Human users are solely responsible for AI errors
AI doesn’t have intent, consciousness, or accountability, it’s software. It doesn’t know what it’s doing. When it goes off-course and generates false, misleading, or simply bad outputs, the responsibility doesn’t fall on the code. It falls entirely on the people who use it.
You can build highly complex, state-of-the-art AI systems, but if someone interacts with them carelessly or delegates tasks blindly, the output won’t magically fix itself. Take AI-generated content in legal cases, journalism, or product design, it’s your job to check, edit, and validate the output before pushing it out into the world. If there’s an error, it’s on the user. No debate.
Real examples keep surfacing. Fantasy romance author Lena McDonald inserted AI-generated content into her book without checking it, exposing a lack of professional review. The same issue showed up in a summer book list written by Marco Buscaglia, a writer for King Features Syndicate, who used AI to fabricate titles and authors that never existed. Chong Ke, a Canadian lawyer, went even further. He cited fake legal cases in a courtroom pulled from ChatGPT. The judge ordered him to pay penalties. In each instance, the human decided to trust AI output without doing the minimal work needed to verify it.
The problem is people treating AI like it’s smarter than it is.
A recent study presented at the Association for Computing Machinery’s FAccT Conference found that OpenAI’s Whisper, a widely used speech recognition model, inserted fabricated content into around 1% of its transcripts. Almost 38% of those errors could literally cause harm in medical scenarios. That’s serious. No automation should run without oversight in use cases where the cost of failure is this high.
Executives should draw a clear line: AI is powerful, but it’s not responsible. Your people are. Set standards. Train for them. Enforce them. That’s how you protect both brand and business.
AI misuse has led to real-world professional and legal failures
AI misuse is happening across industries, and the costs are real.
Professionals in creative and regulated fields have been deploying AI tools without reviewing the results. That lapse is triggering operational failures, public embarrassment, and legal setbacks. The problem isn’t that AI is flawed, it’s that people aren’t doing the human work that still matters deeply: verifying facts, editing outputs, and maintaining quality control.
Marco Buscaglia’s piece for a nationally syndicated summer reading list named fake books by real authors, proving he didn’t check the AI-generated content. Lena McDonald, K.C. Crowne, and Rania Faris, all authors in the romance genre, showed similar issues. They left prompts and AI scaffolding inside their self-published books. Which means they didn’t actually read the material going to market.
In legal settings, it’s worse. Chong Ke, a practicing lawyer in Canada, submitted fabricated court cases to a judge, cases that came straight from ChatGPT. He was held accountable and had to cover the opposing side’s research expenses. This kind of event degrades credibility and adds compliance risk, especially in high-stakes sectors.
The lesson for any executive using AI inside the organization is straightforward: you don’t allow unsupervised tools to circulate unsupported claims, unverified analyses, or decisions with legal weight. The underlying AI models are built to predict likely text or patterns. If your people assume otherwise, it’s not the software’s fault, it’s your governance missing the mark.
Smart businesses will position AI as a co-pilot. That means there’s always a skilled operator in the loop keeping things on track. Anything else is expensive negligence.
Treating AI as an autonomous, legally separate entity is a fallacy
Organizations need to stop pretending AI systems carry independent legal identity. That’s incorrect. AI doesn’t have legal standing, and any attempt to suggest otherwise is just a way to avoid responsibility. That approach won’t hold up in court, and more importantly, it doesn’t withstand logic.
A clear case is Air Canada’s 2023 situation. A customer was given false information about a bereavement fare refund policy by the company’s chatbot. When taken to a small-claims tribunal, the airline made the argument that the chatbot was a “separate legal entity.” The tribunal rejected it immediately. The ruling confirmed what should already be understood: the business owns the tool, owns the infrastructure, and owns the consequences of its deployment.
No AI acts on its own. It processes input, applies machine-learned parameters, and produces output. It doesn’t make decisions. It doesn’t understand responsibility or consequence. If an AI tool provides misinformation, it reflects a breakdown in how that tool was integrated, prompted, or monitored by the organization using it.
For executives, this shifts the focus. It is about why systems were allowed to run without checks, filters, or accountability. Passing blame to an algorithm won’t defend you in front of a regulator, court, or customer base. Business leaders need to ensure responsibility structures are in place, with clear human ownership assigned for all AI-generated decisions and communications.
