Originally published in Forbes Technology Council
Arango CEO Shekhar Iyer shares his perspective on why enterprise AI initiatives struggle—not because of model limitations, but due to missing business context.
As enterprises move from AI systems that answer questions to agentic AI that can take action, a new challenge is emerging: the business context gap. In this Forbes Technology Council article, Shekhar Iyer explains that while AI models and retrieval systems have advanced significantly, they are not enough on their own. The real risk arises when AI agents act on incomplete or fragmented business context.
These failures aren’t model failures—they’re context failures. When AI systems lack a complete understanding of relationships, policies, and real-time operational states, they can make decisions that lead to financial loss, operational disruption, or compliance risk. And unlike human errors, these mistakes can scale quickly across systems.
Shekhar argues that closing this gap requires a shift in how organizations manage data. Business context must be unified across systems, kept current with real-time changes, and governed to ensure trust and traceability. Without this foundation, even the most advanced AI systems will struggle to deliver reliable outcomes. As AI moves deeper into operational workflows, organizations that prioritize context will be best positioned to scale safely and effectively.
Key Takeaways
- The biggest risk in enterprise AI is not model accuracy—it’s incomplete business context.
- Retrieval systems answer what is relevant, but not what is appropriate or permitted.
- AI requires unified, current, and trusted business context to scale reliably and safely.
