Data 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.
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AI
Practical guides to cross-team workflows, documents, shared data and responsible AI.
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23 publications
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.
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.
IDP and AI in document management by 2027
Transitioning from traditional ECM to Intelligent Information Management. How IDP and AI technologies automate corporate document processing while maintaining legal validity.
Common ECM migration errors: why IDP and workflows stall
Successful migration to modern intelligent ECM systems requires mandatory design of fallback rules in workflows and the creation of a high-quality training corpus for IDP models.
Who owns the customer master record: Data Governance for Customer 360
Effective customer data management requires clearly defined responsibilities for master record modifications, especially in the context of AI and cybersecurity.
Checklist for selecting electronic document management systems
A practical approach to choosing an electronic document management system: critical questions during demos to avoid overspending and reduce IT project value.
AI optimizes logistics document flow
AI-driven automation of document management in logistics is essential for enhancing efficiency and regulatory compliance, but requires careful planning.
Integrating AI with ECM/BPM for internal request automation
Integrating AI with existing ECM and BPM systems to automate internal requests. A practical approach to classification, data extraction, and routing.
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.
AI 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.
AI-driven Scriptum: The future of document and workflow automation by 2026
AI-driven Scriptum is set to transform document management and workflow automation by 2026, accelerating document processing and business process automation.