Transforming first-line support by 2027
By 2027, first-line support in large organizations will transform into dynamic systems powered by AI agents. However, granting them full autonomy without systemic boundaries creates operational risks and chaos. Businesses face a gap between theoretical process models and their actual execution, making unprepared AI implementation risky.
Process orchestration and the role of BPM systems
AI agents are highly effective at executing localized tasks, but they cannot independently maintain the integrity of an entire business process. They serve as a dynamic execution layer, while the overall logic must be controlled by a traditional process engine.
According to the Microsoft 2026 Work Trend Index, 49% of conversations in Microsoft 365 Copilot involved cognitive work (data analysis, decision-making). This confirms the role of AI agents as assistants that require strict orchestration.
Process preparation: Process mining and standardization
Before automation, it is essential to analyze the actual state of operations using process mining. Analyzing digital footprints (event logs) helps identify "shadow" processes and bottlenecks before handing tasks over to AI.
To ensure predictable performance, organizations use the BPMN 2.0 standard and separate the logic: decision-making rules are moved to DMN tables, while the AI agent only analyzes text and queries the DMN for a verdict. This prevents unpredictable model behavior.
Security and organizational barriers
According to the OWASP classification (LLM01:2025), prompt injection is the most critical risk for GenAI. Protection requires an isolated architecture: filtering requests through API gateways and restricting the agent's direct access to databases.
The success of implementation also depends on management readiness. Hybrid orchestration solutions are already available on the market. For instance, the Scriptum low-code platform (developed by InBase on top of UnityBase by Intecracy Group) allows integrating AI agents into a secure corporate environment using BPMN 2.0, DMN standards, and access control.
What it means for companies
The rapid shift toward AI-driven support and cognitive tasks means businesses can no longer rely on ad-hoc AI deployments. Unprepared integration leads to operational chaos, unpredictable model behavior, and severe security vulnerabilities like prompt injection. To remain competitive and secure, the industry must transition to hybrid orchestration models where AI is strictly bound by traditional process engines.
Next steps
- Conduct process mining: Analyze digital footprints and event logs to identify bottlenecks and "shadow" processes before introducing AI.
- Standardize process logic: Use the BPMN 2.0 standard and move decision-making rules to DMN tables, leaving the AI agent to only analyze text and query the DMN.
- Secure the architecture: Protect systems from prompt injection by filtering requests through API gateways and restricting direct database access.
- Deploy hybrid orchestration: Adopt secure, low-code platforms like Scriptum to safely integrate AI agents into the corporate environment with proper access controls.
Prepared by a Software Ukraine member. Original publication.