AI Slop: The hidden enterprise risk CIOs can’t ignore

When ChatGPT first burst into the market in 2022 the promise of generative AI became apparent to the masses. Anyone, anywhere could generate content with almost no barriers.  

As it turns out, that promise can also be misused. 

The ability to generate content doesn't necessarily mean that all AI-generated content is good or useful. The proliferation of poor, low-quality AI content has become an increasingly common issue, a phenomenon called AI slop. The issue and use of the term is so common that “slop” it was Merriam-Webster's 2025 word of the year. 

As enterprises race to adopt GenAI tools, AI slop is an unexpected side effect that threatens data quality, model performance and decision-making accuracy. AI slop is the accumulation of low-quality, unverified AI-generated content flowing into corporate systems. Unlike traditional data quality issues, this new class of risk stems from the very productivity tools meant to enhance operations. 

For CIOs, this represents a new category of technical debt that requires attention and generative AI governance frameworks to manage. 

What is AI Slop in the enterprise context 

In an enterprise IT context, the issue of AI slop is particularly threatening. 

The feedback loop is the most worrisome pattern, according to Adnan Masood, chief AI architect at UST. 

"I've seen teams auto-draft FAQs and [knowledge base] articles, ship them and then feed those same pages back into RAG as retrieval sources," he said. "A month later, you're no longer retrieving trusted institutional knowledge; you're retrieving yesterday's synthetic filler. The model didn't get worse, your knowledge substrate did." 

There are several forms of AI slop: 

Low-quality or hallucinated content. GenAI systems can produce what appears to be coherent text, but might contain factual errors, logical inconsistencies or fabricated information. This creates risks when content influences business decisions or customer interactions. 

Synthetic data created without validation. Teams use AI to generate test data or training datasets, but without human review these synthetic datasets contain edge cases and anomalies that don't match real-world conditions. 

Recycled AI outputs that degrade with each generation. Content gets regenerated multiple times, with each iteration losing fidelity to original source material. Ha Hoang, CIO at Commvault, describes this as "invisible residue that builds up when generative systems produce low-quality, unverifiable or repetitive content that creeps into decision-making systems." 

Code, documentation or knowledge base entries written by LLMs without oversight. AI-assisted development tools can insert incorrect patterns into codebases and technical documentation can be written without review. "Whether it's applications, documents or other deliverables, skipping the review step leads to performance issues and quality problems down the road," said Erik Brown, senior partner for emerging tech and AI at West Monroe. 

How AI Slop enters the enterprise 

There are a number of common ways that low-quality AI content enters enterprises: 

Unvetted employee use of GenAI tools. Workers adopt GenAI tools without understanding limitations or following corporate review protocols.  
"AI tools allow people without specialized expertise to create work products quickly, but they often don't have the knowledge to ensure quality and security standards are met," said Brown. 

Auto-generated marketing content or product descriptions. Marketing teams scale content production using AI but sacrifice brand voice and differentiation. "Everything becomes beige. Your differentiated voice, product specifics and institutional nuance flatten into generic best-practice speak," said Masood. 

AI-assisted coding tools inserting flawed patterns. Development environments integrate AI programming tools that write code. Without review, vulnerable patterns enter production. "The failures that hurt are quiet," Masood explained. "Code that passes linting, looks idiomatic, ships fast and later becomes a vulnerability." 

AI-written knowledge articles in support or HR systems. Organizations populate internal wikis with AI-generated content lacking specificity or institutional context. "[This leads to] inconsistent answers across channels and a shift where escalations increase even as content volume goes up," said Brown. 

Synthetic data pipelines without QA. Data teams generate synthetic datasets but skip validation, introducing biases or unrealistic patterns that degrade model performance. This synthetic data risk compounds as flawed datasets propagate. 

Third-party vendors incorporating AI in deliverables without disclosure. External partners use GenAI tools to produce code or documentation but fail to document usage or validate outputs.  

"Our concern isn't just about bad intent; it's about missing metadata," said Hoan. "When a vendor delivers AI-written analysis, we're asking: Can they show who authored it, under what license and using which data?" 

Why AI slop is an enterprise risk 

The scary part of AI slop is not that it can be wrong, according to Masood. 

"The scary part is that it can be wrong beautifully, at scale and with enough confidence that people stop double-checking," he said. 

AI slop doesn't stay contained to a single department or system. The downstream impacts of unmanaged AI content span multiple risk categories: 
 
Operational risks. Degraded decision-making occurs when executives rely on inaccurate AI-generated analysis. AI model drift accelerates when systems train on AI-generated content rather than validated data. This data quality risk manifests as error amplification in automated pipelines. "Organizations are building operational debt because they're not maintaining consistency or considering long-term maintainability," said Brown. 

Reputational risks. Customers who encounter inaccurate content lose confidence and brand trust is fragile, according to Masood. "A single fabricated customer response can do disproportionate damage," he said. "Once customers discount your answers, you pay for it everywhere: support load increases, sales cycles slow and even correct content gets second-guessed." 

Compliance and legal risks. Copyright concerns emerge when AI systems reproduce protected content without proper licensing. Regulatory scrutiny around AI transparency and data lineage intensifies as frameworks like the European Union AI Act take effect. "Regulatory expectations are tightening around traceability, documentation and controls, particularly for customer-facing AI and higher-impact use cases," Masood said. "Audit complexity increases when organizations cannot trace content provenance.  

"We need to prove 'chain of custody' for content that influences business decisions," Hoang said. "Without audit trails showing model and data source, you can't stand behind the work product."