Are enterprise context graphs the next trillion-dollar opportunity in AI or just the latest buzzword in an already crowded landscape?

In this episode of The AI Valley Podcast, Vikas and Sachin explore one of the most important and unresolved questions in the agentic AI era: where does context truly live, and who will own it?

As AI agents move from experimentation to execution, traditional systems of record - CRMs, data warehouses, and SaaS platforms - are proving insufficient. What’s missing is decision context: the why behind past actions, exceptions, approvals, and human judgment that has never been formally captured.

Building on a viral industry debate, the conversation dives deep into:

• Why adding AI and semantic layers to systems of record isn’t enough
• What “decision traces” and enterprise context really mean
• The difference between operational context and analytical context
• Why enterprise data heterogeneity makes context graphs a hard problem to solve
• Who is best positioned to own the context layer - CIOs, business ops, data teams, AI CoEs, startups, or new platforms
• Whether context should be centralized, federated, or embedded in execution paths
• The security, trust, and human resistance challenges of capturing tribal knowledge
• Why context- not models or agents may become the most strategic AI asset inside enterprises
  

Rather than offering simple answers, this episode provides a clear mental framework for leaders, architects, and builders to think about context in a world where AI agents are expected to act, decide, and operate at scale.

If you work in enterprise AI, data platforms, agentic systems, or digital transformation and want to understand where the real moat in AI might emerge, this episode will give you plenty to think about.

Key Quotes

1. Truth may live in systems of record, but decisions live in context and that context has never been captured.
2. Agents can execute tasks, but without context, they stall the moment judgment is required.
3. Models will commoditize. Agents will commoditize. Context will not.
4. The most valuable data in an enterprise isn’t customer data - it’s the reasoning behind past decisions.
5. The future of enterprise AI won’t be decided by better models - it will be decided by better memory.
6. Adding a semantic layer to systems of record explains what happened, not why it happened.
7. In an agent-driven world, context becomes the real moat.
   
   #AIInfrastructure #EnterpriseAI #ContextGraph #AgenticAI #DataPlatforms #FutureOfWork #AIArchitecture