May 23, 2025
Artificial intelligence has become a tool that simplifies and accelerates business processes — from content generation to analyzing large volumes of data. The speed and accuracy with which AI performs tasks motivate companies to implement software based on it. However, the implementation and training of AI not only open new opportunities but also introduce a number of constraints that complicate the process and create uncertainty.
IBM conducted a study and surveyed 2000 companies using AI to automate business tasks. Researchers identified the TOP-5 concerns of business representatives working with artificial intelligence:
- Accuracy and Impartiality of AI Data
This aspect is highlighted by 45% of respondents. AI training must use reliable historical data, as the accuracy of information and the absence of systematic errors are key success factors. Incorrect information can lead to ethical consequences, such as discrimination or unfair decisions.
- Lack of Own Data
42% of organizations believe they lack their own data for effective AI model tuning. Models require large volumes of high-quality data for training. Without sufficient information, their effectiveness decreases, limiting business opportunities to use AI for automating processes and decision-making.
- Lack of Competence in Generative AI
42% of companies experience a lack of knowledge in generative AI, which can lead to the following problems:
- Incomplete utilization of AI potential: without understanding how algorithms work, employees cannot fully leverage the technologies.
- Challenges in implementation: the absence of experts complicates AI implementation, causing employee resistance and issues with adapting business processes.
- Difficulty in Developing an Economic Model
42% of organizations face challenges in planning costs and benefits from using AI:
- Investment planning: without a clear economic model, it is difficult to calculate the payback period and expected economic effect.
- Profitability assessment: the results of AI implementation are often not quick and obvious, making it difficult to calculate profitability.
- Concerns About Data Privacy
40% of organizations express concern about information privacy when implementing AI. This creates serious obstacles:
- Risk of leakage: storing and processing large volumes of data increases the likelihood of personal information leaks, which can lead to legal and financial consequences.
- User trust: users become more concerned about how their data is used. Loss of trust can negatively affect the company’s reputation.
- Regulatory requirements: there are strict laws and regulations for data protection. Non-compliance with these norms can lead to fines and sanctions.
Example of AI Implementation:
One successful example of automating business processes using AI is the automation of data transfer from scanned documents into software. Thousands of companies process thousands of paper documents daily, manually transferring information into systems. This process requires a lot of time and effort and is fraught with the possibility of errors.
AI can not only recognize information on documents but also determine the date, document number, payment deadline, and other content. Based on this information, the document can be sent to the responsible employee and entered into the appropriate sections of the database without manual labor.
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Implementing AI provides opportunities for optimizing business processes; however, it requires careful attention to solving emerging problems. Understanding and overcoming concerns related to data accuracy, lack of information, competencies, and privacy are important steps toward successful AI integration in business.