The most common mistakes in AI automation

What are the most common mistakes when implementing business process automation using AI?

As Sagiton Automation, we help entrepreneurs not only to automate processes themselves, but also to create the right strategies for automating the company — see examples of implementations. Many of our customers, before working with us, tried automation on their own, which did not always end successfully. Companies face challenges that can turn promising technology into a source of problems: from data leaks to logical errors and decisions based on inaccurate analysis. In this article, we will look at five real-life examples of the difficulties that organizations face when implementing AI and show how they can be effectively solved. As Sagiton, we have a separate brand dedicated to cybersecurity, so the topic of security of automation tools is no stranger to us. So the following article is not just a theory — it's lessons learned from practice that can help your company avoid costly mistakes and actually achieve your business goals. The article is based on reports from Cyberhaven Labs, Salesforce, Gartner and McKinsey.

1. Sensitive Data Leak

In recent months we have been hearing more and more about data leaks that are no longer the result of hacker attacks, but... the action of artificial intelligence. Although AI can make life easier and automate many processes, it can also be a source of serious problems, especially when it comes to information security. See with a real example how to avoid leakage of confidential data when implementing business process automation.

Our client's problem: a manufacturing company decided to implement artificial intelligence to automate repetitive bidding processes. Their goal was to speed up the preparation of offers for customers based on the documentation provided. However, a serious concern quickly arose: what happens if the AI analyzes their pricing policies and accidentally reveals them in an uncontrolled way? The risk was real — a 2024 Cyberhaven Labs report found that:

27% of data sent to AI systems is sensitive information‍

Such sensitive data can be considered as, among others, pricing strategies or customer data, so their disclosure poses a real threat to confidentiality.

Solution proposed by us: to minimize this risk, we proposed a hybrid approach. Artificial intelligence has been reduced to the role of extracting raw data from customer documentation, such as order specifications or technical requirements. This data was then fed to a static algorithm that generated the offer on its own. Thanks to this AI as an automation tool did not have access to full pricing policies or final amounts, which significantly reduced the risk of leakage.

The result of the right automation implementation strategy: As a result, the company could safely use the automation of the processes responsible for bidding without exposing its pricing strategies. The process remained fast and precise, and sensitive data was effectively protected from unauthorized access. This solution has shown that the right configuration of automation tools can protect confidentiality while optimizing processes and maintaining confidentiality.

2. Privacy of personal data

In the era of the growing popularity of artificial intelligence, service companies are increasingly deciding to implement automation to process the personal data of their customers. While AI allows for better match of offers and faster service, it also carries the risk of privacy violations, especially when data is collected without the full knowledge of users.

Our client's problem: The insurance company planned to use artificial intelligence to automatically calculate the risk of customers, which was supposed to improve the process of evaluating policies. But before they submitted data — such as names, insurance histories, or health details — there was a concern: what if that information went into a public AI model and was misused or disclosed? Transmission of this type of data without their proper anonymization is one of the most common mistakes when using AI automation tools. The Cyberhaven Labs report from 2024 highlights that:

The most sensitive type of data entered into AI tools is customer service data, which includes sensitive information provided by customers in support requests.

This data is often entered without adequate safeguards, which raises the risk of violating privacy and compliance with regulations such as GDPR.

Solution proposed by us: We started verifying and correcting errors by implementing a multi-layered approach to data protection. The first step was to anonymize the data before entering it into the AI system, which eliminated the possibility of identifying customers. Due to the specifics of the insurance business, we additionally implemented a private AI model — isolated from external access and available only to authorized personnel. In the end, we conducted AI model pentests to verify that the system is fully safe and resistant to potential leaks. Penetration tests are controlled simulations of hacking attacks to check how well a system, network or application is secured.

The result of the right automation implementation strategy: thanks to these actions, the company was able to calmly implement AI for risk calculation without worrying about violating customer privacy. Anonymization and a private model ensured regulatory compliance, and pentests confirmed the reliability of the system. The risk assessment process has become faster and more precise, and customer data has remained secure.

3. Misinterpretations made by AI

Artificial intelligence, despite its advanced technology, is still not free of imperfections — one of the most common mistakes is misinterpretation. Sometimes AI can draw the wrong conclusions based on incomplete or misunderstood data, which can lead to incorrect execution of processes.

