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What accounting firms miss when they rush into AI

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Firm leaders are getting contradictory messages right now. Every conference, every vendor, every LinkedIn post says artificial intelligence is about to transform accounting. The implication: If you’re not implementing it yesterday, you’re already behind.

But the companies actually building AI tools for accounting are working on a different timeline than the one being marketed.

When I talked with Mike Cieri, executive vice president of software at Bill, about implementation timelines, he was direct: “We’ll see rapid change over five years, but I don’t think in six months you’re going to have a complete turnaround on how the accounting industry works.”

Five years of steady change. Not a six-month revolution.

That gap between the hype and the reality is creating unnecessary anxiety. Firms are making decisions about AI adoption while feeling panicked about being left behind, when the actual timeline gives them room to learn and test properly.

What ‘experimenting’ actually means

Most firms fall into one of two camps: diving into AI implementations without testing, or freezing because they don’t know where to start. There’s a middle path.

“Take a couple of associates and say, ‘We’re going to try this in your area. We’re going to try this with a few clients and experiment with it until we feel good that there was value added, and we have the controls we value as a firm in place,'” Cieri suggested.

The word “experiment” matters here. An experiment has defined parameters, a limited scope, and permission to produce unexpected results. A firm-wide rollout has none of those things.

This approach addresses what firms actually worry about: What if this doesn’t work the way the vendor says? What if it creates more problems than it solves? What if we spend money and time and see no real benefit?

Testing small answers those questions with data instead of assumptions.

Where the value actually shows up

During my years in public accounting and later in C-level roles at companies, the frustration I saw repeatedly was talented people spending hours on work that didn’t require their expertise: Transaction coding. Data entry. Repetitive reconciliations.

AI should solve that problem — not by replacing accountants, but by handling the work that doesn’t need human judgment.

In our conversation, Cieri framed it this way: “Human involvement will be high leverage at key moments, where creativity is needed, judgment is needed, advisory is needed. We’re trying to amplify the value of human intervention in those moments.”

This changes what entry-level work looks like. Instead of spending two years learning to code transactions before getting to do analysis, new staff can move into interpretation and pattern recognition faster. The technical skills still matter, but they’re not the bottleneck anymore.

For experienced professionals, it creates bandwidth for work that keeps getting postponed: Strategic client conversations. Team mentoring. The advisory work that actually requires expertise.

From the client perspective: “I should be happier that we’re showing up to meetings and getting a higher-order thinker on the other side, showcasing for me a better picture of my business than I was getting before.”

That’s the actual value — not efficiency metrics on internal processes, but better outcomes for clients because the firm has capacity for meaningful work.

How review processes build trust

The question I hear most often: How do I know the AI did it correctly?

Same way you know a new staff person did it correctly: You review their work.

“We think of AI as just another actor in the system. We record those actions, there’s clear auditability, there’s clear transparency,” Cieri explained. “You’re building trust over time through verification. You see what the AI did, you check its work, you override when needed. The same process firms already use for training staff.”

The difference between firms that scale AI successfully and firms that pull back after problems: The successful ones built review processes from day one. They didn’t assume it would “just work.”

Starting point

If you’re somewhere between “We should do something about AI” and “I don’t know what to do,” here’s a practical framework:

  1. Spend time learning what AI actually does. “People tend to fear things they don’t understand, so just spending some time on that alone … to separate some of the fact from fiction in your own mind” makes the difference between decisions driven by anxiety and decisions driven by clarity, Cieri suggested.
  2. Pick one workflow that’s causing pain. Not the most complex one — the most consistently annoying one. Test with a limited scope. One person, one set of clients, defined timeframe.
  3. Define success before you start. What are you measuring? Time saved? Error reduction? Less end-of-month stress?
  4. Then pause before scaling. What worked? What didn’t? What surprised you? Most firms skip this reflection and miss the learning.

“Firms have a choice. If you want to turn on agents to do some of this work for you, that’s a choice you’re going to be able to make,” Cieri said. “It’s not just going to happen overnight.”
You control the pace of adoption.

What this actually requires

When I work with executives and partners on transformation — technology, workplace culture, business process — the pattern is consistent: The ones who succeed don’t move fastest; they move with intention. They test, reflect, adjust.

The ones who struggle try to do everything simultaneously because someone told them urgency equals importance.

AI creates opportunities for firms to reclaim time for work that matters: Strategic advising. Client relationships. Team development. But only if the adoption process itself doesn’t create the burnout and overwhelm that AI is supposed to solve. AI trends come and go. Fulfillment is evergreen.

Cieri said it best: “This is about supporting, not replacing, human connection.

The technology will wait. The question is whether you’re making decisions from clarity or from the pressure to keep up with what everyone else seems to be doing.

Take a beat. Test small. Build trust through verification. Scale when you understand what you’re scaling.

The firms that will succeed with AI are the ones who test first and scale deliberately.

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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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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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