanned, slightly skewed invoice than on a clean PDF — this is the fastest real test.
- 04Ask what changed in the product when this feature launchedA vendor that can point to an actual change in architecture or output quality has a substantive claim. A vendor that can only point to a marketing refresh does not.
A vendor who gets defensive or vague when asked to separate rule-based outputs from model-generated ones, specifically for the firm's own use cases, is giving a useful signal — whether or not the underlying feature is good, the claim wasn't built with this distinction in mind.
How we think about this
Lekhya's own use of the term is deliberately narrow: a feature is described as AI-powered only where a model is doing something a rule couldn't — extracting unstructured fields, drafting narrative for review — and every number the audit opinion depends on is computed by rule-based code, stated plainly rather than folded into a single marketing claim.
This is practical guidance written for audit teams, not an authoritative statement of any standard or statute. Standards, forms and timelines change. Check the current text issued by the ICAI and the relevant authority before relying on anything here in an audit file.
Related resources
- Technology, data & AI in auditRule-based matching vs. an AI-only reconciliation tool: what each is good atBoth get called 'smart matching' in a demo. One produces the same answer every time on the same data. The other doesn't, and that difference matters for an audit conclusion.
- Technology, data & AI in auditChoosing an audit automation vendor: a checklistThe questions that separate a vendor worth trusting with client data from one that merely demos well: security, what's computed versus generated, exit terms, pricing, and India-specific fit.
- Technology, data & AI in auditAI in audit: the risks and limitations auditors should knowHallucination on numeric tasks, where client data actually goes when it reaches a model, and why junior staff over-relying on fluent output is the risk firms underrate most.
- Technology, data & AI in auditEvaluating an audit software vendor's roadmap claims"It's coming in the next release" has been true and false in equal measure across this industry. Here's how to tell which one you're hearing.
Free tools, no signup
Each one reads a file entirely in your browser — or, for two of them, just answers a few questions — and nothing is ever uploaded.
- Purchase register checkDuplicate payments, round-sum amounts, weekend-dated invoices, incomplete records.
- GSTR-2B reconcilerMatches your purchase register against GSTR-2B — missing invoices either side, amount mismatches.
- Bank reconciliation checkerMatches your cash book against a bank statement — what's outstanding on each side.
- Fixed asset register checkDuplicate asset codes, missing location or useful life, negative net book value.
- Payroll red-flag scannerBank accounts shared across employees, and records missing an ID, PAN or salary.
- CARO applicability checkerAnswer a few questions about the company and see which CARO 2020 clauses likely apply.
- MSME payment delay checkerMSME-flagged invoices paid — or still unpaid — beyond the 45-day Section 22 limit.
- Related-party transaction screenerTransactions above a threshold you set with no approval reference, grouped by counterparty.
- Depreciation useful life checkerCompares a useful life in use against the Schedule II, Part C indicative figure.
- Materiality calculatorOverall and performance materiality against PBT, revenue, assets or equity, with reasoning shown.
- Sample size calculatorStatistical sample size for a test of controls or a substantive test of detail.