Organizations across industries are racing to implement AI agents and machine learning to gain competitive advantages. But many AI initiatives stall or deliver disappointing results because of a fundamental barrier: their data isn't AI-ready.
"AI-ready data means the data must be representative of the use case, encompassing every pattern, error, outlier and unexpected emergence needed to train or run the AI model for a specific use," notes Gartner in the 2024 Top Trends in Data and Analytics guide. "As a result, the exact definition and function of AI readiness varies depending on the AI use case and the context of the technique employed."
However, according to Gartner's 2023 IT Symposium Research Super Focus Group, only 4% of respondents indicated their data was AI-ready, while 37% believed they were well-positioned for AI-ready data. Most importantly, 55% acknowledged that obtaining AI-ready data would be difficult.
Decision intelligence platforms directly address this challenge, giving organizations a faster path to AI readiness that can bypass traditional data transformation projects. But before we explore how decision intelligence platforms accelerate AI adoption, let's examine the five essential criteria that define AI-ready data.
The Five Criteria for AI-Ready Data
So, what exactly does it take to make data AI-ready? Gartner outlines five key criteria every organization should prioritize.
Governed Data
AI-ready data needs consistent oversight. Organizations should establish clear governance frameworks defining who can access the data, under what conditions, and who is responsible for maintaining its quality and integrity. Strong data governance enables transparency, accountability, and compliance across departments, ensuring that data is used ethically and consistently.
Secure Data
Data must be protected throughout its lifecycle, not just during storage. This includes implementing technical measures like encryption, identity and access management (IAM), and continuous threat monitoring. Organizations should treat data security as a shared responsibility to safeguard sensitive information from both internal misuse and external threats.
Bias-Free Data
To ensure fairness and accuracy, organizations should audit datasets for imbalanced representation, outdated assumptions, and structural inequalities. Bias-free, AI-ready data incorporates diverse, representative samples and is continuously tested to avoid reinforcing existing prejudices.
Enriched Data
Raw data isn’t enough. To be AI-ready, data must be contextualized and annotated. Enrichment involves adding metadata, relational links, geospatial tags, timestamps, or other attributes that enhance AI’s understanding and interpretation of the data.
Accurate Data
Data must be complete, current, and consistent, with ongoing validation checks and feedback loops to ensure inputs remain aligned with real-world conditions over time.
How Cloverpop Ensures AI-Ready Data
Even with clear criteria, implementing AI-ready data practices can be daunting. That’s why Cloverpop helps organizations navigate this process with a hands-on, decision-centric approach for fast implementations, strong AI-human collaboration and high-quality decision results.
To start, Cloverpop works decision-back to align its D-Sight AI decision agents to fit each organization’s unique decision-making frameworks and business context. This ensures that recommendations align with the organization’s goals, markets and operations, rather than relying on one-size-fits-all solutions.
After creating decision-centric AI agent frameworks, Cloverpop works directly with clients to collect and validate the required data. Working back from actual decisions is key to identifying gaps, inconsistencies, and quality issues that could affect decision-making. Thus, organizations can address these concerns early on rather than after implementation, and save time and effort by focusing only on specific data that affects critical decisions.
Finally, to ensure high-quality, reliable decisions, all AI agent insights and recommendations are fully transparent and incorporated into decision-making right alongside human input. This ensures a reliable blend of human+AI collaboration. This comprehensive approach helps organizations make confident, data-driven decisions.
Decision Intelligence Is the Key to AI-Ready Data
AI-ready data is critical to successful AI implementation. Organizations have two primary paths: launch comprehensive, organization-wide data transformation initiatives to meet all five Gartner criteria, or adopt decision intelligence platforms that drive AI readiness through targeted, practical use.
Platforms like Cloverpop follow the latter approach by focusing on decision-specific data needs instead of overhauling the entire organization. This enables organizations to make smarter, more reliable decisions while progressively building broader AI capabilities. By delivering immediate value and scaling over time, decision intelligence becomes the foundation for a competitive, insight-driven business.
Want to learn more about decision intelligence and other key trends shaping the future of data and analytics? Download the Gartner Top Trends in Data and Analytics guide.
