Regardless of how a firm ultimately implements AI, Kim Petro, a practice advancement coach from Woodard, stressed the importance of doing so ethically. While AI has opened up a whole new world of possibilities, not all of them are necessarily positive, and so it is important to be mindful of the consequences of using AI solutions.
She pointed to biased hiring algorithms that disadvantage certain people without the developers even realizing it, “so if someone turns in a resume with the wrong name and the algorithm is scrubbing that for appropriateness to the job, it can completely disregard it when it really should not be, there could be discrimination.”
She said there is also evidence people are using AI to create financial advice without disclosing that it came from AI, which “oh my god is so dangerous, the liability we bring on ourselves by giving bad advice and not disclosing where we got it.”
AI ethics or AI law concept. Businessman with ai ethics icon on virtual screen for compliance, regulation, standard , business policy and responsibility.
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Students, it is well known, are also using AI to cheat on their school work, she said. Meanwhile, she added, there are bots trolling social media for purposes ranging from ridiculous to nefarious.
During her own talk at Scaling New Heights in Orlando, Petro outlined some key considerations to avoid some of the less ethical applications of AI.
For one, people should be transparent about their AI use, making sure to always disclose when they use it; Petro herself disclosed she used AI for research and building course content, so “I need to tell people it’s not my own content, it’s scrubbing the Internet and grabbing bits and pieces from other things. I also need to use my own voice when using this content.”
Ethical users also make sure they verify what their AI tells them, noting the propensity of certain language models to make things up wholesale. They should also be aware that not only can the model can wrong, it can also have bias, such as in the aforementioned case of the hiring algorithm.
She also said users overall should try to respect intellectual property; she pointed to an example where board game designers were using AI to make art instead of hiring artists, but the artists the model drew from did not get proper attribution.
Finally, she said that people need to be aware of the privacy and security risk of using AI, especially public models, especially free accounts on public models. This is because inputs can go right into the company’s servers, including any personal or financial information that generally needs to be kept private.
“We don’t want our confidential or sensitive information in there because if you use a free account it informed the generative model for everyone. Not good. Even if you use it just for financials, we highly recommend using a paid account so it is not informing the model,” she said.
While many use AI to draft reports for clients, she said that if there is a risk the information will wind up with an unauthorized third party they should scrub all the identifying details from the prompt before entering it into the model, and then replace the information in the actual report itself.
Other ethical AI uses she suggested include brainstorming, “not content replacement but to help us do the research,” as well as approved image generation for marketing purposes using appropriately licensed artwork, such as “you have a great logo and want to make a banner for LinkedIn.”
Professionally, there is also internal process generation and automation, basically “we can have ChatGPT write me a process for automating monthly close or bank recs, it doesn’t matter as long as we use it internally and, again, has human review. I cannot stress that enough.”
The specific issues that a firm might face regarding AI ethics can vary greatly, and so she also stressed the importance of creating best practices and acceptable use policies, such as making sure a human reviews everything, only using tools that create audit trails, or assigning permissions with user roles.
Overall, she said users should remember to:
“Ask yourself, am I representing this content as my own? If you are, may God strike you down. Well, just kidding. But maybe think about it and really write that this was not your work, it was someone else’s.”
“Could this harm instead of help? If I’m a board game designer, say I’ll save some money and use AI to create this graphic design. I won’t pay an artist to do that. So I scrub the Internet using AI and pull from different artists and it’s blatantly obvious and… [the artist] has nothing to protect their work.”
“Would I be comfortable explaining how I use this to my client? You may have clients who’re not tech savvy and you tell them you put something in AI and they say ‘you put all my information to all robots everywhere!’ They freak out. Would they be okay with you using AI? Are you telling them upfront you use AI but, hey, we won’t have your personal information out there, are you okay with that?”
“Normalize conversations around ethical technology use. This is a bnig thing, especially with policies and enforcing them. AI is changing every day, evolving fast, so fast that we have to keep up with the conversations and learning and make sure we stay on top of it.”
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.
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.
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.