Today’s AI systems are eating themselves sick.
In the rush to generate returns on their AI investments, enterprises are dumping terabytes of raw unstructured content into their models. The idea is that more data means smarter models, but in reality, it means digital “indigestion.”
When AI ingests junk such as redundant, obsolete, trivial (ROT) data and contradictory information, it regurgitates inaccurate and risky outputs. The real issue isn’t how AI thinks, but what it’s fed.
The Cost of a “Junk Data” Diet
Every byte AI processes comes at a cost. Whether it’s cloud compute, storage, or energy, every document ingested consumes valuable resources. If a portion of that data is irrelevant or duplicative, the system wastes the same proportion of processing power. The more junk it eats, the more it bloats.
This inefficiency directly translates into wasted budget and slower performance, but the risks don’t stop there. Poor-quality data degrades model accuracy and reliability. Outdated or conflicting content creates noise that AI systems can’t distinguish from truth.
Recent joint research by Anthropic and institutions including Oxford University and the Alan Turing Institute underscores just how vulnerable AI can be to bad data. The study found that just 250 poisoned documents were enough to compromise models of all sizes, from 600 million to 13 billion parameters. Whether the AI had been trained on 6 billion or 260 billion tokens, those 250 samples were enough to distort its reasoning.
That’s roughly 0.00016% of the dataset, barely a blip. Yet the damage was systemic and irreversible. If just 250 documents can poison an LLM, it’s time for enterprises examine every ingredient they’re feeding into their AI.
The Symptoms of Data “Indigestion”
Across models, hallucinations are on the rise. OpenAI’s 2025 PersonQA benchmark found that hallucination rates in leading models have nearly doubled since last year—from roughly 15–17% in 2024 to as high as 30–48% today.
Researchers often blame model design, suggesting that newer models are built to be more persuasive rather than more accurate. But they’re overlooking a more fundamental cause: AI learns from whatever it’s fed.
If that data is ROT or incomplete, the AI doesn’t just hallucinate. It develops systemic bias, compliance risks, and inconsistent judgment. Here are three of the ways that data “indigestion” manifests:
- Hallucination and Overconfidence: Filling in gaps in knowledge with fabricated but confident responses.
- Bias Amplification: Reproducing or exaggerating social, cultural, and organizational biases.
- Compliance Risk: Pulling sensitive, private, or regulated content into training or inference workflows.
Each symptom can be traced back to ungoverned ingestion of unstructured data. To maintain accuracy, ethics, and trust, enterprises must control what content enters the system.
The Cure: 5 Steps to Healthy Ingestion
Unstructured content makes up over 80% of enterprise information—emails, documents, file shares, chats, and more. Much of it has accumulated for years without classification or curation. Feeding this mass of unfiltered content into AI systems produces poor outputs and risk exposure.
The remedy is a systematic approach to unstructured data ingestion:
1. Classification: Understand what content exists and where. Manual approaches are unfeasible at enterprise data volumes, especially when that data lives in disconnected silos. Enterprises need a unified content and metadata scanning solution to automatically identify ROT and sensitive files across the entire data estate. Visibility is the foundation of control.
2. Curation: Not all data deserves to be fed to AI. Filter for relevant, current, and authoritative content before ingestion. Removing outdated or conflicting documents saves compute cycles and improves model accuracy. Only the best ingredients make it to the plate.
3. Tagging and Metadata Enrichment: Add context through tags using automated content analysis. Custom metadata transforms unstructured files into searchable, traceable assets that can be verified and routed correctly.
4. Segmentation by Use Case: Don’t feed every model from the same menu. A customer service chatbot needs troubleshooting guides, not HR policies. Tailor ingestion pipelines to specific use cases to improve precision and maintain oversight.
5. Continuous Monitoring: Data is never static in the enterprise, where millions of documents and communications are created every day. A systematic approach requires continuous monitoring for relevance, sensitivity, and redundancy to keep AI pipelines clean.
This approach replaces guesswork with governance, and junk data with a clean, curated diet of trusted intelligence.
You Are What You Eat (And So Is Your AI)
By now, you’ve likely heard the adage: AI is only as good as the data it consumes. Feeding it redundant, outdated, or irrelevant data yields poor results, no matter how advanced the model. The path to higher ROI and lower risk begins with smarter ingestion. It’s time to swap the digital junk food for a clean, curated diet—because when AI eats well, it thinks well.
Don’t let your AI eat junk. See how ZL Tech helps enterprises govern and curate unstructured data for AI.
