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
AI
Practical guidance on management accounting, budgeting, treasury, period close, financial controls and evidence-based AI.
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19 publications
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 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 readLow-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.
Valerii Zadorozhnyi6 min readIntegration architecture patterns for AI readiness: from chaos to Data Fabric
AI readiness is defined by architectural data maturity. Transitioning from point-to-point integrations to a managed layer with a unified API contract prevents data processing errors.
Bohdana Zhuravel6 min readEnterprise 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.
Valerii Zadorozhnyi7 min readDomain-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.
Valerii Zadorozhnyi7 min readAI у FinOps: Optimizing cloud spending in multi-cloud environments
AI in FinOps aids in forecasting and controlling cloud expenditure, ensuring budget transparency and resource management discipline.
Yaryna Doroshenko6 min readFinOps in multi-cloud: How banks optimize costs and boost efficiency
FinOps helps banks control rising cloud expenses, identify surplus resources, and ensure budget transparency in multi-cloud environments.
Yaryna Doroshenko7 min readAI у master: data management and system integration
AI analytics is transforming master data management (MDM) in 2026, ensuring accuracy and efficiency in enterprise system integration.
Bohdana Zhuravel3 min readOptimizing multi-cloud costs with AI and NIS2 compliance
AI-driven FinOps optimizes multi-cloud spending while ensuring NIS2 compliance. Effective cloud resource management under new regulations.
Yaryna Doroshenko5 min read