user support incorporating upload terminology

The gap

Supporting evidence — 2 representative gaps

• AI Driven BI: How Large Language Models Transform Enterprise Analytics (2026) · doi

  As LLM driven BI continues to evolve, several areas present opportunities for further research and innovation. 5.1 Multimodal Analytics Future BI systems may integrate text, images, voice, and structured data into a unified analytical experience. This would allow users to upload documents, screenshots, or charts and receive contextual insights generated by LLMs. 5.2 Autonomous Decision Agents LLMs may evolve from passive insight generators to active decision agents capable of recommending actions, simulating scenarios, and triggering automated workflows based on predefined policies. 5.3 Enhanced Explainability and Transparency Research is needed to improve the interpretability of LLM generated insights. Techniques such as chain of thought summarization, confidence scoring, and traceable grounding will help build user trust. 5.4 Domain Specialized LLMs Industry specific LLMs—trained on financial, healthcare, retail, or manufacturing data— could provide more accurate insights and reduce hallucination risk by incorporating domain specific rules and terminology. 5.5 Integration with Real Time Decision Systems As enterprises adopt streaming architectures, LLMs will increasingly be used to interpret real time data, detect anomalies, and support time sensitive decisions such as fraud detection or supply chain optimization. 5.6 Ethical and Regulatory Frameworks Future work must address the ethical implications of AI driven analytics, including fairness, accountability, and compliance with emerging regulations governing AI usage in enterprise environments. 6 https://jrtcse.com/index.php/home These directions highlight the potential for LLM powered BI to evolve into a more autonomous, intelligent, and trustworthy analytical ecosystem.

  Keywords: llms evolve insights decision time driven analytics future systems analytical generated autonomous agents chain domain
• AI-Powered Student Companion (2026) · doi

  Testing will be conducted with larger user groups to provide comprehensive testing support.

  Keywords: testing conducted larger user groups provide comprehensive support