Govinda, Senior Manager at Cognizant, has 15 years of expertise in SAP & Non-SAP Data Analytics, delivering innovative BI solutions.
As artificial intelligence (AI) is now a central pillar of enterprise strategy, many organizations are rushing to integrate AI into their analytics and data ecosystems. From predictive insights to generative AI applications, the promise of AI-driven decision making is compelling. However, in many transformation programs, I've seen organizations struggle not because of limitations in AI models but because their underlying data platforms were never designed to support AI at scale.
Enterprises often treat AI as an add-on layer, expecting existing data architectures to support advanced use cases without significant redesign. This approach rarely works. AI isn't just another analytics workload. It places fundamentally different demands on data platforms, including data quality, semantic consistency, lineage and real-time accessibility.
Over the past few years, I've noticed that many AI discussions in enterprises focus heavily on models, co-pilots and automation features, but far less attention is given to the underlying data architecture that supports them. In my experience working on large-scale analytics programs, the real challenge usually isn't the AI capability itself—it's whether the organization's data platform was designed to support trusted, scalable and connected data across teams.
