A growing backlash against artificial intelligence is reshaping workplace attitudes. What once promised to streamline tasks and eliminate repetition now increasingly feels like an obstacle rather than an aid. According to the Workslop Trust Report from resume services provider Zety, 45% of US professionals say that so-called workslop—AI-generated content that appears polished but lacks accuracy or substance—has made them more cautious about using AI at work. The research found that the top risks of workslop include lower trust in AI (57%), reduced productivity (51%), and damage to a company's reputation (46%). For a technology intended to boost efficiency, these figures present a stark reality: AI is not yet delivering on its promise for many professionals.

Understanding the Workslop Problem

Workslop represents a significant challenge in the AI adoption journey. As organizations rush to integrate generative and agentic AI into daily workflows, the quality of output often falls short. The term captures the frustration of receiving well-formatted but factually hollow or irrelevant content. Jasmine Escalera, career expert at Zety, describes it as an uncomfortable truth: AI is reshaping work, but not always for the better. Professionals are increasingly aware that uncritical reliance on AI can erode trust, waste time, and harm their company's standing.

The impact on productivity is particularly troubling. Instead of saving hours, employees must spend extra time reviewing, correcting, or discarding AI-generated material. This cycle undermines the very purpose of adopting the technology. To break free, business leaders suggest a two-pronged approach: rethink what productivity means in the age of AI, and cultivate persistence in using these tools effectively.

Step One: Rethink Productivity

Joel Hron, Chief Technology Officer at Thomson Reuters, emphasizes that an AI-first mindset is crucial for unlocking value. Rather than starting tasks manually and using AI as an afterthought, professionals should let AI handle the initial heavy lifting and then apply their own judgment, intuition, and expertise. This pattern—AI first, human second—is already reshaping software engineering at Thomson Reuters and will likely spread to other fields in the coming year.

Hron notes that this shift requires a fundamental rethinking of daily workflows. Instead of asking, “How can AI help me do my job?” the question becomes, “How can AI do this job first so that I can add higher value?” This perspective moves beyond simple automation toward true augmentation. It also demands a sophisticated assessment of which tasks benefit from AI and which require human discernment.

Nick Pearson, CIO at Ricoh Europe, echoes this sentiment. Ricoh has developed a model to evaluate its internal AI marketplace tools. The model weighs factors like business risk, financial return, and actual time savings. Pearson warns against chasing ephemeral gains—such as automatically generated meeting notes that nobody reads. True productivity comes from identifying where AI can genuinely save hours or days. His team uses a set of vectors to determine if a tool truly helps or just adds noise.

Richard Corbridge, CIO at property firm Segro, stresses the importance of a learning culture. He argues that professionals must understand the risks of workslop and recognize where AI can operate as a useful assistant—but not a replacement. Gen AI excels at generating output, but oversight remains essential. Corbridge asks teams to differentiate what AI cannot do: it cannot inspire people or create something genuinely new because it is inherently recursive. Human judgment fills that gap.

This step is about redefining productivity as a blend of speed and quality. By prioritizing outcomes over output volume, professionals can avoid the trap of workslop and focus on meaningful contributions. The key is to treat AI as a tool for delegation, not a shortcut to completion.

Step Two: Be Persistent

Implementing AI is only the starting line. Delivering real productivity gains requires sustained effort. Hron describes an all-too-common pattern at Thomson Reuters: employees try an AI tool, find it doesn't meet their expectations, and immediately abandon it. They miss the mark by concluding that “AI just isn't ready.” In contrast, those who persist—who build systems to ground the AI, guide it, and iterate—experience exponential gains. Often, one hyper-curious individual does the heavy lifting, and the entire team benefits.

This persistence pays off not just in immediate efficiency but also in long-term career prospects. Pearson notes that employees who become adept at blending AI capabilities with human expertise will be in high demand. They will also become demanding: when searching for new roles, they will judge potential employers by the quality of AI tools available. The employee experience now includes expectations for sophisticated AI support.

Segro's Corbridge reinforces that persistence is essential because AI is here to stay. Despite occasional predictions of a bubble bursting, the technology continues to evolve. Professionals who invest time in learning how to use AI safely and effectively will secure a competitive advantage. For employers, success lies in creating an environment that encourages experimentation and iteration, balancing risk with reward.

The two steps are interconnected. Rethinking productivity provides the strategic framework; persistence delivers the tactical execution. Without a clear definition of what good looks like, persistence may be misdirected. Without the determination to work through initial frustrations, the best strategies remain unrealized.

In practice, this means organizations must invest in training, provide access to reliable tools, and foster a culture where failure is a learning opportunity. Leaders should model an AI-first mindset, openly discuss workslop risks, and celebrate teams that achieve breakthroughs. The path to AI-driven productivity is not a straight line—it requires constant adjustment and a willingness to fail forward.

As more data emerges about workslop's impact, the imperative for action grows stronger. The Zety report serves as a wake-up call: nearly half of professionals are more cautious about AI, and over half report reduced productivity. But the solution does not lie in abandoning AI. Rather, it lies in being more intentional about its use. By rethinking productivity as a quality-driven, human-centered process, and by persisting through the inevitable bumps, professionals can turn AI from a hindrance into a transformative force.

The next wave of AI adoption will not be about who uses the most advanced models, but about who uses them wisely. The leaders in this space will be those who understand that workslop is a symptom of lazy implementation, not an inevitable byproduct of the technology. With deliberate steps, sustained effort, and a clear focus on value, AI can live up to its promise of boosting productivity without sacrificing trust or quality.

Source: ZDNET News