Organizations often focus on selecting AI models, but a big predictor of AI success is the quality, governance, and accessibility of the data used by the models. When data is disconnected, poorly understood, or insufficiently governed, AI systems can produce inaccurate recommendations and unreliable outcomes, and create compliance risks. According to Gartner, 63% of organizations are unsure whether they have the right data management practices in place for AI, while 60% of AI projects will be abandoned because they aren’t supported by AI-ready data.

The Bad AI Prevention Checklist provides six practical steps to discover, trust, and activate data for AI. It covers stakeholder alignment, data quality standards, governance, observability, data intelligence, and activation strategies that balance innovation with risk management. The checklist helps organizations build the trusted data foundation needed to support AI agents, copilots, analytics, and other AI initiatives with greater speed, transparency, and control.

Most AI failures aren’t model failures. They’re data failures. Organizations often struggle with poor data quality, unclear ownership, inconsistent definitions, and limited visibility into how data is used by AI systems. This checklist provides a practical framework for building AI-ready data by helping teams align stakeholders, establish governance guardrails, improve observability, and create trusted data products that support reliable AI outcomes.

Don’t wait for a bad AI outcome to expose weaknesses in your data foundation. Download the checklist to discover, trust, and activate your data for AI with confidence.