For decades, an auditor tested a sample of transactions and extrapolated to the rest. Today, AI reads the entire set of accounting entries, flags anomalies and prepares the work before a human decides. EY has committed one billion dollars over four years to put AI at the heart of its 160,000 audit engagements, and its platform processes more than 1.4 trillion lines of journal entry data per year. For a Belgian company being audited, as for an audit firm or an internal audit function, the question is no longer whether AI is entering the audit, but how it changes evidence, accountability and compliance.
Traditional audit relies on sampling. For lack of time, the auditor selects a few hundred items out of millions, checks them, then concludes on the whole. That is a practical necessity, not an ideal: a rare deviation can slip through the net. AI and data-analytics tools change that logic because they process the entire population with no significant marginal cost.
EY's audit platform, EY Canvas, processes more than 1.4 trillion lines of journal entry data per year. Testing 100% of entries rather than a sample is no longer an exception reserved for large groups: it is the direction the profession is taking. Standards are following. The IAASB, which sets the international standards on auditing, explicitly recognizes the use of automated tools and techniques and clarifies how to document the work performed with them.
A few selected items, checked manually, then extrapolated. Fast to organize, but blind to the rare anomalies that do not fall into the sample.
The tool reads every entry, isolates the exceptions and ranks the risk. The auditor focuses judgment on what stands out, not on data entry.
Audit is fertile ground for automation: many tasks are repetitive, high-volume and governed by precise rules. That is exactly where AI adds value, by freeing up time for analysis and judgment.
Extraction and comparison of contracts, invoices, leases and bank statements. AI reads, structures and matches documents a human used to comb through by hand.
automated readingAnalysis of the entire general ledger, spotting unusual entries and breached thresholds, without limiting the work to a sample.
full populationSpotting atypical patterns: late-night entries, threshold circumvention, duplicate suppliers, repeated round amounts.
atypical patternsAccount reconciliation, tracking of external confirmations and first-draft summaries, then submitted to the auditor.
less manual workThe investment figures show the scale of the shift. AI is no longer an isolated pilot inside the big firms: it is becoming the very infrastructure of the audit, embedded in the platform teams use every day.
One billion dollars over four years, generative-AI capabilities and a multi-agent framework embedded in EY Canvas. EY aims to cover all phases of the audit by 2028.
Two billion dollars in AI and cloud over five years, in partnership with Microsoft, for an expected growth opportunity of over 12 billion. AI is embedded in the KPMG Clara audit platform.
Deloitte has committed several billion dollars to generative AI, in the order of 3 billion, and has equipped its Deloitte Omnia audit platform with AI agents. PwC has integrated AI into its audit process to cut manual tasks. The consequence for an audited company is simple: your next audit will rely, in part, on AI tools, whether you asked for it or not.
The AI-in-audit market is still young but growing fast. According to market.us, it stands at $1.0 billion in 2023 and would reach $11.7 billion by 2033, a 27.9% annual growth rate. Financial auditing already makes up the largest share, around 40.5% of use cases. Adoption is following: according to Thomson Reuters' 2026 report, based on more than 1,500 professionals, 40% of organizations in professional services (tax, accounting, audit) use generative AI, up from 22% a year earlier.
The enthusiasm is real but unevenly controlled. About 80% of auditors see AI as a turning point for their profession, and 75% of leaders plan to increase their AI budgets within three years. Yet agentic AI is used by only 15% of organizations, and just 18% measure the return on their AI projects. In other words: firms deploy fast and measure little.
An audit remains an opinion that carries liability. An AI tool must therefore be traceable and explainable: one must be able to reconstruct why an entry was flagged, on which data, and who validated the conclusion. Auditing standards require the auditor to understand and document the work, including when relying on automated tools. AI prepares and accelerates, but judgment, skepticism and the signature stay human.
AI-assisted audit inherits precise risks. Hallucination: a generative model can produce a plausible but wrong answer, unacceptable in audit evidence. Over-reliance: delegating judgment to the tool erodes professional skepticism. Confidentiality: a client's financial data is sensitive, and processing it through an opaque remote service raises a governance problem. Compliance: the regulator expects clear documentation of how the tools are used. The rule stays the same: validate on your own data, demand traceability, keep a human on the conclusion.
AI does not erase the standards, it fits within them. The IAASB has published a technology position statement and support material on documentation when using automated tools and techniques. The international standards on auditing (ISA) still apply: the auditor remains responsible for the evidence, whatever machine produced it.
A second front is gaining ground: sustainability assurance. The European CSRD directive requires limited assurance on sustainability reporting. The Commission is building the assurance standard on the international ISSA 5000 standard, whose adoption was postponed by the Omnibus package, to around mid-2027. Auditors who want to certify this information must follow specific training. For the Belgian companies concerned, this adds a volume of non-financial data to be made reliable: an area where AI, properly framed, helps to structure and verify.
Your auditor will test more data, faster, and ask sharper questions. Clean accounting and structured data shorten the engagement and reduce year-end surprises.
AI frees up data-entry time for analysis. Provided you choose traceable tools, train the teams and keep professional judgment at the center.
Audit data is among the most sensitive a company holds: accounts, contracts, salaries, margins. Entrusting it to an AI tool means controlling where it is processed and who can access it. A rollout that respects the GDPR and the EU AI Act, on infrastructure you control, remains simpler to govern than an opaque remote service. A tool that touches audit evidence must be documented, verifiable and supervised, by design.
General ledger, invoices, contracts, statements. Know where the data is, in what format and at what quality before applying a tool to it.
Entry testing, reconciliations, document reading. Start where the gain is measurable and the risk manageable.
Every flag must be reconstructable: which data, which rule, which version of the tool. Without that, no defensible audit evidence.
Train teams to challenge the tool, not follow it blindly. The conclusion, the skepticism and the signature stay a human responsibility.
Molderez Consult helps Belgian companies and firms embed AI in audit and compliance: analysis of 100% of the data, traceability, governance and training, on controlled, GDPR-compliant infrastructure.
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