Automating Document Flow: From Legacy Systems to AI Optimization

The speed and accuracy of document approvals determine operational efficiency. However, many large enterprises and government institutions still rely on outdated document management systems. These systems, often developed decades ago, fail to meet current requirements for speed, security, and integration, leading to operational losses and technological dependency.

Challenges of Legacy Document Management Systems: When Time and Cost Become Critical

For decades, companies have invested in large, complex ECM (Enterprise Content Management) systems that were once cutting-edge. Today, these solutions often become ‘technological anchors.’ High licensing and support costs, a lack of regular updates, difficulties integrating into modern IT landscapes, and reliance on a limited pool of specialists create obstacles to growth. In practice, this means every new automation request or process change turns into an expensive and lengthy project requiring manual coordination and document handling.

Such systems not only slow down business processes but also create risks for data security and regulatory compliance. The lack of flexibility in workflow configuration, inability to scale quickly, and integrate with technologies like AI makes them inefficient for modern needs.

AI as a Catalyst for Transformation: From Classification to Workflow Optimization

Integrating AI into document approval processes goes beyond simple signature automation. AI is becoming a tool for enhancing business process productivity, especially in document management. This year and in the coming years, we are observing in practice how AI is transforming the approach to working with documents, optimizing their entire lifecycle.

In practice, AI can perform several functions:

• Automatic Document Classification: AI models can instantly identify document types (contracts, invoices, applications, memos) based on their content and structure.
• Key Data Extraction: Through Natural Language Processing (NLP), AI can extract data from documents such as amounts, counterparties, dates, numbers, payment terms, and contract subjects. According to the publication “Automating Document Approval with AI: How to Eliminate Manual Coordination” by Scriptum, this data is then directly fed into the approval workflow.
• Workflow Routing and Initiation: Based on extracted data, AI can automatically determine responsible individuals and departments for approval, initiating the corresponding workflow in the document management system. This reduces manual coordination time and minimizes errors.
• Analysis and Decision Support: The 2026 Work Trend Index Annual Report notes that in an analysis of over 100,000 Microsoft 365 Copilot chats, 49% of conversations supported cognitive work: analysis, decision-making, evaluation, and creative thinking. This highlights AI’s potential to assist users in making informed decisions about documents.

Solutions built on UnityBase (a low-code platform from InBase) enable the integration of these AI capabilities, creating flexible and adaptive document management systems. For example, Scriptum.Workflow, built on this platform, allows for the development of solutions that not only automate routine operations but also provide intelligent support at every stage of a document’s lifecycle.

Common Mistake: Risky One-Time ECM Replacement vs. Effective Migration Strategy

One of the most common and costly mistakes in modernizing document management is attempting to replace the entire legacy ECM system in one go. This approach carries significant risks, from critical process failures to multi-million dollar budget overruns and prolonged downtime. It requires a complete overhaul of all integrations, migration of vast amounts of data, and staff training, often paralyzing an organization’s operations for months.

Instead, a phased migration strategy with parallel operation of the old and new systems is recommended. This approach allows for the gradual transition of processes to the new platform, minimizing risks and ensuring business continuity. For instance, one can start by automating individual, less critical workflows using modern low-code platforms like UnityBase, which underpins Scriptum and Megapolis.DocNet. This allows for rapid development and deployment of new functionalities while maintaining the operability of the old system for other processes.

This approach also allows for the gradual integration of AI capabilities, testing their effectiveness, and adapting them to the organization’s specific needs without overwhelming the system or staff.

Operational Scenario: Automating Document Management in the Public Sector with Security and Integration in Mind

Let’s consider a typical scenario for the public sector, where automating document approvals with AI is particularly important. Government institutions process large volumes of documents daily: citizen appeals, interdepartmental requests, internal orders, and tender documentation. Here, not only speed but also compliance with high security standards (КСЗІ) and integration with public electronic services are crucial.

