The 6 foundations for delivering AI-ready data

AI has the power to transform how organizations operate, driving greater efficiency, productivity and innovation. However, successful, scalable AI relies on data, both to train Large Language Models (LLMs) and to power the real-time actions of agentic AI.

This need for AI-ready data is one of the biggest challenges to shifting AI beyond pilot projects and making it part of the business mainstream. To deliver ROI, Chief Data Officers (CDOs) therefore need to focus on providing reliable, high-quality data for both AI and humans, such as through highly-consumable, business-focused data products. Based on our new ebook, this blog explores the key foundations that CDOs need to put in place, and the role of data product marketplaces in driving greater data consumption by AI and humans.

Despite large investments in AI, nearly three-quarters of organizations struggle to scale the value it brings, according to Boston Consulting Group. The primary reason is a lack of AI-ready data – 93% of executives surveyed by Wavestone said that poor quality data was the biggest barrier that they faced when it came to AI. A lack of data may not impact small-scale pilot projects built on specially curated datasets, but when these are extended to the wider enterprise, the cost of unstable data foundations becomes painfully obvious.

AI models are trained by learning patterns from the data that they are fed, while AI agents take actions based on analyzing available information. That means that they both require seamless, rapid access to high-quality, reliable and trustworthy data if they are to deliver on their potential.

Poor quality data impacts AI in ten main ways:

• Inaccurate decision making as AI predictions are based on unreliable information
• Higher compliance and legal risks as inaccurate AI models make potentially biased or discriminatory decisions
• Worsening accuracy as rather than learning and improving, bad data negatively impacts results over time
• An inability to generalize, with AI only accurate under specific conditions due to being trained on narrow datasets
• Security failings, with confidential data exposed outside the business, such as with generative AI engines, breaching governance rules
• Higher costs as projects either fail completely or require enormous rework and additional resources to deliver results
• A lack of consistency, with AI models providing random results, without any understanding of how they operate
• Reputational damage, with AI failures impacting corporate brand and customer and shareholder trust
• Physical danger, with agentic AI optimizing for the wrong outcomes in scenarios such as self-driving cars or causing wider issues in ecosystems such as financial services
• Feedback loop corruption, with incorrect data enabling AI systems and agents to be hijacked by bad actors

AI-ready data shares six key attributes. It has to be:

• High-quality – accurate, consistent, unbiased and up-to-date, without missing fields or values. Quality needs to be continually monitored and guaranteed.
• Reliable – with SLAs to guarantee its quality over time and robust data pipelines to ensure data is collected, processed, enriched and successfully delivered to AI models on an ongoing basis at scale.
• Complete – containing all relevant information, breaking down departmental silos to deliver context through cross-domain data.
• Trusted – well-governed, traceable, and easily understandable/explainable through comprehensive metadata, descriptions, lineage, audit trails and access controls.
• Scalable – able to be shared widely across the organization in a timely manner, meeting AI’s demands for greater volume and velocity of data from all business departments.
• Machine-readable – consumable by AI models and AI agents through clear, readable structures and metadata, automating ingestion and speeding results.

The good news for CDOs is that the first five of these attributes are equally vital to increasing data consumption by humans, especially those that are not data experts. This means that creating and delivering a data strategy that focuses on AI will positively impact wider data democratization, maximizing ROI and data usage.

Successfully delivering AI-ready data requires CDOs to take a step beyond traditional data management approaches, ensuring that data, infrastructure, and processes all align with organizational needs and provide real business value.

CDOs have increasingly been focusing on making this change and proactively turning data into positive ROI. The requirements of AI now accelerate this need and make it a business-critical, C-level priority.

Essentially, to ensure that data is high-quality, reliable, complete, trusted, scalable and consumable, organizations need to focus on putting in place six key foundations to ensure it is AI-ready.

Start by taking a structured look at your existing data ecosystem, pinpointing weaknesses and outlining steps to prepare specific data for targeted AI applications. Because AI relies on numerical analysis, convert raw information into accurate, relevant quantitative metrics. Confirm your data meets requirements for semantics, quality, trust and diversity, and that it is AI-ready with proper labeling, metadata, bias controls and lineage tracking.

AI must address a defined business challenge to generate ROI. Identify the most urgent business priorities and where AI and data can deliver fast, high-value impact. Secure alignment and commitment from business stakeholders and the board, ensure resources are in place for execution, and define clear performance metrics to track success.

Data products are focused, high-value assets built for easy adoption at scale. They package everything a non-expert needs, eliminating the requirement for training or hand-holding. Backed by clear data contracts that define permitted usage and set SLAs for performance and reliability, they are engineered for sustained, high-volume consumption by both users and AI systems.

By integrating and enhancing multiple data sources and exposing them through a simple, machine-readable interface, data products remove barriers around context, access, trust, traceability, ownership and oversight. They provide full visibility into the origin and composition of the data, which is essential for tracking, validating and governing AI models and agentic AI workflows.

If data assets — including data products — can’t be easily governed, found, accessed and used, they won’t gain adoption by people or AI. They must be delivered through a secure, self-service data product marketplace that consolidates all assets into a single source of truth. With an intuitive experience modeled on modern e-commerce platforms, users and AI systems can quickly search, select and deploy the data products they need.

A data product marketplace serves as the consumption layer for AI-ready data, making it organized, usable, machine-readable and dependable. It supports granular access controls for compliance and security, and provides full lineage so organizations can see exactly which data products have been used by models, LLMs and agentic AI workflows.

Data governance defines how an organization classifies, manages, and applies its data to minimize risk and support faster, more confident decision-making. Strong governance resolves challenges linked to compliance, regulations, security and data consistency.

For AI, governance must go further—establishing and enforcing standards for metadata, accountability, and the ethical handling of data, especially personal information used to train models. Without robust governance, AI cannot operate responsibly or deliver reliable, unbiased results.

Years of buying isolated tools have led to sprawling, fragmented data and technology stacks filled with overlapping point solutions that don’t integrate well. The result is costly complexity, difficult data management, and limited ability to deliver consistent, trustworthy, AI-ready data as scalable data products.

To fix the issue, Gartner advises streamlining tech stacks using modern Data Management Platforms (DMPs)—converged, end-to-end platforms that consolidate essential capabilities into one solution and replace scattered tools. Crucially, these platforms must work hand-in-hand with a best-in-class data product marketplace to make high-quality data easily accessible and consumable for both AI and human users.

AI and data must address real business challenges to create measurable ROI. Achieving this demands stronger collaboration between data, IT, governance teams, business stakeholders and data owners. By operating as cross-functional groups, organizations can eliminate silos and strike the right balance between centralized standards and domain autonomy through federated data mesh models, ultimately driving higher data usage and business value.

A data product marketplace enables this collaboration by offering a unified environment to share, govern, access and manage data through a user-friendly experience that serves every stakeholder. Business users can provide ratings and feedback, data owners can respond to questions, and administrators can control access so the right users and AI systems can leverage the right data products.

AI success starts with data, meaning organizations need to have the right foundations in place to deliver lasting ROI. Harnessing the transformative power of LLMs and AI agents requires data to be AI-ready, reliable, trusted and easily available through data products and data product marketplaces. This not only delivers the benefits of AI, but ensures that data can be consumed by employees across the business, driving data democratization and maximizing value.

Find out more about making your data AI-ready in our new ebook. Learn about the key challenges and how to overcome them, best practices and the role of data product marketplaces in AI success. Download the ebook here.