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Low-effort AI “workslop” increasingly irritating workers

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AI can, in theory, make people more productive, efficient and smarter at work. But the results of a recent study show this is not guaranteed to be the case, as a significant number of people report receiving low quality AI “workslop” outputs that paradoxically increase the amount of work they have to do. 

The authors of the study, who discuss their findings in the Harvard Business Review, define “workslop” as “AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.” For example, reports that look polished and read well but make no sense, computer code missing vital context, or a slide deck that looks fine until you realize half the information is outright wrong. 

Of 1,150 U.S.-based full-time employees across industries polled, 40% report having received such content in the last month. Typically, those who receive it have to then spend time verifying information, correcting errors, and otherwise doing work that the person who sent it should have already done. One subject said they had to waste time following up on information and checking it against their own research, and then had to waste even more time setting up meetings with other supervisors to address the issue, and then finally they wasted even more time simply by having to redo the work entirely. Another said an email, while nicely written, was very unclear and so had to spend time tracking down all the relevant people for additional clarification. 

Human in the loop 3
Efficient AI robot working in the office and lazy employees having a coffee break

StockPhotoPro – stock.adobe.com

Employees said such low-effort, low-quality content makes up about 15.4% of all content they receive at work. The researchers noted that people spend an average of one hour and 56 minutes dealing with each instance of workslop. Based on participants’ estimates of time spent, as well as on their self-reported salary, the researchers found that these incidents carry an invisible tax of $186 per month per person. 

As one might imagine, people do not like getting this content, and they think less of the people who send it to them. When asked how it feels to receive workslop, 53% report being annoyed, 38% confused, and 22% offended. When asked about their feelings towards those who generated the content in question, about half viewed them as less creative, capable and reliable than they did before receiving the output. Further, 42% said they were less trustworthy and 37% said they were less intelligent. Further, 32% said they are less likely to want to work with the sender again in the future. 

Where does this content come from? Mostly between peers, at 40%, but not entirely. The study found 18% was from direct reports to avengers and 16% was from managers to their team members or even higher up in the chain than that. While workshop occurs across industries, researchers found that professional services and technology are disproportionately impacted.

The insidious effect of workslop, said the researchers, is that it shifts the burden of the work downstream, requiring the receiver to interpret, correct, or redo the work. In other words, it transfers the effort from creator to receiver. It was described as passing the cognitive buck, not so much doing work but shifting it around the organization. 

Part of the blame comes from organizations themselves, as the researchers believe this phenomena is fed by unclear AI mandates—while organizations are advocating for people to use AI all the time, not everyone specifies how, as they lack discernment in how the technology is applied. They also said there is a mindset difference in how people use AI, divided between what they called pilots and passengers. Basically, pilots are much more likely to use AI purposefully to enhance their own creativity and achieve their goals. Passengers, in turn, are much more likely to use AI in order to avoid doing work entirely. 

The researchers said that leaders need to address both the mindset and the mandate issues if they want to get the most from AI. 

“Workslop may feel effortless to create but exacts a toll on the organization. What a sender perceives as a loophole becomes a hole the recipient needs to dig out of. Leaders will do best to model thoughtful AI use that has purpose and intention. Set clear guardrails for your teams around norms and acceptable use. Frame AI as a collaborative tool, not a shortcut. Embody a pilot mindset, with high agency and optimism, using AI to accelerate specific outcomes with specific usage. And uphold the same standards of excellence for work done by bionic human-AI duos as by humans alone,” said the paper. 

AI strategy still work in progress

These findings call to mind recent data released by Wolters Kluwer at its recent North America conference, which found that while 86% of North American finance leaders report that their organizations are either beginning to explore AI use cases (53%), or are piloting AI in select areas (33%), ROI and strategy efforts lag behind. 

The survey found that only 24% of respondents said their finance leadership is fully aligned on the strategic role of AI. A larger portion, while 43% reported partial alignment, with engagement varying across leaders. Meanwhile, 9% said leadership is misaligned, 8% noted no alignment, and 16% were not sure of the level of alignment. Wolters Kluwer said this highlights the need for finance leaders to develop and clearly communicate a unified vision on how they expect AI to shape their finance operations.

These mixed results could be a consequence of the mixed feelings finance leaders have regarding AI. The survey found that while 14% said they are very comfortable with their organization’s level of AI investment, a larger number 38% reported being only somewhat comfortable. Further, 17% felt AI investment was too low, but 28% were not sure, highlighting the need for more clarity around AI budgeting decisions

“Finance leaders are clearly recognizing the potential of AI, but the journey from exploration to scaled deployment is complex” said Madhur Aggarwal, executive vice president and general manager of corporate performance management at Wolters Kluwer. 

 

Seventy-nine finance leaders responded to this survey, conducted on Sept. 17, 2025, during the North America CCH Tagetik inTouch25, in Houston, Texas.

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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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