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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

Continuous Auditing Transforms Corporate ERPs

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continuous auditing transforms corporate erps

As corporate accounting departments cross the threshold into late July 2026, the adoption of continuous, automated auditing systems has reached a definitive turning point. Driven by advances in artificial intelligence and deep integration with modern Enterprise Resource Planning (ERP) platforms, leading finance organizations are moving away from traditional, periodic post-hoc audits in favor of real-time, 100% transactional verification. This technological transition is redefining internal control environments, reducing compliance costs, and eliminating the structural delays inherent in legacy quarterly closing processes.

Unlike traditional auditing frameworks that rely on statistical sampling—a process that inevitably leaves operational blind spots—continuous auditing software monitors operational data feeds continuously. Every purchase order, electronic invoice, payroll disbursement, and cross-border wire transfer is automatically cross-referenced against established corporate governance parameters, regulatory tax schedules, and anti-fraud algorithms in real time. Anomalies or unauthorized ledger entries are flagged instantly, allowing internal audit teams to investigate and remediate compliance gaps immediately rather than months after the close of a financial period.

The implications for executive financial management are far-reaching. By embedding continuous verification directly into daily transaction workflows, chief financial officers gain uninterrupted visibility into the organization’s true financial standing. Real-time balance sheet auditing eliminates the severe operational bottlenecks associated with month-end and quarter-end financial reconciliations, freeing accounting professionals to focus on strategic financial modeling, tax planning, and capital allocation rather than manual data entry and spreadsheet consolidation.

However, implementing continuous auditing requires accounting leadership to invest heavily in data governance and technical upskilling. Internal audit teams must evolve from manual ledger reviewers into system architects capable of auditing complex algorithms and validating automated data pipelines. Accounting firms and corporate controllers that master continuous auditing will establish a resilient compliance framework capable of meeting stringent international regulatory standards with total transparency.

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Accounting

U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

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U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

WASHINGTON — In a major escalation of cross-border trade friction, U.S. President Donald Trump has signed executive orders imposing new 50% tariffs on a wide selection of Canadian exports, citing discriminatory practices by Ottawa targeting American auto, dairy, and beverage industries.

The new duties, announced Monday, will take effect in 30 days. They target a broad spectrum of consumer and industrial goods—ranging from wine, liquor, and milk products to commercial cement, furniture, clothing, and hockey equipment.

Untested Legal Mechanism

To enact the sweeping measures, the administration invoked Section 338 of the Tariff Act of 1930—a rarely used legal provision allowing the executive branch to levy additional tariffs of up to 50% on foreign nations deemed to discriminate against U.S. commerce.

White House officials noted that Section 338 addresses trade discrimination rather than national security or economic emergencies. The move comes months after prior global emergency tariffs faced legal challenges in domestic courts, signaling Washington’s pivot toward alternate statutory authorities to maintain import duties.

Senior administration officials briefed reporters that the measure directly responds to Canadian provincial bans on U.S. alcohol, restrictions on American vehicle exports, and import quota disparities affecting U.S. dairy and cheese producers relative to third-party trading partners.

“While the administration continues to secure reciprocal trade agreements globally, Canada retaliated against efforts to protect domestic industry,” U.S. Trade Representative Jamieson Greer stated.

USMCA Impact and Carve-Outs

Significantly, the newly ordered 50% duties will apply to designated items even if they otherwise comply with the United States-Mexico-Canada Agreement (USMCA).

However, the administration confirmed key targeted exemptions:

  • Energy products (including oil and natural gas)
  • Potash and critical minerals
  • Fish and seafood
  • Goods already governed by sector-specific duties (such as existing steel and aluminum tariffs)

Administration representatives emphasized that the tariffs do not stem from recent disputes concerning drifting Canadian wildfire smoke, noting that policy options regarding environmental spillover remain under separate review.

Canadian Response and Market Reaction

Following the White House announcement, the Canadian dollar experienced a sharp decline against the U.S. dollar, falling approximately 0.4% during evening trading.

Canadian Prime Minister Mark Carney issued a statement emphasizing that Canada’s earlier counter-duties had merely matched previous U.S. trade actions. “Canada stands ready to engage intensively to address outstanding issues with the U.S. to the mutual benefit of our citizens,” Carney stated, pointing to detailed proposals Ottawa submitted to modernize the USMCA framework.

Ontario Premier Doug Ford took a firmer stance, urging a “dollar-for-dollar” reciprocal response if the measures go into effect on August 19.

With a 30-day implementation window before the duties officially lock in, industry associations and trade groups on both sides of the border are calling for urgent bilateral negotiations to avert further supply chain disruption across North America.

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Accounting

Automated Continuous Auditing: Transforming Compliance and Real-Time Financial Oversight

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Transforming Compliance and Real-Time Financial Oversight

The traditional accounting paradigm—defined by periodic monthly closures and post-hoc annual audits—is rapidly giving way to continuous, automated financial oversight. As of July 2026, forward-thinking accounting practices and multinational corporate finance departments are leveraging continuous auditing systems powered by advanced machine learning models. These systems monitor operational transactions in real time, shifting audit methodologies from sample-based post-analysis to absolute, 100% transaction-level verification.

The operational advantages of continuous auditing are transformative. Standard auditing procedures historically relied on statistical sampling, which, despite rigorous methodology, inherently left gaps where anomalies or fraudulent transactions could go undetected for months. Modern continuous auditing platforms integrate directly with enterprise resource planning (ERP) databases, instantly cross-referencing purchase orders, invoices, bank feeds, and tax records. Any deviation from established control parameters or unusual transaction behavior triggers immediate flags for internal audit teams, dramatically reducing detection lag from quarters to seconds.

Beyond fraud prevention, continuous auditing fundamentally alters internal reporting and decision-making. Executive leadership no longer has to wait weeks after the close of a quarter to evaluate precise financial standing; real-time verified ledger data provides an uninterrupted view of operating margins, tax liabilities, and cash flow dynamics. This real-time visibility enables corporate controllers to adjust capital allocation strategies dynamically, mitigating liquidity constraints and capitalizing on emerging commercial opportunities far more efficiently than competitors bound to legacy reporting cycles.

However, implementing continuous auditing requires accounting professionals to acquire new analytical capabilities. The role of the auditor is evolving from manual data reconciliation toward system validation, algorithmic model governance, and strategic risk interpretation. Accounting firms and corporate finance departments must invest in continuous technical education, ensuring that audit staff possess the data engineering skills necessary to design, maintain, and evaluate complex automated compliance systems.

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