How to prevent AI slop from costing your business

How to prevent AI slop from costing your business

Artificial intelligence in the workplace offers compelling benefits, such as faster execution, increased output, and better-informed decision-making. However, as organizations rush to implement AI, they often discover that speed and efficiency alone don’t translate into effective outcomes. Without the right guardrails and processes in place, overreliance on technology can introduce AI slop, which undermines productivity, trust, and quality.

In this article, Upwork, an online marketplace for hiring skilled freelancers, explains what AI slop is, the hidden costs of limited oversight, and how to maintain both productivity and quality while incorporating AI into your business.

What Is AI slop?

AI slop is output generated by artificial intelligence that seems adequate on the surface but falls short in substance. Outputs may include reports, presentations, messages, or code that appear grammatically correct and formatted properly but are missing depth, context, accuracy, or relevance. The end result is often content that creates more work than it saves.

Because AI outputs can seem accurate and look complete, they’re often accepted without adequate review. AI slop typically emerges when users don't fully understand the limits of the tools they’re using, fail to apply appropriate oversight, or lack subject matter expertise. Unfortunately, this can mean passing along work that is flawed, vague, or simply wrong.

The hidden costs of AI slop

The effects of AI slop can compound quickly. At first glance, AI slop may seem like a minor inconvenience. But recent data shows that consequences are significant and widespread.

Low-quality work and reputational damage

Researchers from Stanford Social Media Lab and BetterUp Labs explored in September 2025 the implications of AI slop and coined the term “workslop” to describe the issue. Based on a survey of 1,000 full-time U.S. office workers, the research found that nearly 40% of respondents reported receiving some form of workslop — incomplete, low-quality content — in the previous month. Respondents estimated that more than 15% of the content they receive at work qualifies as workslop.

Put into perspective, this means that nearly one in six messages, deliverables, or reports may be unfinished, unclear, or require additional edits and cleanup before they can be used.

The emotional and reputational impacts can be significant. The research found that over half (53%) of the respondents say they feel annoyed, 38% feel confused, and 22% even feel offended when they encounter workslop. About half of the respondents said they view colleagues who send workslop as less capable, less reliable, and less creative. Additionally, 42% perceive those coworkers as less trustworthy, while 37% see them as less intelligent.

Burnout and lack of clarity

Even when productivity appears to be improving on paper, other implications of AI may be overlooked. Data from The Upwork Research Institute report From Tools to Teammates: Navigating the New Human-AI Relationship found that 77% of executives surveyed reported seeing gains from AI adoption, and employees reported being 40% more productive when using AI tools.

However, the same report found that among workers who reported high productivity levels with AI, 88% also reported feeling burned out. This combination — higher output and lower well-being — highlights the productivity paradox: Faster doesn’t always mean better.

Another report from The Upwork Research Institute, From Burnout to Balance: AI-Enhanced Work Models, found that half of full-time employees surveyed who use AI indicated they have no idea how to actually meet the productivity goals set by their employers. Nearly two-thirds (65%) also said they’re actively struggling with productivity expectations.

6 steps to prevent AI slop

Given the hidden costs of AI slop, organizations need to be proactive and intentional about how they introduce AI tools and platforms, set expectations, and manage outputs.

Avoiding AI slop isn’t about limiting the use of AI. Rather, it’s about building the right systems and processes around how team members leverage AI. Here are six steps teams can take to ensure AI outputs add value rather than clutter.

1. Treat AI as a tool, not a replacement

Think of AI as a capable but inexperienced team member. An AI tool can quickly create drafts and suggest ideas but still requires guidance and oversight. Review AI outputs with the same scrutiny you would apply to any junior team member’s contributions.

For example, if you're using AI to draft marketing copy, consider the content a starting point — not a final draft. A marketer on your team with domain knowledge should still revise tone, validate facts, and ensure the message aligns with brand strategy. The AI saves time on structure and wording, but the worker can ensure the content is up to standard.

2. Implement a standardized review process

Because all AI outputs require feedback from your team before approval or publication, implement a standardized process to review and refine content. Designate AI content checkpoints within your workflows and project timelines, ensuring worker review isn't skipped under pressure. Encourage workers to ask whether the output is actually solving a problem or simply adding volume and more work for the team.

Consider implementing a rubric or checklist to evaluate AI-generated outputs. Answering the right questions as part of a review checklist can significantly improve output quality.

