Automation projects often stall or fail because companies rely on idealized process maps rather than the actual state of task execution. Business leaders struggle to bridge the gap between how they believe processes work and actual operational practice. This leads to failed pilot projects and budget erosion. Evidence-based discovery using real data is becoming the new industry standard for success.
Success in business process automation depends on shifting from subjective perceptions to evidence-based analysis (process mining), clearly separating process logic from business rules (using BPMN and DMN standards), and iterative scaling through pilot launches. This approach reduces technical debt and IT system maintenance costs.
The trap of the ideal process: Why traditional design leads to pilot failure
Most automation initiatives begin the same way: analysts gather department heads and draw detailed flowcharts. This approach is based on the false assumption that employees know exactly how they work and describe it accurately. In practice, such maps reflect only the "happy path"—an ideal scenario that exists in regulations but rarely matches operational reality.
If design is based solely on subjective assumptions, scaling becomes problematic. In practice, only 13% of companies successfully scale their solutions under these conditions, while up to 49% of pilot projects stall halfway due to the discovery of critical exceptions not accounted for during the design phase. When developers try to hard-code a chaotic process, they simply automate the chaos, creating custom monoliths that become impossible to maintain.
Fact-based process discovery: How event log analysis exposes shadow routes
To avoid the trap of subjectivity, modern process engineering uses process mining technology. Instead of interviewing employees, objective digital footprints—event logs that always remain in a company's information systems—are analyzed. Process mining provides an evidence base for prioritizing automation by showing how a process is actually executed.
Analyzing this data allows for the reconstruction of the real workflow and the identification of bottlenecks and "shadow processes." For example, in a classic invoice processing workflow, the route looks sequential: receipt, verification, manager approval, payment. However, event log analysis often reveals shadow scenarios where employees bypass standard approval steps to speed up processing. Automation that ignores these workarounds will block real work or lead to user sabotage of the system.
Divide and conquer: Why business rules (DMN) should be separate from process logic (BPMN)
One of the biggest mistakes in automation is mixing the process flow with decision-making logic (business rules). If complex verification conditions are "hard-coded" directly into the script or process branches, any change in corporate policy will require developer involvement and code rewriting.
To solve this, standards from the Object Management Group (OMG) are used: BPMN 2.0 and DMN (Decision Model and Notation). The BPMN 2.0.2 standard (also published as ISO/IEC 19510:2013) is executable: the same model serves as both documentation for the business and the logic that drives the process in the engine. In turn, the DMN standard allows complex business rules to be moved out of process diagrams into separate, easy-to-manage decision tables.
Consider separating complex eligibility verification logic in a loan application process. Instead of creating dozens of branches in a BPMN diagram, the process is reduced to a single step that calls a DMN table. If the policy changes, a business analyst simply updates the decision table without needing to redeploy the IT solution.
Launching a viable pilot: From executable model to initial metrics
The goal of a pilot project is to prove the viability of an architectural approach and obtain initial real metrics on speed and execution cost, without trying to cover the entire enterprise landscape at once. During the pilot phase, it is critical to ensure end-to-end orchestration.
For example, orchestrating a multi-step customer onboarding process using a process engine maintains visibility into the current state of each individual request. Instead of manual control, the engine automatically distributes tasks, recording the execution time of each stage.
The technology alliance Intecracy Group offers tools for implementing this approach. Specifically, the low-code platform Scriptum (developed by Scriptum) uses the Camunda engine and supports BPMN, CMMN, and DMN standards. This allows for the rapid launch of executable process models and the separation of business rules from task flows.
The platform foundation for building custom and integrated enterprise solutions is UnityBase (a joint development by the Intecracy Group alliance, where InBase is a key, but not the only, developer). Using Domain metadata mechanisms, automatic REST API generation, and flexible role-based access (RBAC/RLS), UnityBase enables the rapid deployment of robust systems. For projects with high security requirements or heavy loads, the official platform page recommends Enterprise or Defence commercial editions, which support detailed audit trails and enhanced integration capabilities.
Continuous improvement: How orchestration ensures development without chaos
Launching a pilot is just the beginning of the continuous improvement cycle. When a process is managed through an executable BPMN 2.0 model, every step is logged automatically. This creates a constant stream of clean data for repeated process mining.
However, it should be remembered that tools like process mining, BPMN, or DMN create transparency and management flexibility, but they are not a magic solution for overcoming organizational problems. Automation works in the long term only when a company is ready to transform its operational culture based on the objective data obtained.
| Criterion | Sign of low readiness (manual chaos) | Sign of high readiness (evidence-based approach) |
|---|---|---|
| Source of process knowledge | Description based on subjective interviews; no digital footprints. | Event logs available in information systems; process reconstructed via process mining. |
| Decision-making logic | Rules hard-coded in legacy systems or existing only in regulations. | Rules structured and moved to separate DMN standard decision tables. |
| Execution stability | Each case handled uniquely in manual mode. | Process has clear phases described by the BPMN 2.0 standard for automatic orchestration. |
FAQ
What is the difference between a subjective process description and evidence-based process mining?
A subjective description is based on interviews and regulations, which often ignore actual employee workarounds. Process mining, conversely, analyzes objective event logs from IT systems, reconstructing the actual state of processes and identifying "shadow" routes.
How does the DMN standard help business users?
DMN allows for the separation of complex decision-making logic from the process diagram and program code by placing it in convenient decision tables. This enables business rules to be updated without involving developers or rewriting the IT system.
What is an executable BPMN 2.0 model?
It is a standardized process diagram that serves simultaneously as understandable graphical documentation for the business and as code that directly manages process execution within a specialized process engine.