AI in system integration: Overcoming challenges in multi-system interoperability

13^th Global Summit on Artificial Intelligence and Neural Networks

March 14, 2025 | Webinar

Shashank Pasupuleti

Senior Product Systems Engineer, Capgemini Engineering, USA

Scientific Tracks Abstracts: J Comput Sci Syst Biol

Abstract :

Statement of the Problem: In today's digital landscape, integrating multiple systems that vary in software, hardware, and data formats is a major challenge. Artificial Intelligence (AI) has the potential to overcome these challenges by improving system interoperability, but barriers such as inconsistent data, real-time processing issues, and a lack of standardized frameworks often hinder its full potential. This study explores how AI can address these challenges and streamline multi-system integration. Methodology & Theoretical Orientation: This study uses a mixed-methods approach that combines case study analysis with AI-driven integration strategies. The research evaluates AI tools and their ability to enhance data consistency, automate error detection, and improve communication across diverse systems. Special focus is given to AI applications in large-scale environments, where integration complexities are most pronounced. Findings: AI tools have shown strong potential in simplifying system integration. By enhancing data consistency, automating conflict resolution, and speeding up the integration process, AI c an significantly reduce manual intervention and system downtime. Applications like machine learning-based predictive analytics and data transformation techniques have been particularly effective. However, challenges remain in data privacy, AI transparency, and regulatory compliance. Conclusion & Significance: AI holds transformative potential for overcoming multi-system interoperability challenges. By improving system integration and scalability, AI can enhance the efficiency of digital systems. To fully unlock AIâ??s benefits, further progress is needed in data standardization, transparency, and real-time decision-making. The study recommends adopting AI-driven frameworks that prioritize automation, seamless data flow, and robust governance to improve system integration outcomes. Data Table: AI Technique Benefits Challenges Predictive Ana lytics Identifies poten tial issues before they occur. Machine Learning-Based Models Automates error detection and conflict resolu tion. Requires high-quality data to be effective. Can be complex to train and require large datasets. Automated Data Transformation Real-Time Deci sion-Making AI-Driven Data Governance Ensures data consistency across systems. Optimizes per formance and reduces down time. Data privacy con cerns. May face laten cy issues with large-scale sys tems. Improves tracking and security of data flow. Lack of standard ized frameworks. Table 1: a data table could help highlight the key AI techniques used in system integration, their benefits, and the challenges encountered.

Biography :

Shashank Pasupuleti is a Senior Product Systems Engineer with specialized expertise in systems engineering, mechanical system integration, and AI driven solutions. With extensive experience in developing and integrating complex mechanical systems, Shashank has successfully led multi disciplinary teams to design and optimize high-performance products, particularly in the fields of digital engineering and robotics. His work focuses on applying systems engineering principles to ensure seamless integration across various system components, addressing challenges in compatibility, data flow, and performance optimization.