Autonomous Document Processing for Smart Office Workflows: Design, Implement, Evaluate

Thesis Abstract

The rapid digitization of office environments necessitates intelligent management of document-intensive workflows to enhance efficiency, accuracy, and information governance; however, many organizations struggle with fragmentation between legacy systems and emerging autonomous processing technologies. This study investigates the design, implementation, and evaluation of an autonomous document processing (ADP) system integrated into smart office workflows to streamline document intake, processing, routing, and archival, while maintaining compliance with governance and security requirements. The aim is to develop a reusable architectural framework and an operational prototype that demonstrably improves processing speed, accuracy, and decision support across departmental workflows. Specific objectives are to (i) analyze current office document processes and identify bottlenecks amenable to automation; (ii) design an ADP architecture incorporating optical character recognition, natural language processing, entity extraction, workflow orchestration, and policy-driven access control; (iii) implement a modular prototype using open-source components and cloud-based services; (iv) evaluate the system’s performance against baseline manual processes and a non-autonomous automated baseline in terms of throughput, error rate, and user satisfaction; and (v) derive guidelines for governance, security, and change management in smart office environments. The methodology adopts a mixed-methods research design, combining a rigorous engineering evaluation with a qualitative assessment of user acceptance. The population comprises administrative units within a mid-sized university campus and a collaborating corporate partner, with a total of 12 departments across both organizations. A stratified random sample of 120 users (60 from the university, 60 from the industry partner) participated in usability and acceptance testing. Data collection employed (i) automated log data from the prototype capturing processing time, OCR accuracy, extraction precision, and routing latency; (ii) a structured survey measuring perceived usefulness, perceived ease of use, and system usability scale (SUS); (iii) semi-structured interviews with 20 workflow managers to explore governance, risk, and change-management implications; and (iv) archival performance metrics from existing document handling software for baseline comparisons. Validity and reliability were ensured through pilot testing of instruments (n=15), triangulation of quantitative and qualitative findings, and expert review of the architectural design against established standards (ISO/IEC 27001 and NIST SP 800-53). Analytical techniques include descriptive statistics and inferential analyses such as paired t-tests to compare processing times before and after ADP deployment, and ANOVA to assess differences across departments. Regression analysis examines the relationship between system trust, perceived usefulness, and adoption rates. For the technical performance, precision, recall, and F1-scores are calculated for OCR and information extraction components, while process mining is used to evaluate workflow efficiency gains. Thematic analysis of interview transcripts identifies barriers and enablers to adoption, with coding guided by the Technology-Organizational-Environmental (TOE) framework and the Diffusion of Innovations theory. The expected findings indicate substantial improvements in processing throughput (anticipated 35–50% reduction in cycle times), higher information extraction accuracy (F1-scores for key entities in the range of 0.92–0.97), reduced human error, and improved compliance indicators through policy-driven routing and audit trails. User acceptance is anticipated to rise with SUS scores exceeding 75, moderated by perceived task fit and trust in automated decisions. Qualitative insights are expected to reveal critical success factors such as robust data governance, explainable AI components for auditability, and organizational change strategies. The study contributes to knowledge by delivering an end-to-end ADP design and an empirical evaluation framework for smart office workflows, bridging the gap between academic models and practical deployment in real organizational contexts. It advances theory by integrating AI-enabled document processing with governance and change-management considerations within the TOE and Diffusion of Innovations frameworks. Practically, the research provides a modular reference architecture, a blueprint for integrating ADP with existing enterprise systems, and actionable guidelines for data governance, security, and user training to maximize adoption and long-term sustainability. The main conclusion posits that an autonomous document processing system, when properly governed and integrated with stakeholders’ workflows, can deliver significant productivity gains, improved accuracy, and enhanced governance without increasing cognitive load on users. Recommendations include adopting a phased rollout with continuous monitoring, investing in explainable AI and auditable decision logs, aligning governance policies with regulatory requirements, and conducting longitudinal studies to assess long-term impacts on organizational performance and employee experience.