DECISIONS & DELIVERY
Practical guides to operating models, process automation, transformation governance, management data and controlled AI.
38 publications
Secure IT/OT convergence requires eliminating direct connections between ERP and SCADA. We analyze the architecture of an industrial demilitarized zone (IDMZ) and asynchronous data exchange.
Why digital signatures are insufficient for court and how to build an architecturally resilient document management system that ensures non-repudiation.
Integrating autonomous agents into enterprise ERP and ECM environments requires strict architectural discipline, using DDD and RBAC to mitigate security risks.
Integrating generative AI into corporate processes using BPMN 2.0 and DMN for secure AI agent orchestration and maintaining system control.
Transitioning from experimental ML models to robust enterprise systems using AWS Well-Architected Framework, Google SRE, and 12-Factor App principles.
How SRE practices, DORA metrics, and engineering discipline help prevent technical debt, while low-code platforms standardize core architecture.
Transitioning to microservices without considering operational complexity creates technical debt. We examine approaches for building stable enterprise systems.
How to combine the flexibility of custom development with the speed of low-code while maintaining engineering discipline, version control, and robust system architecture.
Transitioning to a Data Mesh architecture resolves integration chaos through data decentralization, data contracts, and moving away from fragile point-to-point connections.
A practical analysis of architectural patterns for connecting legacy systems to modern ecosystems: how to avoid point-to-point chaos and why RPA is only a temporary solution.
Why ignoring organizational principles, lacking data owners, and "big bang" migrations doom corporate integration, and how to build a managed architecture.
The success of AI initiatives depends on data lineage transparency and Data Governance maturity. Learn how to avoid poor data quality traps and prepare enterprise IT architecture for AI scaling.