Written by: Fatima Yasmin, Lintang Fajar, Maximilian Setjadi, Shafiqa Pohan, Vivyan Idatama
Designed by: Sarah Aretha Julemima Tambunan
Adapting to the evolution of the 21st century, corporations are changing how they process information by integrating technology and artificial intelligence (AI). Digital transformation is optimizing how businesses operate and meet consumers’ expectations by leveraging the large amount of data, a key asset for insightful actions. With the help of data entry automation, the manual process of computing data can be eliminated. Data entry automation can be enhanced by reasoning-based processing with the help of AI and machine learning (ML) that uses logical references and contextual evidence to handle raw data. Instead of manually inputting data into the system that increases areas of errors and resource-intensive, the adoption of AI technologies eliminates these liabilities ensuring an accurate output through a structured and logical manner.
The problem arises from organizations’ continued reliance on manual data entry processes, which are highly susceptible to human error, inconsistencies, and delays. As businesses undergo digital transformation, the volume and complexity of data continue to grow rapidly, making traditional data processing methods increasingly inefficient. Manual data entry consumes significant time and human resources, leading to higher operational costs and reduced productivity. In addition, inaccuracies in data input can compromise data quality and negatively affect analysis, decision-making, and overall business performance. These challenges are further exacerbated by the limitations of conventional systems, which lack the ability to process data using reasoning-based and contextual analysis, making them inadequate for managing large and complex datasets in the modern business environment.
Reasoning based data entry automation significantly improves data accuracy and consistency by reducing human errors commonly found in manual input processes. Automated systems ensure higher data quality, which enhances the reliability of business analysis and decision-making. In addition, automation increases operational efficiency by accelerating data processing and reducing the need for extensive human intervention, allowing employees to focus on higher-value tasks. From a strategic perspective, reasoning-based processing enables systems to interpret data contextually using business rules and learned patterns, transforming business information systems into intelligent decision-support tools. As highlighted by the OECD (2025), AI-driven automation enhances efficiency and decision quality when supported by appropriate governance frameworks, contributing to long-term organizational competitiveness.
Business workflows in practice often rely on detailed and complex procedures to ensure data accuracy and properly handle exception cases, making them time-consuming and frequently hindering operational efficiency. Addressing this problem requires companies to go beyond a basic data entry automation. A practical example of reasoning-based processing in business information systems can be observed in Nokia’s travel and expense management workflows. The company adopted Concur’s auto-itemisation and intelligent audit instrument to shift towards operational systems that implement context-aware processing.
The auto-itemisation categorizes invoice structures contextually from uploaded documents into individual cost components instead of treating the invoice as a single total. This tool progressively reduces the need for manual efforts while allowing human correction, from which the system learns over time. Subsequently, expense reports are evaluated by Concur’s Intelligent Audit, which places 30 policy checkpoints through rule-based reasoning prior to managerial approval. As stated by Nokia’s Head of Digital Travel Experience, Mario Pires, “Nokia can now audit 100 percent of invoices for the same cost, whereas it was previously considered a complicated procedure.” Although human examination is still required, this case illustrates how reasoning and decision-making are continuously demanded by business information systems instead of entirely recording transactional data.
The transition to autonomous reasoning in business information systems presents several challenges related to data quality and consistency, system complexity, and autonomous decision-making. Reasoning-based systems depend on accurate, consistent, and well-integrated data. However, many companies continue to face issues such as data silos, inconsistent formats, and legacy system constraints, which can undermine automated reasoning and decision accuracy. In addition, autonomous reasoning systems often struggle with complex, multistep tasks that require continuous self-assessment and adaptation. There are also concerns over transparency, trust, and governance, as automated reasoning processes may be difficult for users to interpret and audit. Thus, ensuring explainability, accountability, and ethical use of AI-driven systems remains a key concern for organizations adopting advanced automation.
In conclusion, the evolution of data entry automation toward reasoning-based processing marks a significant shift in how business information systems function in the digital era. By integrating AI and machine learning, organizations can move beyond manual processes, which are labor-intensive and prone to error, toward more intelligent systems that enhance efficiency, accuracy, and decision-making quality. The case of Nokia demonstrates how reasoning-based automation can streamline complex workflows while still allowing human oversight. However, it also brings some notable challenges, including data quality issues, system integration complexity, and concerns regarding transparency and governance. Therefore, while reasoning-based processing offers substantial benefits, its successful implementation requires careful system design, strong data governance, and a balanced approach that combines technological innovation with human oversight.
REFERENCES
Aquino, J. & Jonker, A. (2025, December 19). Top data integration challenges and solutions. IBM Think. https://www.ibm.com/think/insights/data-integration-challenges.
Cohen, A. (2025, May 22). Case study: Nokia applies AI to travel and expense programme. Business Travel News. https://www.businesstravelnewseurope.com/Technology/Case-study-Nokia-applies-AI-to-travel-and-expense-programme
OECD. (2025). Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions. OECD Publishing. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/governing-with-artificial-intelligence_398fa287/795de142-en.pdf
Thompson et al. (2024, September 24). An Implementation of Autonomous Reasoning in Large Language Models through Token-Level Inner Dialogue Mechanisms. https://osf.io/preprints/osf/c3bnd_v1
