Chris Dukich, Owner of Display Now, notes the following errors:
“1. Exaggerating AI’s Abilities:
There is a common misconception that AI can handle problems fully by AI systems, or carry out tasks such as a human being with no aid whatsoever. For instance, AI tools for customer service tend to lack the recognition of emotional contexts, thus leaving customers with negative experiences.
2. Dismissing Data Quality:
AI is only effective with the information it processes. Businesses often feed machines incomplete information or biased inputs, leading to substandard outputs or reinforcing existing biases.
3. Overestimating Human Monitored Tools:
AI marketing tools are sometimes deployed with the common assumption that these automation tools are perfect. A well-used example is ad campaigns that AI driven tools ran that were targeted to the wrong audiences, once again.
4. Ignoring Future Ethical Considerations:
From data privacy issues to surprising outcomes, AI technology may be introduced in a company without proper planning for ethical issues, and this may hurt their reputation.”
How critical are these errors? According to Chris, these mistakes range from being an inconvenience to being disastrous:
“1. Losses suffered: AI-driven processes and decisions that have gone awry can lead to financial loss due to wasted resources cognizant marketing campaigns and inefficiencies in supply chains being the norm.
2. Loss Of Trust: Biases or lack of ethics in AI constructs erodes consumer trust. A case in point is the AI systems used at times in recruitment, which have been known to discriminate on the basis of gender or race AI.
3. Operational Self inflicted Disruption: Mistakes in AI predictions (e.g., demand estimation) can freeze business from functioning properly by having a lack of stock or too much being produced.
4. Fines And Refunds: Almost every state in the world is attempting to develop more robust legal frameworks targeting the use of AI. AI misuse, especially around user data, can attract very hefty fines.”
Cache Merrill, the Founder of Zibtek, adds:
“In most cases, companies feel that AI integration into their system will be a simple task and without understanding its vast nature, they start treating it as a plug and play module. Some of these advices tend to be misleading:
1. Simulating Intelligence Exceedingly: For tasks AI is solely required to run, for some misplaced reason, business decision makers expect AI to work by itself for any and every task.
2. Insufficient Amount of Quality Data: An AI model builds up virtuous outputs from massive amount of quality data. If data is placed in the model that is not accurate or is biased then the outcome is going to be faulty which will eventually lead to poor decision-making.
3. Failure of the Ethical Framework: During the conversation of trust frame, many forget to mention the great ethical framework universal laws such as bias social implication, privacy social implication, and unwanted consequences.
4. Absence of Domain Professionals: The executives implementing AI without integrating with domain specialists are going to have crippled AI models without any functionality.5. Disregarding AI normal upgrades: AI needs normal upgrades and retraining, but often businesses do not have any allocation for these.
These mistakes can lead to different type of outcomes, starting from something of worse kind to catastrophic:
1. Problems of Cash Flow: Every business including investment companies risk a loss to AI due to poor implementation, the wasted finances can reach millions. For instance, directing cash into marketing chatbots who can not even understand what normal conversation is, expecting severe losses and damages to all the sales.
2. Problems Related to Reputation: The use of chatbots for example can go down the drain if they are unable to withstand even an ordinary marketing conversation, leading to serious AI related issues including discriminatory mujeres hiring algorithms.
3. Failures in Operation: Incorrect AI tools when depended on can wreak havoc on operational activities, like in the case of healthcare and finance.
4. Risks of Regulation: Fines or lawsuits may occur due to failure to comply with existing regulations or legislation, as well as adhering to ethical standards.”
Anbang Xu, the founder of JoggAI, also adds:
“The most critical mistake I see is using AI as a shortcut rather than a strategic tool. Many businesses adopt AI to solve surface-level problems, such as automating customer responses, without considering how these systems integrate with broader goals. This leads to fragmented experiences for users and unmet expectations for the company. For example, at JoggAI, we initially underestimated how diverse client needs could break generalized AI systems. It wasn’t just a technical problem; it highlighted how AI must adapt to specific business strategies rather than the other way around.
