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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

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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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.

Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.

Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.

Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.

This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.

Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.

By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.

Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.

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