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Managing generative AI in your accounting firm

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Generative artificial intelligence, or gen AI, is a type of artificial intelligence that can create content, generate insights and even simulate human-like conversations. Gen AI tools like ChatGPT and Microsoft Copilot are transforming the business world and accounting firms.

While the technology offers many benefits, its rapid adoption also creates challenges for firm leaders. We’ve talked to many firm leaders who think they can simply block these sites at their firms or forbid employees from using them, avoiding the inherent risks. However, this approach is short-sighted and risky. This article explains why and offers a better alternative.
 
What is gen AI?

Generative AI refers to machine learning models that can produce new data similar to the data they were trained on. These models can create text, images, music and more, making them incredibly versatile tools. You might not realize it, but AI is likely already a part of your everyday activities. Here are some common examples:

  • Browsing social media. AI algorithms suggest content tailored to your interests.
  • Using digital assistants. Virtual assistants like Siri and Alexa use AI to understand and respond to your commands.
  • Online shopping. Websites generate personalized recommendations based on your browsing and purchase history.
  • Unlocking your phone. Facial recognition systems utilize AI for secure access.
  • Navigational apps. AI optimizes routes and provides real-time traffic updates.
  • Editing photos. AI tools enhance and modify images seamlessly.
  • Autocorrect and autocomplete. AI improves typing accuracy and speed.
  • Playing video games. AI opponents provide dynamic and challenging gameplay.
  • Auto-generated playlists. Music-streaming services curate playlists based on your listening habits.
Generative AI

The dangers of gen AI

While there are many benefits to using gen AI, it also brings several risks that firm leaders must address.

  • Data protection and privacy. AI systems often require vast amounts of data, raising concerns about how tech companies collect, store and use that data.
  • Ethical guidelines. We’re still working out how to ensure that AI operates within ethical boundaries to prevent misuse.
  • Industry-specific regulations. Because this technology is moving so quickly, accounting and tax-specific regulations haven’t yet caught up.
  • Data leakage. Protecting sensitive information from unauthorized access and leaks is a top priority. How can you stop employees from copying and pasting sensitive client or firm data into a Generative AI tool?
  • Intellectual property protection. AI-generated content can blur the lines of intellectual property rights.
  • Bias and discrimination. AI models can inadvertently perpetuate biases present in the training data.
  • Fake content and misinformation. Generative AI is prone to “hallucinations” or incorrect or misleading results. It’s easy to create realistic fake content without verifying authenticity.

Establishing usage policies and guidelines

Given the potential risks, firm leaders must develop comprehensive AI usage policies.

Proper guidelines help minimize the dangers of AI usage and give employees a reference point for ethical AI use. Trying to prohibit AI tools outright can lead to unauthorized use.

Consider the following findings from Microsoft and LinkedIn’s 2024 Work Trend Index Annual Report:

  • 75% of global knowledge workers are using generative AI;
  • 78% of AI users are bringing their own AI tools to work (BYOAI); and,
  • 52% of people who use AI at work are reluctant to admit using it for their most important tasks.

You don’t have to start from scratch — many of your existing data protection and privacy guidelines can be adapted for AI.

If you’re wondering where to start, create an exploratory committee to oversee AI implementation. This committee should include a cross-functional group of people from multiple departments and be led by IT. The committee can vet AI tools and opportunities, compare the cost to the potential ROI and establish priorities. This helps ensure a structured approach to implementing and using GenAI.

It’s also crucial to train employees, helping them understand how to ethically and responsibly use AI tools. This proactive approach safeguards the firm and empowers your team members to leverage AI’s benefits responsibly.

Generative AI offers firms exciting opportunities to accomplish more and free up employees for higher-value work, but it also creates challenges for CPA firms. By developing an AI usage policy, exploring AI tools in your firm and educating your team members on how to use AI responsibly, you can harness the power of AI while minimizing risks. Remember, while the technology is new, you likely established principles of governance, ethics and data protection long ago. Embrace the innovation, but do so cautiously and responsibly.

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Accounting

Mandatory ESG Reporting Standards Demand Standardized Non-Financial Audit Trails

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Mandatory ESG Reporting Standards Demand Standardized Non-Financial Audit Trails

Corporate accounting departments face an expanded regulatory mandate as mandatory sustainability and Environmental, Social, and Governance (ESG) reporting frameworks take full effect internationally. Governed by the European Union’s Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) IFRS S1 and S2 standards, enterprise financial controllers are now legally required to track, verify, and report non-financial data with the same internal controls and auditability as traditional financial statements.

The expansion shifts ESG compliance

This regulatory expansion shifts ESG compliance from marketing departments to corporate accounting offices. Financial managers are now responsible for gathering, consolidating, and verifying carbon emissions metrics, supply chain labor conditions, water usage, and climate risk exposures across multi-tiered corporate structures. These non-financial metrics must be integrated into standardized general ledgers to withstand rigorous third-party audit assurance processes.

To comply with these rigorous reporting mandates, accounting software providers have added dedicated ESG modules designed to aggregate data from IoT sensors, utility platforms, and vendor management systems. Controllers are implementing internal control frameworks—modeled after traditional COSO frameworks—to ensure the completeness, accuracy, and consistency of sustainability disclosures, protecting organizations against greenwashing penalties and litigation risks.

The transition requires significant cross-functional collaboration between accounting teams, legal counsel, and operational directors. Accounting professionals are expanding their technical expertise beyond financial ledgers to master carbon accounting methodologies, lifecycle assessment standards, and non-financial data governance protocols, fundamentally expanding the role of the modern corporate accountant.

Why This Information Matters
Mandatory ESG disclosures require companies to treat environmental and social metrics as audited financial records. Executives, accountants, and board members must institute formal tracking and assurance processes to satisfy legal mandates, maintain investor confidence, and mitigate regulatory non-compliance risks.

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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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