The AI revolution is at an impasse – not due to insufficient computational power, but because organizations are solving the wrong problems.
While global GenAI spending is expected to reach $644 billion in 2025, experts also warn that over 40% of agentic AI projects will be canceled by 2027. Indeed, recent M&A activity – such as Snowflake's $250 million acquisition of Crunchy Data and Rubrik's acquisition of Predibase – signals a fundamental shift: enterprise AI's next phase is about more than compute capabilities… It's about deeper comprehension.
Smart Money Is Moving
According to S&P Global Market Intelligence's 2025 survey, 42% of businesses have scrapped most of their recent AI initiatives, up from only 17% in 2024. Another 46% abandoned proof-of-concept demos before production even began.
These AI projects aren't failing due to technical limitations, but rather due to semantic gaps. If an AI system can process petabytes of data but can't understand what “customer lifetime value” means across varying departmental needs, the failure points will likely be contextual.
Consider the strategy behind Snowflake’s integration of Postgres’ semantic AI capabilities, which aims to create a foundation where AI agents can understand transactional context and business semantics — enabling developers to “build trustworthy AI agents” with “greater agility, visibility and control.” Rubrik's Predibase acquisition similarly aims to help customers “securely deploy agentic AI” by prioritizing contextual accuracy alongside computational power.
Where Context Meets Scale
The success of Palantir's recent collaboration with Qualcomm to extend AI comprehension capabilities is another demonstration of the transformative power of context-first AI architecture. Their “Ontology” approach — creating linguistic precedents for mapping business concepts, relationships, and rules into machine-readable formats — transforms AI from pattern recognition into plain-speak business reasoning and shows how semantic understanding enables AI to operate effectively, even in offline or resource-constrained environments.
For example, in regard to their nuclear energy initiatives, Palantir's AI doesn't just predict equipment failures — it understands the cascading business impacts across supply chains and regulatory compliance that either lead to or result from these failures. Similarly, in manufacturing, their systems comprehend interdependencies between quality control, inventory management, and customer commitments, allowing a holistic overview of operations that helps predict and preemptively mitigate issues.
As one Palantir executive noted, “The Ontology [based approach] enables users to construct workflows that incorporate and combine heterogeneous logic assets,” allowing AI to be “securely introduced into increasingly complex decision-making contexts.”
The Context-First Infrastructure Revolution
The shift from efficiency-first to meaning-first architectures represents a fundamental rethinking of enterprise AI. According to Gartner's 2025 Data & Analytics Summit his transformation hinges on three critical factors:
- Semantic Data Architecture: Every data point must carry business meaning, not just computational value. As consulting company Enterprise Knowledge research shows, semantic layers serve as bridges between raw data and applications, providing “unified and contextualized views” that enable intuitive user interactions.Business Logic Integration: In order to deliver maximum value, modern AI requires integration with pre-determined business contexts, proprietary to the needs of any given organization. Oracle's AI Agent Studio exemplifies this approach by providing access to Oracle Fusion Applications APIs, knowledge stores, and predefined tools which preserve enterprise-specific business logic within AI-powered workflows. Such solutions empower agentic AI systems by integrating business ontologies with Model Context Protocols (MCP), which enable seamless, context-rich data interpretation and allows AI agents to function across various enterprise data sources.Contextual Decision Engines: McKinsey's 2025 AI workplace report emphasizes that successful enterprise AI systems must thoroughly understand the business implications of any given task, for any given organization. Yet only 1% of companies believe they've reached AI maturity, highlighting the gap between current capabilities and contextual requirements.
The Competitive Implications
Organizations that can successfully establish context-rich AI systems will create self-reinforcing advantages for themselves.
Each business interaction has the potential to deepen Agentic AI’s nuanced understanding of the specific needs of any given business, improving performance and creating competitive moats that will be difficult for others to replicate through computational power alone. Deloitte's State of Generative AI report confirms that while 60% of organizations pursue up to 20 AI experiments, those focusing on “industry- and business-specific challenges” see dramatically better outcomes.
The talent implications are equally significant. While AI engineers command premium salaries, the real scarcity is professionals who understand both AI implementation as well as business domain ontology. PwC's 2025 predictions emphasize that “AI success will be as much about vision as adoption, with companies needing systematic, transparent approaches to confirming sustained value.” In other words, if the people training AI to understand business needs don’t understand those needs themselves, neither will the AI agents they create.
The Strategic Imperative
So, what exactly are the architectural changes organizations must make?
Gartner's Data & Analytics Summit underscores the importance of moving from technical metadata to semantic metadata—data that is enriched with predefined business definitions, ontologies, and relationships. This ‘Semantic-First Design’ shift is critical for organizations aiming to derive meaningful insights and ensure clarity across systems. At the same time, effective contextual AI governance is crucial for differentiating true agentic AI capabilities from insufficient models that merely offer basic automation but are misleadingly marketed as agentic.
The companies which succeed with agentic AI will be those whose AI agents have been strategically configured to understand business contexts deeply enough that they can act autonomously and effectively.
The Agentic AI Opportunity
Gartner predicts that 33% of enterprise software will include agentic AI by 2028, up from less than 1% in 2024. The rise of agentic AI makes semantic infrastructure essential; to achieve this, AI systems require:
- Deep contextual understanding to make autonomous decisions aligned with business goals.Semantic consistency across all data sources to prevent conflicting actions between differing departments and tasksBusiness logic integration to ensure compliance with organizational rules and regulations
As organizations pour billions into agentic AI development, those without semantic foundations will face escalating failure rates.
The Context Imperative
As agentic AI systems become more prevalent, the divide between organizations with semantic infrastructure and those without will only widen. For enterprises investing in agentic AI, the choice is clear: build semantic foundations now or watch as context-aware competitors turn savvier AI investments into unbeatable advantages.
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.
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