Corporate AI Adoption Sparks Concerns Over ‘Work Slop’ and Quality Control

The Rise of AI-Generated “Work Slop”

Companies implementing artificial intelligence tools are encountering a new workplace phenomenon termed “work slop”—content that appears professional but lacks substantive value, according to reports from business experts and research organizations. This automated content, while quick and inexpensive to produce, creates hidden costs as employees struggle to process and correct it, sources indicate.

The Rise of AI-Generated “Work Slop”
Quantifying the Problem
Real-World Consequences
Internal Organizational Impact
Governance and Training Solutions
Framework for Responsible Implementation
Structural Considerations

André Spicer, dean of Bayes Business School and author, describes this development as “a new form of automated sludge in organizations.” He explains that while traditional bureaucratic processes like meetings and reports required significant time investment, “this new form of sludge is quick and cheap to produce in vast quantities. What is expensive is wading through it.”

Quantifying the Problem

A recent study by coaching platform BetterUp and Stanford Social Media Lab found that desk-based employees in the United States estimate approximately 15% of the work they receive qualifies as AI work slop. Researchers define this term as “AI-generated work content that masquerades as good work but lacks the substance to meaningfully advance a given task,” potentially resulting in content that is “unhelpful, incomplete, or missing crucial context.”

Michael Eiden, managing director at Alvarez & Marsal’s digital technology services, observes that “the accessibility of generative AI has made it easier than ever to produce work quickly—but not necessarily to the highest standard.” This accessibility creates tension between efficiency gains and quality control, analysts suggest.

Real-World Consequences

The professional services sector has already experienced tangible repercussions. Deloitte reportedly provided partial refunds to the Australian government for a report containing AI-generated errors, demonstrating the reputational and financial risks for firms relying heavily on automated content generation.

Legal professionals have also faced challenges, with the UK High Court urging vigilance after cases where lawyers using AI submitted documents containing false information, including fabricated citations and quotations. These incidents highlight the potential for serious professional consequences when AI tools are deployed without adequate oversight., according to additional coverage

Internal Organizational Impact

The problems extend beyond external reputation damage. Internally, poor AI-generated content often results in “bloated reports with mangled meanings and excessive verbiage,” creating additional work for colleagues who must decipher or correct the material, according to business analysts.

Kate Niederhoffer, social psychologist and vice-president at BetterUp Labs, emphasizes that employees typically generate work slop not for “nefarious” reasons but because they “have so much work to do.” She categorizes AI users into “pilots” who use technology to augment their capabilities and “passengers” who use it primarily to save time when overwhelmed.

Governance and Training Solutions

Experts unanimously recommend establishing clear policies and training programs to mitigate these challenges. “Firms shouldn’t simply hand employees these tools without guidance,” Eiden advises. “They need to clearly define what good looks like.”

James Osborn, group chief digital officer at KPMG UK and Switzerland, stresses the importance of both staff verification and “suitable governance processes” to ensure appropriate technology use. For high-stakes work, human review remains “non-negotiable,” with AI serving as an assistant rather than final author, according to industry leaders.

Framework for Responsible Implementation

Mark Hoffman of Asana’s Work Innovation Lab advocates four core foundations for AI use: comprehensive guidelines balancing various concerns; training that extends beyond technical skills to include delegation; clear accountability rules; and quality control standards prioritizing accuracy alongside efficiency.

Joe Hildebrand, managing director of talent and organization at Accenture, emphasizes “reversibility” as a critical principle: “Every AI deployment should include a human override or kill switch. Monitoring how often humans reverse AI decisions and using those insights to improve the system can enhance trust.”

Structural Considerations

Some experts suggest that the solution may involve returning to more traditional evaluation methods. Spicer notes that universities are increasingly requiring written exams or verbal presentations instead of electronic submissions, predicting that companies will similarly “rely on analogue input and processes to make high-stakes decisions.”

Stuart Mills, assistant professor of economics at Leeds University, cautions that managers risk being distracted by “the excitement of AI and immediateness of the results” instead of asking fundamental questions about organizational structure and value creation. He warns that measuring output by volume rather than quality can create “an illusion of productivity” that ultimately undermines genuine efficiency.

As organizations continue to integrate AI tools, the challenge remains balancing technological capabilities with human judgment to prevent the proliferation of work slop while harnessing AI’s genuine potential, according to industry observers.