Top 7 AI Mistakes Pharma Companies Make

AI can do all sorts of marvelous things these days. It can create beautiful images, craft excellent articles, and even write great songs. Even though the technology is capable of many things, it definitely cannot prevent its users from making bad decisions and huge mistakes.

From Google’s Bard AI chatbot making accusations about Big Four consulting firms to deep fakes about famous singers and politicians flooding the internet, many controversial AI incidents impact our society. So, who is to blame: the AI or its user? Our guide will tell you more about AI failures and mistakes and what you can do to resolve these issues and make the most out of artificial intelligence in the pharma industry.

What Are AI Mistakes?

Artificial intelligence is a truly revolutionizing technology, but it is not flawless: its mistakes can take many shapes and forms. It’s not just about AI giving you the wrong output; it can be any type of error, from biased information to data leaks. It’s sometimes hard to say who’s responsible for some mistakes: a company that implemented AI incorrectly, a developer of a particular AI-powered solution, or just a coincidence that led to big problems. No matter the answer, AI incidents, and mistakes can happen at any time with both small and large language models.

Even big corporations might suffer from poor AI decisions: Microsoft had to cancel the launch of CoPilot+ Recall due to backlash over users’ data being continuously recorded and archived, and Netflix was accused of using AI-generated images in a true crime documentary. It seems like almost every other big company has faced negative reactions from society because of AI, and most of the time, it’s poor decision-making to blame.

Understanding AI Errors and Incorrect Answers

AI, just like any other technology, is far from flawless. Many things about it can make it a dangerous tool, especially in the wrong hands. Let’s take a look at the nature of AI errors and why they happen.

Why mistakes occur

Even though there are many types of AI failures, we can generally divide them into two categories: mistakes made by machines and mistakes made by humans. We’ll discuss the later ones in another section, but what about the situation where the AI system fails? The reason behind every AI failure is rooted in many limitations and challenges in AI development and deployment.

For instance, AI systems trained on poor-quality data will carry over those errors, leading to consistently inaccurate results. Other factors, such as overfitting/underfitting, explainability issues, and the complexity of the tasks, also increase the risk of mistakes or incidents occurring. To understand why something might go wrong, it’s important to understand what types of AI errors there might be, which we will discuss next.

Types of AI errors

This list will go over some of the most common mistakes. Keep in mind that this list is not exhaustive, and your unique workflows might have other problems and vulnerabilities.

Bias

AI is capable of being biased, all because it is often trained on biased data. For example, a study by The Conversation exposes how Midjourney, a generative artificial intelligence tool, displays bias in the different types of images it creates. In some pictures, women were mostly shown younger than men and had fewer wrinkles; all images were conservative in how they portrayed different people, showing no tattoos, piercings, etc.

Lack of common sense

Common sense is innate to human beings, and it is not found anywhere else in either the natural or human-made world. It’s common sense for us that it’s dark at night and light during the day. But is it the same for AI? It does not “think” the way people do, meaning that it doesn’t have common sense. It’s trained on certain sets of data, which can be of bad quality, causing AI to sometimes spur out nonsensical information and untrue facts.

AI hallucination

Have you ever asked an AI chatbot, like ChatGPT, a question only to receive an answer that sounded both adequate and inadequate at the same time? If that happened to you, it’s okay: things like that occur more often than you might imagine. This type of error happens due to the AI’s ability to generate content even when it does not know the right answer. Its response might sound coherent, but the factual part of it will be far from the correct answer. This is an especially important mistake to remember since many users believe that AI, since it’s trained on large datasets, will never give you faulty information.

Catastrophic interference

Neural networks, inspired by the human brain, help machines learn and understand information and make predictions based on that knowledge. Unlike the human brain, a neural network may struggle to connect new information with previously learned data. This is a so-called catastrophic interference or catastrophic forgetting, and because of it, it might be harder to train some AI models.

While humans can retain previous knowledge even as they learn new information (e.g., knowing that 1 + 1 = 2 or that the sun rises in the morning and sets in the evening), many neural networks struggle to connect all the information they learn after being updated. However, methods such as meta-learning, regularization techniques, parameter isolation, and other approaches can help mitigate catastrophic interference.

TOP AI Mistakes Companies Make That Are Worth Your Attention

Mistakes are not only about errors in how machines think and operate; many failures and incidents also occur due to human factors. Let’s take a look at some of these and discuss how they can be overcome.

The shiny object syndrome 

When any type of technology becomes popular, sooner or later, everyone tries to integrate it into their workflows. Right now, numerous pharmaceutical companies are investing in AI tools that don’t align with their goals, and they do that without even realizing the problem behind this investment. AI is great, and everyone uses it to stay ahead of the competition, right? Well, this is only true if artificial intelligence is used correctly. Your company doesn’t need another expensive software if it’s not clear what kind of value it will bring.

