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New data from Testlio highlights growing risks associated with hallucination and accuracy failures in enterprise AI systems, prompting a shift towards advanced testing approaches that combine human oversight and AI automation to ensure trustworthiness and safety.
New data from Testlio reveals a growing set of reliability challenges in enterprise artificial intelligence deployments, with a striking 82% of identified bugs attributed to hallucinations and accuracy failures. These issues, often described as “invisible failures,” occur when AI systems provide incorrect or fabricated information while maintaining an appearance of flawless operation, creating significant risks for organisations relying on AI-driven services.
Testlio’s findings come from thousands of tests conducted on enterprise AI products over six months. The data highlights that most errors do not manifest as visible crashes or error messages, as seen in traditional software, but rather through misinformation generated by AI models. This subtlety makes such issues harder to detect and address until their consequences emerge, potentially causing significant harm to user trust and company reputation. Enterprise applications such as chatbots, retrieval-augmented generation (RAG) systems, and other AI solutions are particularly vulnerable to partly inaccurate or wholly fabricated outputs, which may go unnoticed by users.
The severity of these problems is notable, with 79% of detected AI issues rated as medium or high in impact, directly affecting user experience and trust. This underscores a growing realisation among corporate leaders that ensuring fundamental truthfulness and reliability in AI systems poses a greater challenge than previously appreciated, surpassing concerns around bias and fairness, which represented a smaller fraction of identified bugs. Dean Hickman-Smith, Chief Revenue Officer at Testlio, emphasises this point: “The most dangerous AI failures are the ones you can’t see. When traditional software breaks, it crashes visibly. AI systems, by contrast, often appear flawless while quietly fabricating information. The real crisis in AI isn’t bias, it’s basic truth.”
In response to these challenges, Testlio has expanded its AI Testing solution to better address the specific needs of validating AI systems. The enhanced service incorporates hallucination detection, agentic behaviour assessment, consumer safety, and enterprise security. By leveraging a global network of over 80,000 vetted testers combined with AI-powered automation, Testlio aims to uncover subtle errors and contextual failures that traditional testing methods might miss. This comprehensive approach extends beyond verifying functionality to include evaluations of fairness, consistency in reasoning, and trustworthiness under realistic, practical conditions.
Testlio’s validation capabilities support a wide array of AI applications including generative AI, large language model integrations, retrieval-augmented generation, agentic AI, recommendation engines, and predictive technologies. The company also assesses response delivery, formatting, and system integration reliability across an extensive range of languages, real devices, and payment methods. Underpinning these efforts are proprietary technologies like LeoAI Engine and LeoMatch, which utilise vast testing data to streamline test orchestration and precisely match testers with specialised cases.
Industry experts highlight the necessity of evolving AI testing practices to meet the sophisticated challenges posed by AI behaviour. Traditional QA methods, effective for conventional software, fall short when applied to AI, where issues such as hallucinations, ethical drift, and model degradation require continuous behavioural monitoring and crowd-sourced red teaming. Human-in-the-loop (HITL) testing models have become essential, combining human judgment with automation to identify nuanced failures, mitigate bias, and ensure AI systems remain aligned with ethical principles and responsible AI standards throughout their lifecycle. Testlio’s approach reflects this paradigm shift, emphasising human insight as critical to evaluating AI quality comprehensively and maintaining enterprise trust.
This expanded AI testing model is gaining traction as organisations increasingly adopt AI-powered technologies but face growing awareness of the complexity involved in making these systems both impressive and fundamentally reliable. “Testing AI systems demands a new level of sophistication,” said Kristel Kruustük, co-founder of Testlio. “Our testers go beyond finding bugs to evaluate fairness, reasoning, and trust. By integrating human oversight and AI education into our platform, we’re helping the industry build safer systems from the inside out.”
As enterprises continue to integrate AI into core operations, the battle to avoid invisible failures and build trustworthy AI systems will be crucial to preserving user confidence and protecting brand reputation in an era increasingly defined by artificial intelligence.
📌 Reference Map:
- [1] (IT Brief) – Paragraphs 1, 2, 3, 4, 5, 6, 7
- [2] (Testlio Blog) – Paragraphs 1, 4
- [3] (Testlio Solutions) – Paragraph 4
- [4] (SD Times) – Paragraph 5
- [5] (Testlio Blog) – Paragraph 5
- [6] (Testlio Blog) – Paragraph 5
- [7] (Testlio Blog) – Paragraph 5
Source: Noah Wire Services
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
10
Notes:
The narrative is fresh, published today, 20th November 2025, with no prior appearances found.
Quotes check
Score:
10
Notes:
Direct quotes from Testlio executives are unique to this report, with no earlier matches found.
Source reliability
Score:
9
Notes:
The report originates from IT Brief Australia, a reputable technology news outlet.
Plausability check
Score:
9
Notes:
The claims align with known issues in AI systems, and Testlio’s findings are consistent with industry concerns.
Overall assessment
Verdict (FAIL, OPEN, PASS): PASS
Confidence (LOW, MEDIUM, HIGH): HIGH
Summary:
The narrative is fresh, with unique quotes and a reliable source. The claims are plausible and consistent with industry knowledge. No significant credibility risks identified.
