As of August 2026, 88% of companies said they use AI.1 Yet that same research found 95% of GenAI pilots fail, 56% of CEOs report zero financial benefit from their AI investment and only 39% of organizations report a measurable EBIT impact from AI.
The main reason why these AI failures happen? Context.
When context is fragmented, incomplete or outdated, AI can’t deliver important, trusted outcomes because at that point, even the best models are just guessing. AI’s dependence on context is so absolute, Gartner recently established a new market category for it (“AI Context Platforms”) that they’ve valued at $28 billion for 2026 and $78 billion by 2030.
Context is, in fact, the most consequential fight in enterprise AI right now. And every vendor seems to suddenly be rebranding around it. CIOs are being pitched “context engines.” Productivity platforms, data catalogs and knowledge-graph vendors (among many others) are calling their technologies “context platforms.” And context platform providers are racing to become the “contextual AI system of record.”
That convergence is so dizzying, it’s nearly impossible to easily identify which ones are genuine, comprehensive contextual intelligence offerings—unless you know exactly what to look for.
Read this guide to learn 7 key factors for evaluating contextual AI, including:
- What “continuously learning” really means, from automatic online updates to offline updates on a regular release schedule;
- Which knowledge graphs can capture important business relationships that traditional RAG may miss; and
- Which data-preparation strategies can help you deploy contextual AI in days instead of months.
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