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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

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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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.

Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.

Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.

Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.

This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.

Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.

By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.

Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.

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