Indian IT firms view data platforms as IP amid cloud‑partner reliance
Indian IT companies are wrestling with a paradox that sits at the heart of their AI ambitions. On paper, they tout home‑grown data platforms as a source of competitive advantage, branding the underlying code and architecture as proprietary assets. Yet the same firms repeatedly turn to global cloud giants and niche technology partners to plug technical gaps, especially when scaling analytics workloads or deploying machine‑learning models.
This tension surfaces in boardrooms across Bangalore, Hyderabad and Pune, where senior executives balance the desire for self‑sufficiency against the practicalities of cost, speed and talent scarcity. Policy makers watch closely, aware that the shift toward internal intellectual property could reshape licensing norms and data‑sovereignty debates. At the same time, industry veterans remain wary of over‑promising on “built‑in” capabilities.
It’s against this backdrop that the following perspective emerges, highlighting both the optimism and the skepticism that define the current moment.
In his view, data platforms are increasingly treated as internal intellectual property, even as vendors continue to depend on cloud providers and specialist partners to fill gaps. Pravat Jena, a senior data scientist at Dell with 14 years of experience in data strategy, took a more sceptical view. He said most Indian IT firms remain "strong at pilots and PoCs (proofs-of-concept)," but only a subset are consistently ready for production AI at scale.
The limiting factors, he argued, are operating models and governance maturity rather than technical capability. "Most firms are not building core platforms fully in-house," Jena said. "They rely heavily on hyperscale-native stacks, vendor tools and partnerships, with limited proprietary differentiation." Both Datta and Jena agreed that data quality and governance remain the weakest layers in AI programmes.
Jena described governance frameworks as often existing "on paper," with uneven implementation across legacy systems and multi-cloud environments. The result is that AI systems scale slowly beyond controlled use cases. Datta noted that despite regulatory pressure from India's Digital Personal Data Protection Act and emerging AI governance guidelines, only a minority of enterprises feel adequately prepared to support scalable AI workloads.
According to a 2025 McKinsey report, while almost all companies worldwide invest in AI, only 1% believe they are at maturity.
Data platforms are now being treated as internal intellectual property, even as Indian IT firms still lean on cloud providers and specialist partners to plug obvious gaps. Yet practitioners on the ground describe data readiness across the sector as only “partial.” This partial readiness limits the speed at which AI initiatives can move from pilot to production. Consequently, firms find themselves juggling clean‑data demands with the reality of fragmented governance.
While the promise of faster innovation and better customer experiences is clear, the underlying data quality often falls short of regulatory expectations. Pravat Jena, a senior data scientist at Dell, remains skeptical about the current trajectory, noting that reliance on external cloud services may undermine the very IP advantage firms hope to secure. In short, the enthusiasm for AI deployment is tempered by unanswered questions about how consistently high‑quality data can be supplied.
Whether Indian IT companies can close that gap without sacrificing their strategic partnerships is still uncertain. A lingering concern.
Further Reading
- Papers with Code - Latest NLP Research - Papers with Code
- Hugging Face Daily Papers - Hugging Face
- ArXiv CS.CL (Computation and Language) - ArXiv
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Common Questions Answered
How are Indian IT firms positioning data platforms as intellectual property despite reliance on cloud providers?
They market the code and architecture of their data platforms as proprietary assets, claiming competitive advantage. However, they still depend on global cloud giants and specialist partners to fill technical gaps when scaling analytics or deploying machine‑learning models.
What does Pravat Jena identify as the main limitation for Indian IT firms moving AI from pilot to production?
Jena notes that most firms excel at pilots and proofs‑of‑concept but lack consistent readiness for production‑scale AI. The partial data readiness and fragmented governance hinder the speed of scaling AI initiatives.
Why do Indian IT companies continue to lean on cloud partners when building their data platforms?
Cloud partners provide the scalable infrastructure and specialized services needed for large analytics workloads that in‑house platforms cannot yet handle. This reliance allows firms to bridge gaps in expertise and capacity while still promoting their platforms as internal IP.
What impact does “partial” data readiness have on AI initiatives within Indian IT firms?
Partial data readiness slows the transition from pilot projects to full production, limiting the speed and reliability of AI deployments. It forces firms to juggle clean‑data demands with fragmented governance, reducing overall AI effectiveness.