Technological progress in artificial intelligence (AI) is accompanied by terminological ambiguity surrounding concepts like AGI or intelligent agents. This creates risks of budget misallocation due to the difficulty of assessing the real value of solutions. Standards like NIST AI RMF 1.0 emphasize the importance of governability and transparency in AI systems, rather than abstract promises.
EDMS as a foundation for practical results
Unlike declarative promises in the AI field, electronic document management systems (EDMS) deliver measurable results today. Implementing EDMS allows companies to ensure the legal validity of documents using QES, optimize approval workflows, centralize data in a single archive, and increase operational transparency for audits.
A common mistake is attempting to implement complex AI solutions without proper process preparation. According to the Microsoft Work Trend Index, organizational factors have twice the impact on AI success compared to individual efforts. Without the structured data provided by EDMS, AI efficiency remains low. Gartner predicts that by 2028, over 50% of enterprise GenAI models will be focused on industry-specific data, highlighting the importance of preparing your own information base.
What this means for the market
For the software industry and enterprise businesses, rushing into AI adoption without a structured data foundation leads to failed digital transformations and wasted budgets. Companies that prioritize EDMS will secure a competitive advantage by training AI models on high-quality, legally validated internal data, while those neglecting this step will struggle with low AI efficiency and integration bottlenecks.
Action plan
To build a reliable foundation for future intelligent solutions, organizations should follow this practical roadmap for EDMS-based digitalization:
- Audit and model optimal electronic document workflows.
- Deploy an EDMS to ensure a full document lifecycle and signing with QES.
- Integrate the system with state registries for the exchange of legally binding data.
- Create a centralized archive for rapid access and version control.
- Structure metadata to make information suitable for subsequent analysis by AI tools.
Prepared by a Software Ukraine member. Original publication.