Top 5 Mistakes Businesses Make When Working with AI and Benefits of Real Data Analytics from Real Humans

Artificial intelligence (AI) continues to grow at a rapid pace and has become much more present in our everyday lives and the workplace.

While many companies have gone all-in on AI in a variety of use cases, simply using an AI program or AI chat bot “off-the-shelf“ can have some substantial shortcomings and important considerations.

Especially in the analytics and data science sphere, many complex problems and solutions benefit greatly from human knowledge, skills, and creativity.

In this article, we take a closer look at 5 key mistakes businesses make when using AI programs and ways that human experts can add value to analytics implementations, whether it’s an easy-to-understand visualization for a client, a GIS-driven analysis of where to locate a new business branch, or predictive forecasting of sales trends for the next quarter and beyond.

1. Mistake One: Assuming That AI Programs Are Always Right

The first mistake that businesses — and really anyone who uses AI — can make, is assuming (or hoping) that the AI program, tool, or chat bot are always correct in their responses, data analytics results, or conclusions.

As we covered in another recent article on our site, AI programs can provide partially or fully incorrect responses to user prompts, with the implications ranging from minimal to very severe.

For example, an editorial team at a Chicago newspaper asked an AI chatbot to make a list of summer reading books, which the bot did, but also included several fake books that didn’t exist.

A more severe example included content from fake court cases that was provided in AI chatbot responses, which of course could have even more important negative implications.

Although humans are of course also not always correct, humans often reference better sources for their conclusions and can be asked questions about their responses, whereas this is not always seen with AI chat bots.

2. Mistake Two: Relying on Entirely AI for All Data Work and Analytics Tasks

A second mistake that we often encounter in businesses and organization is the reliance on AI tools for virtually all data science and analytics work.

While fully-AI or assisted analytics platforms such as claude.ai can be useful for simple jobs or low volume analytics work, human-driven analytics have several benefits.

For example, when importing datasets for a data merge before conducting exploratory data analyses, correctly identifying and treating different types of missing data is crucial.

Having a well-trained human expert in data and analytics to identify and analyze the missing data and how to code, classify, and merge it is important for ensuring accuracy and utility in any data science consulting work.

3. Mistake Three: Hoping That Data Privacy and Integrity is Always Protected

Another mistake related to AI programs is the hope that data privacy and the integrity of results from data analytics is always protected in all cases.

As data breaches continue to grow in frequency and scale, AI chatbots are not immune to potential data breaches and the unauthorized use of user data.

Much like other tech and social media platforms, AI programs have extensive, many-page-long Terms and Conditions, and Privacy Policies.

Making sure to actually read these documents and to understand how data is treated, stored, and shared is essential when working with any AI program, whether it’s an enterprise-wide platform or smart phone-based app.

Working with a real professional can have benefits here too, as clients are able to ask questions and discuss data privacy terms in real time virtual or in-person meetings, which is not possible with AI programs or chat bots.

4. Mistake Four: Selecting the Wrong AI Tool for the Job

Yet another mistake businesses can make with AI is selecting the wrong AI tool for a particular job, use case, or application.

Just like using the right software patch or firmware update on a computer, or having the right tool to fix a flat tire on a car, the quality, functionalities, and cost of AI tools is always changing.

As we discuss in more detail below in #5, AI programs often follow a “freemium“ model, where a limited number of features are available in the free version and more features become available with one-time, monthly or annual payment.

In addition, the learning curve and extent to which programming knowledge is required differs across AI programs, which is another key consideration when selecting one or more of these tools to work with in your business or organization.

For much more about the different AI tools and programs available in today’s consumer and business markets, check out our recent article: Four Ways Data Science and Artificial Intelligence Can Enhance Your Business and Productivity: AI Defined, Top AI Programs and Key Considerations

5. Mistake Five: Overspending on AI Tools and Subscriptions

The fifth and final mistake we’ve seen, especially recently, is overpaying for AI tools via one-time payments and recurring subscriptions.

As we described above, AI programs and chat bots often utilize a freemium model, with different tiers of subscriptions, ranging from the most popular free tier, to progressively more expensive tiers.

These upper tiers can become considerably expensive, for example, with xAI’s SuperGrok AI plan selling for $300 per month and Google’s AI Ultra plan selling for $250 per month (as of fall 2025).

Although these higher rates allow for access to all mainstream and (in almost all cases) beta features as well, they are not always worth it, and subscriptions are very likely to increase in cost even more in the future.

How Real People Can Provide Real Insights in Data Science and Business Analytics

So if these are five key pitfalls and easy-to-encounter mistakes with AI, what is the solution?

Our response is two-fold: 1) make sure to keep up-to-date on the latest AI changes, trends, and regulations via our Knowledgebase and leading sources such as Wired, and 2) view AI as one of many tools available to human experts to create, refine, and deploy data analytics, visualization, and predictive modeling tools.

The Bottom Line: AI Has Benefits…But We Still Need Humans

So there you have it: while AI tools have experienced tremendous growth in their sophistication recently, they still suffer from these important limitations.

We hope that learning more about these five key mistakes encountered when working with AI is helpful and improved the understanding of what to look out for when working with AI.

Here at Chestnut Hill Analytics Insights, our experts have extensive real-world experience with data science software programs and AI tools.

Whether it’s a business expansion plan or scientific research study, our consultants are here to help you make sense of complex data of all varieties and provide informative visualizations, actionable steps, and easy-to-understand recommendations.

We offer a large portfolio of data science, analytics and business consulting services, which we describe in more detail on our Services page:

Thanks so much for reading this piece on mistakes that can happen when working with AI and benefits of human insights — we’d love to hear your thoughts below in the comments on this Knowledgebase article!

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