As generative AI tools move from pilot programs into daily workflows, researchers are beginning to examine their unintended consequences. A new analysis published in Harvard Business Review expands on the concept of “workslop,” a term the authors introduced last year to describe low-effort, AI-generated output that appears polished but shifts the burden of verification, interpretation, and correction to the recipient.

The latest research suggests workslop is not an isolated annoyance. In survey data cited by the authors, 41 percent of respondents reported receiving a specific instance of workslop that affected their work, and 53 percent admitted to sending at least some AI-generated material they considered unhelpful, low effort, or low quality. Rather than framing the issue as individual misuse, the researchers argue that workslop reflects unclear AI mandates, overloaded teams, and declining workplace trust.

For laboratory managers, the findings arrive at a critical moment. Labs are increasingly experimenting with generative AI to draft standard operating procedures (SOPs), summarize experimental data, prepare internal reports, and streamline administrative documentation. In highly regulated environments, however, superficially complete AI output that lacks contextual accuracy can introduce operational risk.

What the new research reveals about workslop

The researchers position workslop as a management issue rather than a technology flaw. According to the article, 41 percent of surveyed employees said leadership encouraged AI use without detailed instructions or task-specific standards. At the same time, more than half acknowledged sending at least some low-quality AI-generated work to colleagues.

The data suggest that broad directives to “use AI” can drive performative adoption. When expectations emphasize visible AI usage over measurable quality, employees under workload pressure may prioritize speed and compliance over depth and accuracy. The result is content that appears complete but requires additional cognitive effort from reviewers.

The researchers also identify trust as a key variable. In their findings, stronger team trust significantly reduced the likelihood of workslop, while unclear expectations increased it.

Why workslop matters in laboratory operations

Although the research spans industries, the operational implications are particularly relevant for laboratories, where documentation quality directly affects compliance, traceability, and reproducibility.

In lab management contexts, workslop may manifest as:

• Generic or imprecise language in SOP drafts
• Incomplete contextualization of experimental data summaries
• AI-assisted performance evaluations that lack individualized assessment
• Internal communications that require clarification or correction

In regulated or clinical environments, even small inaccuracies can complicate audits, delay approvals, or create downstream interpretation issues. If AI-generated sections require significant revision, the net productivity gain may be limited or negated.

Lab Management Certificate

The Lab Management certificate is more than training—it’s a professional advantage.

Gain critical skills and IACET-approved CEUs that make a measurable difference.

Beyond documentation quality, workslop can affect culture. When team members perceive that AI is substituting for professional judgment without adequate oversight, trust and morale may erode.

What lab managers can do to prevent workslop

The research outlines several protective factors that laboratory leaders can translate into operational safeguards.

Clarify appropriate AI use

Rather than issuing broad AI mandates, lab managers should define acceptable use cases. Clear guidance can specify:

• Which document types may include AI-generated drafts
• Required human review and sign-off procedures
• Tasks where AI support is inappropriate due to scientific or regulatory risk

Integrating these standards into existing quality management systems reinforces accountability.

Build AI competence

The researchers report that employees who feel competent and in control of AI tools are significantly less likely to produce workslop. In laboratory settings, AI literacy training should emphasize:

• Crafting precise, context-rich prompts
• Verifying technical accuracy against primary data
• Distinguishing drafting assistance from analytical judgment
• Recognizing limitations of generative models

Reinforce review culture and trust

Structured peer review and supervisory oversight remain essential safeguards. Encouraging disclosure of AI-assisted work and normalizing validation processes can reduce stigma while preserving quality standards.

Focus on outcomes, not usage frequency

Tracking the volume of AI use may incentivize superficial adoption. Instead, lab leaders can evaluate AI integration against operational metrics, including turnaround time, error rates, audit findings, and workload distribution.

A management signal, not just a technology issue

The Harvard Business Review research frames workslop as a signal of organizational strain—an indicator that expectations, capacity, and governance structures may be misaligned. For laboratory operations navigating automation, staffing pressures, and digital transformation, that framing is instructive.

Generative AI can support productivity in lab management when deployed thoughtfully. The emerging research suggests, however, that without clear standards, training, and reinforced human oversight, AI adoption may shift cognitive burden rather than reduce it.

For lab managers, the takeaway is not to slow experimentation, but to structure it deliberately. Defined policies, disciplined review practices, and strong team trust remain essential as laboratories integrate generative AI into operational workflows.

This article was created with the assistance of Generative AI and has undergone editorial review before publishing.