AI Glitches or Warnings? Tech Leaders on Why You Should Always Double-Check
The rapid integration ofAI into professional workflows promises unprecedented gains in efficiency and speed.
Yet, amidst the promises of productivity, a concerning new challenge is emerging: AI’s capacity for creating convincing, yet entirely fabricated, information.
This isn’t merely about simple errors; it’s about a phenomenon known as “hallucination,” where AI generates plausible-sounding research, citations, and facts that are completely fictional.
This can pose a significant risk to organizations across all sectors, from legal and healthcare to marketing and finance.
How are business leaders, thought leaders, and tech professionals confronting this unique new threat to accuracy and trust?
This Techronicler article compiles invaluable insights from those on the front lines of AI integration, revealing their firsthand experiences with AI’s “confident lies” and the critical, often painstaking, strategies they are now implementing to verify AI-generated content and protect their organizations from costly, credibility-damaging mistakes.
Read on!
AI is a Tool, Not a Shortcut
After testing more than 50 AI platforms for content creation—across both text and image generation—I can say this confidently: every single one will hallucinate.
Even with platforms that allow you to train models on your brand voice, enforce tight editorial rules, and specify how to reference your product and competitors… I still get it wrong. I’ve seen these tools invent features we don’t offer, create imaginary pricing structures, and—even worse—write favorably about competitors with claims that aren’t even accurate. These aren’t edge cases. They happen more frequently when you’re building fast-growing SaaS platforms like we are at ez Home Search and our subsidiaries, where content volume is high and the landscape is constantly shifting.
The dedicated content platforms—like Byword, Arvow, Frase, SurferSEO, and MarketMuse—handle nuance better, but they’re not immune. Jack-of-all-trade tools like Semrush, Mindpal, Jasper, or Rytr? Still useful for SEO planning or research, but content creation with them is risky if you’re not incredibly careful and willing to invest a large amount of human effort editing and rewriting.
If you’re not editing with a sharp eye, it’s only a matter of time before AI-generated inaccuracies make it onto your site, confuse your prospects, and erode your team’s credibility. That erosion leads to real losses—lower lead quality, missed revenue targets, and damaged brand trust. And the worst part? It’s avoidable.
There’s a growing, misfounded belief in some leadership circles that you can throw content creation on autopilot or slash your headcount by 90% and still maintain quality. That might get you through Q3, but it’ll tank your revenue and EBITDA targets next year. The smarter move is to recalibrate your team for acceleration. The goal isn’t to replace people—it’s to equip the right ones to do 5x, 10x, or even 50x more with AI.
AI is a force multiplier. But only for those who treat it like a tool—not a shortcut.
Kurt Uhlir
Chief Marketing Officer, eZ Home Search
AI Needs Human Fact-Check, Oversight
Recent artificial intelligence failures demonstrate the critical need for systematic fact-checking and human oversight in AI-powered systems. This analysis examines verified incidents and provides actionable recommendations for organizations deploying AI technologies.
Major AI System Failures
Google Gemini Image Generation Crisis (February 2024)
Google’s Gemini AI image generator produced historically inaccurate illustrations, including racially diverse Nazi soldiers and anachronistic depictions of historical figures. The company paused the feature on February 22, 2024, following widespread criticism. Senior VP Prabhakar Raghavan acknowledged the system “missed the mark” due to over-corrective diversity tuning and excessive safety filters.
Air Canada Chatbot Legal Liability (2024)
Air Canada’s customer service chatbot promised bereavement fare refunds contradicting company policy. The British Columbia Civil Resolution Tribunal ordered the airline to pay CAD $812.02 plus court fees to customer Jake Moffatt, establishing legal precedent for AI-generated promises.
Microsoft Copilot Hallucinations
Microsoft’s coding assistant has documented issues with fabricated code suggestions, non-existent URLs, and false information about individuals. These incidents highlight risks in automated content generation across professional applications.