Our client's problem: one of the companies we worked with wanted to implement a chatbot based on artificial intelligence — it was supposed to answer questions about the company's activities directly on their website. Initially, the system worked well, but the problem arose when the automation tool analyzed a case study in which the project budget was given. AI began to give this amount as standard for all queries, which, of course, was misinterpretation of the input data — The actual costs could have been completely different. As the Salesforce 2023 Report points out:

42% of companies do not trust the accuracy of AI due to lack of context.

Solution proposed by us: to solve this problem, we adjusted the chatbot configuration. The AI model was fed with knowledge of standard minimum costs (“from” amounts) for individual services, which gave it a solid base to answer. When the customer asked for a different amount, the system displayed the message: “Costs depend on the details, please contact customer service”. In addition, if it was not possible to provide a price, an automatic notification was generated to the sales department, which decided to supplement the model with relevant information, in order to avoid future input errors. We designed the knowledge update process in a simple user interface, allowing non-technical people — e.g. from marketing or sales — to easily add new information.

The result of the right automation implementation strategy: The chatbot began to provide reliable and precise answers, eliminating the risk of misinformation. Customers received clear messages, and the company gained an automation tool that not only supported the service, but also grew through cooperation with the sales department. This approach showed how with AI solutions, it is still important to collaborate with the team.

4. No rationale for AI automation

Although the strategy of implementing automation with the use of AI is often seen as the most modern solution, it is not always justified. In many cases, classical, static programming algorithms prove to be faster, more predictable and sufficient to perform specific calculations.

Our client's problem: a construction company wanted to automate the design and pricing process based on customer documentation. Their task was to create a design concept, and then to accurately price it. The client insisted that artificial intelligence carry out the entire process — from design to final calculations — in order to achieve his business goals even faster. It quickly became clear that the results of the AI were inconsistent, and the system did not cope with the exact calculation of the project, which increased the risk of errors. Gartner in 2024 states that:

62% of companies face difficulties in adopting AI.

Often precisely because of its unjustified use in tasks requiring precision.

Solution proposed by us: we have put forward a more sustainable approach. Artificial intelligence was used exclusively in the conceptual phase, generating design variants based on customer requirements. The AI model received instructions on how to prepare the design to make it countable, but we entrusted the validation and final calculations to a static algorithm. This solution checked that the project met the requirements according to the construction art, eliminating the risk of relying solely on AI at key moments in the process.

The result of the right automation implementation strategy: the company received an effective tool for creating design concepts, and a static algorithm provided a precise estimate in accordance with construction standards. This approach minimized the risk of errors, speeded up the process and allowed the technology to be optimally used where it actually brought value — without unnecessarily forcing AI in every aspect.

5. Poor quality and input errors

The effectiveness of the process of implementing automation based on artificial intelligence largely depends on the quality of the data on which it is based. Poor quality or errors in the input can lead to incorrect results, wrong decisions and loss of trust in the system.

Our client's problem: the trading company decided to implement artificial intelligence to optimize sales forecasting processes. The client hoped for better inventory planning and faster achievement of business goals. However, the input sent to the automation tool — outdated, duplicated, and incomplete — rendered the AI results chaotic and useless. McKinsey's The state of AI in 2023 report from 2023 indicates that:

60% of companies find it difficult to get value from AI.

This happens precisely because of the poor quality of the data, which leads to inaccurate analyzes and decisions.

Solution proposed by us: before we launched the AI system, we did a thorough database tidying up — we removed duplicates, updated outdated entries, and filled in missing information. We then implemented automated data update processes to ensure ongoing consistency and quality. Only after these steps could AI work on a solid foundation, generating reliable sales forecasts.

The result of the right automation implementation strategy: after providing high-quality data, sales forecasts became precise and useful, which allowed the company to better manage inventory and optimize business decisions. This experience has confirmed that without solid data preparation, even the most advanced automation of business processes with AI will not bring the expected results.

Summing up — how to avoid mistakes when using AI?

The above examples — sensitive data leaks, privacy issues, AI misinterpretations, unwarranted use of technology, and poor data quality — show that the success of implementing automation tools depends on more than just sophisticated algorithms. The key is an automation strategy based on prudence: from data security, through precise adjustment of tools to needs, to solid foundation preparation in the form of structured databases. Solutions such as static algorithms, anonymization, private models with pentests, intuitive interfaces for non-technical users or automation of data processes can turn challenges into real benefits. On our blog https://www.sagiton.pl/en/blog we share lessons so that your company can use AI head-on — without unnecessary risks and disappointments. What conclusions do you draw for your business?