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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

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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