10 Common Hallucinations: When Bad AI Impacts Trust and Revenue

We have moved past the era of the "chatty" LLM. Organizations are no longer just using AI to draft emails; they are deploying autonomous agents to execute high-stakes business processes—from managing supply chains to auditing legal contracts. This is the "agentic shift."

While these agents offer unprecedented efficiency, they carry a structural risk: they are designed to provide an answer even when the data is incomplete. In a chatbot interface, a hallucination is a nuisance; in an autonomous agent, it is a business failure. When models invent facts in the middle of a live workflow, the result is direct liability, frozen transactions, and eroded trust. To secure the agentic future, we must move from blind trust to rigorous, continuous observability.

Examples of common AI hallucinations

AI hallucinations are hard to spot. To show how these common hallucinations can be prevented, we’ll be highlighting features and capabilities from Splunk Agent Observability.

1. Phantom vendor contracts in automated procurement

Your autonomous procurement bot can generate a 30-page contract for a supplier who has never heard of it. Unauthorized spend and fraud investigations follow. In Boston Consulting Group's (BCG) 2026 survey of tech procurement functions, trust in autonomous decision-making was the top barrier to agentic deployment at 71%, with accountability for agent actions at 53%.

The Fix: The Context Adherence metric inSplunk Agent Observability scores whether a response draws only from the documents your bot actually retrieved. Luna evaluation models run that check on every clause and flag vendor names or stock-keeping units (SKUs) with no source document. Anchor retrieval to your certified contract repository and reject unsupported entities in continuous integration and continuous delivery (CI/CD).

2. Synthetic risk alerts in banking compliance agents

Your compliance agent flags a wire transfer for North Korean sanctions violations, complete with convincing Office of Foreign Assets Control (OFAC) identification numbers (IDs) and detailed backstories. None of it exists, yet the alert can slip past rule-based filters, freeze legitimate transactions, and trigger mandatory disclosures. The Financial Industry Regulatory Authority's (FINRA) 2026 Annual Regulatory Oversight Report defines hallucinations as output that "is inaccurate or misleading, yet is presented as factual information" and calls for "storing prompt and output logs for accountability and troubleshooting."

Signals clusters similar anomalies across production traces, while Context Adherence exposes named individuals with no match in retrieved watchlist data. Require dual-source verification and human review before flagging a transaction.

3. Ghost parts in manufacturing schedulers

Your scheduling agent insists a "ZX-17 torque plate" must ship tomorrow. That part never existed. A study of autonomous supply-chain agents found GPT-5 mini and Llama 4 Maverick violated basic ordering rules in more than 25% of cases, with coefficients of variation of 37–46% across repeated runs. Gartner forecasts spend on supply chain software with agentic AI will grow from under $2 billion in 2025 to $53 billion by 2030, so the exposure widens every quarter.

The Fix: Score suggestions against your bill of materials. When output drifts, Tool Selection Quality flags incorrect tools or arguments, and the Agent Graph traces the step that produced the order. Require a verified part number before purchase.

4. Imaginary drug interactions in clinical decision support

You're testing a clinical decision-support agent before release when it flags a "novel" interaction between warfarin and a common probiotic. The reference looks authoritative, yet the citation doesn't exist in PubMed. A Communications Medicine study fed six LLMs 300 vignettes, each containing one fabricated clinical detail; hallucination rates ran 50%–82% across models and prompting methods. In Clinical Pharmacology & Therapeutics, decision-support systems found 280 clinically relevant drug-drug interactions across 30 polypharmacy patients versus ChatGPT's 80; repeat queries changed 27 of the 30 answers.

The Fix: Pipe every suggested interaction through your pharmacy database. Correctness catches open-domain fabrications, Context Adherence flags claims that stray from the supplied literature, and anything unsupported gets redacted and routed to a pharmacist.

5. False stock replenishment in retail inventory agents

You're monitoring a retail inventory agent after launch when post-holiday audits reveal inflated purchase orders for items that never moved because the agent fabricated a demand spike. Research on LLM newsvendor decisions found GPT-4 deviated up to 70% more than human benchmarks and chased demand with adjustment rates approaching 100% where humans rarely exceeded 40%. IHL Group estimates global inventory distortion at $1.73 trillion annually, $572 billion of it overstock.

The Fix: Execution tracing maps each reasoning hop to point-of-sale, loyalty, and supplier data, exposing orders with no upstream signal. Require two live signals or human approval.

