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The backlash against AI is in full swing. What once seemed like a clever way to shortcut tasks and remove repetition has started to feel like a hindrance rather than a hand.
Almost half (45%) of US professionals said "workslop" has made them more cautious about using AI in the workplace, according to the Workslop Trust Report from resume templates service Zety.
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The research, which defined workslop as AI-generated work that appears polished but lacks accuracy, substance, or adequate review, found that professionals believe this low-quality output has lasting consequences for teams and organizations.
Zety's research suggested the top risks of workslop are 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, not less, the potential implications of these risks are considerable for professionals who see generative and agentic AI as a solution to some of their biggest workplace challenges.
As Zety's in-house career expert, Jasmine Escalera, suggested succinctly, her firm's research presents an uncomfortable reality: "AI is reshaping how work gets done, but not always for the better."
So, what can professionals do to ensure that AI-enabled services are a hand rather than a hindrance?
The answer, suggested the business leaders who spoke with ZDNET, is twofold: rethinking productivity and being persistent.
Joel Hron, CTO at Thomson Reuters, is tasked with helping the global content and technology specialist exploit gen AI, machine learning, and agentic technologies.
He told ZDNET that one of the key lessons his organization has learned over the past two years is that rethinking what AI productivity means is a constant work in progress.
"An AI-first mindset is an important change that's happening right now," he said. "That approach means looking at the jobs that you're doing every day and figuring out, 'How do I get AI to do this job first, so that I can come in second with a higher layer of judgment or intuition, rather than me doing it first?'"
Hron described this shift as an interesting change in working style that his firm is experiencing now, particularly in software engineering.
He said this trend foreshadows similar transitions across other roles in the future: "I think that 'AI first, human second work pattern' is one area to pay attention to over the course of the next year."
The professionals who excel during this shift in working practices, Nick Pearson, CIO at technology specialist Ricoh Europe, told ZDNET, will be those who take a sophisticated approach to AI's value-adding capabilities.
To help in this process, Ricoh has created a model to assess whether the tools that people select from its internal AI marketplace generate productivity gains. The model considers a range of vectors, such as business risks and financial returns.
"The model asks, 'Does this thing help or not? Does it really save hours or days? Where does AI save this time? Is it generating notes on a meeting that, frankly, no one cares about?' Because that's not something that's adding value," said Pearson.
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Rather than focusing on ephemeral targets, Richard Corbridge, CIO at property specialist Segro, suggested the key to realizing productivity gains is for professionals to be part of a learning culture that understands the risks of workslop and recognizes where AI can operate as a useful assistant.
"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.
"We make sure that the workslop element is really understood and that if you don't use this tool wisely, then the risks are much higher. I'm a big fan of saying, 'Where do we differentiate what can't AI do?' It can't inspire people, per se; it can't naturally create something new, because it's quite recursive. We need human judgment."
Implementing AI is just the starting point. Delivering actual productivity gains requires hard graft.
Hron said Thomson Reuters uses a mix of in-house models and off-the-shelf tools to power its AI-enabled services. However, not every professional sees the value straightaway.
"People would sometimes pick up these tools, and they wouldn't quite do what they wanted them to do or wouldn't do things as well as they wanted," he said. "But when these people just said AI wasn't ready, and they turned it off, they missed the mark."
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Hron said his team learned that persistence pays off when using AI services.
"The people who built systems around the AI tools necessary to ground the AI and guide it in the right direction were ultimately the ones who hit a new exponential that the rest of the organization wasn't on," he said.
"They, suddenly, got on a new curve. But it took effort and persistence. Often, there was one individual who was hyper-curious and put in the work, and then everyone else on the team tended to benefit from that effort."
Persistence matters, suggested Ricoh's Pearson, because employees who become effective at blending AI capabilities with human expertise will be in high demand and, consequently, become highly demanding.
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When these professionals look for new roles, they will judge potential employers on the AI tools that they can use in their work: "An employee experience is bubbling where people are saying that these are the tools and capabilities I expect to have in a company."
Persistent professionals who learn how to use AI safely and effectively will be in a strong position. And for employers, Pearson suggested that success will be about establishing the right blend between risk and reward.
"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?'"
The simple message, said Segro's Corbridge, is that persistence will pay off. While the backlash against AI might be on the rise, the technology will continue to evolve and grow, and professionals must focus on understanding how to exploit its capabilities effectively.
"There's a lot of debate about when the AI bubble is going to burst," he said. "I'm not convinced. I think it's here to stay. AI isn't going to go away."
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