Technical Root Causes
AI hallucinations stem from several systemic issues:
– Training Data Limitations: Internet-sourced datasets contain biases and gaps that surface unpredictably
– Overconfident Presentation: Models express certainty disproportionate to actual accuracy
– Lack of Grounding: Systems predict plausible sequences without accessing verified sources
– Alignment Problems: Safety measures can inadvertently create new forms of bias
Verification Protocols
Organizations must implement robust fact-checking procedures:
– Cross-reference AI outputs with minimum two authoritative sources
– Require human review for legal, medical, and historical claims
– Implement automated validation systems before public release
– Display clear disclaimers about AI limitations
Strategic Recommendations
– Establish AI Governance: Create cross-functional teams responsible for AI system oversight and quality assurance
– Implement Continuous Monitoring: Regular testing of AI outputs for accuracy, bias, and appropriateness
– User Education: Train employees and customers on AI limitations and proper verification procedures
– Legal Preparedness: Develop policies addressing liability for AI-generated content and decisions
Conclusion
While AI systems offer significant productivity benefits, the Google Gemini incident and similar failures underscore the necessity of human oversight. Organizations must balance AI efficiency with systematic verification to avoid costly errors and maintain credibility. Success requires treating AI as a powerful tool requiring careful management rather than an autonomous solution.
Supratim Sircar
Software Engineer, Cisco
AI Fabricates Research with Fake Academic Credentials
So here’s an AI glitch that slapped me in the face: I asked a well-known AI tool to help draft a partner pitch deck. I gave it a rough outline and asked it to fill in supporting stats. Simple enough.
It confidently cited research from “The University of Amsterdam, 2022” claiming that auditory learning improves retention by 48%. That stat looked perfect. I was tempted to just roll with it. But something felt… too neat. Too clickable. So I searched for the paper. Nothing.
Turns out the study didn’t exist. The university didn’t even publish anything remotely related in that timeframe. The number was completely fabricated—but it was wrapped in just enough academic polish to seem real.
Here’s the creepy part: it even generated a DOI. A fake DOI. Like, the AI hallucinated a paper, a title, an author and gave it a fake serial number that looked legit. That’s not just an error. That’s confidence theater.
That moment reminded me AI isn’t just capable of being wrong—it’s capable of being persuasively wrong. It doesn’t hesitate. It doesn’t blink. It lies with flair.
Now, anytime AI gives me “research-backed” anything, I default to “guilty until proven real.” I’ve added a new line to our pitch deck review checklist: “Verify every statistic, no matter how plausible it sounds.” Painful, but necessary.
Derek Pankaew
CEO & Founder, Listening
Google AI Overviews Display Blatantly Wrong Information
There have been a handful of times now where I have Google something and the AI Overview that popped up at the top of the page had blatantly wrong information.
For example, I am someone who often Googles the actors in the shows/movies I’m watching because I like to know where I’ve seen them before. Not that long ago, I Googled an actor and in the AI Overview that popped up, it said they were most known for their role in a TV show that they had never even been in. It’s because of situations like that why I don’t trust AI Overviews to be accurate.
Edward Tian
CEO, GPTZero
AI Presents Fictional Cybersecurity Attacks as Real Events
I asked AI to share cybersecurity attack scenarios for my personal knowledge, and it confidently presented a few detailed, totally made-up situations as if they were real, recent events.
It was a stark reminder that even the most advanced AI can hallucinate and generate inaccurate information.
This experience underscored the importance of always double-checking AI-delivered content and solutions, especially when accuracy is critical. You can’t just blindly trust what the AI tells you, even if it sounds plausible.
Prompt Change Reduces RAG System Hallucinations
Shantanu Pandey
Founder & CEO, Tenet
Misleading Responses from RAG-Based Q & A System
While building a QnA application using a Retrieval-Augmented Generation (RAG) approach, I encountered an issue where the LLM would sometimes generate misleading or hallucinated responses even when the answer wasn’t present in the knowledge base.
