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Inside ISO 42001 framework on AI management systems

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Artificial intelligence, particularly generative AI, has advanced rapidly in a very short time, with the technology insinuating itself into businesses big and small across the world. But the speed at which it has been adopted, and the scale of its impact, has led to many concerns about its use and misuse. This, in turn, has highlighted the importance of adequate governance for these complex systems.

Accounting professionals are long used to helping clients through the governance challenges of other complex systems, from financial data integrity to cybersecurity protocols. Consequently, they are uniquely positioned to help with AI governance challenges as well, especially through standards such as the recently released ISO/IEC 42001.

The ISO/IEC 42001 Standard, released towards the end of last year, concerns artificial intelligence management systems; it specifies requirements for establishing, implementing, maintaining, and continually improving an AIMS within an organization. Having developed the standard in response to the rapid development of AI technology, the ISO said it is meant to be applied to organizations of any size involved in developing, providing, or using AI-based products or services. It is applicable across all industries and relevant for public sector agencies as well as companies or nonprofits.

The standard defines an AI management system as a set of interrelated or interacting elements of an organization intended to establish policies and objectives, as well as processes to achieve those objectives, in relation to the responsible development, provision or use of AI systems. ISO/IEC 42001 specifies the requirements and provides guidance for establishing, implementing, maintaining and continually improving an AI management system within the context of an organization.

It is distinct from other standards that pertain to AI, such as ISO/IEC 22989, which establishes terminology for AI and describes concepts in the field; ISO/IEC 23053, which establishes an AI and machine learning framework for describing a generic AI system using ML technology; and ISO/IEC 23894, which provides guidance on AI-related risk management for organizations.

ISO/IEC 42001, on the other hand, is a management system standard. 

Implementing this standard means putting in place policies and procedures for the sound governance of an organization in relation to AI, using the Plan‐Do‐Check‐Act methodology. Rather than looking at the details of specific AI applications, it aims to provide a practical way of managing AI-related risks and opportunities across an organization. 

Top 50 Firm Schellman, in a published guide on the standard, requires that organizations first identify the scope of their AIMS, all the issues relevant to the purpose and strategic direction of their AIMS, and the needs of both internal and external stakeholders, who may include customers, suppliers, employees, and regulatory bodies. To this end, Schellman recommended that organizations clarify their strategic business objectives, relevant risks and customer expectations. 

They must also demonstrate the commitment of top management to AI governance through policy, roles, responsibilities and authorities. Overall, management must be actively involved in support, especially through the artificial intelligence policy and communicated roles and responsibilities. 

Organizations must also outline their AI objectives; determine AI risks, impact and opportunities; and plan actions to address them. Schellman noted that the required completion of an AI impact assessment goes a little further than other ISO standards.

Organizations are recommended to:

  • Define a process to assess the potential consequences that can result from AI systems on individuals, groups, and societies;
  • Outline the potential consequences of an AI deployment, intended use, and potential misuse for individuals, groups, and societies;
  • Understand the context — both technical and social — where the AIMS is primarily deployed considering applicable jurisdictions;
  • Retain documented information of the AI impact assessment, available to internal and external interested parties (as determined by the organization’s strategic alignment); and,
  • Use the results of the AI impact assessment as inputs for their AI risk assessment as required by ISO 42001.

They must also demonstrate allocation of adequate resources to support the AIMS, appropriate competence for persons doing work under the AIMS, and personnel’s awareness of the AIMS, as well as communication and documented information regarding the AIMS. This includes employing adequate personnel, but also deploying the necessary data, tooling, systems, and assets (including human capital) to support the AIMS. The framework also mandates a certain level of competence, awareness, communication, and documented information as part of that support.
In addition, organizations must outline the implementation of processes regarding artificial intelligence offerings to ensure the conformance of AI operational planning and control within the design, development, and production processes through effective, efficient, and agile implementations.

There must also be monitoring, measurement, analysis, and evaluation of AIMS processes and performance, and internal audit against the AIMS framework and other applicable controls, as well as a dedicated management review. 

Finally, the standard calls for the correction of nonconformities and continual improvement of the AIMS. The compliance journey will necessitate the correction of major or minor nonconformities, which can be raised by the organization, the internal auditors, or by an external certification body performing a readiness assessment or initial certification.

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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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