Ask most CIOs what's holding back enterprise AI, and the answer rarely starts with algorithms, it starts with plumbing. For Partha Protim Mondal, Chief Information Officer at Berger Paints India, that plumbing problem was the real starting point of the company's technology overhaul, long before any AI model entered the picture.
With over 25 years of IT leadership experience across the chemical, automotive, and technology sectors before joining Berger, Mondal brought a hard-earned instinct: unify the foundation first, then layer intelligence on top. Under his watch, Berger Paints migrated its core business systems, including its Oracle E-Business Suite and HR operations, onto Oracle Cloud Infrastructure, moving away from a patchwork of legacy, siloed systems that made getting a single unified view of the business a persistent headache.
That decision wasn't just about modernization for its own sake, it was about economics. Elastic cloud scaling means Berger's infrastructure can flex up during peak retail seasons and scale back down afterward, rather than paying to keep excess capacity running year-round. For a company running 16 manufacturing units across India, plus operations in Nepal, Poland, and Russia, that kind of flexibility translates directly into cost control, not just convenience.
The bigger point Mondal makes is one enterprise AI conversations often skip: connecting disparate systems and creating a unified data view is the unglamorous, expensive part of the job, and it has to come before AI can meaningfully move the needle on customer experience, supply chain decisions, or operational resilience. Berger, India's second-largest paint company by revenue, has treated that sequencing as a competitive advantage rather than a delay.
It's a reminder that in enterprise AI, the flashiest use case rarely wins, the company with the cleanest data foundation usually does.
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