Low-code, RPA, or AI agent: choosing the right approach
Balancing deterministic orchestration and probabilistic AI in enterprise systems to avoid technical debt and critical security risks.
AUTHOR / Digital Line
Khrystyian Zadorozhnyi
Editorial contributor
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Contributes Digital Line editorial materials on data, integration, enterprise platforms and practical AI use.
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Author’s publications
Current articles · 42Process modeling standards: BPMN, CMMN, and DMN
Effective enterprise process automation requires separating workflow orchestration, flexible case management, and business rules. Learn how to avoid hardcoded logic and identify shadow routes.
Understanding BPM versus RPA and workflow automation
A guide to BPM, RPA, and workflow automation for IT architects. Learn how to combine BPMN, DMN, and Process Mining to avoid technical debt and achieve end-to-end orchestration.
BPM and document management: defining the boundary
Documents store facts, while business processes manage actions. Distinguish between DMS/ECM and BPM to avoid data silos and securely integrate AI agents.
Architectural defense against AI Prompt Injection and leaks
How to protect corporate LLM systems from Prompt Injection and data leaks at the architectural level in 2025, following NIST AI RMF and OWASP standards.
RPA after ERP: A Bridge or a Digital Patch?
RPA is a useful temporary bridge to legacy systems following an ERP deployment. However, automating Excel-based workflows, email approvals, and fragmented responsibilities often accelerates technical debt rather than resolving it.
AI-Native System Architecture: Ensuring 'Keepalive' Quality and AI Risk Management
AI-native system architecture demands an integrated approach to quality and AI risk management. We examine frameworks, security platforms, and organizational factors.
Enterprise developer: from code to AI system management
AI-native development is transforming the role of the enterprise developer in 2026-2027, shifting focus from writing code to managing AI systems, security, and domain-specific models.
Domain-specific models: when corporate AI needs its own language
Corporate systems require domain-specific language models for security, efficiency, and compliance with unique business processes.
Recursive AI self-improvement: preparing businesses for new challenges
The concept of recursive AI self-improvement is transforming process automation. We explore how companies can prepare their data and manage risks.
AI and RPA: How data and risks define automation success
Integrating AI into RPA is transforming business processes, but success hinges on data readiness and effective risk management. Prepare your data.
Domain-specific AI models for enterprise applications
Domain-specific AI models are becoming a key tool for modernizing enterprise applications in 2026-2027, overcoming legacy system limitations.