AI cost forecasting and FinOps automation: the future of cost management by 2027
Shifting from reactive invoice analysis to proactive cost modeling during architectural design using unit economics and AI assistants.
Yaryna Doroshenko6 min readCONTROLLED FINANCE
Intecracy Group
Practical guidance on management accounting, budgeting, treasury, period close, financial controls and evidence-based AI.
Choose a topic
16 publications
Yaryna Doroshenko6 min readCloud-native architecture and cost: how design impacts TCO
Why cloud bills rise after migration and how to embed financial efficiency (FinOps) into infrastructure design during the application planning stage.
Yaryna Doroshenko6 min readFinOps in enterprise: from chaotic cloud spending to unit economics
An overview of implementing a FinOps culture, the Inform-Optimize-Operate cycle, and unit economics principles to effectively manage cloud spending in the enterprise sector.
Yaryna Doroshenko6 min readScaling AI-assisted development in enterprise: from code generation to engineering discipline
By 2027, successful AI adoption in development will depend not on code generation speed, but on integrating AI tools into controlled testing, security, and architectural oversight processes.
Valerii Zadorozhnyi7 min readIntegrating AI/ML into products: From prototype to production
Transitioning from experimental ML models to robust enterprise systems using AWS Well-Architected Framework, Google SRE, and 12-Factor App principles.
Valerii Zadorozhnyi5 min readDevOps 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.
Valerii Zadorozhnyi6 min readHybrid 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.
Valerii Zadorozhnyi6 min readData governance and data lineage: quality for AI
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.
Bohdana Zhuravel7 min readAPI Management and Data Contracts: Overcoming Integration Chaos
How to transition from fragile point-to-point connections to a managed integration layer using API gateways and schema registries to ensure enterprise system stability.
Bohdana Zhuravel5 min readMaster Data Management: The role of Data Stewards and Owners
The transition to decentralized architectures and AI integration has revealed a critical bottleneck: the lack of clear data ownership undermines the reliability of enterprise systems.
Bohdana Zhuravel6 min readProcess 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.
Valerii Zadorozhnyi6 min readIntegrating ECM with ERP/CRM and government registries
How to eliminate information desynchronization between accounting systems and document archives, ensure legal validity, and build a seamless architecture based on ISO 15489-1 and NIST CSF 2.0 standards.
Viacheslav Haiduk6 min read