6. Fabricated competitive intelligence in strategy agents

Your strategy agent is tasked with summarizing competitive threats for a quarterly M&A report. It produces a professional-looking analysis claiming a key competitor is pivoting to a new hardware-as-a-service model, complete with projected revenue figures and "leaked" market share data. None of this is true—the model hallucinated the strategy based on a mix of industry rumors and its own training data. Leadership decisions based on this fabrication could lead to millions in misallocated R&D investment.

The Fix: Anchor strategy agents to verified third-party data feeds and internal market research silos. Use Context Adherence to block any assertion that cannot be traced back to a specific, authorized document in your research library.

7. Hallucinated pricing and features in sales copilots

Your sales copilot is assisting a prospect with a quote. In an effort to close the deal, the agent invents a "bundled discount" and a set of features that don't exist in your product roadmap, promising delivery by Q3. The customer signs the proposal based on these false terms. When the error is discovered, the company faces a contract breach, reputational damage, and an inevitable churn event.

The Fix: Constraint-gate your sales agent to query only your official pricing engine and current product feature API. Use Correctness checks to validate that every offer or promise made in the conversation is cross-referenced against your CRM’s active product catalog. Block the agent from outputting any price or date not verified by a live source.

8. Invented legal citations in contract review

You're evaluating a contract-review agent against an approved corpus when its summary of a 90-page supplier agreement comes back peppered with case law that doesn't exist. Damien Charlotin's AI Hallucination Cases database counted 2,041 cases worldwide involving AI-hallucinated content, 1,367 of them in the US. The Sixth Circuit fined two lawyers $30,000 in Whiting v. City of Athens; the Ninth Circuit added six-month suspensions in Lnu v. Bondi. Dedicated legal research tools hallucinate 17%–33% of the time, per Stanford Law's study in the Journal of Empirical Legal Studies. If a fabrication reaches your contracts, you, not the model, own the liability.

The Fix: For example, Custom Metrics cross-reference each authority against your retrieved corpus and flag unsupported or outdated cases. Retrieval-only citation generators confine models to your approved law library, and CI/CD gates reject ungrounded references.

9. Invented customer personas in marketing segmentation

You've seen a segmentation model churn out oddly specific audiences ("eco-luxury millennials in suburban zip codes with an affinity for artisanal cold brew") that no one can trace to real data. Interactive Advertising Bureau (IAB) research found 70% of marketers had experienced at least one AI incident, 40% had to pause or pull ads, and only 6% believe current safeguards are sufficient.

The Fix: Keep models inside approved customer tables. Run Context Adherence against those tables and verify persona size and claimed behaviors against customer relationship management (CRM) counts.

10. Bogus incident root cause in IT service management

Picture an after-hours outage: dashboards glow red and your AI service-desk agent blames "a corrupted Transport Layer Security (TLS) certificate on node 42." No such node exists, and engineers chase the phantom fix while service-level agreement (SLA) penalties mount. InfoQ's reporting on ClickHouse's site reliability engineering (SRE) evaluation put zero-shot LLM root-cause accuracy at 44%–58% versus 62% for human SREs; prompt engineering lifted models to 60%–74% while humans stayed above 80%.

Gartner's survey of 782 I&O leaders found only 28% of AI use cases fully succeed, with failures concentrated in auto-remediation and agent-led workflows. I&O refers to infrastructure and operations.

The Fix: Validate each root-cause claim against retrieved logs with Correctness and Context Adherence checks. Use CI/CD gates to block models that fail factuality tests.

Operationalize trustworthy AI agents with Splunk

A production agent needs continuous observability and evaluation. Splunk Agent Observability, part of Splunk Observability Cloud, traces a rogue claim to the prompt or data chunk that created it:

• Automated quality gates in CI/CD: Run experiments inside your unit tests and pipelines, assert on metric aggregates, and block releases that fail quality thresholds.
• Multi-dimensional response evaluation: Score every output across Context Adherence, Correctness, and Tool Selection Quality with Luna evaluation models at up to 96% lower cost than frontier LLM-as-judge evaluation, with under-200ms latency while running multiple evaluation metrics.
• Agent Graph visualization: Render every branch, decision, and tool call as an interactive graph and see the step where a fabricated entity entered the workflow.
• Signals for automatic failure detection: Cluster similar failures across production traces and surface recurring failure patterns.

Splunk's visibility and insight keep digital systems secure and reliable. Digital resilience must cover the agents making decisions inside those systems, and we're building the measurement layer that decides which decisions you can trust.

Read the report The Agentic Shift: Redefining Observability for the AI Era to see how agent observability fits into your resilience strategy.

FAQs about the impact of AI hallucinations on business