The root cause?
In the system prompt, I had instructed the LLM to “include the top 3 relevant files.” This led the retrieval step to always supply exactly 3 documents to the LLM regardless of whether all of them were truly relevant.
As a result, when a user asked a question that wasn’t covered by the knowledge base, the LLM still received and tried to stitch together an answer from loosely related or completely irrelevant documents. The responses sounded confident but were often wrong, a classic LLM failure mode amplified by poor retrieval filtering.
I updated the instruction to:
“Include only the files that are relevant to the question, up to a maximum of 3.”
This subtle prompt change dramatically improved the precision of document retrieval and reduced hallucinations when the answer wasn’t available.
Since then we have improved our LLM workflows to be observed through LLM as judge metrics to catch these issues at Scale.
Kanish Manuja
Principal Engineer, Twilio Inc.
AI Sanctions Tool Flags Innocent Customer Wrongly
Incorrect Sanctions Match from an AI Tool
Yes, one recent AI glitch that stood out involved a sanctions screening tool powered by AI. We were testing the system’s ability to flag high-risk names against global watchlists. During one test, the AI flagged a false positive: it matched a customer named “Mohamed A. Khan” to a sanctioned individual named “Mohammed Al-Khanji.”
At first glance, the alert seemed valid due to the name similarity. However, upon manual review, we found that:
The date of birth didn’t match
The nationality and passport details were different
The sanctioned individual had known affiliations and identifiers that the customer clearly lacked
Despite all this, the AI scoring engine had assigned a high-risk score and flagged the customer for escalation. If we had blindly trusted the AI, it could have led to:
Unnecessary delays in onboarding
Customer dissatisfaction
And even internal escalation costs or reputational damage
Lesson Learned:
This experience was a strong reminder that AI is a support tool, not a decision-maker. It works best when paired with human judgment, especially in high-stakes areas like compliance, fraud, or legal screening. Since then, we’ve made it a point to always review AI-generated results manually, especially when they carry significant operational consequences.
This also reinforced the importance of fine-tuning AI models and providing context-based override options for compliance teams.
Umair Ahmed Qureshi
SEO Specialist, Organic Growth Marketer & Content Marketing, FOCAL
AI Falsely Claims Clinic Offers Psychedelic Therapy
We experienced a serious AI-related glitch while creating content for a mental health counselling centre in Canada. We used AI to help draft a list of blog posts and generate initial content to speed up the process. One of the articles included a section claiming the clinic offered psychedelics and MDMA-assisted therapy. It sounded polished and relevant, but it wasn’t true.
The blog post was published live on the client’s site. A few weeks later, while reviewing content, the client flagged the section. The problem? They don’t offer psychedelic treatments at all. Worse, stating that they do could’ve risked their professional license.
It was a sobering reminder that while AI can save time, it must be carefully reviewed, especially in regulated industries like healthcare. The tech may sound confident, but it doesn’t always know where the line is. Since then, we’ve strengthened our editorial process to include mandatory human review, particularly for sensitive content.
Nirmal Gyanwali
Founder & CMO, WP Creative
ChatGPT Lives in the Past, Dates Wrong
I recently learned the hard way why trusting AI blindly is a risky proposition.
I had ChatGPT write a blog post introduction about “affiliate marketing trends,” and the first sentence proudly announced it was still 2023—except we’re now deep into 2025. Luckily, I caught it during editing, but it served as a sharp reminder to always review AI-generated content carefully. Now, I ensure that I thoroughly double-check any AI-generated content, particularly dates, numbers, and critical details.
AI can be amazing, but it still needs human eyes to keep it grounded in reality.
Georgi Petrov
CMO, Entrepreneur & Content Creator, AIG MARKETER
On behalf of the Techronicler community of readers, we thank these leaders and experts for taking the time to share valuable insights that stem from years of experience and in-depth expertise in their respective niches.
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