Originally published in Forbes Technology Council
Arango CEO Shekhar Iyer shares his perspective on how enterprise data must evolve to support the next generation of agentic AI.
Agentic AI is rapidly moving from concept to reality, marking what Nvidia CEO Jensen Huang calls a fundamental inflection point in computing. But while AI capabilities are advancing quickly, most enterprise data architectures are not keeping pace.
In this Forbes Technology Council article, Shekhar Iyer explains that the biggest barrier to scaling agentic AI isn’t the models—it’s the data. Many organizations either rely on fragmented “Frankenstack” architectures built from stitched-together tools, or they underestimate the complexity of maintaining high-quality, connected data over time. Both approaches limit scalability, increase costs, and make it difficult to trust or explain AI-driven decisions.
To unlock the full potential of agentic AI, enterprises must rethink their data foundations. This means building a unified, contextual data layer that connects structured and unstructured data, stays current with business changes, and ensures governance, traceability, and reliability. With the right data architecture in place, organizations can enable AI systems that don’t just respond—but reason, decide, and act with confidence.
Key Takeaways
- Agentic AI is a fundamental shift, but most enterprise data architectures aren’t ready to support it.
- Fragmented “Frankenstack” systems limit scalability, increase cost, and reduce trust in AI decisions.
- A unified, contextual, and governed data layer is essential for enabling reliable, explainable agentic AI.
