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AI errors in Deloitte report underscore need for care

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In an object lesson for reviewing your AI outputs, Big Four firm Deloitte will partially refund the Australian government for an advisory report containing inaccuracies that were introduced by one of its AI models. 

The report in question pertained to an Australian study on a targeted compliance framework to prevent people from abusing government benefits that was initially released over the summer. A statement from the Australian government earlier this month said, “There have been media reports indicating concerns about citation accuracies which were contained in these reports,” and added, “Deloitte conducted this independent assurance review and has confirmed some footnotes and references were incorrect.”

The changes were made after an expert in welfare law noted several errors in the report. It contained numerous references to studies that did not actually exist, cited made-up publications, falsely quoted a judge, and faked a reference to a court decision. The revised report, which Deloitte published after excising the inaccuracies, discloses that it was at least partially developed using a generative AI large language model. 

Generative AI

The Australian government said that despite the errors, the main substance of the review was retained and there were no changes to the actual recommendations.

Governance concerns

The incident underscores the need for strong AI governance in order to mitigate the risks of this new technology. This ranges from finding ways AI fits into current governance and compliance structures to developing policies that specifically pertain to AI

Yet, while organizations generally are aware of the need for AI governance, actual execution has tended to lag behind. Governance, risk and compliance solutions provider AuditBoard came to this conclusion as a result of a survey it conducted that found over 80% of respondents said their organizations are either very or extremely concerned about AI risks but, at the same time, only 25% said they have fully implemented an AI governance program. 

Meanwhile, though 92% of respondents said they are confident in their visibility into third-party AI use, just 67% of organizations report conducting formal, AI-specific risk assessments for third-party models or vendors. That leaves roughly one in three firms relying on external AI systems without a clear understanding of the risks they may pose.

Further highlighting the issue is that organizations seem to struggle with actually controlling AI use among employees. A survey from Top 100 Firm EisnerAmper found only 22% of people said their organizations even monitor AI use in the first place, and only 11% block ChatGPT and other public models. The survey also found that only 36.2% have an AI policy, only 34.2% say their company emphasizes transparency when discussing AI, and only 34% say their company has an AI strategy. In addition, a significant portion of professionals don’t really tell their supervisors they’re using AI. While slightly more (22.4%) say they get permission first before using AI, almost as many (21.7%) have no such reservations; 22.2% either might or might not. 

Another issue highlighted by this most recent incident is that while people know they should not blindly trust AI outputs due to the possibility of error, most do anyway. The EisnerAmper survey found that while about 81% of respondents were very or somewhat confident in the results of their outputs, when asked how often they find errors, 28.4% said “not very often” and 3.4% never find errors. Only 10.3% were supremely confident in their ability to spot errors. 

Other studies are similarly grim. A McKinsey survey found that just 27% of respondents whose organizations use generative AI say that employees review all content created before it is used. A similar share say 20% or less of gen-AI-produced content is checked before use. And another study from trend analytics company ExplodingTopics found the problem was even more severe: Only 8% of people regularly bother to verify AI information, and 42.1% of web users have experienced inaccurate or misleading content in AI overviews. 

We can see this playing out in the rise of what a Harvard Business Review article dubbed low-effort “AI workslop,” which was defined as “AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.” For example, reports that look polished and read well but make no sense, computer code missing vital context, or a slide deck that looks fine until you realize half the information is outright wrong. It is not difficult to imagine that those sending such content likely did not take the time to verify it before passing it on to another worker. 

Of 1,150 U.S.-based full-time employees across industries polled, 40% report having received such content in the past month. Typically, those who receive it have to then spend time verifying information, correcting errors, and otherwise doing work that the person who sent it should have already done. Employees said such low-effort, low-quality content makes up about 15.4% of all content they receive at work. The researchers noted that people spend an average of one hour and 56 minutes dealing with each instance of “workslop.” Based on participants’ estimates of time spent, as well as on their self-reported salary, the researchers found that these incidents carry an invisible tax of $186 per month per person. 

What all these studies indicate is that while having a human in the loop is vital for organizations using AI, it’s more important that those humans actually work to scrutinize AI outputs.

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Accounting

SEC’s Semiannual Reporting Proposal Faces Investor Pushback: What CFOs Need to Know

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U.S. Securities and Exchange Commission (SEC)

A proposal from the U.S. Securities and Exchange Commission to potentially shift some public companies away from quarterly financial reporting toward a semiannual model is drawing significant pushback from investors, even as it continues moving through the regulatory process. The debate has direct implications for corporate finance teams, auditors, and the broader transparency of U.S. capital markets.

What the SEC Proposed

According to a summary published by accounting advisory firm Cohen & Co., the SEC issued a proposed rule on May 19, 2026, aimed at simplifying financial reporting requirements for many U.S. public companies. The proposal would potentially reduce the frequency of certain mandatory disclosures from quarterly to semiannual, a structural change that has not been made to core U.S. reporting requirements in decades.

The proposal follows an extended debate within U.S. policy circles, with proponents arguing that reduced reporting frequency could lower compliance costs and free up management time for longer-term strategic planning rather than quarter-to-quarter results management.

Why Investors Are Pushing Back

Comment letters submitted in response to the proposal have been extensive, and according to Cohen & Co.’s review of the public record, investors “appear to be largely opposed” to the shift, viewing frequent interim reporting as a core benefit of U.S. capital markets relative to other jurisdictions.

Accounting and law firms have taken a more measured position, generally urging any changes to remain aligned with the Financial Accounting Standards Board (FASB), whose existing disclosure requirements and guidance are built around a quarterly reporting cadence. A shift to semiannual reporting without corresponding changes to FASB guidance could create friction between SEC filing requirements and GAAP-based disclosure expectations.

Lessons From the U.K. Experience

The debate is not without precedent. The United Kingdom moved away from mandatory quarterly reporting for listed companies in 2014, returning to a semiannual disclosure requirement. According to Cohen & Co.’s analysis, that experience offers a cautionary data point: there was no measurable increase in capital expenditure or R&D investment following the change, while analyst coverage of affected companies declined as reliable interim information became less available — a particular risk for smaller and newly public companies that rely on analyst coverage to maintain investor visibility.

Practical Implications for Finance Teams

Beyond the debate over disclosure philosophy, the proposal carries practical complications. Many companies have debt covenants and credit agreements structured around quarterly financial delivery; a shift to semiannual reporting could require renegotiating those terms. Reduced reporting frequency would also extend the “window of market silence” between disclosures, a factor that governance and investor-relations teams would need to manage carefully to avoid information asymmetry.

Separately, and unrelated to the reporting-frequency debate, the SEC and FASB have continued finalizing more routine updates this year. New Accounting Standards Updates are taking effect for December 31, 2026, fiscal year-ends covering income tax disclosures, credit loss measurement, induced debt conversions, and stock compensation, according to Eide Bailly’s review of 2026 ASU activity. Additional guidance on paid-in-kind dividends and environmental credits is also on the near-term horizon.

What to Watch Next

The semiannual reporting proposal remains in the comment and review phase, and no final rule has been adopted as of this writing. Finance leaders should monitor the SEC’s regulatory agenda for further movement, while treating the current quarterly reporting requirement as the operative standard until any final rule is issued and an effective date is set.

Given the extent of investor opposition documented in the comment file, a full shift to mandatory semiannual reporting appears more likely to result in either a scaled-back compromise or continued study rather than swift adoption — though the SEC’s ultimate direction remains uncertain.

 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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