The backlash against artificial intelligence in the workplace is intensifying. What once seemed like a clever way to shortcut tasks and remove repetition has started to feel like a hindrance rather than a hand. According to a recent survey by resume services firm Zety, 51% of professionals say that AI-generated work—dubbed 'workslop'—has actually lowered their productivity. The term refers to content that appears polished but lacks accuracy, substance, or adequate human review. This trend is causing widespread concern, with many workers becoming more cautious about using AI tools.

The research found that the top risks associated with workslop include lower trust in AI (57%), reduced productivity (51%), and damage to a company's reputation (46%). For a technology meant to make people more productive, these findings are alarming. Yet business leaders argue that the problem is not AI itself, but how it is deployed. They suggest two key steps to stop workslop: rethinking what productivity means in an AI-first world, and being persistent in refining AI use.

Rethinking Productivity

Joel Hron, CTO at Thomson Reuters, emphasizes that an 'AI-first, human-second' mindset is crucial. Instead of starting tasks manually and then using AI to polish, professionals should let AI do the initial heavy lifting, then apply human judgment and intuition on top. This shift is already happening in software engineering, and Hron predicts it will spread to other roles. The goal is to move from 'human first, AI second' to a workflow where AI generates a draft, and humans refine it—saving time and reducing errors.

Nick Pearson, CIO at Ricoh Europe, advocates for a sophisticated approach to measuring AI value. Ricoh has created an internal AI marketplace with a model that assesses whether tools actually save hours or days. The model considers business risks, financial returns, and genuine productivity gains. For example, if AI generates meeting notes that nobody reads, that is not adding value. Pearson stresses that professionals must focus on tasks where AI truly accelerates output, such as data analysis or content summarization, rather than trivial activities.

Richard Corbridge, CIO at property specialist Segro, adds that a learning culture is essential. Professionals need to understand the risks of workslop and recognize where AI can operate as a useful assistant. 'AI is very good at generating outputs, but we must not do things without oversight,' he says. Corbridge encourages teams to differentiate between what AI can and cannot do. AI cannot inspire people or create something truly new; it is recursive by nature. Human judgment remains irreplaceable for innovation and strategic thinking.

This rethinking of productivity also involves resetting expectations. Many organizations initially see AI as a magic bullet for efficiency, but quickly realize that effective use requires investment in training and process redesign. The 51% productivity loss from workslop often stems from employees spending extra time verifying and correcting AI outputs. By prioritizing high-quality AI adoption—such as using domain-specific models or retrieval-augmented generation (RAG)—companies can turn the tide.

Being Persistent

Implementing AI is just the starting point. Delivering actual productivity gains requires hard graft and persistence. Hron notes that at Thomson Reuters, some employees initially dismissed AI tools because they didn't perform perfectly out of the box. 'People turned them off instead of building systems around them to ground the AI and guide it,' he says. Those who persisted—often a single hyper-curious individual—managed to achieve exponential improvements that benefited entire teams.

Pearson agrees that persistence matters because employees who become skilled at blending AI with human expertise will be in high demand. These professionals will expect employers to provide cutting-edge AI tools as part of their work environment. Companies that fail to offer such capabilities may struggle to attract and retain top talent. The bar is rising: workers now view advanced AI assistants as a baseline, not a perk.

Corbridge underscores that the backlash against AI should not deter organizations from pursuing its potential. 'The AI bubble is not going to burst,' he says. 'It's here to stay.' The key is to integrate AI safely and effectively. That means continuous training, regular audits of AI outputs, and fostering a culture where employees feel empowered to question and improve AI-generated content. Workslop is a symptom of rushed deployment, not an inherent flaw in the technology.

Historical context supports this view. The productivity paradox—where new technologies initially appear to harm productivity before eventually boosting it—has been observed with previous innovations like computers and the internet. AI is following a similar trajectory. The early phase of hype and haphazard adoption leads to disappointment, but persistent, strategic use yields long-term gains. Companies that invest in the right infrastructure, data governance, and training will reap the rewards.

For individual professionals, the advice is clear: do not give up on AI after a few frustrating attempts. Experiment with different tools, refine prompts, and learn to work alongside AI as a co-pilot rather than a replacement. The 51% who report lower productivity may be those who expect AI to work perfectly without human oversight. By contrast, the minority who successfully blend AI with their expertise are already seeing significant efficiency gains.

In practice, being persistent means developing a habit of iterative improvement. For instance, a marketer using AI to generate blog posts should not accept the first draft verbatim. Instead, they should use the draft as a starting point, adding their own voice, verifying facts, and refining structure. Over time, the AI learns from their corrections, producing higher-quality outputs with less human effort. This feedback loop is essential for reducing workslop.

Organizations must also support persistence by creating safe spaces for experimentation. Employees need to feel free to try and fail without fear of blame. When workslop occurs, it should be a learning opportunity, not a reason to abandon AI. Leaders like Hron, Pearson, and Corbridge all stress the importance of celebrating small wins and sharing best practices across teams. This collaborative approach accelerates the learning curve.

Finally, the economic implications of persistent AI adoption are significant. Companies that master AI integration will outperform competitors in speed, cost, and innovation. The 51% productivity loss from workslop is not inevitable—it is a warning sign that organizations must address gaps in strategy and execution. By rethinking productivity and committing to persistent improvement, businesses can transform AI from a hindrance into a powerful ally.

Source: ZDNET News