Agentic Business Intelligence Architecture for Autonomous Data Exploration, Insight Generation, and Strategic Decision Orchestration

Abstract

Business Intelligence (BI) is critical for supporting tactical, operational, and strategic decision-making in enterprises. Traditionally, BI solutions have focused on creating data use dictionaries that allow decision leaders to collect and reference those data narratives that are most suited to their decision making. Contemporary digital business ecosystems generate immense data streams, and the creation of dictionaries for these streams is impractical and insufficient. Autonomous BI that allows autonomous data exploration and narrative generation is therefore a necessity. To meet this need, the Decision-Orchestration Adaptive Business Intelligence (DOABi) architecture enables the formation of autonomous decision-supporting BI nodes. These nodes connect to existing data streams with little or no dependence on human or administrative processing. Decisions needing support are inquired through BI agent entities that serve the interest of the decision-maker. A formal exploration model specifies the autonomy and supporting mechanisms of such service providers. These service BI agents operate at three cognitive levels and can ingest and explore data under a controlled-adaptive mechanism. The architecture is supported by an illustrative live enterprise case study and is further strengthened by literature analysis and industrial, academic, and entrepreneurship expert opinions. Data exploration is not new to data science. Making discovery easier, faster, and more fully realized is the question of the day. Meyer et al. [2018] highlight the shortcomings of traditional BI systems, which have focused on creating use dictionaries for data resources. Data streams generated by information systems, IoT devices, and social media, along with imagery and video captured by drones and mobile devices, evolve too rapidly for such dictionaries to be a fitting solution. The exploration of such uncharted data sources is better left to intelligent agents and the AI behind them. In a framework for autonomous business decision orchestration, autonomous exploration is a major enabler.