Is your data ready for the AI rush? Questions IT leaders should ask before scaling AI

Enterprise AI adoption is accelerating, but moving from experimentation to production depends on more than the choice of model. Organizations need data that is accurate, accessible, searchable, governed, and up to date. The Dell AI Data Platform is designed to connect enterprise data and prepare it for use by models and agents by creating a unified data context layer.

Here we examine two important areas IT leaders should assess before scaling AI: data readiness and infrastructure. We also explore how the Dell AI Data Platform supports the move from pilot to production.

TL;DR

● Data readiness encompasses quality, access, preparation, governance and freshness, all of which can affect enterprise AI outcomes

● Disconnected data sources and inefficient pipelines can limit AI performance and make production workloads harder to manage

● The Dell AI Data Platform connects enterprise data and coordinates its preparation, retrieval and governance through an open, modular architecture

According to Accenture’s 2026 Pulse of Change research, 49% of companies surveyed are piloting or deploying AI agents, while 81% of employees surveyed feel AI tools have increased their overall productivity at work.

The same Accenture research also found that 82% of C-suite leaders are investing more money in AI. Yet many organizations still struggle to move AI initiatives into production.

MIT Project NANDA’s preliminary 2025 State of AI in Business research reported that 95% of organizations studied were seeing no measurable return from their GenAI investments. Meanwhile, separate Gartner® research predicts that “Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls.”*

AI initiatives can struggle for several reasons, including unclear business value, weak workflow integration and inadequate risk controls. Project NANDA’s research emphasizes systems that can retain context, learn from feedback and adapt to day-to-day work. Data readiness is a separate, important consideration: inaccurate, inaccessible or outdated information can undermine the reliability of AI outputs.

Gaurav Chawla, Dell Technologies Fellow and Vice President in the Infrastructure Solutions Group Chief Technology Office, describes the importance of data quality: “The most expensive infrastructure in the building can only deliver outcomes as good as the quality of data driving reasoning and action.”

For IT leaders, assessing data readiness means looking at both the information itself and the infrastructure and workflows that make it available to AI applications.

How does data quality impact AI adoption?

The adage of “garbage in, garbage out” has never been truer – especially in the age of generative AI. Large language models (LLMs) and AI applications require data to provide users with accurate answers to various queries, while agents also need data to perform tasks reliably.

Hitachi Vantara’s 2024 State of Data Infrastructure Survey highlights the importance of data quality. Using high-quality data was the most commonly cited reason for successful AI projects, with 41% of U.S. respondents selecting it.

When embarking on an AI initiative, IT leaders should assess data accuracy, completeness and freshness, alongside whether the application can access the information it needs under appropriate governance controls.

How does infrastructure affect AI adoption?

Infrastructure affects how reliably enterprise data can be prepared and delivered to AI applications. Organizational fragmentation can also complicate oversight: WRITER’s 2026 AI Adoption in the Enterprise survey found that 79% of executives said AI applications were being created in silos across their organization.

When departments expand AI independently, IT teams need a consistent way to coordinate access, governance and operations across applications.

Deloitte’s State of AI in the Enterprise 2026 report describes leading organizations enabling modular, cloud-native platforms that securely connect, govern and integrate different data types. It also emphasizes privacy, sovereignty, data quality and interoperability.

The aim is to make enterprise data usable across applications while maintaining consistent governance and operational control.

Questions to ask before scaling AI

● Which enterprise data sources does the intended use case need, and can the application access them?

● How will we assess data accuracy, completeness and freshness?

● Will access permissions and governance policies carry through preparation and retrieval?

● Can pipelines refresh data and indexes reliably as information changes?

● Can storage and processing capacity grow independently, and how will we measure cost per workload?

● What business result will justify moving the project into production?

How can the Dell AI Data Platform help my business?

