What it really takes to get enterprise data AI-ready

Enterprise AI projects may target a wide range of functions, from customer support to financial forecasting. But no matter what the goal, all such projects depend on data: if the AI model doesn’t have the right information to work with, in a form it can interpret and process, it’s unlikely to deliver high-quality outputs, no matter how much computing power or dedicated infrastructure you throw at it. Gartner predicts that 60% of AI projects in 2026 that aren’t supported by AI-ready data will end up being abandoned.

But what does “AI-ready data” really mean? The Dell AI Data Platform helps businesses prepare information for AI processing by cleaning and sorting data sets, cataloging unstructured information, and making files from diverse sources consistently available. The platform also helps organizations create standard pipelines to turn scattered data assets into a scalable, reusable AI foundation. Here are five practical steps to get more value from enterprise data for AI.

TL;DR

• Successful AI projects start with identifying the data each model or application needs
• The Dell AI Data Platform helps make information consistently available to the AI, even if it’s spread across multiple locations
• Cleaning, organizing, and enriching data helps AI systems to interpret it more reliably
• Governance, data protection, and cyber-resilience functions need to underpin AI data projects from the outset
• Orchestration and automation help to keep AI systems fed with current data, and support the development of new applications as requirements evolve

1. Start by identifying the data that AI actually needs

The first step in making data AI-ready is discovery: applications such as chat support, data forecasting and retrieval-augmented generation may call for different data sets, both at the point of ingestion and in operation. The Dell AI Data Platform helps businesses identify and locate the information required for their purposes, and present it in a form that’s suitable for AI processing.

2. Make data from all sources consistently accessible

In modern businesses, data is commonly spread across different systems, platforms and physical locations, including public clouds. Even if it’s all technically findable, therefore, access to the information required by an AI model may be complicated by the need to use a variety of access models and methods.

Is a central repository the answer?

Probably not. It may be tempting to copy or move everything to a central repository, but this creates governance challenges and can lead to versioning problems when the data set needs to be updated or expanded. The Dell AI Data Platform enables unified access to different types of information in place, even when the data is scattered across multiple silos and storage engines, reducing the management overhead and the risk of inconsistencies in the data foundation.

3. What does AI-ready data actually look like?

To be AI-ready, data must be not only accessible but clean, organized, and contextualized enough to be accurately interpreted and used by the AI model. Achieving this might involve tasks such as organizing and labeling data files, resolving inconsistent formats, and connecting metadata. A common mistake is to focus on structured data while leaving unstructured data under-prepared; the Dell AI Data Platform includes dedicated data engines designed to help query, process, search, and enrich both structured and unstructured data for effective AI use.

4. Build in security and governance from the outset

The data used by AI services is often sensitive or covered by data-protection regulations. This means security and governance are crucial, both in the workflows that prepare and present the data to the AI and in the operations and outputs of the engine.

The Dell AI Data Platform supports data security with access controls, native encryption and protection measures against external threats and accidental exposure. Cyber resilience features are built into the platform rather than layered on, including immutable snapshots and automated threat detection, to avoid data loss and support business continuity in the event of an incident.

5. Make processes repeatable, not one-off

Deploying AI isn’t a one-off operation – even for smaller businesses, it’s an ongoing process of preparing, protecting, and refreshing data. It may involve multiple pipelines to support different applications, from retrieval-augmented generation to analytics and real-time decision-making.

Thankfully, there’s no need to start every project from scratch. The Dell AI Data Platform includes the Dell Data Orchestration Engine, which helps create, automate, and reuse data pipelines across all stages of AI workflows. In this way, organizations of all sizes can maintain an enterprise-grade operating model, where data keeps pace with new information and use cases.

Making enterprise data AI-ready is a multi-stage discipline

It typically involves locating information and making it available to be searched, queried, ingested, and processed within a framework of data protection and governance. The Dell AI Data Platform brings together components that support all these different aspects of AI readiness in an integrated platform with a single line-of-support experience.

This helps organizations get the best from their AI data no matter what scale they’re working at, or what their focus may be.

Whatever your goals, the Dell AI Data Platform can help you establish a solid and secure data foundation for AI projects, one that can evolve with your use cases and business requirements.

If you think the Dell AI Data Platform could benefit your business, find out more on the Dell website: US readers click here and CA readers here.