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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

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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