Published on: August 3, 2026
AI is transforming modern marketing and customer experience, but its effectiveness depends on the quality of the data behind it. In this guest article, Rahul Singh, Senior Director, Business Development at The Trade Desk, explains why organisations must prioritise data readiness before scaling AI initiatives.
AI is rapidly becoming a core component of modern Martech and CX stacks. But without the right foundation, AI doesn’t create intelligence – it simply scales bad decisions faster. It’s like installing a high-end GPS in a car with a broken engine – the tech is impressive, but you’re still not getting very far.
Many brands are rushing to deploy predictive models, generative tools, and automation layers before addressing basic data challenges. Inconsistent customer records, unclear consent policies, and siloed systems remain common, and it’s further complicated by evolving data privacy norms in India & around the globe. This is similar to trying to cook a gourmet meal with expired ingredients. No matter how skilled the chef – or how advanced the tool – the outcome will disappoint.
When these issues exist, AI outputs become unreliable, biased, and difficult to govern. It’s like asking for directions from ten different people who all give you a different route. Confusion replaces clarity, and progress slows down.
A strong data foundation, on the other hand, ensures that AI recommendations are trustworthy, explainable, and aligned with business goals. Think of it as laying solid tracks before running a high-speed train. When the foundation is right, everything moves faster, smoother, and more safely. It also reduces operational risk and improves adoption across teams, because people trust what they see.
Before scaling AI, brands must focus on building true “data readiness.” And below are a few Practical steps which brands can undertake:
AI is not a shortcut to maturity. It is a force multiplier for whatever foundation already exists. Brands that invest first in clean, unified, and governed data will be the ones that unlock sustainable value from AI—while others risk running faster in the wrong direction.
