What is AI-ready data?

Properly prepared and managed data is fundamental to AI success—as the adage goes, “garbage in, garbage out.” Data that is accurate, complete and consistent drives better performance and productivity gains from enterprise AI. Meanwhile, a data strategy for well-governed and protected data helps ensure regulatory compliance and safeguard user privacy.

As AI-powered decisions increasingly become a competitive advantage, many organizations are realizing that traditional data management practices may not be enough to deliver AI-ready data. According to a 2024 survey from the IBM Institute for Business Value, only 29% of technology leaders strongly agree that their enterprise data meets the quality, accessibility and security standards needed to efficiently scale generative AI (gen AI).¹

To achieve and sustain data readiness for AI adoption, organizations can focus on a few essential data practices: Unified access, governance, security and support. By putting these foundational elements in place, organizations can ensure their data is truly AI-ready—and in doing so, transform AI from an expensive experiment into a powerful engine of enterprise value.

Without trusted, high-quality and well-managed data, the outcomes of AI tools can be disappointing at best—and inaccurate, biased or a privacy risk at worst.

AI-ready data helps ensure that AI technologies deliver real business value and actionable insights by enabling:

Organizations that struggle to realize ROI from their AI initiatives often face significant data-related barriers to true AI readiness, including:

• Data sprawl and fragmentation
• Poor data quality
• Operational bottlenecks and skills gaps
• Security and governance risks

Data silos are a plague on modern data ecosystems. Their spread is driven by several factors, from organizational structure and culture to IT complexity and regulatory constraints. This data fragmentation creates barriers to both daily operations and strategic initiatives, such as AI.

Disconnected data is inherently inefficient and often unstructured, requiring extra steps for effective data preparation and use. It’s inconsistent across the organization and more difficult to manage for regulatory requirements and privacy policies. These issues significantly slow the access and preparation of AI-ready data, potentially driving up the cost and complexity of AI programs.

Poor data quality stems from a variety of sources. While data silos and fragmentation are one example, other common causes include inconsistent data quality management practices, outdated systems and architecture, and integration challenges. Often, it’s a combination of several of these factors.

Even the most advanced AI models are affected by poor-quality data, leading to unreliable, inaccurate and potentially biased outputs. The consequences can be severe: financial losses from failed AI projects, reputational damage from biased decisions or reduced trust in AI’s overall value.

Human expertise remains critical for AI implementation. However, the rapid advancement of AI and new technologies is shifting roles and widening the AI skills gap. Many organizations lag in employee training and upskilling, often due to ineffective learning formats, budget limitations or insufficient access to the right tools and data.

Without adequate tech talent, existing data teams might find themselves stretched thin. They are managing complex, siloed data environments while simultaneously under pressure to quickly deliver AI-ready data for critical projects.

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With data fragmentation and complexity arises the reality that sensitive and protected data is often spread across business units, data platforms and repositories. This data sprawl raises concerns regarding compliance, access control and trust.

Scaling enterprise AI without the proper security and governance in place increases exposure to risk and regulatory complexity. Organizations aware of this barrier but struggling to fix it might see their AI projects stall. For those unaware, the risks compound as they move forward and scale their AI.

Modern AI (especially generative AI) relies on large volumes of data to deliver real value. Fortunately, data generation isn’t limited to large enterprises. Organizations of all sizes produce substantial volumes of data each year through their websites, social media, internal systems and customer interactions.

Yet most organizations are underutilizing their data. Estimates suggest that only around 1% of enterprise data is leveraged in traditional large language models (LLMs).²

Why let such valuable AI fuel go to waste? Because most enterprise data is unstructured. It lacks a predefined format and comes from diverse data sources such as PDFs, social media posts, images, instant messages and emails. Less than 1% of this unstructured data is in a format suitable for direct AI consumption.³ In other words, the vast majority of enterprise data is not AI-ready.

While structured data remains immensely valuable, failing to tap into the potential of unstructured data—diverse, flexible and rich with insights—is a strategic misstep and significant barrier to scaling enterprise AI.

