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AI Leaders on: 2025 and AI regulation

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While AI is still in the wild west phase that many new technologies go through, as the technology has spread there have been increasing calls from both organizations and individuals to make the field slightly less wild. Not so much that it completely kills the innovation and vibrancy of this burgeoning field, but enough that series players will feel safe entering this space without being worried they’re putting themselves at risk. 

In particular, our experts are interested in measures that can improve the transparency and accountability of AI systems, such as clear labeling of AI-generated content, the ability to trace the model’s decision-making process, and disclosure of the data and algorithms involved. There was also strong support for ensuring these systems are explainable and, especially important for the accounting community, auditable. 

“An AI regulation that emphasizes transparency in the training of large language models (LLMs) would be highly beneficial. Understanding how these models are trained, including the data sources and methodologies used, is crucial for ensuring accountability and trust in AI systems. This transparency would be particularly advantageous in fields like accounting, where leveraging AI to enhance audit quality requires a clear understanding of how AI decisions are made,” said Mike Gerhard, chief data and AI officer with BDO USA. 

Respondents also expressed strong support for regulations aligned with principles-based or risk-based approaches, such as the EU AI Act, which focus on safety, fairness and non-discrimination while still providing space for innovation. This is especially important given the stakes involved with AI’s ascendency, especially for traditionally marginalized communities. 

“I believe we need to get ahead of the eight ball when it comes to the ethical issues stemming from AI’s inherent bias problem. When we let AI perform tasks such as sifting through resumes, making creditworthiness decisions, or assessing job interviews, we ought to be sure it does so without (hidden) biases. Part of this problem is on the vendor side, but part of this ought to be codified (and thus protected) by law,” said Pascal Finette, founder and CEO of training and advisory firm Be Radical. 

At the same time, virtually everyone cautioned against going too hard on regulation, especially at this early stage of the technology’s evolution.

“As further governance emerges, I hope we don’t see overly restrictive rules that stifle creativity and progress. Rather, I’d love to see further regulations that strike the right balance between ensuring the ethical and secure use of AI while encouraging innovation. Public-private partnerships and feedback loops from organizations doing the assessments will be crucial in getting that right,” said Avani Desai, CEO of Top 50 firm Schellman.

Will we see more focus on AI regulation in 2025? Well, the only thing we know for sure is we don’t know anything for sure. But we can make educated guesses. While no one outright said we’d definitely see new regulations rolled out, some predicted scandals that would likely draw attention to the need for further oversight for AI systems. 

“AI’s capability will continue to evolve. The cost of using AI (e.g., Open AI’s API service) will continue to go down. There will be more AI applications. At the same time, we will also see more AI-related negative incidents, particularly those that raise important ethical concerns and debates,” said Abigail Zhang-Parker, an accounting professor at the University of Texas at San Antonio. 

Overall, when asked for their most confident predictions, many said the widespread integration of AI into workflows will accelerate, especially given the rising prevalence of autonomous AI agents with limited decision-making power. The rise of these virtual workers are widely predicted to increase productivity and efficiency at firms. At the same time, some experts warned how this might shift employment dynamics, as well as increase risk of ethical dilemmas. 

“I am confident that AI will either reduce the number of new hires the largest accounting firms plan to hire or lead to further staff reductions, if not both. The largest firms have planned for this stage of AI for years and they thought this day would come sooner. They know they can do more with less. I’m also quite confident we’ll see a scandal where a firm misuses AI or subjugates its judgment to AI that leads to a fraud or material error getting through an audit.  We’ve already seen this occur in the legal field. It’s only a matter of time until it happens to an accounting firm,” said Jack Castonguay, a Hofstra University accounting professor and the vice president of learning and development at Surgent. 

In this, the second of three parts, we look at our experts’ answers to: 

  • What is an AI regulation you’d love to see? What is an AI regulation you’d hate to see?
  • What AI prediction for 2025 are you most certain of? Something you are very confident we’ll all see next year?

We’ll have our third and final part—where we get into one of the more esoteric aspects of AI—next week.

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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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