Essential ingredients for an AI-ready data foundation

Enterprise data is moving from supporting individual businesses to powering entire organizations as agentic AI promises to transform how enterprises operate. Modern AI requirements, diversity of workloads and data, and growing scale demands are creating a need for businesses to rethink their data strategies. Today’s enterprises need data platforms that can supercharge agentic AI development and automated workflows.

The shift from an incremental-improvements approach to comprehensive data strategies reflects the evolving needs of AI-powered businesses. While capturing insights used to be the primary goal for data teams, the focus has shifted to taking action on that data.

This exciting period of growth and possibility is defined by data: moving from traditional apps to AI agents, from SQL queries to natural language interfaces, from basic search to understanding images, text, and voice. These developments require a technical foundation that’s simple, holistic, secure, and flexible. This doesn’t require starting from scratch, but it does mean carefully evaluating current capabilities and planning for future needs. The organizations who will be successful in building the future of data are those with a fully integrated stack. 

So what are the key ingredients of an integrated data and AI foundation? This modern foundation requires readiness for AI success and an ambitious vision for how AI can transform the way the business runs and grows. Here’s what we’ve found is essential:

1. Unified access to all your data

Modern data platforms need unified, AI-ready access to comprehensive data sources, whether it’s by data federation, a centralized data lakehouse, or both. This approach enables teams to focus on building AI-powered workflows rather than managing data integration tasks. A decentralized system with AI agents embedded throughout creates competitive advantages through accessibility and automation.

Including all data means more than just current or new information coming in — it also includes legacy and historical data, and from multiple sources, like databases, data lakes, and data warehouses. It needs to include structured, unstructured, and semi-structured in any format or media type. And all the data has to be connected to AI with deep semantics and understanding, so it can be activated and actually used for accurate and safe agentic AI.

This can bring strong results for the bottom line: Retailer Walmart's team created a better data platform to collectively run their payments transactions and accurate customer search, and they’re now using robotics and supply chain automation for faster delivery and generally improved user outcomes.

2. Real-time performance at scale

Modern data foundations require both real-time data and enterprise-grade security and scale. It’s the only way a business will be able to consistently beat out competition in any industry, since it powers all the necessary actions, like building better apps, creating better products, and acting on data insights immediately. AI itself can only succeed when it can access data consistently from every source in milliseconds, with continuous reads and writes happening in real time. 

Data-driven, AI-powered automation frees up data and other tech teams to focus on truly innovative work. But for a business to build data-driven apps faster, perform large-scale analytics, share insights, and, importantly, act on those insights, it requires fresh data, wherever it resides, in addition to massive speed and scale.

The Home Depot, as an example, built its growth on sophisticated data usage for forecasting, replenishment, and more. But as pressure on the data warehouse grew and use cases became more complex, capacity fell short. The Home Depot chose an integrated data platform to serve customers better without wasted resources.

3. AI-powered productivity for every team

This new type of cloud data platform doesn’t just matter to data and IT teams. When enterprises move beyond data management and into data action, every team can work much more efficiently and make smarter decisions based on access to trusted enterprise data. A modern data foundation should offer multiple access and usage points, even for non-technical users, so data is activated and available to everyone, whether it’s data analysts, scientists, engineers, developers, or business users.

A flexible, autonomous data platform includes all workloads, wherever they run, with multi-modal querying options including natural language alongside SQL. When that’s in place, data scientists and data engineers can choose their preferred AI-powered tools to speed up innovation, insights, and action. Development teams are more productive using AI to build better agents and apps, and built-in AI allows all teams to use capabilities like data prep, code assistance, analysis, and anomaly detection.

Orange had plenty of ideas to use AI for better customer relationships, business operations, and financial outcomes, but lacked a powerful data foundation. With that new foundation in place, the company found success with their initial AI projects, including personalization and telecom installation accuracy. 

As you plan your organization’s AI-powered future, consider how unified data platforms can accelerate innovation and drive better business outcomes. Data should be active, useful, and easily available, so everyone can access what they need and ultimately make customer experiences better.