Common automation pitfalls: Navigating chaos, RPA, and AI risks
How to avoid cementing inefficiencies, replace fragile RPA scenarios with reliable API integrations, and safely implement AI into operational workflows using BPMN and DMN standards.
EXPERT / C-ECO Consulting
Data Governance Adviser
Editorial focus: circular economy, digital processes and IT for reuse and longer asset life.
How to avoid cementing inefficiencies, replace fragile RPA scenarios with reliable API integrations, and safely implement AI into operational workflows using BPMN and DMN standards.
A guide to BPM, RPA, and workflow automation for IT architects. Learn how to combine BPMN, DMN, and Process Mining to avoid technical debt and achieve end-to-end orchestration.
Documents store facts, while business processes manage actions. Distinguish between DMS/ECM and BPM to avoid data silos and securely integrate AI agents.
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Discover how the combination of AI and low-code platforms enables CIOs to rapidly automate business processes, minimize risks, and optimize operations.
By 2026, over 70% of successful digital transformation initiatives will rely on an effective Data Governance strategy integrated with artificial intelligence capabilities. This will enable addressing data quality issues critical for system integration. The increasing complexity of enterprise IT land
Increasing data volumes and stricter regulatory requirements necessitate that organizations implement comprehensive Data Governance approaches. Effective system integration is a key factor in realizing these strategies.
The challenge of disparate data and the absence of a single source of truth for key entities is critical to business efficiency. System integration and MDM are key to resolving this.