From Automation to Agentic AI: The Evolution of Business Process Management
Business process management has changed and evolved significantly over time. Traditionally, companies have used a BPM (Business Process Management) approach, which allows them to describe, control, and optimize end-to-end business processes across the entire organization.
In parallel, RPA (Robotic Process Automation) technology has developed, focusing on the automation of individual, localized, and repetitive tasks—often directly at the user’s workstation. As a result, such operations are performed faster and with fewer errors.
However, modern business has become significantly more complex: data volumes are growing, conditions are changing, and simply following set rules is no longer enough. That is why AI is beginning to be used as a complement to traditional automation—primarily in scenarios with high variability or dynamic data, where maintaining a large number of rules becomes difficult.
One area of development for this approach is agentic AI—systems that not only perform individual tasks but can also analyze context, adapt to changes, and assist in decision-making.
At the same time, traditional approaches such as BPM and RPA are not going away—they remain effective for clearly defined, structured processes. The best results are achieved precisely by combining these approaches, with each used in the scenario for which it is best suited.
This approach is already being applied in practice. For example, the German bank NORD/LB uses the Camunda platform as the foundation for orchestrating its business processes and integrating various automation tools into a single system. At a bank with over a thousand processes, automation had long been fragmented—individual solutions operated locally but did not provide full transparency.
Thanks to Camunda, the bank was able to transition to end-to-end process orchestration and gradually integrate AI where it is truly appropriate. For example, in the process of handling incoming correspondence, an AI agent is used to handle complex or ambiguous cases, while BPM provides control, rules, and process manageability, and a human is involved only when necessary.
This approach allows us to combine the predictability of traditional automation with the flexibility of AI, ensuring process transparency, reducing technical complexity, and enabling more effective decision-making.
Why Traditional Automation Needs to Evolve
Traditional automation (BPM, rule-based RPA) works well for stable and repetitive processes. However, in scenarios with high variability or frequent changes, building and maintaining a large number of rules becomes complex and resource-intensive.
This is precisely where AI comes into play. Its key value lies not in replacing traditional automation, but in enabling its faster implementation where variable or unstructured information must be handled. AI can interpret data, understand context, and help select the most relevant course of action without additional complex processing.
At the same time, it is important to understand that all approaches work in parallel:
- traditional automation — for clear, stable, and well-defined processes;
- AI — for scenarios with high variability or frequent changes in conditions.
Key points:
- AI reduces reliance on complex rule logic;
- accelerates the adaptation of processes to changes;
- works effectively with variable data.
Next, we’ll take a closer look at what AI-driven automation is and how it integrates into existing processes.
AI-Driven Business Process Automation
AI-driven automation is an approach to business process management that leverages artificial intelligence technologies such as machine learning (ML), large language models (LLMs), and other intelligent solutions. These technologies integrate with traditional processes to make them more efficient, flexible, and autonomous.
Unlike RPA, which focuses on automating routine tasks at the user’s workstation and operates within clearly defined scenarios, the AI approach allows for handling more flexible situations. It helps interpret data, take context into account, and select the appropriate course of action.
Machine learning allows systems to improve their performance based on historical data—in other words, they “learn” from experience. Large language models help process natural language and understand context, which is particularly useful when handling documents or communicating with customers. Specific solutions related to decision intelligence allow for more accurate modeling of the decision-making process and the application of these models in real time.
Agentic AI: A New Approach to Business Process Execution
Agentic AI is an approach that utilizes autonomous AI agents. These agents can do more than just perform individual tasks; they can independently understand what needs to be done, plan their actions, analyze the situation, and adapt to changes. Such agents can interact with various systems, make decisions, and even adjust their action plan while working.
Unlike traditional systems, AI agents operate as a cycle of continuous improvement. First, they understand the goal and determine exactly what needs to be done. Then they plan a sequence of actions based on available information. Next, they proceed to execution, using APIs or other services. After that, they store the results and context to perform even better in the future. And finally, they learn from the experience gained and optimize their behavior.
Let’s consider an example from the banking sector. When processing incoming correspondence, agentic AI can independently analyze documents, determine their type, and identify errors or missing data. For example, if the information is incomplete or contradictory, the system can forward the document for additional review or generate an automated response to the customer. This approach significantly reduces the workload on employees and allows for faster processing of complex tasks.
Camunda’s Role in AI-Driven Automation
The Camunda platform helps organize and manage business processes using the open standards BPMN and DMN. This enables companies to build automation systematically—not just for individual tasks, but for the entire process as a whole, including decision-making at various levels.
