DevOps and CI/CD: accelerating enterprise time-to-market safely
Balancing software delivery speed and system reliability using DORA metrics, 12-Factor App principles, SRE error budgets, and platform engineering.
EXPERT / Digital Line
Alina Lisova
Editorial contributor
About
Contributes Digital Line editorial materials on data, integration, enterprise platforms and practical AI use.
Articles with comments12Explore articles
Expert commentary
Current articles · 12Managing technical debt and code quality in enterprise systems
How SRE practices, DORA metrics, and engineering discipline help prevent technical debt, while low-code platforms standardize core architecture.
Pragmatic architecture for enterprise systems: avoiding distributed monoliths
Transitioning to microservices without considering operational complexity creates technical debt. We examine approaches for building stable enterprise systems.
Hybrid development in enterprise: balancing custom code and low-code
How to combine the flexibility of custom development with the speed of low-code while maintaining engineering discipline, version control, and robust system architecture.
AI agents in workflow: first-line processing and BPM future
How to combine the flexibility of AI with the rigid logic of BPMN 2.0 and DMN to automate first-line request processing without security risks or operational chaos.
Managing automation projects: Process discovery, pilots, and continuous improvement
Why traditional process maps doom IT projects to failure and how to build effective automation based on real logs, BPMN 2.0, and DMN decision tables.
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.
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 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.
Phased legacy ERP migration for uninterrupted transformation
Phased migration of legacy ERP systems in the public sector helps avoid downtime and ensures process continuity by focusing on automation and data.