Technological progress is measured by the speed at which new acronyms emerge, and business leaders face a paradox: the more promises, the less concrete information. This is particularly true in the realm of artificial intelligence, where terms like AGI (Artificial General Intelligence), “Agents,” and “Superintelligence” are often used to justify new investments, but without clear definitions or verifiable capabilities. This uncertainty poses a risk of misallocated budgets, as it’s difficult to assess the real value of a solution when its ultimate goal remains vague.
AI uncertainty: a challenge for business and investors
Artificial intelligence is developing rapidly, but along with progress comes terminological confusion. When companies promise “AGI solutions” or “intelligent agents” that will change everything, yet cannot clearly explain what they mean by these terms, it creates room for speculation. Investors pour in funds, and business clients expect miracles, while tangible, measurable results remain elusive.
This problem is not new. The history of technology is replete with examples where ambitious promises outpaced actual capabilities. Today, when it comes to AI, it is especially important to distinguish marketing hype from practical value. NIST AI RMF 1.0, for instance, structures AI risk management around the functions Govern, Map, Measure, and Manage, emphasizing the need for clear control and measurement at every stage of AI system implementation. This indicates that even regulatory frameworks focus on manageability and transparency, rather than abstract promises.
Electronic document management as a foundation for real business results
In contrast to the vague promises in the AI space, there are proven tools that deliver tangible business results today. Electronic document management (EDM) is one such foundation. It is a comprehensive system that ensures the legal validity of documents, process transparency, data manageability, and a significant increase in operational efficiency.
Implementing EDM allows companies to:
- Ensure legal validity: Through the use of qualified electronic signatures (QES) and compliance with regulations such as the Law of Ukraine “On Electronic Identification and Electronic Trust Services” No. 2155-VIII, electronic documents gain the same legal force as paper documents.
- Optimize processes: Automating approval workflows, signing, and archiving significantly reduces the time spent on routine operations.
- Centralize data: All documents are stored in a single electronic archive, simplifying information retrieval, access, and management.
- Enhance transparency and manageability: Every step of document handling is recorded, ensuring full process transparency and auditability.
Members of the Intecracy Group alliance, when implementing EDM systems like Megapolis.DocNet and Scriptum.DMS (document management systems from InBase), understand that these practical benefits are key for businesses. These systems do not promise AGI, but they guarantee clear, measurable improvements in daily operations.
Over-enthusiasm for vague AI promises: a common mistake
One of the common mistakes we are observing this year and in the coming ones is the attempt to implement complex AI solutions without proper data and process preparation. Companies, captivated by the idea of “intelligent assistants,” often overlook the basic needs for structured data and automated processes. For example, expecting AI to effectively handle unstructured, disparate documents stored across various systems without unified rules is an illusion.
Microsoft’s “2026 Work Trend Index Annual Report” notes that organizational factors, such as culture and managerial support, have twice the impact on successful AI adoption as individual efforts. This confirms that technology alone is not a panacea. Without clear business processes, which EDM can provide, and without high-quality, structured data, any AI solution will be ineffective. Even if 49% of conversations in Microsoft 365 Copilot chats supported cognitive work (analysis, decision-making, evaluation, and creative thinking), as stated in the same report, this was possible due to integration and data structure within the Microsoft ecosystem.
Instead of chasing abstract promises, businesses should focus on building a solid foundation. This includes implementing an effective EDM system that allows for the collection, structuring, and management of data in a way that makes it suitable for future AI utilization. Gartner predicts that by 2028, over half of the GenAI models used by enterprises will be domain-specific, meaning tailored to particular industries and data. This further underscores the importance of preparing one’s own data effectively.
Practical scenario: digitalization of the public sector with a focus on EDM
Let’s consider a typical scenario of EDM implementation in the Ukrainian public sector, where Intecracy Group members have extensive experience. Government institutions often face enormous volumes of paper-based document flow, complex approval routes, and the need to ensure a high level of security and transparency.
In practice, this looks like:
- Process analysis and modeling: Specialists conduct an audit of existing document management processes, identify bottlenecks, and model optimal electronic workflows.
- EDM system implementation: Based on the UnityBase platform (an open-source low-code platform developed by InBase), a system such as Megapolis.DocNet or Scriptum.DMS is deployed. These systems ensure the full document lifecycle: from creation and registration to approval, signing (using QES), and archiving.
- Integration with public services: The EDM system is integrated with external state registers and services, enabling the exchange of legally significant documents. This is critical for ensuring trust in electronic data, as required by Law No. 2155-VIII.
- Electronic archive creation: All documents are stored in a centralized electronic archive, ensuring quick access, version control, and adherence to retention periods.
- Data preparation for future AI: At this stage, thanks to the structure and metadata collected by the EDM, data becomes suitable for further analysis and use by AI tools. For example, Nectainium (an automation platform from Nectain) can leverage this structured data for routine operation automation or intelligent search.
This approach not only resolves current document management issues but also builds a robust foundation for future AI initiatives, ensuring that any “intelligent” solutions will operate with high-quality, verified data.
Key steps to successful EDM implementation for future AI
To avoid the trap of vague AI promises and achieve real value, companies should focus on sequential steps that prepare their infrastructure and data. This is not just about installing software, but about strategic preparation for an era where data is the key asset.
AI readiness checklist based on EDM
- Strategy: Are the business problems that AI should solve clearly defined, rather than just technological trends?
- Data: Has an audit of data quality and consistency within the EDM been conducted? Are there metadata standards?
- Governance: Are there internal policies for AI usage and data governance in place?
- Legal validity: Is document integrity ensured through QES and long-term storage in an electronic archive?
- Processes: Have iterative processes for data cleansing and structuring been implemented, rather than attempts at a global “one-off” cleanup?
- Integration: Is the EDM system integrated with other key business systems (ERP, CRM) to create a unified information space?
By focusing on these practical steps, companies can build a strong foundation that allows them not only to optimize current operations but also to effectively leverage AI technologies when they become mature and well-defined enough for real business applications. This is the path to achieving measurable results, not chasing undefined promises.