Process Orchestration
Camunda allows you to integrate various systems, services, and automated components into a single process. Instead of isolated solutions, a cohesive logic is formed where all elements work in harmony. This helps avoid “automation silos” and ensures process transparency and manageability.
Decision Automation (DMN)
DMN (Decision Model and Notation) enables the formalization of business rules and their management separately from the process. This allows for the rapid adaptation of decision logic without changing the core process, which is particularly important in a dynamic environment.
Event-Driven Architecture
Camunda operates on an event-driven basis, allowing the system to respond to changes in real time. The use of the distributed Zeebe process engine ensures scalability and stability even with a large number of concurrent processes.
Integration via APIs and Microservices
Camunda supports an API-first approach, making it easy to integrate microservices, external systems, and other automation tools. Each component can perform its function independently while remaining part of a single process.
End-to-End Automation
The platform allows for the complete automation of processes—from initiation to the final result. Business rules, data processing, AI logic, and user interaction are combined within a single process, ensuring integrity and efficient execution.
Hyperautomation and Agent-Based AI Powered by Camunda: A Practical Scenario
Hyperautomation is an approach that integrates various automation technologies into a single system. This includes BPM, RPA, AI/ML, and event-driven architecture, which together enable a significant increase in the efficiency of business processes. The Camunda platform helps integrate all these components into a single, coordinated process.
Let’s consider this using the example of handling a customer request. When a request enters the system via CRM or a web form, a business process is triggered, coordinated through BPM and, if necessary, using rules (DMN) to make standard decisions.
In cases where data or conditions fall outside standard scenarios, AI can be engaged—for example, to select the most relevant course of action without creating additional complex rules.
In parallel, RPA is used to automate routine operations at the interface level—for example, to fill out forms or interact with other systems, such as ERP.
If atypical or critical situations arise, the process can be handed over to a human for a final decision.
This approach allows for the combination of clear process orchestration, rapid execution of routine tasks, and flexible data handling where needed. As a result, the company gains not just automation, but a managed and adaptive process that works effectively in real-world business conditions.
Where to start implementing AI-driven automation with Camunda
To successfully implement business process automation using AI tools on the Camunda platform, it is important to proceed gradually and follow a clear plan.
Process audit — first, you need to analyze existing business processes, identify the key ones, and find areas where problems or inefficiencies arise.
Selecting use cases — next, you should identify specific scenarios that have the greatest potential for automation and can deliver the maximum business impact (ROI).
Architecture — at this stage, the technical structure of the solution is formed, determining exactly how Camunda, AI solutions, and RPA tools will interact.
Pilot project — launching a minimum viable product (MVP) to test hypotheses, evaluate effectiveness, and adjust the approach as needed.
Governance and security — implementing data management, access control, and monitoring policies to ensure the system’s reliability and secure operation.
These steps help gradually build a flexible, scalable, and secure automation ecosystem that meets business needs.
The Benefits of AI-Driven Automation for Businesses in Ukraine
AI-driven automation offers Ukrainian businesses a number of significant advantages, particularly in the face of market volatility and rapid change.
It provides flexibility in challenging conditions, allowing for a quick response to changes in the economy, the market, or regulatory requirements. As a result, companies can adapt their processes to new realities more quickly.
Such automation also simplifies scaling. Businesses can expand their processes and implement new automated solutions without significant additional costs or complex system changes.
Another key benefit is cost optimization. Automating routine tasks reduces the workload on employees and cuts operational costs while improving efficiency.
Ultimately, these benefits help businesses become more competitive, adapt faster to digital changes, and enhance the customer experience.
Conclusion
The evolution of business process management—from classic BPM to RPA and on to AI-driven automation—opens up new opportunities for businesses. Traditional approaches remain an important foundation, but the use of AI provides the necessary flexibility in scenarios where this was previously difficult or required significant effort, and enables faster decision-making in non-standard situations.
The Camunda platform demonstrates how the combination of process orchestration, decision-making automation (DMN), event-driven architecture, and AI services can create a unified ecosystem for hyper-automation and improved business efficiency.
Today, automation goes beyond simply following rules and has become a flexible process management tool. And it is the combination of orchestration, classic automation, and AI that allows us to build systems that are not only efficient but also capable of adapting to changes in real time.
If you want to effectively implement AI automation and agentic AI into your business processes, you should turn to the experts at Integrity Vision. They will help you analyze processes, identify the best scenarios for automation, build the right architecture, and launch pilot solutions, all while ensuring transparency, security, and maximum efficiency.
