Why Enterprise Al won't be Plug-and-Play

From training its own LLMs on $20M worth of in-house infrastructure to rethinking AI privacy at the data layer, Zoho is proving that AI for enterprises isn’t about size—it’s about fit, context, and control. Ramprakash Ramamoorthy, Director of Al Research at Zoho, explains why real-world AI adoption demands more than APIs and flashy demos—and why Zoho is betting on contextual, private, ground-up intelligence.

- Aanchal Ghatak

As organizations rush for rapid AI implementation, many worldviews are burrowing in the foundational complexity underneath the surface. Zoho’s strategy is fundamentally different; they have invested over $20 million building foundational infrastructure-256 NVIDIA GPUs-to construct their own Zia LLM models, completely deployed on-premise for enterprise workflows, instead of relying on third-party APIs, or fine-tuned consumer models. While the LLMs vary from 1.3B to 7B and were constructed from scratch, each model is designed to create privacy-first, scalable value across an ecosystem of 55+ apps.

What are some of the biggest challenges businesses face while deploying AI—and how are you helping them navigate these?

The first, and possibly the biggest challenge is expectation. Many organizations very much view AI as plug-and-play, expecting instantaneous change. When in reality, the complexity of the domain is far deeper.

Secondly, organizations often operate in fragmented data channels and have siloed partial processes—this makes deploying AI more difficult. Even at Zoho, we have dealt with this. As an example, when we explored running automation for legal and product support, we found out that the “final miles plumbing” took quite a while - despite owning the entire platform. That says a lot.

And thirdly, the lack of integration between internal systems creates friction. A really obvious example: I raise a travel request to attend a press event, which gets approved. I then, separate from the travel request, need to submit an on-duty request that also requires approval for the same trip. Those are disconnected processes. This friction can severely limit the effectiveness of AI. When systems aren't able to talk to each other, the Al will only be able to achieve a small fraction of its potential.

Are there any underrated challenges that companies often overlook while adopting Al?