Disconnected data silos within enterprises obstruct the extraction of actionable insights, diminishing efficiency in areas such as product development, client engagement, meeting preparation, and analytics-driven decision-making. This paper introduces a framework that uses large language models (LLMs) to unify various data sources into a comprehensive, activity-centric knowledge graph. The framework automates tasks such as entity extraction, relationship inference, and semantic enrichment, enabling advanced querying, reasoning, and analytics across data types like emails, calendars, chats, documents, and logs. Designed for enterprise flexibility, it supports applications such as contextual search, task prioritization, expertise discovery, personalized recommendations, and advanced analytics to identify trends and actionable insights. Experimental results demonstrate its success in the discovery of expertise, task management, and data-driven decision making. By integrating LLMs with knowledge graphs, this solution bridges disconnected systems and delivers intelligent analytics-powered enterprise tools.

The foundation of enterprise intelligence is technology platform that is increasingly being driven by artificial intelligence (AI). IDC has identified three pillars that drive enterprise intelligence: 1) an organization's ability to synthesize information, 2) its capacity to learn and 3) its ability to apply insights at scale. AI has immense potential to super-charge all three of these pillars. However, most enterprises still struggle with AI, and achieving enterprise intelligence at scale remains a challenge for most organizations. According to IDC's 2020 survey of analytics, AI, and RPA services buyers, 80% of respondents said they were at some stage of AI adoption, though most were only in pilots or using AI for limited business functions.

An automatic system is being developed to disseminate information to the various sections of any industrial, scientific or government organization. This intelligence system will utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the "action points" in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points. This paper shows the flexibility of such a system in identifying known information, in finding who needs to know it and in disseminating it efficiently either in abstract form or as a complete document. The premise of BI systems has remained pretty much the same: to collect an organization's data from disparate sources and process it in the best possible way to produce useful information to -- and perhaps this is the most important part -- help decision makers to make the best informed decision for the benefit of an organization.

An automatic system is being developed to disseminate information to the various sections of any industrial, scientific or government organization. This intelligence system will utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the "action points" in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points. This paper shows the flexibility of such a system in identifying known information, in finding who needs to know it and in disseminating it efficiently either in abstract form or as a complete document. The premise of BI systems has remained pretty much the same: to collect an organization's data from disparate sources and process it in the best possible way to produce useful information to -- and perhaps this is the most important part -- help decision makers to make the best informed decision for the benefit of an organization.