So, how can you leverage the capabilities of AI without making unnecessary investments? Know your goals. Define what you’re trying to achieve with the help of AI, and seek the right tools that will help you meet your goals. Based on the proven results, scale gradually and expand AI integration.

Data dilemmas 

So many businesses rush into AI implementation that they forget to assess the risks the technology poses, including concerns regarding data access and safety. There is still a lot of bias towards the use of AI in many industries, and for a good reason: AI-based projects might go in a completely wrong direction due to inaccurate and incomplete data, and all of this can happen just because of one simple mistake that can go unnoticed just because the companies trust AI too much.

Of course, the described situation sounds a little bit too dramatic. However, let’s not forget that whenever there is a new tech, there are many worries about data safety. If you want to make the most of AI and mitigate all possible risks, invest not only in AI-powered software but also in some security measures and data governance.

AI-generated content risks

According to the reports, 73% of marketers these days use AI to create different types of content. Almost everyone in the marketing field utilized artificial intelligence to some degree. For sure, AI-generated content has many benefits, such as faster content delivery, increased efficiency, and reduced content production costs. Still, there are many issues AI-generated content entails, such as factual inaccuracies, ethical and compliance concerns, lack of emotional connection, and plagiarism. GenAI is excellent at assisting content creators, but it is not yet a perfect solution for standalone content generation.

To avoid creating and distributing misleading content that might cause reputation damage, focus on the usage of specialized AI models and maintain human oversight of all processes delegated to AI. Remember that there are no AI systems capable of fully replacing humans, nor should you do as well.

Navigating regulatory minefields with custom tools 

Artificial intelligence can assist companies in the early detection of safety risks and other issues related to compliance. Everything sounds great on paper until the benefits of AI turn into disadvantages. Many custom solutions, especially made by third parties, can result in validation, compliance, and transparency challenges, putting companies at risk of data leaks and breaches.

AI-based projects might be delayed or even derailed due to regulatory problems, often tied to unsafe utilization of technology. To prevent anything like this from happening, it’s important to work with regulatory agencies and follow industry guidelines, even if it sometimes seems too much.

The talent gap

Many companies are having difficulty attracting and retaining experts in the current competitive market due to talent shortages. In 2024, the hiring gap for all AI positions was estimated to be around 50%. Moreover, according to Deloitte, only 17% of organizations are looking for solutions to the problem. Both junior-level workers and seniors in many industries are experiencing difficulties getting used to the new technology, and for some, it is especially hard to adjust to the changes.

The scarcity of experts in the new technology is a common problem. This happened many years ago when computers were first introduced to the general public. The first companies to solve this problem were those willing to invest in training their staff rather than solely searching for newly educated experts. If you’re dealing with the same problem, consider partnering with reliable technology vendors instead of seeking new people outside of your organization.

Lack of change management

The world never stands still. New technologies emerge, and old traditions go away. It’s important to always remember that and change your ways as the world evolves. Many companies are now making the same mistake again: instead of preparing their organization for AI adoption, they choose to either completely ignore it or force their employees to figure out everything by themselves. AI is here to stay, and the sooner you develop a comprehensive change management plan that covers all aspects of staff training and support, the better.

Misaligned incentives

Many businesses rush into AI implementation without even considering their goals. As a result, they don’t get any long-term value from the AI tools and solutions they choose and focus on short-term gains instead. For example, some companies deploy AI too quickly, which is already a huge problem itself, and don’t create any training programs for the employees. Even though organizations still receive the desired solution as soon as possible, in this case, without adequate staff training, it is impossible to achieve high-quality, long-term outcomes.

Here is another example. Let’s say a clinic decided to implement a facial recognition system. Immediately, the establishment would improve their safety and increase patient identification. Also, such a system would save the clinic a lot of money. But is it everything a facial recognition system can do? In the long term, it can provide data-driven insights that can help the clinic personalize its greetings and services, which would definitely increase patient satisfaction and loyalty.

No matter what type of AI solution you decide to implement, aligning it with patient-centric goals and considering the broader impact on your patients is crucial. With AI-powered analytics, organizations can learn more about their customers and deepen their relationships. Keep your patients and their needs in mind every time you consider introducing a new strategy or integrating different tools into your workflows, as all of it will have just as much impact on your customers as it has on your employees.

Closing Thoughts

Don’t underestimate human capabilities, and don’t overestimate AI’s. This technology is still evolving, and even when it does reach its peak, it will still need us, humans, to fully unleash its potential. You should not only embrace AI but also learn its strengths and weaknesses.

Hundreds of thousands of businesses are looking for the best AI solutions right now. If you are ready to implement artificial intelligence into your workflows, contact us today to start your transformational journey. Our experts are always ready to provide you with detailed information on all AI-driven solutions we offer.

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