AI Hallucination on OECD Digital Technologies and Environment Recommendation (2025) for Accountants (CA/PA) in international jurisdictions

Executive Summary

Accountants advising clients on digital infrastructure governance, ESG reporting, or technology-related disclosures under the OECD's 2025 Digital Technologies and Environment Recommendation face a specific exposure when they rely on AI tools for quantitative benchmarking: the figures AI produces can be wrong in ways that are invisible at first glance. In the one aggregated question tested against this regulation, AI tools returned a confidently stated statistic that diverged from the figure in the primary source, and when pressed, acknowledged the uncertainty.

The failure pattern here is not vague mischaracterisation but precise numerical fabrication: a specific country-level energy consumption percentage, attributed to a named statistical authority and a named OECD publication, but materially incorrect. For a CA or PA drafting a sustainability opinion, a due diligence memo, or a board briefing that cites OECD or national statistical data on data-centre energy loads, an error of this type will survive internal review if no one independently verifies the underlying source.

How AI gets this regulation wrong

The dominant failure mode on this regulation is confident fabrication of specific quantitative data, percentages, time-series figures, and source attributions, presented as verbatim reproductions of named publications when the underlying numbers are wrong. AI tools tested on this regulation did not hedge or caveat; they committed to precise figures with full citation scaffolding, then reversed themselves only under direct challenge. The table below maps this pattern to the specific finding in this cell.

What that means for your practice

For Accountants in international jurisdictions, the risk from this regulation's AI failure concentrates in a single category: producing a wrong deliverable, an opinion, memo, or briefing that contains a materially incorrect statistic attributed to a credible source. The table below maps that risk to the finding that generated it, showing where in a CA/PA workflow the exposure is highest and what the downstream consequence looks like if the error travels uncorrected into a client document or regulatory submission.

When this affects Accountants (CA/PA)

The OECD Digital Technologies and Environment Recommendation enters an accountant's workflow most often in two contexts: sustainability-linked assurance or advisory work touching technology infrastructure, and cross-border digital economy transactions where an OECD policy baseline is used to frame risk or benchmark a client's environmental exposure. A CA/PA advising a multinational on its data-centre footprint, preparing disclosures for an ESG-linked financing, or scoping a due diligence exercise that includes digital infrastructure assets will routinely want a quick read on the quantitative benchmarks the OECD and its member-country statistical offices have published.

That is precisely the query type where AI tools are most likely to be used, and most likely to fail silently.

The specific risk this regulation creates for accountants is statistical misattribution. The OECD recommendation cites country-level energy consumption data sourced from national statistical authorities; those figures carry the weight of official statistics, and practitioners use them as anchors in client reports, fairness opinions, and regulatory submissions. When an AI tool returns a wrong percentage, plausible-looking, precisely expressed, attributed to the correct authority, a junior team member drafting a memo has no obvious reason to challenge it. The error passes review because it fits the expected order of magnitude and bears a real citation.

The liability surface for a CA/PA is sharp here: if the incorrect figure appears in a signed opinion, a prospectus disclosure, or a board paper prepared for a regulated entity, correcting it after the fact is expensive. In cross-border contexts, the compounding effect is worse, different jurisdictions' regulators may have referenced the same OECD data independently, so an error in one filing can create inconsistency across a multi-jurisdiction engagement that is difficult to walk back.

The findings at a glance

The table below summarises the one finding tested against this regulation for the Accountants (CA/PA) audience, including the question area, the AI failure type, and the risk impact classification.

Aggregate impact

With one finding in this cell, the pattern is tight but instructive. The failure does not involve misreading a policy obligation or mischaracterising a procedural requirement, it involves fabricating a specific number. The AI committed to 14% as Ireland's 2021 data-centre share of metered electricity, constructed a four-point time-series around it (5% / 14% / 18% / 21%), and attributed the whole sequence to Ireland's Central Statistics Office as cited in the OECD Digital Economy Outlook 2024. The actual figure in the primary source is 11%. The fabricated time-series does not appear in any part of the underlying material.

For accountants, this failure mode is more dangerous than a vague policy error precisely because it mimics good research practice. A wrong percentage with a real citation chain and a plausible trend line looks like diligent sourcing to a reviewer who does not go back to the primary document. The error is structural: the AI produced a number that is internally consistent, directionally credible (data-centre energy loads have been rising steeply), and anchored to a named authority, but materially wrong on the specific figure the OECD text records.

The systemic risk for international accountants is that engagements touching digital infrastructure sustainability often aggregate data across jurisdictions, and country-level statistical anchors from OECD publications are treated as settled. A practitioner who uses an AI-generated figure for Ireland's data-centre load as a benchmark when scoping a comparable analysis for another jurisdiction, or who cites it as context in a multi-country advisory, compounds the error across multiple deliverables before the source discrepancy is noticed.

What your team should do

The default position for any CA/PA using AI on this regulation should be: do not carry forward any specific statistic, percentage, absolute figure, or growth rate, without pulling the primary source yourself. That applies even when the AI has provided what looks like a precise attribution. The finding in this cell shows that a correctly-identified source (CSO 2023, cited via OECD Digital Economy Outlook 2024) does not guarantee a correctly-recalled figure. The citation apparatus can be right while the number it nominally supports is wrong.

The practical safeguard is a one-step check: before a quantitative benchmark from an OECD publication or a national statistics authority lands in any client-facing document, a team member confirms the figure against the actual text. This is not a large burden on most engagements, OECD publications and most national statistical releases are publicly accessible, but it needs to be a standing instruction, not an ad hoc call.

For engagements where the data-centre energy or digital infrastructure context is peripheral rather than central, the risk of an AI-supplied figure going unchecked is highest, because the reviewer's attention is naturally on the primary regulatory or transactional question.

AI tools are reliably useful on this regulation for orientation work: understanding the structure of the recommendation, identifying which annexes or chapters address which policy areas, and drafting initial summaries of obligations or policy intent for client briefings. The failure point is narrow, specific quantitative data drawn from third-party statistical sources, and it is avoidable with a single verification step. Teams that build that step into their workflow as a matter of course will use AI productively on this regulation without the downside risk that the finding in this cell illustrates.

How RLB Can Help

RegLeg's published Hallucination Research is available as open reference, use it as a pre-flight check before relying on AI output on regulatory questions that matter to your sign-off. The findings are organised by regulation and failure mode, so if you are working across IFRS application guidance, PCAOB standards, or cross-border group reporting obligations, you can pull the relevant regulation page and see, specifically, where AI tools have fabricated citations, misstated effective dates, or collapsed jurisdiction-specific carve-outs into a single incorrect answer. That is faster and more defensible than discovering the error after the advice has gone out.

For firms running multiple Accountants on the same regulatory portfolio, group reporting, audit quality frameworks, independence requirements across jurisdictions, RegLeg offers bespoke deep-dives. We work through the specific regulations in scope, map the failure modes that surface most consistently in that regulatory space, and produce a structured briefing your team can use as a standing reference. This is not a one-size engagement: the output is scoped to the regulations you are actually using AI tools against, and framed around the workflow decisions those findings affect, materiality judgements, disclosure drafting, cross-border reconciliation.

We also produce training and CPD-aligned material built around the failure modes your team should be stress-testing in their own AI use. Not generic AI literacy content, specific failure patterns documented against the regulations accountants in international practice touch most, presented in a format that maps to the professional judgement calls your team makes daily. If your firm has an existing AI-use policy, we can review it confidentially against RegLeg's failure-mode catalogue and flag where the policy's assumptions about AI reliability are not supported by what the research actually shows.