Agent-based automation supports processes with scattered information and manual steps. In this context, agents perform tasks that were previously manual and use current information to make decisions based on an up-to-date overall picture.
Agent-based automation structures processes spanning multiple systems or stages. Automating routine tasks with AI agents improves control and accelerates decisions as volumes grow.
Focus shifts from automating single tasks to entire workflows. Process challenges often involve information transfer between stages and timely detection of issues, not just a single work step.
The agent’s role is to streamline processes that are burdened by manual work, scattered information, and the need to manage deviations, slowing operations. In these situations, the agent integrates directly into the workflow rather than functioning as an independent tool.
What is meant by an AI agent in business processes
An AI agent is an assistant designed to fulfill a defined task or role by making decisions and taking actions based on predefined rules, context, and the organization’s data. Unlike a traditional assistant, an agent answers questions and proactively carries out the process related to their assigned role.
An agent can recognize document types and extract relevant information. It can also compare data from different sources to form an overall view before the next step.
An action can start when a specific event happens, such as a document arriving. The agent can also monitor the process and highlight areas needing attention. A clear task definition keeps the solution transparent and manageable at work.
Benefits of agents from a process perspective
The introduction of agents changes the structure of work. Routine data processing is automated, and expert work focuses on monitoring, handling deviations, and decision-making. Manual data entry and comparison are reduced, thereby minimizing the risk of errors and improving the reliability of the process.
Structured data enables up-to-date reporting without extra summaries. Structured data is stored in a uniform format and linked to process stages. This is different from information in separate files or free-form text. Transparency improves when information is easy to follow and trace.
Scalability matters when event volume increases or new stages join. The agent-based model handles more work without needing more resources. The process stays predictable and manageable, even as situations change.
Processing paper and attachment-based data in the process
In many processes, key information is still made in attachments and free-form documents. These appear in construction, infrastructure, industrial maintenance, logistics, property maintenance, and many permit-related processes. Documents often arrive as images or scans, and the data is then entered by hand into systems.
An agent can be connected to the process as soon as the document arrives. When the attachment is directed to the agent, it recognizes the document type and extracts predefined information, such as quantities, dates, and identifiers. The operating model is suitable for environments where documents are created in a distributed manner, and processing occurs across multiple stages and systems.
The data is stored in a structured format, and the original document is attached for traceability. Work centers on reviewing and handling deviations, not just entering data. The information is available right away to support projects, finances, or reporting.
Data reconciliation and deviation management
Often, information about a single event is stored in many systems at different stages. This happens across fields such as financial management, logistics, energy, production, and services. Operational, payment, and reporting data are often made separately. Reconciliation is often done by comparing reports afterward, which takes time and may lead to errors.
An agent can be configured to automatically compare data from different sources. Events are matched by quantity, time, or identifiers—no matter which system provided the data. This fits environments with many events and scattered chains.
Matched cases move forward automatically. Deviations go for review. Experts focus only on unclear or judgment-based cases. There is ongoing visibility into process status and repeated deviation points.
Agent-based automation as part of a managed whole
The value of agents lies in how they connect to different stages, not in a single function. Across these examples, information is spread out, processing is manual, and timely reactions to deviations are needed.
The agent-based model supports staged process development. A limited use case keeps the process manageable and reveals where process automation works best. This process-focused approach extends to other processes without requiring model rebuilding.
If agent-based automation gives you ideas, you can start development safely during a joint workshop. The workshop finds the processes where agents help most and identifies the next concrete steps.
Discover the workshop on developing processes using artificial intelligence.
Marko Koskela
Chief Commercial Officer, partner
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