Another frequent issue is data arrogance—believing that “more data” equals “better AI.” While data volume is important, quality matters more. I’ve seen businesses feed unstructured, biased datasets into AI models and then wonder why the outputs are inconsistent or skewed. Garbage in, garbage out—it’s that simple, yet it’s a lesson businesses often learn the hard way.
How Critical Are These Errors?
Errors in AI aren’t just technical; they’re existential for businesses. A single poorly designed AI system can erode brand trust and, worse, alienate customers. Take the case of an AI-driven recruitment platform that unintentionally discriminates against specific demographics. It’s not just a mistake; it’s a PR nightmare that impacts hiring equity and public perception.
What makes these errors particularly dangerous is their cascading effect. A single misstep in an AI system can ripple through entire workflows, multiplying inefficiencies and amplifying customer dissatisfaction. It’s why I think businesses need to treat AI like they would any other mission-critical system—holistically and cautiously.
Addressing and Preventing These Mistakes
I think the only real way to address AI mistakes is through human-first design. At JoggAI, we ensure every AI system we deploy has a safety net: human oversight that actively monitors and corrects outputs. This isn’t a limitation—it’s a feature. AI alone cannot make qualitative decisions that require context, empathy, or creativity. Humans fill that gap.
Another approach I’d advocate is piloting AI in low-stakes environments before scaling. Too often, businesses deploy systems organization-wide without understanding their full impact. Testing AI in controlled, real-world scenarios uncovers blind spots and provides the opportunity to refine systems before they face high-pressure use cases.
Current Limitations of AI
One of AI’s most misunderstood limitations is its reliance on static models. AI models don’t think—they follow patterns based on historical data, which makes them inherently reactive, not proactive. This poses a significant challenge for dynamic environments like marketing or healthcare, where the context shifts rapidly.
AI also struggles with ethical consistency. Despite best efforts, models often replicate human biases embedded in their training data. These limitations mean AI remains a tool, not a decision-maker—a nuance that gets lost in discussions about its potential.
Can AI Be a Completely Safe Tool?
I don’t believe AI can ever be entirely safe, but I think that’s the wrong question to ask. Instead, we should focus on whether AI can be responsibly managed. For me, safety comes from transparency: businesses need to design AI systems that are explainable and auditable. Users deserve to know how decisions are made and what data drives those decisions.
Safety is less about eliminating risks and more about creating systems that can adapt, learn, and improve over time. AI’s power lies in its ability to complement human capabilities, not replace them.”
Edward Tian, CEO of GPTZero, shares his point of view:
“A lot of people think that AI is making businesses and individuals “lazy,” and with that can come various issues and errors. Personally, I don’t think there is a sure answer of ‘yes’ or ‘no’ when it comes to whether AI is making us lazy. AI is one of those things that can be either incredibly valuable or incredibly unnecessary, and when it comes to businesses implementing AI, the motivations behind that can be either very intentional and wise or frivolous.
So, whether AI is making a person or business lazy largely depends on what they are implementing and why. For example, if engineers are using it to help with testing, that could be a very helpful, strategic choice, as AI testing is proving to be valuable due to its capabilities of running a lot of tests in a short amount of time. But, at the same time, if those engineers are implementing AI in this way in order to completely free their own hands from dealing with testing, simply accepting whatever results the technology finds without looking into them further personally, that could be deemed as lazy.
Another error companies and individuals often make in regard to AI is failing to think about the ethical considerations at play. One of the biggest ethical considerations is simply user awareness. Of course, there is the legal necessity of disclosure when applicable, but there is also the reality that people often click ‘accept’ on terms they either don’t read or don’t understand. Even if a person technically grants permission for an AI tool to be trained on their data, that doesn’t mean they actually know that’s happening. If they knew, they may have chosen not to save or create certain data for their own privacy’s sake. Not to mention, if people don’t realize what they’ve agreed to and then come to find out, that could create a lot of backlash.
