Artificial intelligence (AI) plays a key role in business process automation. Combined with traditional tools such as Robotic Process Automation (RPA) and Business Process Management (BPM), AI enables operational improvements and reduces employee workload. In this article, we will discuss how AI is used in automation, the benefits it brings, and the challenges associated with its implementation.

Business process automation involves using technology and IT tools to automate, streamline, and optimize various processes within an organization. It replaces or supports manual and repetitive tasks with computer systems, improving efficiency, shortening execution times, and reducing costs. Automation plays a crucial role in eliminating human errors, minimizing delays, and enhancing customer satisfaction.

Artificial intelligence (AI) is a technology that mimics human thinking and makes decisions based on data analysis. AI can learn from experience, allowing it to continuously improve its capabilities. When combined with automation, AI can transform traditional business processes into more intelligent and efficient systems.

AI and automation are two different but complementary concepts. Automation is based on executing programmed, repetitive tasks through robots (RPA), while AI makes decisions based on data. The combination of these technologies leads to intelligent automation, enabling systems to adapt and optimize processes in real time.

Automation allows for time savings and increased efficiency, which is crucial for both large and small businesses. Additionally, it enhances customer service quality and optimizes processes, contributing to the long-term growth of enterprises. Decision-making process automation can be applied across various industries, significantly improving organizational operations by increasing efficiency, reducing costs, and improving service quality.

A perfect example illustrating the difference between automation and AI is chatbots. Basic chatbots automate processes, while AI-powered chatbots can analyze user intent and adjust responses based on context.

AI-based automation is one of the most important technological development directions. Combining AI with traditional automation, such as Robotic Process Automation (RPA), Business Process Management (BPM), and other business process automation solutions, enables the creation of intelligent systems capable of performing more complex tasks than simple algorithms.

Intelligent Process Automation (IPA) integrates RPA with data analytics and AI, making it applicable in complex business processes, such as generating advertising creatives and analyzing large data sets to predict trends and suggest actions.

Intelligent automation (IA) combines RPA, AI, and BPM. RPA performs repetitive tasks, AI with machine learning analyzes data and makes decisions, while BPM organizes the entire process. With natural language processing, systems can provide intelligent, personalized customer service, improving interaction quality and service efficiency. As a result, systems can operate more autonomously and effectively.

Examples of Applications:

• Customer service – AI-based chatbots respond to customer inquiries, reducing wait times.
• Finance and banking – AI analyzes transactions and detects potential fraud.
• Healthcare – intelligent automation accelerates the analysis of medical test results.

Integrating AI into automation processes, including business process automation, brings several benefits:

• Cost reduction – less manual work means lower operational costs.
• Faster data processing – AI can analyze vast amounts of information in a short time.
• Elimination of human errors – automation reduces the risk of mistakes.

In the past year, 60% of business leaders have optimized at least one process within their organization. Across various departments, the results are as follows:

• 57% of IT teams have implemented process optimizations,
• 53% of finance and shared services departments,
• 54% of process and operations teams,
• 50% of supply chains have introduced improvements.

However, there is still a gap between perception and the actual level of process intelligence maturity. One of the reasons may be an overreliance on traditional process mapping tools.

As many as 87% of operations and process leaders express confidence in their current optimization tools. Over the next two years, they see the most potential for value generation in technologies such as:

• Process mapping and modeling
• Task mining
• Process enablement and knowledge distribution tools
• System observability
• Process mining
• Lean and Agile methodologies
• Process orchestration
• Simulation and data analysis
• Six Sigma tools

In today’s dynamic environment, business process automation has become a crucial element for organizations looking to increase their efficiency and competitiveness.

Despite its vast potential, AI is not always suitable for every field. There are situations where its implementation causes more problems than benefits. Below are some common cases where artificial intelligence may not be the best solution and why alternative approaches should be considered.

AI requires stable and extensive data sets for learning and operation. If input data changes rapidly or unpredictably, AI models may become outdated, leading to incorrect decisions.

Examples:

• Supply chain – AI can optimize logistics, but in emergencies, such as raw material shortages, its predictions may be unreliable.
• Workforce management – AI models can analyze staffing needs but may fail to anticipate sudden changes in employment, such as mass layoffs or strikes.

Advanced AI systems require significant computing resources and specialized expertise, leading to high implementation and maintenance costs.

Examples:

• Small businesses – implementing AI in HR automation or customer service may be too expensive compared to traditional methods.
• Public sector – government administration often operates on limited budgets and cannot afford full AI implementation for process automation.

Artificial intelligence can reinforce biases and discrimination, especially if models learn from incomplete or biased data.

Examples:

• Automated recruitment – AI used for candidate selection may unintentionally favor certain groups based on historical patterns.
• Financial scoring systems – AI assessing creditworthiness may misclassify clients, denying loans to people who are creditworthy.

AI is effective in repetitive and predictable processes but may fail in situations requiring intuition and flexible decision-making.

Examples:

• Customer service – AI chatbots can handle common questions but require human intervention for complex customer issues.
• Healthcare – AI assists in diagnostics, but for rare or unknown diseases, doctors still need to rely on their expertise and experience.

AI in process automation can significantly improve organizational efficiency by reducing costs and streamlining routine tasks. However, its implementation requires a well-thought-out approach, analysis of potential risks, and cost evaluation. In many cases, traditional automation methods remain more practical and cost-effective.

If you want to discover how AI can help automate your business, get in touch.

With our solutions, we support companies across various industries, adapting technology to individual needs and processes. We offer comprehensive implementations, consulting, and optimization of existing systems.