How to prevent AI slop from costing your business

Artificial intelligence in the workplace offers compelling benefits, including faster execution, increased production, and better-informed decision-making. But as organizations rush to adopt AI, they often find that speed and efficiency alone aren’t enough to deliver effective results. Without the right guardrails and processes in place, over-reliance on technology can lead to AI slump, compromising 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.

AI slop is the output produced by artificial intelligence that appears superficially sufficient but is substantively insufficient. The output may include reports, presentations, messages, or code that appear grammatically correct and well-formatted but lack depth, context, accuracy, or relevance. The end result is often content that creates more work than it saves.

Because AI output appears accurate and complete, it is often accepted without proper review. AI delays typically occur when users do not fully understand the limitations of the tools they are using, fail to apply appropriate oversight, or lack subject matter expertise. Unfortunately, this can mean handing over research that is flawed, ambiguous, or just plain wrong.

The effects of AI slop can quickly worsen. At first glance, AI slop may seem like a minor inconvenience. However, recent data shows that the impact is significant and far-reaching.

Poor quality work and reputational damage

Researchers from the Stanford Social Media Lab and BetterUp Lab investigated the effects of AI slop in September 2025 and coined the term “workslop” to describe the issue. A survey of 1,000 full-time office workers in the United States found that nearly 40% of respondents reported receiving some form of work (incomplete, low-quality content) in the past month. Respondents estimate that more than 15% of the content they receive at work falls under Work Crop.

To put this into perspective, this means that almost 1 in 6 messages, artifacts, or reports are incomplete, unclear, or require additional editing or cleanup before they can be used.

The emotional and reputational impact can be significant. According to the survey, more than half (53%) of respondents said they felt frustrated when they encountered a failure at work, 38% felt confused, and a further 22% said they were upset. About half of respondents said they view colleagues who submit work crops as less competent, less reliable, and less creative. Additionally, 42% perceive those colleagues to be untrustworthy, and 37% perceive their colleagues to be less intelligent.

Burnout and lack of clarity

While productivity may appear to be improved on paper, other impacts of AI can be overlooked. According to data from Upwork Research Institute’s report, From Tools to Teammates: Navigating the New Human-AI Relationship, 77% of executives surveyed reported seeing benefits from AI adoption, and employees reported a 40% increase in productivity when using AI tools.

However, the same report also found that among workers who reported being more productive due to AI, 88% also reported feeling burnt out. This combination of increased production and decreased happiness highlights a productivity paradox. In other words, faster isn’t necessarily better.

Another Upwork Research Institute report, “From Burnout to Balance: An AI-enhanced Work Model,” found that half of full-time employees using AI surveyed said they don’t know how to actually achieve the productivity goals set by their employer. And almost two-thirds (65%) said they were actively struggling with productivity expectations.

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

Avoiding AI slop doesn’t mean limiting the use of AI. Rather, it’s about building the right systems and processes based on how your team members leverage AI. Here are six steps teams can take to ensure their AI output adds value rather than clutter.

1. Treat AI as a tool, not a replacement

Think of AI as a competent but inexperienced team member. AI tools can quickly draft and suggest ideas, but they still require guidance and oversight. Review the AI ​​output with the same scrutiny you apply to the contributions of junior team members.

For example, if you’re using AI to draft marketing copy, consider that content to be a starting point rather than a final draft. Marketers on teams with domain knowledge need to adjust the tone, verify the facts, and make sure the message is aligned with the brand strategy. AI saves time on structure and wording while allowing workers to ensure content is compliant with standards.

2. Implement a standardized review process

All AI output requires feedback from your team before approval or publication, so implement a standardized process to review and refine content. You can specify checkpoints for AI content within workflows and project timelines to ensure that workers don’t skip reviews due to pressure. Encourage employees to ask themselves if their work is actually solving a problem or just adding to the team’s workload and workload.

Consider implementing a rubric or checklist for evaluating AI-generated output. Answering the right questions as part of your review checklist will greatly improve the quality of your output.

Answer questions such as:

• Does this provide accurate information?
• Is the output brand-related?
• Does your content serve your target audience?
• Is data and evidence properly cited?
• Are the insights unique or just a surface-level summary?
• Can I confidently include my name and company name in this output?
• Does the output raise follow-up questions or require additional clarification?

Rather than measuring productivity by the number of artifacts produced by AI, focus on the valuable outputs produced. For example, measure whether engagement metrics improve or whether customers respond more positively to an automated process powered by AI. It also tracks time saved or added after accounting for revisions, rework, and team clarification.

A team that produces 50 AI-generated reports every month may appear to be productive. But if half of your reports require major revisions or are flagged for inaccuracies, this is a sign that the volume is more than worth it. Instead, organizations should track net productivity metrics, including how much useful work is produced after accounting for reviews, refinements, and revisions. This reconfiguration can drive better strategic decisions about how and when to use AI.

Prompting, editing AI output, and identifying when content doesn’t match context or purpose are all essential in an AI-driven workplace. Provide your employees with training, shared resources, and opportunities to try out AI tools in a low-risk environment. This instills confidence and encourages responsible use.

Conduct in-house workshops focused on how to write better prompts. For technical teams, consider pair programming sessions where software developers co-develop using AI tools and reflect on what worked and what didn’t. If you’re a content team, take the time to compare AI-generated drafts with human-generated drafts to identify areas for improvement. Incorporating this type of hands-on learning accelerates adoption while reducing misuse.

In addition to investing in training and AI literacy, set expectations for when and where AI tools should be used and which tools are approved for use in your organization. Many employees report that productivity expectations are unclear, so it can be helpful to outline which tasks should be handled by AI and which should be supervised by your team.

5. Build a culture of experimentation and feedback

Openly encourage and create a safe space for team members to share feedback on what is and isn’t working with your AI tools. When something doesn’t work, ask what the original prompt was and how you can improve it. Share ideas for better prompts, iterate together, and make feedback part of your team’s growth.

Start your team meeting with a quick review of recent AI-assisted projects. Discuss what worked well and what could have been more powerful. Ask individuals to share immediate versions that led to clearer or more accurate output. This approach will help everyone learn how to collaborate more effectively with AI. Creating transparent feedback loops turns individual learning into team power.

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

6. Bring in outside expertise as 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 output, and maintain quality. To address this, many companies turn to skilled freelancers for added flexibility, structure, and oversight.

Freelancers bring specialized skills, subject matter expertise, and fresh perspectives to your company. And because we often work across multiple clients and industries, we bring tested strategies for implementing AI responsibly and effectively. When organizations implement standardized review processes and other AI guardrails, freelancers can become a powerful extension of in-house teams.

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

For example, companies can hire translation experts to catch nuances that AI-powered tools often miss, and freelance QA testers can validate AI output before it goes live. As annual planning progresses and companies look to effectively integrate AI into their core business processes, demand for freelance project managers is on the rise, especially among small and medium-sized businesses.

The rapid adoption of AI in the workplace brings both benefits and drawbacks to organizations and employees. While this technology can accelerate workflows and inspire creativity, it can also produce AI slop, or output that is misleading, incomplete, or counterproductive if not carefully reviewed.

Organizations that treat AI as a collaboration tool, invest in the skills of their employees, and prioritize quality over quantity are more likely to see sustainable results from AI integration. And by leveraging expertise where needed, teams can ensure that their AI investments deliver real value.

this story produced by upwork Reviewed and distributed by stacker.