AI optimizes logistics document flow

Logistics today faces a paradox: despite technological advancements, a significant portion of document flow remains analog or fragmented. According to Scriptum, 73% of logistics teams still manage documents in Excel and non-integrated systems. This not only slows down processes but also creates fertile ground for errors and regulatory risks. A single goods transport operation can require up to 50 paper documents exchanged between 30 counterparties, and 70% of companies process waybills (TTN) manually, as noted in the article ‘AI document flow in logistics: applications, invoices, acts, and waybills’ (Scriptum, 2023). This year and in the coming ones, given the mandatory nature of electronic waybills in Ukraine from 2027, the digitalization of document flow becomes a strategic necessity.

Challenges in logistics document flow: from Excel to AI

Manual document processing in logistics is not just a matter of speed but also accuracy. The human factor inevitably leads to errors, which can cost companies financial losses, delivery delays, and problems with regulatory bodies. Fragmented systems and reliance on Excel create ‘information silos’ where data is not integrated, making a unified view of operations and effective analysis impossible.

The implementation of AI in logistics document flow aims to solve these problems. AI systems can automate routine tasks such as recognizing data from applications, invoices, acts, and waybills, validating it, and routing it. This not only speeds up processing but also minimizes errors, freeing up personnel for more complex and analytical tasks. However, successful implementation requires not just replacing paper with digital, but a profound transformation of approaches.

Common mistake: migrating old workflows without optimization

One of the most common mistakes when transitioning to electronic document management is the mechanical migration of existing, often outdated and overly complex, approval workflows. Companies simply transfer paper-based processes ‘as is’ into a digital format without analyzing them for efficiency or eliminating redundant steps. For example, if a paper document passed through five departments, each placing their signature, then during digitalization, an identical electronic workflow is often created, even though AI can automatically verify most data and reduce the number of approvals.

This approach does not allow for the full realization of the potential of digital systems. Instead of acceleration and simplification, companies get a digital version of their own bureaucracy. Before implementing any ECM (Enterprise Content Management) / DMS (Document Management System), especially with AI components, a business process audit and re-engineering must be conducted. Only after optimizing workflows can solutions like Scriptum.DMS (a document management system from InBase), which allow for flexible workflow configuration and AI integration for document validation and processing, be effectively implemented.

Architectural example: integrating ECM with government services for the public sector

For public sector organizations involved in logistics, integrating electronic document management systems with government services is mandatory. Let’s consider a typical architectural scenario. The central element is an ECM system, such as Megapolis.DocNet (an electronic document management system from InBase), which ensures document storage, management, and routing. This system must be integrated with government services through secure communication channels.

For example, for legally significant actions, such as submitting documents to court or exchanging official correspondence, the ‘Electronic Court’ subsystem is used. As indicated on the id.court.gov.ua portal, it operates through an electronic cabinet and utilizes electronic identification and signatures. This requires the ECM system to support qualified electronic signatures (QES), based on Law of Ukraine No. 2155-VIII ‘On Electronic Identification and Electronic Trust Services’ (Law of Ukraine, 2017). Integration with QES allows for signing documents directly within the system, ensuring their legal validity.

Furthermore, for the public sector, compliance with the requirements of the Comprehensive Information Security System (CIS) is critical. This means that all components of the architecture, including the ECM system and integration channels, must be certified and comply with cybersecurity standards. AI components that process confidential data must also be part of this CIS, requiring careful risk management.

Components of successful AI у document management in logistics

The implementation of AI in logistics document flow is based on several components:

1. Intelligent Document Processing (IDP): AI models, particularly domain-specific GenAI, which Gartner predicts (2025) will constitute over half of the models used by enterprises by 2028, can recognize various document types (applications, invoices, acts, waybills) and extract key data with high accuracy. This reduces the need for manual input and verification.
2. Automated Validation and Verification: AI can check extracted data against rules, compare it with information in ERP systems (e.g., SAP, Microsoft Dynamics), and identify anomalies or potential errors.
3. Optimized Routing and Approvals: Based on recognized data and established rules, AI can automatically direct documents to the appropriate employees or departments for approval, reducing processing time.
4. AI Risk Management: AI implementation carries certain risks related to model accuracy, data bias, and cybersecurity. To manage these risks, Intecracy Group follows the NIST AI RMF 1.0 framework, which structures management around the functions of Govern, Map, Measure, and Manage (NIST, 2023). This allows for systematic assessment, monitoring, and mitigation of potential threats.
5. Integration with Existing Systems: Effective AI у document management cannot exist in isolation. It requires seamless integration with ERP, CRM, accounting systems, and government electronic services. Products like Scriptum.DMS and Megapolis.DocNet provide such integration, allowing companies to avoid vendor lock-in and build a flexible architecture.

Checklist for implementing AI in document management

Before embarking on a project to implement AI in logistics document management, it is recommended to go through the following checklist:

• Audit of existing document workflows has been conducted, and processes for optimization have been identified.
• A registry of AI model risks (accuracy, bias, security) has been compiled according to the NIST AI RMF 1.0 approach.
• An assessment of the compatibility of the current infrastructure with AI solutions has been performed.
• An integration architecture with ERP, CRM, and other accounting systems has been developed.
• Measurable KPIs for evaluating effectiveness have been defined (e.g., waybill processing time, validation error rate).
• A staff training plan for working with new tools has been created.
• A policy for monitoring AI models has been developed, including human-in-the-loop mechanisms for critical decisions.

Implementing AI in logistics document management is not just a technological upgrade but a transformation that enables a new level of operational efficiency and regulatory compliance. It requires a comprehensive approach, including process optimization, risk management, and integration with existing and government systems.