Colleagues discuss business data displayed on screens during a meeting.

Is Your Data Foundation Ready To Scale AI?

As AI moves from pilot to production, it often brings long-standing data issues to the surface. Only 7% of companies have fully scaled AI across their organizations.¹ And 43% of data leaders cite data readiness as the most significant barrier to aligning AI with business objectives.²

Promising pilots are often built with carefully prepared and limited data. Production introduces the full complexity of the enterprise environment: fragmented sources, inconsistent quality, unclear ownership, technical debt and disconnected infrastructure. The limiting factor may not be the AI model itself, but whether the data beneath it is trusted, traceable and reusable — making data readiness a business priority, not simply an IT concern.

The goal is not to fix every data issue before moving forward. It is to understand which capabilities matter for each priority use case, establish a fit-for-purpose foundation and strengthen it as AI expands.

This checklist can help leaders identify the data, governance, architecture and operating practices needed to turn AI experimentation into sustainable business value.

Six Areas To Strengthen the Data Foundation for AI

Business Outcomes and Data Requirements: Define What the Data Must Deliver

Data readiness is not a one-size-fits-all standard. Define what each use case needs from the data, how much risk is acceptable and how success will be measured before modernizing the foundation around it.

Have you defined the business outcome, KPI, and decision or workflow the AI initiative should improve?
Have you identified the structured and unstructured data the use case requires and confirmed that it is available, accessible and permitted for the intended purpose?
Have you set fit-for-purpose quality, freshness and latency requirements, along with the appropriate level of human oversight based on the use case’s risk?
Do you understand the infrastructure, licensing, integration and operating costs required to move from pilot to production?

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Data Estate and Ownership: Make Data Discoverable and Accountable

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AI depends on data that often spans databases, documents, messages, images and applications. A clear view of the data estate helps teams reduce duplication, address silos and assign accountability.

Do you have a current inventory of the data sources, formats and dependencies used by priority AI initiatives?
Is each source assigned an accountable owner, steward or product team with defined usage and service expectations?
Can teams consistently identify sensitive, regulated, proprietary or third-party data before it enters AI pipelines?
Have you identified legacy dependencies, duplicated data sets and disconnected systems that could constrain performance, increase cost or prevent the use case from scaling?

Data Quality and Context: Define What “Good Enough” Means

Perfect data is not a prerequisite, but AI can amplify missing context, stale content and small inconsistencies. Quality standards should be measurable and proportional to the business impact of the use case.

Have you defined measurable thresholds for accuracy, completeness, consistency, timeliness and relevance?
Can you monitor and address quality issues as data is transformed, retrieved and used — not just when it first enters the environment?
Are structured data and unstructured content connected to shared business entities, definitions and context?
Are quality issues assigned to accountable owners with clear priorities and processes for remediation?

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Metadata, Lineage and Governance: Preserve Trust Through Every Transformation

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AI systems continually transform, recombine and generate data across the lifecycle. Organizations do not need a fully mature governance program before moving forward, but they do need fit-for-purpose controls embedded in data pipelines and scaled to the risk of each use case.

Does metadata capture ownership, sensitivity, version, purpose and allowed usage for source data and derived artifacts?
Can you explain and reproduce how an AI output was created, including the source data and transformations behind it?
Do governance and security controls follow data as it is transformed, retrieved and generated — as well as where the original information is stored?
Are AI-generated summaries, decisions and other outputs governed when they are written back into business systems and reused?

Architecture and Reusable Data Services: Build Once, Use Across Use Cases

Production AI requires repeatable pipelines and shared services rather than a new data stack for every pilot. Reuse improves consistency, speeds deployment and helps reduce duplicated cost.

Can ingestion, extraction, enrichment, indexing and retrieval capabilities be reused across applications?
Are common metadata models, entity definitions and policy controls applied consistently across business units?
Is data placed across on-premises, cloud and hybrid environments based on latency, data gravity, security, compliance and cost?
Can the architecture scale storage, networking and compute as data volumes, formats and AI usage grow?

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Observability, Operations and Cost Control: Manage Data as AI Evolves

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AI data readiness is not a one-time project. Production environments need continuous visibility into data freshness, pipeline health, retrieval behavior, output quality and cost.

Can teams monitor pipeline failures, stale content, retrieval gaps and quality degradation before they affect users?
Are reliability, reuse, governance and scalability measured across AI data products and services?
Do data, AI, security, infrastructure and FinOps teams share ownership of service levels, usage and cost per business outcome?
Are data products and AI-created assets versioned, refreshed and retired through a defined lifecycle?

Sources:
¹ McKinsey & Company, “AI data readiness: The key to scaling impact,” June 2026
² Drexel University’s LeBow College of Business and Precisely, “2026 State of Data Integrity and AI Readiness,” 2026

Why CDW

CDW helps organizations connect business priorities to the data and technology capabilities needed to scale AI with confidence. Because an AI performance issue may originate in data quality, infrastructure, networking, cybersecurity or cloud architecture, CDW brings specialists across these disciplines together to diagnose root causes.

Drawing on experience working with hundreds of customers, CDW can apply proven best practices and governance standards to help accelerate progress and build an integrated path forward.

Business-led data and AI advisory: Identify high-value use cases, assess readiness and build an integrated roadmap across data, AI and operations.
Data modernization and governance: Improve quality, metadata, lineage and access while embedding fit-for-purpose governance into modern data pipelines.
Cross-functional architecture and security: Bring together data, cloud, infrastructure, networking, cybersecurity and FinOps expertise to address enterprise dependencies.
Implementation and managed services: Design, deploy and operate reusable data platforms, pipelines and controls that support AI as needs evolve.