White Papers Data Readiness Gap

Data Readiness Gap: A Tech Leader’s Guide to Scaling AI Beyond Pilots

  • Why AI pilots fail to scale when they move into live business workflows.
  • The gap between AI adoption and AI readiness, and why using AI does not mean your organization is ready to scale it.
  • The five dimensions of AI-ready data: quality, accessibility, integration, governance, and operations monitoring.
  • How fragmented systems, inconsistent records, weak ownership, and limited access controls create AI production risks.
  • A practical roadmap for prioritizing AI use cases, closing data readiness gaps, and scaling AI responsibly.

Build the data foundation your AI initiatives need

AI adoption is accelerating, but scale remains limited. According to  McKinsey88% of organizations now regularly use AI in at least one business function, yet only about one-third have begun scaling AI programs across the organization. Gartner also predicts that 60% of AI projects unsupported by AI-ready data will be abandoned. The message is clear: AI activity is not the same as AI readiness. Many pilots succeed because teams manually prepare the data, narrow the workflow, select ideal examples, and correct issues before they become visible. But in production, AI systems must work with live data, existing applications, permission rules, security requirements, governance policies, and real users who expect dependable outputs every time. This whitepaper helps technology and transformation leaders evaluate whether their data foundation can support AI beyond pilots and into real business workflows.

How to build AI data readiness effectively

  • Identify high-impact AI use cases: Start with the workflows most likely to improve revenue, efficiency, customer experience, or operational performance.
  • Map the data behind each use case: Identify the systems, datasets, owners, access requirements, and quality gaps involved.
  • Prioritize gaps by business impact: Focus on the data issues that most directly affect deployment readiness, business value, and risk.
  • Define ownership, policies, and access controls: Clarify who owns the data, which AI use cases can use it, and how access should be governed.
  • Strengthen metadata, lineage, and discoverability: Help teams understand where data comes from, how it has changed, and whether it can be trusted for AI use.
  • Integrate the systems AI depends on: Connect the applications, databases, and workflows required to support production AI.
  • Operationalize monitoring before scaling: Track data quality, pipeline reliability, AI outputs, drift, and performance before expanding usage.
  • Scale from one production use case to adjacent workflows: Build repeatable AI value by expanding from validated workflows into related areas with reusable data foundations.
With this whitepaper, you’ll gain a practical framework to identify the data gaps holding your AI initiatives back and build a roadmap for production-scale AI.

Who should read this white paper

  • CIOs and IT leaders modernizing legacy systems, data infrastructure, and enterprise applications for AI adoption
  • CTOs and technical leaders responsible for moving AI initiatives from pilots into production workflows
  • Data, analytics, and AI leaders working to improve data quality, accessibility, governance, and operational reliability
  • Transformation and operations leaders using AI to improve efficiency, customer experience, and business performance
  • Executive sponsors evaluating why AI pilots are not scaling and what data foundation is needed before further investment
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