This challenge is reflected in grim AI outcomes: According to the IBM Institute for Business Value’s (IBV) 2025 CEO Study, just 16% of AI initiatives have reached enterprise scale.

Now is a critical moment for businesses. The success or failure of AI initiatives depends on how effectively organizations manage and prepare high-quality data—both structured and unstructured—for AI.

Data that embodies the following characteristics can support trusted, reliable and valuable AI use cases:

• Unified and accessible
• Governed
• Secure
• Supported

AI can’t act on what it can’t access. An essential first step toward AI readiness is establishing unified access to enterprise data. This means breaking down silos and creating a single, manageable view of information spread across databases, data lakes, applications and document repositories.

The broader the access, the greater the data-driven insights and value AI can deliver. AI can go beyond just providing internal answers and start improving customer experiences or operational efficiency.

Unified data access also transforms isolated data into reusable assets that are easier and more cost-effective to work with. It supports multiple workloads and enables economies of scale, turning data into a strategic resource.

Technologies such as data integration and data fabric architectures make unified access possible:

Data integration transforms and harmonizes data from hybrid and multicloud environments into unified, coherent formats ready for AI use cases. Real-time data integration specifically supports AI and automation use cases.

Data fabrics create a virtual, unified view of all enterprise data without physically moving it. They combine capabilities such as data catalogs, federated metadata, data integration, virtualization and machine learning to help users quickly discover, access and use AI-ready data. 

Effective data governance helps ensure data integrity, security, quality and access through clear policies, processes and standards. A robust governance foundation transforms enterprise data into high-quality, trustworthy AI-ready assets—which are essential for responsible AI development.

Data privacy laws and AI-related regulations are evolving rapidly, and often require detailed model documentation. This includes information on data provenance, lineage and fitness for purpose—backed by steep penalties for noncompliance. For example, under the EU AI Act, penalties can reach EUR 35 million or 7% of a company’s worldwide annual turnover, depending on the violation.

Bias and accuracy are also growing concerns, with nearly half of surveyed CEOs worrying about these risks. In high-stakes sectors such as healthcare and finance, where AI might influence critical decisions, robust data governance is critical to safeguarding fairness and trust.

Strong governance frameworks mitigate these risks and support high-quality data through measures such as:

• Access controls, document lineage and usage guidelines that support data privacy and regulatory compliance
  
  
• Clear and enforceable standards across the AI lifecycle and automated bias detection tools for fair and accurate data practices
  
  
• Data cleansing, data validation and data observability solutions that help ensure data accuracy, cleanliness and timeliness
  
  
• Metadata management tools that categorize datasets with descriptive, structural and administrative metadata, so AI models are trained on accurate, relevant information

While data security is often considered part of broader governance, it warrants special focus when it comes to AI-ready data. Generative AI presents a new set of data security challenges, such as data leakage and prompt injection attacks, that demand proactivity.

A single breach can devastate an organization’s bottom line. According to IBM’s 2025 Cost of a Data Breach Report, the global average cost of a data breach has reached USD 4.4 million.

To keep data safe throughout the AI lifecycle (from collection and preparation to training and disposal), organizations should consider three key tenets of data security: discovery, protection and monitoring.

AI-ready data isn’t valuable in a vacuum. It only delivers real impact when supported by the appropriate human skills and data infrastructure.

To successfully adopt and scale AI systems, teams across functions will require varying levels of training and reskilling. Employees should develop foundational understanding of AI concepts, workflows, decision-making and responsible usage.

While not everyone needs to become a data scientist, a culture of data literacy and data democratization can empower people to confidently use AI applications and make data-informed decisions. In addition, AI ethics and bias identification training can reinforce governance for trustworthy AI.

Organizations should also consider whether their data storage infrastructure is ready to meet the performance and capacity demands of AI workloads. LLMs, in particular, require significant storage resources across multiple environments. To meet these needs, many organizations today are adopting storage solutions such as cloud object storage, flash storage and data lakes, warehouses and lakehouses.