Another one of the biggest ethical, and legal, dilemmas businesses face when implementing AI at the moment is copyright. Right now, there are still a lot of questions, and not a lot of strict laws, to guide people on how to use AI technology without infringing on copyright protections. One aspect of this conversation that is a bit more solidified right now is that content generated by AI generally cannot easily be copyrighted, since a person did not technically create it. So, if you were to use generative AI to write a poem or design a piece of digital art, you would typically have no legal copyright ownership over it. It is too difficult to prove how AI programs source their data to create content to be able to qualify for copyright according to fair use.
Something else to consider is that AI poses a lot of answers and also a lot of challenges for cybersecurity. On one hand, it is exceptional in many ways. The way in which it is able to locate weak spots in security frameworks and facilitate improvements intelligently is a fantastic proactive cybersecurity measure. It’s not just reacting to attacks when they happen – it’s locating places where attacks are most likely to be attempted, solving those issues before hackers even get there. On the other hand, AI is certainly not perfect. Algorithms can get skewed, and when synthetic data is used to train AI there may not be enough nuance to develop software that is as complex as it needs to be to handle real-world cybersecurity issues.
One of the best ways for businesses to ensure that they are not making errors or mistakes when using AI is having AI governance. To ensure true transparency and fairness with AI usage in the workplace, having AI governance is essentially these days. There should be one person, or a small handful of people, on your staff who is in charge of AI governance – overseeing that it’s implemented ethically, used correctly, and staying within its designated bounds. If you want to use an AI governance platform, this team should compare all available options and select the platform best suited to the company’s specific needs. It’s important to ensure that all AI usage follows your company’s existing values and code of ethics. These actions will help make sure that your AI usage is in alignment with what your company stands for, and that will bring about fairness and transparency.
As far as whether AI can be a “completely safe” tool, it’s very difficult to say with certainty that it can be. The thing about technology in general is that it can almost always be potentially hacked. That alone means AI is generally not “completely safe.” Also, when using AI tools, you often just don’t know exactly how they are using your data, so how safe it is for you can depend on what data you provide it.”
Another interesting point of view is shared by Ayush Garg, the founder of AnswerThis:
“To answer your questions, the biggest mistake both businesses and people face when using AI is overreliance. It’s the most critical error you can make, but most people don’t even think about it. While artificial intelligence has developed rapidly, there is still more than a 1% rate of error, with over 1 billion queries made per day just on ChatGPT. That translates to a lot of incorrect or misleading answers making their way into real decisions.
Overreliance often starts with a fundamental misunderstanding of what AI models really are. People tend to see a string of coherent sentences and assume it must be correct. Yet the truth is that these models are still tools. Really great tools, but tools nonetheless. They don’t actually understand the content they produce; the models are just predicting the next best word based on patterns in their training data. This means AI can generate plausible-sounding but factually incorrect information. It might misinterpret a question, provide outdated details, or subtly shift the context so that what appears to be a neat answer is actually off the mark.
Generally, this is not that big of a deal when the model is just answering something for novelty’s sake, but these mistakes do become critical when the AI’s suggestions influence decisions, especially in multidisciplinary fields. Research especially can really suffer from inaccuracies or misappropriation of data. Even if 9 of 10 pieces of information are totally accurate, the 1 is enough to ruin an entire paper and even spread to other research papers that cite it. AI might overlook contextual factors that would be obvious to a human, and the results can be costly or even damaging to a company’s reputation and operations.
One of the clearest ways to address and prevent these problems is to maintain a healthy dose of skepticism. AI should serve as a starting point for research or ideation, not a final authority. If it drafts an outline for some kind of strategy or action, have someone with domain expertise double-check the logic and the facts thoroughly, or even create an outline beforehand. Training teams and individuals to critically assess AI outputs goes a long way. Instead of blindly trusting a neatly generated sentence, ask: “Does this align with what I know? Can I verify this with a reliable source?” Creating internal guidelines can help keep these mistakes in check.