As a critical component of Dell AI Factory with NVIDIA, the Dell AI Data Platform serves as an open, secure, modular foundation designed to turn enterprise data – structured, semi-structured, and unstructured – into production-ready AI outcomes at scale across three core layers: storage engines, data engines, and an orchestration engine. By connecting these capabilities, it turns siloed data into an enterprise-wide data context layer.

The platform integrates specialist engines and existing data sources using open standards, helping organizations coordinate workflows from ingestion through retrieval and inference without forced consolidation into a proprietary repository.

Dell’s Data Orchestration Engine automates data workflows, working with the platform’s Data Engines – which analyze, process, and search enterprise data – to prepare and serve enterprise context:

● Data Analytics capability: Powered by Starburst and built on Trino, it supports federated SQL queries across data lakes and databases without first copying source data into a central repository.

● Data Processing capability: Based on Apache Spark, it supports large-scale batch, streaming and AI/ML workloads.

● Data Search capability: Powered by Elastic, it combines full-text and vector search, with GPU acceleration for vector indexing.

Meanwhile, the platform’s AI storage engines are built to keep pace with that data movement. In one inference benchmark using KV-cache reuse, Dell reports up to 19x faster time to first token.**

Separately, Dell Lightning File System delivers up to 150 GB/s of throughput per rack unit in Dell's internal analysis.*** These figures measure different capabilities, and results vary by workload and configuration.

By deploying the Dell AI Data Platform, organizations can realize several outcomes that directly connect to AI success:

● Innovate without lock-in: Keep your data where it is and use open formats – like Apache Iceberg, Delta Lake, and Parquet – instead of rip-and-replace.

● Scale on your terms: Storage, data services and GPUs scale independently, helping organizations reduce overprovisioning as workloads grow. Preparing and retrieving relevant context also helps manage the data supplied to models, supporting more efficient AI operations.

● Keep your AI secure: Rely on enterprise-grade governance, resilience, and protection built-in from day one.

● Faster time to value: Replace manual, brittle pipelines with automated production data pipelines to move from discovery to value faster.

According to Dell, CTBC Bank built an AI-powered fraud prevention capability on the Dell AI Data Platform, processing transactions in real time against more than 400 risk factors. Dell reports a fraud detection response time of 0.03 seconds and a 40% reduction in fraud losses between 2024 and 2025.

Take the next step

Before expanding an AI pilot, establish whether its data can support everyday production use. That means checking quality and access, defining governance responsibilities, and testing how pipelines perform as demand grows. The Dell AI Data Platform provides capabilities to support that work, but the starting point remains a clearly defined use case and a measurable business outcome. Those decisions give IT teams a firmer basis for choosing infrastructure and planning deployment.

You can find out more about the Dell AI Data Platform on the Dell website. US readers click here . CA readers click here.

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FAQs

What problem does the Dell AI Data Platform solve?
It helps address data barriers that can stall enterprise AI projects, including siloed information, unpredictable AI costs, complex manual pipelines, and security and governance gaps.

What are the main business outcomes of the Dell AI Data Platform?
The Dell AI Data Platform enables open innovation without vendor lock-in through open formats, independent scaling of storage and GPUs, embedded data security, and faster time to value via automated pipelines.

What are the Dell AI Data Platform's core architectural layers?
The Dell AI Data Platform combines storage engines for AI data infrastructure, data engines for analytics, processing, and search, and data orchestration to coordinate workflows across the AI lifecycle.

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* Gartner Press Release, Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, June 25, 2025. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

**Based on internal Dell testing (October 2025) using the vLLM inference engine, LMCache with Dell connector, and NVIDIA NIXL library. Testing measured a 100% Key-Value (KV) cache hit rate on four NVIDIA H100 GPUs, using the LLaMA-3.3-70B Instruct model with Tensor Parallelism=4. Results may vary based on system configuration, workload, and other factors.

***Based on Dell internal analysis of sequential and random read I/O for Dell Lightning File System, February 2026. Actual results may vary.