The backlash against artificial intelligence is intensifying. What once seemed like a clever shortcut for routine tasks now often feels like a burden rather than a boon. Nearly half of US professionals – 45% – report that so-called "workslop" has made them more cautious about using AI in the workplace, according to a survey by a career services firm. Workslop refers to AI-generated content that appears polished but lacks accuracy, substance, or proper human review. The consequences are tangible: 57% say workslop reduces trust in AI, 51% say it lowers productivity, and 46% worry it damages their company's reputation.

For a technology marketed as a productivity multiplier, these numbers sting. Yet the solution is not to abandon AI. Instead, business leaders and technology executives who have navigated this challenge offer a clear path forward built on two essential steps: rethinking what productivity really means and cultivating persistence in the face of imperfect tools.

Rethinking Productivity

The first step is to shift how you measure and pursue productivity. Rather than asking "How fast can I produce?" the smarter question is "What work should AI do first so I can apply higher judgment?" One CTO leading AI adoption at a global content and technology company described this as an "AI-first, human-second" pattern. In software engineering, for example, AI can generate initial code drafts; the engineer then reviews, refines, and adds creative logic that AI cannot supply. This pattern is poised to spread across all knowledge work.

But simply handing tasks to AI without oversight is a recipe for workslop. A CIO at a European technology firm explained that his organization created an internal AI marketplace where tools are assessed against multiple vectors: business risk, financial return, and actual time saved. The model filters out activities that appear shiny but add zero real value – such as AI generating meeting notes that nobody reads. True productivity gains come from focusing on high-impact tasks where AI genuinely saves hours or days.

Another CIO, from a property specialist, emphasized that professionals must be embedded in a learning culture that understands both the risks and the strengths of AI. "If you think about gen AI, it's by definition very good at generating outputs. But let's not just do things without oversight. Let's use AI as a tool to help educated, experienced colleagues," he said. This means clearly differentiating what AI cannot do – it cannot truly inspire, nor can it create something genuinely novel, because its outputs are inherently recursive. Human judgment remains irreplaceable.

Organizations that succeed in rethinking productivity do not treat AI as a magic wand. They treat it as a junior assistant that requires clear instructions, context, and a human supervisor. They invest in training employees to prompt effectively, to verify outputs, and to know when not to use AI. This nuanced approach transforms AI from a source of workslop into a reliable productivity partner.

Being Persistent

The second step is persistence. Implementing AI is just the starting line; delivering consistent gains requires sustained effort. The same CTO observed that many professionals try an AI tool once, find it doesn't meet expectations, and then switch it off permanently. That is a mistake. The people who ultimately unlock exponential productivity are those who persist – who build systems around the AI to ground it, guide it, and iterate until it works well.

Often it takes just one hyper-curious individual on a team to put in the initial grind, testing different prompts, adjusting parameters, and integrating AI with existing workflows. That person's success then benefits the entire team. But the effort is real. Short-term disappointment must not be mistaken for long-term failure.

Persistence also matters from an employee experience perspective. Professionals who become skilled at blending AI capabilities with human expertise will be in high demand. They will come to expect advanced AI tools in their workplace, and they will judge potential employers by the quality of the AI ecosystem offered. Companies that fail to provide effective AI tools risk losing top talent to competitors that do.

The same CIO from the European firm noted that an employee experience trend is emerging: workers now expect agents and automations that meaningfully assist them. "We're looking at those issues because, ultimately, in the attraction of talent and retaining people, professionals will say, 'Hang on, I had a couple of agents at my last place that really helped me. Do you have those agents available to me in this workplace?'"

To support persistence, organizations should create feedback loops where employees can report workslop incidents, share effective prompting techniques, and celebrate wins. They should also allocate dedicated time for experimentation – allowing professionals to explore AI tools without the pressure of immediate production results. Over time, this culture of persistent learning builds institutional knowledge that reduces workslop across the board.

The broader context is that AI is not a passing trend. While some skeptics question whether the AI bubble will burst, the consensus among leaders is that generative and agentic AI are here to stay. The technology will continue to evolve, and professionals must evolve with it. Those who take the two steps – rethinking productivity and being persistent – will not only avoid the pitfalls of workslop but will position themselves as the indispensable human experts AI needs.

Consider a typical scenario: a marketing professional asks an AI to draft a client proposal. The first output contains generic language, outdated statistics, and a slightly off tone – classic workslop. Instead of abandoning the tool, the professional persists: they add role-specific context, correct the statistics with real data, and adjust the tone via improved prompts. The second draft is much better. The third is nearly ready. Through persistence, the professional reduces proposal writing time by 40% while maintaining quality. This pattern repeats across countless tasks, from code generation to financial analysis to customer support.

The risk of workslop will never disappear entirely, but it can be managed. Organizations that embed the two steps into their AI strategy will see the productivity gains they originally hoped for. Those that ignore the backlash or treat it as a temporary glitch will find their teams disengaged and their investments wasted.

In the end, the professionals who thrive will be those who see AI as a collaborator that needs direction, not a replacement that works alone. They will approach each AI interaction as a chance to learn and refine. They will demand that their employers provide the tools and culture necessary to make AI work well. And they will measure success not by output volume, but by the value created when human and machine intelligence combine.

Source: ZDNET News