In practice, this looks like:

1. Document Receipt: A citizen’s appeal is received via the public services portal or email.
2. AI Classification and Extraction: An AI module integrated into a system like Scriptum.Workflow automatically classifies the document as a ‘citizen appeal,’ extracts key data (full name, address, nature of the issue, date), and identifies the responsible department.
3. Workflow Initiation: Based on the extracted data, the system automatically creates a registration card, assigns a responsible executor, and launches an approval and execution workflow, which includes managerial review, transfer to the relevant department, and deadline monitoring.
4. Interdepartmental Interaction: If information from another agency is required to resolve the issue, the system automatically generates a request using integration with public services (e.g., СЕВ ОВВ) and tracks its status.
5. Security and Compliance: All document management processes occur within a secure environment compliant with КСЗІ requirements, utilizing electronic digital signatures and access control. Alliance companies, such as Softengi, have experience in developing and implementing such solutions, ensuring security and reliability.

This scenario demonstrates how AI can not only accelerate document processing but also ensure transparency, accountability, and regulatory compliance, which is vital for the public sector.

Managing AI Risks in Document Management: Security, Reliability, and Compliance

Implementing AI in critical business processes like document management requires careful risk management. The need for secure and reliable AI solutions is growing, as evidenced by the development of relevant risk management frameworks.

Key aspects of AI risk management include:

• Data Security and Confidentiality: AI systems process sensitive information, making data protection against unauthorized access and leaks a priority.
• Accuracy and Reliability of AI Models: Inaccurate AI classification or data extraction can lead to incorrect document routing, delays, and legal risks.
• Bias Management: AI models can inherit biases from training data, potentially leading to discrimination or unfair decisions.
• Regulatory Compliance: AI solutions must comply with standards such as GDPR, NIS2, and Ukrainian information protection requirements.

International frameworks exist to structure AI risk management. For example, the Artificial Intelligence Risk Management Framework (AI RMF 1.0) from NIST structures AI risk management around the functions Govern, Map, Measure, and Manage. This helps organizations systematically identify, assess, and mitigate risks.

Particular attention should be paid to risks specific to large language models (LLMs), which are often used for natural language processing. OWASP’s Top 10 Risk & Mitigations for LLMs and Gen AI Apps 2025 lists Prompt Injection as LLM01:2025, the first risk in the list for LLM/GenAI applications. This means attackers can manipulate AI systems through specially crafted prompts, potentially leading to information leaks or unauthorized actions.

To mitigate these risks, approaches such as using domain-specific AI models are employed. According to Gartner’s Top Strategic Technology Trends for 2026 forecast, by 2028, over half of GenAI models used by enterprises will be domain-specific. This allows for the creation of more accurate and secure models trained on organization-specific data, reducing the risks associated with general LLMs.

Business Outcome: Freedom from Technological Dependence and Document Flow Unification

The ultimate business outcome of implementing AI in document approval processes is not just accelerated operations but strategic transformation. It’s about breaking free from technological dependence on legacy vendors and their expensive, inflexible solutions. Transitioning to modern platforms like UnityBase with integrated AI capabilities allows for the unification of document management across the entire organization, creating a single, transparent, and efficient space for working with documents.

Such unification ensures:

• Reduced Operational Costs: Through automation of routine tasks and workflow optimization.
• Increased Speed and Quality of Decision-Making: Via rapid access to information and intelligent support.
• Improved Compliance and Security: Through centralized document management and built-in control mechanisms.
• Flexibility and Scalability: The ability to quickly adapt the system to new business requirements and integrate with other IT systems.

Companies within the alliance, particularly InBase and Softengi, possess the expertise to build such solutions, helping organizations modernize their document management systems and leverage AI potential to achieve strategic business goals.

Readiness Checklist for Implementing AI in Document Management

• Have business goals for automation been defined (e.g., reducing contract approval cycle from 5 to 2 days)?
• Has a process audit been conducted to identify bottlenecks where AI will have the greatest impact (e.g., manual classification of incoming mail)?
• Is there an AI risk management policy in place covering data security, confidentiality, and compliance with GDPR/NIS2?
• Has a response plan for LLM-specific threats (e.g., Prompt Injection, data leaks) been developed?
• Have success metrics for AI solutions been defined (classification accuracy >95%, data extraction speed, approval time)?
• Is there a staff training plan for working with new AI tools and understanding their capabilities and limitations?
• Have individuals been assigned responsibility for monitoring, supporting, and further developing AI systems in document management?