Successfully delivering AI-ready data – recommendations from Gartner

The availability of high-quality, reliable data is recognized as the number one challenge to scaling AI projects. Failure to provide the right data, in an AI-ready form, leads to inconsistent, unreliable results, wasted investment and lower business performance.

Data issues impact AI models – and also hold back the success of agentic AI. As organizations look to automate complex business workflows through agents, seamless access to high-quality data is essential. And given the real-time, interconnected nature of agentic AI, data issues can cause even greater problems, such as inconsistent decisions and incorrect actions that then quickly propagate through your company and ecosystem.

To help build and execute AI-ready data strategies, Gartner has published a range of reports and best practices, many of which will be explored in more depth at the forthcoming Gartner Data Summit London. Ahead of the event, which Huwise will be attending, here’s our synthesis of the latest thinking from the analyst firm.

Simply making existing data assets available to AI models and agents is not enough. They have to meet stringent requirements around quality, but also be focused on the specific needs of AI. Gartner highlights three, key, interconnected areas:

Align data

Data must be well-structured, accurate and accessible. To guard against bias and inconsistency it has to be trustworthy, annotated and labelled correctly. This requires consistent, complete metadata and a semantic layer to bridge raw data and AI (and human users).

Govern contextually

Strong governance is vital to ensuring that the data used for AI is shared responsibly, and in line with ethical and legal standards. That means it must be fully documented and any sensitive data protected, with processes in place to limit access to relevant AI use cases, without putting unnecessary barriers in place to slow adoption.

Qualify continuously

Data shifts and changes, potentially impacting AI outputs over time. This requires ongoing monitoring, observability, and assessment of factors such as consistency to ensure that it remains relevant and valid for a specific use case.

AI agents bring new challenges. Data is being continuously ingested by agents in real-time, meaning that data flows must be autonomous, coordinated, and reliable. Due to the potential issues if agents fail to operate correctly, agentic AI requires tighter, more automated governance to ensure reliability and control at scale. 

This complexity can hold back the deployment of AI agents. According to McKinsey, nearly two-thirds of organizations globally have experimented with agents, but under 10% have scaled them to deliver tangible value.

Research by Gartner highlights that data availability & quality is the top barrier to AI, with 30% of CDOs ranking it as a top three issue. This is backed up by other statistics – eight in ten companies in a McKinsey study highlighted data limitations as a challenge to scaling agentic AI.

Availability and quality are not the only barriers – context and governance also hold back success.

When it comes to data, it is crucial to understand that humans and AI perceive information differently. Humans use their knowledge and experience to provide context to the data they see, filling in gaps and immediately recognizing what data represents and seeing when something is wrong. 

AI does not have this innate ability to add context to the data it consumes. It sees data as complete and neutral, and bases its actions on the data it receives. 

Many existing data architectures are rightly focused on protecting data, but at the cost of what Gartner refers to as “inertia”. Essentially data is not made available in a timely manner due to concerns about compliance and security, with the technical infrastructure holding back secure sharing, particularly outside technical experts. 

Gartner highlights that data management must expand to meet AI needs. This requires a three part infrastructure:

• Traditional data management foundations, such as data warehouses, data governance and lineage
• Advanced data management capabilities, such as data fabric, DataOps, data mesh and data products
• AI-specific techniques, such as data labelling, data enrichment and data bias mitigation

Building on this, AI-ready data, particularly for agents, requires two key components – context and self-service access.

The context layer is the shared infrastructure that sits between raw data and AI models and agents. It builds in the business rules, relationships and operational processes needed to interpret data correctly, It combines metadata, lineage, quality signals, decision history and governance policies to capture what the data represents, how it is used in the business, and how it is used over time. Essentially, it represents the context and understanding that humans have when they look at data.

High-quality, contextualized data does not generate value if it is not then discovered, accessed and consumed by AI agents and models. It must be transparent and trustworthy, so that AI knows that it meets its requirements and can easily ingest it in real-time. 

This pressing need is driving the rise of data products, curated, high-value data assets that are designed to meet a specific business need. However, simply creating data products does not guarantee that they will be discovered or used. Organizations are therefore turning to data product marketplaces to provide a centralized access point to data products and other data assets. Through an intuitive, self-service experience, data marketplaces connect AI and humans with relevant, trusted and contextualized data, driving usage and value. Security and governance is enforced through granular access controls, ensuring that data is used in line with company and regulatory policies. 

In a recent study which listed Huwise as a representative vendor, Gartner stated that data marketplaces are essential for organizations seeking to maximize ROI from their data investments, transforming data into a strategic asset by breaking down silos and enabling secure, scalable data product sharing.

Gartner also has a warning for companies who may feel that a cautious approach to AI is the best strategy. Looking ahead to 2030 it points to a growing transformation gap between AI-first leaders and the AI cautious. By solely focusing on incremental improvements from AI, rather than audacious reinvention, this group risks not only missing out on the benefits of AI, but also of being out-performed by rivals.

Instead, Chief Data Officers and other data leaders need to understand where AI can transform how they operate, both as a technical function and across the wider business. For example, Gartner highlights that while in 2025 81% of IT work was done by humans without AI by 2030 this drops to 0%. Data management and engineering will become much more of a human-agent collaboration, with adaptive, context-aware AI agents automating the majority of data engineering workflows, freeing up capacity for higher-value reinvestment.

The rapid rise of AI has demonstrated the importance of high-quality, trustworthy data, and the need for many organizations to focus on building the right foundations for AI. This goes beyond traditional data management to cover key, AI-specific areas such as context, with data products then seamlessly made discoverable, accessible and available to AI and humans through centralized data marketplaces. Only then will AI usage scale to deliver the performance and effectiveness required to compete moving forward.

As part of its commitment to supporting organizations on their data product journey, Huwise will be exhibiting at the forthcoming Gartner Data & Analytics Summit in London between 11-13 May. Find out more about how we can help transform your data strategy by visiting us on booth 113 or booking a meeting here.