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.
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.
As corporate accounting departments cross the threshold into late July 2026, the adoption of continuous, automated auditing systems has reached a definitive turning point. Driven by advances in artificial intelligence and deep integration with modern Enterprise Resource Planning (ERP) platforms, leading finance organizations are moving away from traditional, periodic post-hoc audits in favor of real-time, 100% transactional verification. This technological transition is redefining internal control environments, reducing compliance costs, and eliminating the structural delays inherent in legacy quarterly closing processes.
Unlike traditional auditing frameworks that rely on statistical sampling—a process that inevitably leaves operational blind spots—continuous auditing software monitors operational data feeds continuously. Every purchase order, electronic invoice, payroll disbursement, and cross-border wire transfer is automatically cross-referenced against established corporate governance parameters, regulatory tax schedules, and anti-fraud algorithms in real time. Anomalies or unauthorized ledger entries are flagged instantly, allowing internal audit teams to investigate and remediate compliance gaps immediately rather than months after the close of a financial period.
The implications for executive financial management are far-reaching. By embedding continuous verification directly into daily transaction workflows, chief financial officers gain uninterrupted visibility into the organization’s true financial standing. Real-time balance sheet auditing eliminates the severe operational bottlenecks associated with month-end and quarter-end financial reconciliations, freeing accounting professionals to focus on strategic financial modeling, tax planning, and capital allocation rather than manual data entry and spreadsheet consolidation.
However, implementing continuous auditing requires accounting leadership to invest heavily in data governance and technical upskilling. Internal audit teams must evolve from manual ledger reviewers into system architects capable of auditing complex algorithms and validating automated data pipelines. Accounting firms and corporate controllers that master continuous auditing will establish a resilient compliance framework capable of meeting stringent international regulatory standards with total transparency.
WASHINGTON — In a major escalation of cross-border trade friction, U.S. President Donald Trump has signed executive orders imposing new 50% tariffs on a wide selection of Canadian exports, citing discriminatory practices by Ottawa targeting American auto, dairy, and beverage industries.
The new duties, announced Monday, will take effect in 30 days. They target a broad spectrum of consumer and industrial goods—ranging from wine, liquor, and milk products to commercial cement, furniture, clothing, and hockey equipment.
Untested Legal Mechanism
To enact the sweeping measures, the administration invoked Section 338 of the Tariff Act of 1930—a rarely used legal provision allowing the executive branch to levy additional tariffs of up to 50% on foreign nations deemed to discriminate against U.S. commerce.
White House officials noted that Section 338 addresses trade discrimination rather than national security or economic emergencies. The move comes months after prior global emergency tariffs faced legal challenges in domestic courts, signaling Washington’s pivot toward alternate statutory authorities to maintain import duties.
Senior administration officials briefed reporters that the measure directly responds to Canadian provincial bans on U.S. alcohol, restrictions on American vehicle exports, and import quota disparities affecting U.S. dairy and cheese producers relative to third-party trading partners.
“While the administration continues to secure reciprocal trade agreements globally, Canada retaliated against efforts to protect domestic industry,” U.S. Trade Representative Jamieson Greer stated.
USMCA Impact and Carve-Outs
Significantly, the newly ordered 50% duties will apply to designated items even if they otherwise comply with the United States-Mexico-Canada Agreement (USMCA).
However, the administration confirmed key targeted exemptions:
Energy products (including oil and natural gas)
Potash and critical minerals
Fish and seafood
Goods already governed by sector-specific duties (such as existing steel and aluminum tariffs)
Administration representatives emphasized that the tariffs do not stem from recent disputes concerning drifting Canadian wildfire smoke, noting that policy options regarding environmental spillover remain under separate review.
Canadian Response and Market Reaction
Following the White House announcement, the Canadian dollar experienced a sharp decline against the U.S. dollar, falling approximately 0.4% during evening trading.
Canadian Prime Minister Mark Carney issued a statement emphasizing that Canada’s earlier counter-duties had merely matched previous U.S. trade actions. “Canada stands ready to engage intensively to address outstanding issues with the U.S. to the mutual benefit of our citizens,” Carney stated, pointing to detailed proposals Ottawa submitted to modernize the USMCA framework.
Ontario Premier Doug Ford took a firmer stance, urging a “dollar-for-dollar” reciprocal response if the measures go into effect on August 19.
With a 30-day implementation window before the duties officially lock in, industry associations and trade groups on both sides of the border are calling for urgent bilateral negotiations to avert further supply chain disruption across North America.
The traditional accounting paradigm—defined by periodic monthly closures and post-hoc annual audits—is rapidly giving way to continuous, automated financial oversight. As of July 2026, forward-thinking accounting practices and multinational corporate finance departments are leveraging continuous auditing systems powered by advanced machine learning models. These systems monitor operational transactions in real time, shifting audit methodologies from sample-based post-analysis to absolute, 100% transaction-level verification.
The operational advantages of continuous auditing are transformative. Standard auditing procedures historically relied on statistical sampling, which, despite rigorous methodology, inherently left gaps where anomalies or fraudulent transactions could go undetected for months. Modern continuous auditing platforms integrate directly with enterprise resource planning (ERP) databases, instantly cross-referencing purchase orders, invoices, bank feeds, and tax records. Any deviation from established control parameters or unusual transaction behavior triggers immediate flags for internal audit teams, dramatically reducing detection lag from quarters to seconds.
Beyond fraud prevention, continuous auditing fundamentally alters internal reporting and decision-making. Executive leadership no longer has to wait weeks after the close of a quarter to evaluate precise financial standing; real-time verified ledger data provides an uninterrupted view of operating margins, tax liabilities, and cash flow dynamics. This real-time visibility enables corporate controllers to adjust capital allocation strategies dynamically, mitigating liquidity constraints and capitalizing on emerging commercial opportunities far more efficiently than competitors bound to legacy reporting cycles.
However, implementing continuous auditing requires accounting professionals to acquire new analytical capabilities. The role of the auditor is evolving from manual data reconciliation toward system validation, algorithmic model governance, and strategic risk interpretation. Accounting firms and corporate finance departments must invest in continuous technical education, ensuring that audit staff possess the data engineering skills necessary to design, maintain, and evaluate complex automated compliance systems.