It’s also important that businesses and individuals understand the technical and infrastructural limitations that lead to such errors. AI is not just a magical box; it’s part of a pipeline that includes multiple APIs, different models, and complex data flows. For instance, structure generation is an area with huge, yet underexplored, potential. AI can fill out JSON schemas or adapt its outputs to the input requirements of different applications, enabling them to communicate seamlessly even if they expect different data structures. This would be a big leap forward in making two apps “talk” to each other in a more natural and flexible way. At the moment, though, this is still a developing field and ensuring that the generated structure consistently meets all constraints and formats is no small challenge.
From my own experience running enterprise AI, I’ve seen that even some of the largest providers (think industry giants like Google) have around a 2–3% error or “elate” rate in certain tasks. At first glance, 2–3% might not seem huge, but consider that when you chain multiple APIs together in a pipeline to complete a single complex task, those error rates compound. By the time you’ve passed data through several steps, the cumulative error risk is far from negligible. You end up needing constant reruns, retries, and even exponential back off strategies to ensure you get a clean result. For the future, I believe we need to push AI infrastructure towards far more robust internal systems, aiming for error rates below 0.5%. That’s where true reliability lies. When the infrastructure that supports these models is stable and predictable enough that the final output is not just the best guess, but something we can trust with minimal manual oversight.
There’s also the issue of reliance on the largest, most advanced models. Many startups default to using the biggest, “best” models for their processing. While these models are incredibly powerful and can handle huge conceptual domains, there’s a trade-off. For plenty of applications, you don’t actually need that level of complexity. Sometimes a smaller, more efficient model with carefully crafted prompts can be a better fit for both quality and efficiency. It can produce results that are easier to validate, faster to run, and cheaper to maintain. Large models certainly have their place, they’re excellent at handling abstract reasoning and complex topics, but if your task is straightforward and doesn’t require that expansive conceptual depth you might be better served by a leaner model plus some smart prompt engineering. It’s about choosing the right tool for the job, not just grabbing the biggest hammer.
You asked how AI is being restricted right now and what limitations users might face. Regulatory bodies and governments are starting to lay down rules, recognizing that AI’s influence is so widespread it needs guardrails. The European Union’s AI Act, for instance, attempts to categorize AI applications based on their risk and impose different levels of scrutiny. This can limit how AI is deployed in certain sensitive contexts from facial recognition to something like automated decision-making in lending. In the U.S. and elsewhere, similar conversations are happening. We’re seeing more emphasis on data transparency, bias mitigation, and accountability mechanisms. Companies might find that, as they deploy AI in certain sectors, they need to comply with strict data governance rules, explainability requirements, and user consent standards.
As for whether AI can be completely safe, we have to acknowledge that no technology is 100% foolproof. Just like cars, airplanes, or even the internet itself, AI comes with inherent risks. The key is to manage these risks responsibly. By continuously improving infrastructure, refining models, reducing error rates, and adopting better verification processes, we can approach a state where AI is as safe and reliable as any well-established tool. As I said, the maturity of AI infrastructure is still in progress.
The main takeaway is to remember that AI is a powerful assistant, not a full-fledged replacement for human judgment. If we treat it as a collaborator rather than a perfectly authoritative source, we can harness all its strengths: speed, scalability, and pattern-recognition humans can never achieve, while safeguarding against its weaknesses. We’ll need better infrastructure, more robust error controls, and the willingness to experiment with smaller, more efficient models that can be tuned precisely for a given task. We’ll need to think about how to use structure generation effectively, enabling different systems to communicate fluidly without injecting more complexity. And we must stay aware of evolving regulations and standards to ensure that, as AI matures, it does so in a way that benefits humanity in its totality.”