This means reviews can’t be occasional. They need to be systematic. Across legal, service, and communications workflows, any AI deployment must be traceable, auditable, and controlled. That’s what future-proof governance looks like. And it’s your edge, if you commit to it early.
Unsupervised AI use leads to widespread hallucinations and dangerous errors
When AI is unsupervised, the results get unreliable fast. It’s been proven through direct examples, from glitchy customer support interactions to outright dangerous recommendations made by public-facing systems.
Google’s AI Overviews prompted headlines globally when it suggested putting glue on pizza or eating small rocks. Apple’s AI-powered notification summaries added fake headlines about political arrests. These aren’t edge cases, they happened at the world’s most advanced tech companies. And that’s the warning. If their processes let these outputs reach users, it shows just how critical human oversight remains.
Failures aren’t limited to consumer tech either. In healthcare, AI transcription tools using OpenAI’s Whisper routinely produced inaccurate output. One study presented at the Association for Computing Machinery’s FAccT Conference found that 1% of Whisper transcriptions included content that was never spoken. Of those, nearly 38% had the potential to cause medical harm.
These outcomes confirm that letting AI operate on unattended pipelines or unsupervised processes is reckless. AI-generated outputs are based on probabilities, not understanding. Without someone checking context and accuracy, error rates multiply and compound.
For executives rolling out AI in communications, healthcare, fintech, or legal services, these are threats to compliance, trust, and operational stability. Fixing this doesn’t require overhauls. It requires assigning ownership, setting up review layers, and tuning deployment architecture to ensure AI doesn’t “publish” until verified. That’s a simple operational discipline. But it needs enforcing from the top down.
Striking a balance, responsible AI adoption requires active human oversight
There’s a clear path ahead. Companies don’t need to reject AI. But they can’t fully trust it to run operations autonomously either. Leaders need to get comfortable with the middle ground, using AI to support work but never replacing the oversight that ensures results are accurate, relevant, and aligned with business goals.
Plenty of organizations have gone to extremes. Some prohibit any use of generative AI over security or quality fears. Others integrate it without proper controls, assuming the system will somehow manage itself. Both approaches are inefficient. If you’re not using the technology at all, you’re missing tangible gains in productivity, idea generation, and speed. If you’re using it without verifying its outputs, you’re gambling with execution and introducing systemic risk.
The real solution is disciplined deployment. Train your teams to use AI as a support asset. Make review checkpoints non-negotiable. Automate what makes sense, but establish clear rules for when, where, and how human verification is required. That’s what control looks like in an AI-integrated business.
Executives should also consider governance a strategic asset. It’s not just about compliance, it’s about predictable performance. If your teams understand where automation ends and their judgment begins, they’ll use the tools more effectively. That delivers stronger results, steadier output, and smoother innovation cycles.
The recurring theme in high-profile AI failures is always the same: someone handed off too much responsibility to a system that doesn’t reason, doesn’t understand, and doesn’t correct itself. Eliminating that risk means making oversight part of the design.
AI isn’t going away. Its capabilities will scale. The companies that benefit most won’t be the ones using it the fastest. They’ll be the ones using it well, with operators who understand it, systems that control it, and leaders who enforce accountability from the top down. That’s where the advantage is. .
Key executive takeaways
- Human decisions drive AI errors: AI is not autonomous, it reflects the quality of human input and oversight. Leaders should enforce clear responsibility standards to ensure AI-generated content is reviewed, verified, and aligned with organizational expectations.
- AI misuse leads to tangible risk: From legal penalties to reputational damage, unchecked AI output introduces liabilities. Executives must establish review protocols across creative, legal, and operational teams to prevent avoidable failures.
- Legal accountability stays with the user: Claiming AI as a separate legal entity won’t hold under scrutiny. Decision-makers need to recognize that legal responsibility always resides with the organization deploying the AI tools.
- Unsupervised AI generates harmful misinformation: Leading AI systems have produced false instructions and fabricated content, even in critical areas like healthcare. Leaders should mandate human-in-the-loop processes to intercept and correct flawed output before it escalates.
- Disciplined AI adoption unlocks value: Total restriction or blind trust in AI both undercut performance. Executives should guide teams to integrate AI as a supervised tool, supporting workflows without surrendering accountability.
