What is Contextual Intelligence?
Contextual Intelligence is a form of AI which leverages a business context graph to understand the business intent of analytics efforts. Contextual Intelligence applies the business metrics and business logic of a use case to uncover areas of improvement and propose specific actions, with a much higher degree of relevance and personalization to an end user. It generates a recommendation to improve performance against a specific metric and identifies the impact of proposed actions on the metric in order to predict the butterfly effect associated with a potential action.
Why it matters
Without context, AI recommendations are generic and low-trust. Contextual Intelligence raises relevance by grounding every recommendation in the actual metrics, rules, and relationships that govern a specific business — letting operators act on AI output with confidence.
See how Diwo operationalizes Contextual Intelligence.
Read the decision-intelligence playbooks that put this concept to work at Fortune 50 scale.
Related concepts
A knowledge graph is a collection of linked entities (objects, events, concepts) that acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.
Decision intelligence is a data-driven process that enables you to rapidly make faster, more accurate fact-based decisions rather than relying on intuition or gut feel. The approach combines decision-making techniques with AI, ML, contextual intelligence, and automation to generate actionable business recommendations. Rather than replacing human judgment, Decision Intelligence augments human ability to make better and more consistent decisions.
Diwo's AI-driven Insight Engine framework delivers contextually relevant views into organizational data and business operations. The framework integrates with Diwo's Business Context graph to capture relationships between entities and their impact on decision-making. The engine analyzes trends and time-varying data changes, automatically surfacing and correlating actionable insights.
Composite AI, also known as Multidisciplinary AI, represents the combined application of different AI techniques to improve learning efficiency and broaden knowledge representations. Rather than treating machine learning as a universal solution, organizations combine various AI approaches for better results when addressing complex business challenges. A composite AI system should be built on a "composite architecture" that integrates multiple methodologies: traditional rules-based systems, natural language processing (NLP), optimization techniques, and graph techniques.