Understanding, Not Processing Power: The Emerging Wave of AI Prioritizes Context Over Computational Abilities
At the Gartner's Data & Analytics Summit, the importance of moving from technical metadata to semantic metadata for deriving meaningful insights and ensuring clarity across systems was underscored. This shift is seen as crucial in establishing context-rich AI systems in enterprise software.
According to Gartner’s 2025 insights, the key factors for realizing context-rich AI include high-quality, AI-ready data, multimodal data integration, trustworthy AI governance, robust infrastructure, business alignment, and integration of real-time, context-aware decision making.
AI-Ready Data is critical for enterprise software, requiring data preparation to minimize bias, reduce hallucinations, and enable responsible, compliant AI development. Enterprises must rethink data governance and management frameworks around this need.
Multimodal AI using diverse data types (text, audio, images, video) enables deeper and more contextual understanding by AI systems. Gartner projects multimodal AI will be mainstream in enterprise software within five years, enhancing context-rich AI capabilities.
AI TRiSM (Trust, Risk, and Security Management) is essential for managing AI risks. Organizations need layered AI risk management to maintain policy enforcement and protect data.
Alignment with Business Goals and Collaboration is vital. AI must be aligned with business objectives, and success depends on infrastructure benchmarking and productive collaboration between AI and business teams.
Integration of Real-Time, Context-Aware Decision Making is necessary for enterprises, providing AI decisioning that is transparent, explainable, compliant, and adaptive, blending business rules with machine learning to achieve smarter, accountable outcomes.
Infrastructure Enablement for AI Performance is crucial for mature enterprises. They invest heavily in cloud and optimized compute resources such as GPU-accelerated environments to support real-time inference and reduce latency, which are necessary for context-rich AI functionality.
Gartner predicts that 33% of enterprise software will include agentic AI by 2028, up from less than 1% in 2024. However, over 40% of agentic AI projects are expected to be canceled by 2027, and 42% of businesses have scrapped most of their recent AI initiatives. Effective contextual AI governance is crucial for differentiating true agentic AI capabilities from insufficient models that offer basic automation.
Investing in agentic AI, enterprises have the choice to build semantic foundations now or watch as context-aware competitors turn savvier AI investments into unbeatable advantages. Deloitte's State of Generative AI report highlights that organizations focusing on "industry- and business-specific challenges" see better outcomes from AI experiments.
Oracle's AI Agent Studio provides access to Oracle Fusion Applications APIs, knowledge stores, and predefined tools which preserve enterprise-specific business logic within AI-powered workflows. To achieve semantic infrastructure, AI systems require deep contextual understanding, semantic consistency across all data sources, and business logic integration.
In the age of abundant compute power, context is the new gold, and those who can teach their AI systems to truly comprehend the business they serve will earn their Midas Touch. As agentic AI systems become more prevalent, the divide between organizations with semantic infrastructure and those without will only widen.
Notable acquisitions and collaborations in the AI sector include Snowflake acquiring Crunchy Data for $250 million, Rubrik acquiring Predibase, and Palantir collaborating with Qualcomm to extend AI comprehension capabilities. Palantir's AI doesn't just predict equipment failures, but also understands the cascading business impacts across supply chains and regulatory compliance.
[1] Gartner (2025), [Link to the source] [2] Gartner (2025), [Link to the source] [3] Gartner (2025), [Link to the source]
Artificial Intelligence (AI) development in enterprise software is enriched by high-quality, AI-ready data, which aids in minimizing bias, reducing hallucinations, and promoting responsible AI development (AI-Ready Data). Additionally, the integration of artificial-intelligence into AI systems, specifically multimodal AI that can process various data types like text, audio, images, and video, fosters a deeper, more contextual understanding by these systems (Multimodal AI).
