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Poll: People trust AI less, but use AI more

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People trust AI tools less and are more worried about their negative impacts than they did two years ago but, despite this, their use has been growing steadily, as many feel the benefits still outweigh the risks. 

This is according to what Big Four firm KPMG said was the largest survey of its kind, polling over 48,000 people across 47 countries, including 1,019 people in the U.S. 

The poll found, among other things, that the proportion of people who said they were willing to rely on AI systems went from 52% in 2022 to 43% in 2024; the proportion of those saying they perceive AI systems as trustworthy went from 63% to 56%; and the proportion of those saying they were worried about AI systems rose from 49% to 62%. 

Yet, at the same time, most people use AI today in some form or another. The poll found that the proportion of organizations reporting that they’ve adopted AI technology went from 34% in 2022 to 71% in 2024; consequently, the proportion of employees who use AI at work went from 54% to 67% in the same time period.

Outside of work, in terms of their personal lives, 20% of respondents said they never use AI, but 51% said they use AI daily, weekly or monthly. For the most part, when people are using AI, it is usually a general purpose public model: 73% said this is what they use for work, versus 18% who are using AI tools developed or customized to their particular organization. 

However, while more people are using AI, fewer say they know enough about it. The poll found that nearly half, 48%, reported their AI knowledge as “low” while a further 31% rated it as “moderate.” Only 21% said they had a high amount of knowledge on AI. 

Despite this, most who use AI believe they’re pretty good at using it effectively. The poll found 62% saying they could skillfully use AI applications to help with daily work or activities; 60% said they could communicate effectively with AI applications; 59% said they can choose the most appropriate AI tool for the task; and 55% said they can evaluate the accuracy of AI responses. Those saying they lacked confidence in any area hovered between 21% to 24%.

The report suggested that this disparity might be due to AI solutions having intuitive interfaces that people can quickly grasp: just as one may not need to know much about cars to drive one, maybe people don’t need to know how AI works to use it well. 

This could be borne out by the benefits people say they have personally witnessed from using AI. A clear majority, 67%, of those using AI at work said they have become more efficient, 61% say it has improved access to accurate information, 59% say it has improved idea generation and innovation, 58% say the quality or accuracy of work and decisions has improved, and 55% say they have used it to develop skills and ability. 

However, other viewpoints are more contentious. Yes, 36% say it has saved them time on repetitive and mundane tasks but 39% say it has increased time; 40% say it has decreased their workload but 26% say it has increased it; meanwhile, 36% say it has led to less pressure and stress at work, but 26% say it has added more. Tellingly, while 19% say AI has reduced privacy and compliance risks, 35% say it has made them worse, and while 13% think it has led to less monitoring and surveillance of employees, 42% say AI has amplified it.  

While more people are using AI, they are not always doing so in ways their organizations would approve. The poll found, for example, that about 31% have contravened specific AI policies at their organizations, 34% admit they uploaded copyright material or intellectual property to a generative AI tool, and 34% said they uploaded company information. Meanwhile, 38% admitted to using AI tools when they weren’t sure if it was allowed and 31% used AI tools in ways that might be considered inappropriate (though the specifics of what that might mean was not mentioned.) 

People are also not entirely forthcoming when they have used AI, as the survey found 42% avoided revealing AI use in their work and 39% have passed off generative AI content as their own. 

The poll also found that AI has had impacts on how people work: 51% concede they’ve gotten lazier because of AI, 42% say they’ve relied on AI output without evaluating the information, and 31% admit they’ve made mistakes in their work because of AI. 

This might explain, at least partially, why 43% overall have reported personally witnessing negative outcomes from AI. The three biggest problems people have personally seen with AI are “loss of human interaction and connection” with 55% saying they’ve seen this; inaccurate outcomes, at 54%; and misinformation or disinformation, at 52%. Meanwhile, though they remain the lowest in the list, a still-troubling 31% said they saw bias or unfair treatment due to AI, 34% have witnessed both environmental impacts and the undermining of human rights due to AI, and 40% said they have seen manipulation and harmful use of AI (though, again, the specifics of this were not elaborated upon.) While right now many still believe the benefits outweigh the risks, this proportion has actually lowered from 50% in 2022 to 41% in 2024. 

However, 83% report they would be more willing to trust an AI system when such assurance mechanisms are in place. The survey also found strong support for the right to opt out of having their data used by AI systems, 86%, as well as for monitoring for accuracy and reliability, 84%, training employees on safe and responsible AI use, 84%, allowing humans to override the system’s recommendations and output, 84%, and effective AI laws or regulations, 84%. The poll also found that the clear majority, 74%, support third party independent assurance for AI systems. 

“Employees are asking for greater investments in AI training and the implementation of clear governance policies to bridge the gap between AI’s potential and its responsible use,” said Bryan McGowan, trusted AI leader for KPMG. “It’s not enough for AI to simply work; it needs to be trustworthy. Building this strong foundation is an investment that will pay dividends in future productivity and growth.”

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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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