Address questions such as:

• Does this deliver accurate information?
• Is the output on brand?
• Does the content serve its intended audience?
• Is the data or evidence cited properly?
• Are the insights original or merely surface-level summaries?
• Would I feel confident putting my name or the company’s name on this output?
• Does the output raise follow-up questions or require additional clarification?

3. Shift your metrics

Instead of measuring productivity by the number of deliverables produced by AI, focus on the value outputs created. For example, measure whether engagement metrics improve or customers respond more positively to automated processes powered by AI. And track time saved — or additional time added — after accounting for revisions, rework, and team clarification.

A team producing 50 AI-generated reports per month may appear productive. But if half the reports require extensive revisions or are flagged for inaccurate content, this is a sign that volume is eclipsing value. Instead, organizations should track net productivity metrics — including how much usable work is produced after factoring in review, refinement, and revisions. This reframing can drive better strategic decisions about how and when to use AI.

4. Invest in AI literacy

Prompting, editing AI outputs, and identifying when content doesn’t align with context or objectives are all essential in AI-driven workplaces. Provide employees with training, shared resources, and opportunities to experiment with AI tools in a low-stakes environment. This builds confidence and encourages responsible use.

Run internal workshops focused on how to construct better prompts. For technical teams, explore pair-programming sessions in which software developers co-create with AI tools and then reflect on what worked and what didn’t. For content teams, allow time to compare AI- and human-written drafts to identify areas for improvement. Embedding this kind of hands-on learning accelerates adoption while reducing misuse.

In addition to investing in training and AI literacy, set expectations around when and where AI tools should be used — as well as which tools are approved for use at your organization. Because many workers reported a lack of clarity with productivity expectations, outlining which tasks should be handled by AI and which tasks should be overseen by your team can be helpful.

5. Build a culture of experimentation and feedback

Openly encourage and create a safe space for team members to share feedback about what’s working and what’s not with AI tools. When something isn’t working, ask what the original prompt was and how it may be improved. Share ideas for better prompts, iterate together, and make feedback part of how teams grow.

Start team meetings with short reviews of recent AI-assisted projects. Discuss what went well and what could have been stronger. Ask individuals to share prompt versions that led to clearer or more accurate outputs. This approach can help everyone learn to collaborate more effectively with AI. Creating transparent feedback loops turns individual learning into team capabilities.

As part of your culture of feedback, also consider distributing employee engagement surveys or meeting with team members one-on-one to gather feedback about their experience using AI tools, as well as their overall workload. Collecting and addressing feedback can help improve the efficiency of AI tools, show employees their input is valued, and minimize burnout.

6. Bring in outside expertise when needed

In some cases, organizations — especially small and medium-sized businesses (SMBs) with limited resources — may not have the internal bandwidth needed to effectively manage AI tools, review outputs, and maintain quality. To address this, many companies turn to skilled freelancers for the flexibility, structure, and oversight they add.

Freelancers bring to a company specialized skills, subject-matter expertise, and fresh perspectives. And because they often work across multiple clients and across industries, they bring tested strategies for deploying AI responsibly and effectively. Once organizations have standardized review processes and other AI guardrails in place, freelancers can be a powerful extension to internal teams.

Freelancers can help bridge gaps in quality control by reviewing, validating, and refining outputs. Data published in the September 2025 Upwork Monthly Hiring Report found that demand for localization and translation services jumped 29%, quality assurance testing increased 9%, and project management spiked by 102% in September.

Companies can hire translation experts, for example, to catch nuances that AI-powered tools often miss, while freelance QA testers can validate AI outputs before they go live. Demand for freelance project managers has particularly risen among SMBs as annual planning gets underway and companies look to effectively integrate AI into core business processes.

Engage workers to produce high-quality outputs

Rapid AI adoption in the workplace presents both benefits and drawbacks for organizations and workers. While the technology can accelerate workflows and spark creativity, it can also produce AI slop — outputs that are misleading, incomplete, or counterproductive if not reviewed carefully.

Organizations that treat AI as a collaborative tool, invest in employee skills, and prioritize quality over volume are more likely to see sustainable results from AI integration. And by tapping into specialized expertise when needed, teams can ensure AI investments truly deliver value.

This story was produced by Upwork and reviewed and distributed by Stacker.