AI plays a crucial role in creating next-level customer experiences, but the real hero is data. While AI garners the spotlight, it’s the underlying data platform that connects the dots between every data point, making personalized and impactful customer interactions possible.

Imagine this scenario: you’re a global retail brand with physical stores, an e-commerce site, and social media storefronts. One of your long-time in-store customers “likes” a product on Instagram. This triggers an autonomous agent to send them a personalized discount code, nudging them toward a purchase. The customer visits your website, where they see a customized shopping page featuring products they love, in their size, and within their budget. After the purchase, the agent follows up to ensure they’re satisfied and offers a discount on complementary items. This kind of seamless, curated experience makes the customer feel like a VIP.

From generating personalized messages to creating bespoke shopping experiences, AI is becoming pivotal in the customer journey. But these experiences aren’t powered by a custom-built AI model; they’re driven by a data platform that aggregates and connects every data point across the customer’s journey.

Without the proper connection between data points, AI alone falls short. In fact, by 2025, around 30% of generative AI projects are expected to fail due to poor data quality.

AI and Data: The Essentials for Next-Level Customer Experiences

AI needs vast amounts of data to work effectively. Some businesses think training their own large language model (LLM) on proprietary data will give them an AI system that perfectly understands their customers. However, training an LLM is expensive, time-consuming, and requires specialized expertise. Additionally, LLMs lack built-in permission controls, and integrating them into existing systems adds another layer of complexity.

Even if you could train your own LLM, it would quickly become outdated. Every time a customer record is updated, the LLM would need to be retrained, which is impractical for most companies.

The Better Approach: Pairing AI with a Real-Time Data Platform

Instead of building a custom LLM, a more effective strategy is to use a pre-trained LLM in combination with a data platform that pulls data from multiple systems, regardless of the source or format. By feeding the AI a continuous stream of real-time, relevant data, businesses can improve the accuracy of the AI’s outputs and quickly identify key patterns.

Consider the earlier example of the shopper who engaged with a social media post and received a personalized message from an autonomous agent. These isolated interactions may seem insignificant on their own, but when combined, they reveal a clear buying intent. Now imagine this at scale, with hundreds or thousands of interactions happening daily. AI agents can work alongside sales teams to identify high-value opportunities and suggest next steps, from resolving service cases to securing more sales.

This future isn’t limited to retail. In healthcare, for example, AI agents can quickly verify patient benefits, gather insurance and pharmacy information, and summarize it for medical professionals. The potential applications of AI span industries like healthcare, financial services, manufacturing, and life sciences.

How Metadata Enhances Every Customer Interaction

AI can only deliver these advanced capabilities if it can find the right data—and that’s where metadata comes in. Metadata is like a labeling system that organizes and describes data, making it easier to locate and use. It provides crucial details such as when data was created, who modified it, and what its purpose is.

By organizing data with metadata, businesses can transform raw data into something actionable. This enhances AI’s ability to deliver sharper predictions, more accurate insights, and highly personalized interactions, all powered by a unified platform that breaks down data silos.

Data: The Key to Effective AI

Without a data platform that integrates and orchestrates all your data, AI will struggle to make a meaningful impact. A well-organized data platform is the backbone of an agile, responsive business that can anticipate changes, seize opportunities, and deliver what customers want, when they want it.

This level of precision leads to better customer experiences and increased loyalty. In essence, data maturity is the foundation for AI success. A comprehensive data strategy, which includes aligning business and data teams and leveraging tools to uncover hidden insights, is essential for driving growth and new opportunities.

Data Drives AI Success

To turn a simple social media “like” into a sale, businesses need more than just AI—they need a robust data platform that connects every interaction and insight across the customer journey. Without it, AI is just making guesses. Data is the key to unlocking AI’s full potential and delivering the personalized, next-level experiences that win customers and foster long-term loyalty.