Companies that have rapidly adopted generative artificial intelligence tools have been undermining institutional knowledge and reducing the quality of their work unless they carefully manage how the technology is used, according to an analysis published by the Harvard Business Review.
The report argues that due to excessive reliance on AI, such companies have accelerated “knowledge decay,” a process in which employees lose skills over time while organizations become increasingly dependent on outdated or low-quality information.
The cycle can begin when workers use AI to produce substandard work that requires colleagues to spend additional time checking facts and correcting errors, gradually weakening confidence in internal processes and institutional knowledge.
“Errors compound and pile up,” the report says. “Trust in information erodes. People spend more time verifying facts or risk costly and dangerous mistakes. Eventually, people start to lose trust in the processes that they rely on to do their jobs.”
The report comes as businesses across industries invest heavily in generative AI tools in hopes of improving productivity, lowering costs and reducing dependence on human labor. But concerns have grown over AI systems that can generate inaccurate information, commonly known as hallucinations, forcing employees to review and correct machine-generated output.
Some companies have hired workers specifically to identify and fix AI-generated mistakes, while others have faced employee resistance over mandates to use the technology.
The study reveals that AI has also complicated recruitment, with automated systems making it harder for employers and candidates to trust hiring processes.
“The overall impact of AI ‘augmenting’ each step is that it has sunk trust in the process to all-time lows for both job seekers and recruiters,” the publication said.
To avoid what it described as the “slopification” of workplace knowledge, the report said organizations should establish processes to verify AI-generated content and limit the technology’s use to tasks where it provides clear benefits.
“For many tasks, using public LLMs often adds little to no real value,” the researchers wrote. “It creates generic prose that often contains mistakes. But the use of proprietary models and/or leveraging proprietary data may well add value.”
The findings add to a growing debate over whether generative AI will deliver the productivity gains promised by technology companies or simply shift more work toward reviewing and correcting machine-generated content.
Stay ahead of the stories shaping our world. Subscribe to Impact Newswire for timely, curated insights on global tech, business, and innovation all in one place.
Dive deeper into the future with the Cause Effect 4.0 Podcast, where we explore the ideas, trends, and technologies driving the global AI conversation.
Got a story to share? Pitch it to us at info@impactnews-wire.com and reach the right audience worldwide
Faustine Ngila is the AI Editor at Impact Newswire, based in Nairobi, Kenya. He is an award-winning journalist specializing in artificial intelligence, blockchain, and emerging technologies.
He previously worked as a global technology reporter at Quartz in New York and Digital Frontier in London, where he covered innovation, startups, and the global digital economy.
With years of experience reporting on cutting-edge technologies, Faustine focuses on AI developments, industry trends, and the impact of technology on society.
Discover more from Impact Newswire
Subscribe to get the latest posts sent to your email.
