Now Available. The Modern Data Report 2026: The Data Activation Gap

Is Your Data Ready for AI?

15 questions. 5 minutes. A clear picture of where your data ecosystem stands — and exactly what to fix first.

15 scored questions          4capability dimensions             Results emailed instantly

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In just a few minutes, you’ll uncover: 

Cover and pages of a Modern WhitePaper titled 'AI Starts with Data: Are you Ready? A Scorecard for Enterprise AI Readiness, Volume 2,' showing a scoring sheet and a data readiness diagram.
  • Where your organization stands today 
  • The biggest gaps holding back AI success 
  • Practical next steps to accelerate ROI
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How It Works

Answer 8 questions on a 1 – 10 scale.

  • 1 = No capability in place. ‍
  • 5 = Some capability, but with gaps or manual processes. ‍
  • 10 = Strong capability with automated systems.

After you complete the assessment, enter your email to get your results. You'll discover your organization’s AI readiness level: Early Stage, Progressing, or Strong Foundation.  

Want a deeper analysis? Our team can review your score and help you prioritize next steps.  

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I'm Ready
Step 1/5
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Please select your response on the 1–10 scale.
  • 1: Raw data with no lineage visibility. Impact analysis is manual, updates take weeks with a high level of rework. ‍
  • 5: Partial and manually maintained lineage with basic tagging and categorization. Impact analysis can take days, and at least 30% of changes require manual rework. ‍
  • 10: Comprehensive lineage tracking across all systems. Impact analysis and changes propagate the same day, almost automatically, with less than 5% rework.
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Step 1/5
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Please select your response on the 1–10 scale.
  • 1: Workflows are fragmented across tools; governance is manual or absent; runs are sequential and slow; outcomes take weeks or months.
  • ‍5: Partially governed workflows; parallelism is limited; outcomes are delivered in days to weeks with frequent rework.
  • ‍10: Fully integrated workflows, with automated governance; parallel execution is standard, and outcomes are delivered in hours to days with minimal rework.
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Step 1/5
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Please select your response on the 1–10 scale.
  • 1: No recognition of context; data is interpreted differently in each use case, with no business and technical alignment.
  • ‍5: Some contextual differences are documented, and a few shared definitions exist, but ownership is fragmented and inconsistent.
  • ‍10: Context is consistently recognized across use cases, with business and technical teams co-owning definitions.
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Step 1/5
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Please select your response on the 1–10 scale.
  • 1: No semantic layer; business concepts and technical data are disconnected, with no traceability. Changes roll out per tool, over weeks. ‍
  • 5: A partial semantic layer exists, with some business concepts linked to data and limited lineage documentation. New metrics reach priority tools in days.
  • ‍10: Unified semantic layer links all business concepts to technical data, with automated lineage. Publish once goes live across BI and APIs in hours.
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Step 1/5
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Please select your response on the 1–10 scale.
  • 1: No visibility into AI decision paths, model inputs/outputs, or interpretability.
  • ‍5: Some audit logs and partial traceability of model inputs/outputs exist, but interpretability is limited and not standardized.
  • ‍10: Full transparency with traceable model inputs/outputs, clear interpretability of decisions, clear documentation, and audit-ready compliance reporting.
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Step 1/5
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Please select your response on the 1–10 scale.
  • 1: AI systems require custom integration for each data source, with no standard APIs. Weeks to expose new endpoints.
  • ‍5: Some APIs are available, but coverage is limited and not optimized for AI. Days to expose.
  • ‍10: A comprehensive AI-native execution layer exposes consistent, well-documented APIs (REST, SQL, GraphQL, MCP). Endpoints can be exposed in hours.
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Step 1/5
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Please select your response on the 1–10 scale.
  • 1: No direct access; teams must rely on exports or IT-managed pipelines. The setup takes weeks.
  • ‍5: Some notebook integrations exist, but coverage is inconsistent and limited to technical users. Set up in days.
  • ‍10: Full support for direct, secure access to production data for teams via notebooks, APIs, and modern development tools. Environment Setup in hours.
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Step 1/5
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Please select your response on the 1–10 scale.
  • 1: No support for real-time access; data must be batch-processed before use. Seconds-to-minutes latency is unacceptable for inference. ‍
  • 5: Some workloads support near real-time queries, but latency is inconsistent. 95th percentile latency seconds.
  • ‍10: Infrastructure is optimized for low-latency, real-time AI inference across all workloads. 95 / 99th percentile latency seconds across critical workloads.
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Step 1/5
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Step 1/5
Step 1/5
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Early Stage
(Significant gaps to address)
Progressing
(Strong capabilities, room to strengthen)
Strong Foundation
(Purpose-Driven, Al-Ready)
Your AI Readiness
A Strong Foundation 👏

You have built a strong data foundation for AI readiness. As you scale, DataOS can help you go further by bringing unified governance, reusable data products, and faster activation across BI, apps, and AI.

Recommendations 💡

You are building momentum, but a few gaps remain. Here are targeted actions to improve:

  • Start by automating data lineage tracking to quickly assess change impacts and minimize manual rework.
  • Focus on integrating workflows under a unified governance model to enable parallel execution and faster delivery.
  • Work toward aligning business and technical teams around shared, context-aware data definitions.
  • Invest in building a unified semantic layer to link business concepts with technical data for consistent reporting and faster rollout.
  • Enhance AI governance by improving traceability of model inputs, outputs, and decision paths.
  • Prioritize creating standardized, AI-ready APIs to give systems consistent and rapid access to trusted data.
  • Enable secure, direct access to production data through notebooks and APIs to accelerate experimentation and delivery.
  • Upgrade infrastructure to support real-time, low-latency data access for AI and analytics use cases.

This result is a preview, based on 8 of the 15 questions from our AI Readiness white paper. One of our experts will reach out to work with you on translating your score into a clear, actionable roadmap for strengthening your data foundation and advancing AI excellence.

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For the comprehensive 15-question assessment and the complete AI Readiness Scorecard & Framework, download the white paper for free.

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