Now Available. The Modern Data Report 2026: The Data Activation Gap
Step 1/5

Your AI Readiness
Scorecard

Find out if your data stack is ready for AI, and what to fix first.

• 8 questions          • ~3 minutes           • Get a personalized report​

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Your results will be sent to your email along with a personalised breakdown.

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Step 1/5

Trust . Built-in Governance

Can your teams trust the data powering your AI systems?

Please select your response on the 1–10 scale.

1. Vulnerable

Raw data with no lineage visibility. Impact analysis is manual; updates take weeks with a high level of rework.

5. Capable

Partial lineage documented with basic tagging. Impact analysis takes days; at least 30% of changes require manual rework.

10. Transformative

Comprehensive lineage across all systems. Impact analysis and changes propagate same-day with less than 5% rework.

Please select your response on the 1–10 scale.

1. Vulnerable

Workflows are fragmented across tools; governance is manual or absent; runs are sequential and outcomes take weeks or months.

5. Capable

Some workflows are connected with partial governance; parallelism is limited; outcomes delivered in days to weeks with frequent rework.

10. Transformative

Workflows fully integrated end-to-end with automated governance; parallel execution is standard; outcomes delivered in hours with minimal rework.

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Context . Semantic Consistency

Does your data mean the same thing across every team and AI system?

Please select your response on the 1–10 scale.

1. Vulnerable

No recognition of context; data is interpreted differently in each use case with no business and technical alignment.

5. Capable

Some contextual differences are documented and a few shared definitions exist, but ownership is fragmented and inconsistent.

10. Transformative

Context is consistently recognised across use cases, with business and technical teams co-owning all definitions.

Please select your response on the 1–10 scale.

1. Vulnerable

No semantic layer; business concepts and technical data are disconnected with no traceability. Changes roll out per tool over weeks.

5. Capable

A partial semantic layer exists with some business concepts linked to data and limited lineage documentation. New metrics reach priority tools in days.

10. Transformative

A comprehensive semantic layer links all business concepts to technical data with automated lineage. Publish once, live across BI and APIs within hours.

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Productized . Reusable Data Assets

Is your data engineered as products teams can reuse, or rebuilt from scratch each time?

Please select your response on the 1–10 scale.

1. Vulnerable

Nearly all data preparation is manual and custom for each project. 70–90% manual effort per project; heavy one-off pipelines.

5. Capable

Some reusable data products reduce repetitive work, but most preparation is still case-by-case. More than 30% manual effort.

10. Transformative

Preparation is largely automated using production-grade, reusable data products across projects. Less than 10% manual effort.

Please select your response on the 1–10 scale.

1. Vulnerable

Data is raw and unorganised, requiring extensive manual cleaning before AI can use it. Each use case needs weeks of preparation.

5. Capable

Some data is catalogued and reused for specific use cases, but versioning and consistency are limited. New use cases take days to weeks.

10. Transformative

Fully versioned, catalogued, AI-ready assets that are composable and immediately usable across domains. New use cases live within hours to days.

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Actionable . Data at the Speed of AI

Can AI systems actually consume your data at the speed they need?

Please select your response on the 1–10 scale.

1. Vulnerable

AI systems require custom integration for each data source with no standard APIs. Weeks to expose new endpoints.

5. Capable

Some APIs are available but coverage is limited and not optimised for AI. Days to expose new endpoints.

10. Transformative

A comprehensive AI-native execution layer exposes consistent, well-documented APIs across all data domains. New endpoints live within hours.

Please select your response on the 1–10 scale.

1. Vulnerable

No direct access; teams must rely on exports or IT-managed pipelines. Environment setup takes weeks.

5. Capable

Some notebook integrations exist but coverage is inconsistent and limited to technical users. Setup takes days.

10. Transformative

Full support for direct, secure access to production data for both technical and business teams via notebooks, APIs, and dev tools. Environment setup in hours.

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Step 1/5
50
AI Readiness

Progressing

You have strong foundations in place. Targeted improvements in your weakest dimensions will unlock meaningful AI acceleration.
TRUST

Built-in Governance

26 /20
Governance foundations are in place but inconsistently applied. Focus on automating policy enforcement and broadening lineage coverage.
CONTEXT

Semantic Consistency

26 /20
Governance foundations are in place but inconsistently applied. Focus on automating policy enforcement and broadening lineage coverage.
Productized

Reusable Data Assets

26 /20
Governance foundations are in place but inconsistently applied. Focus on automating policy enforcement and broadening lineage coverage.
Actionable

Speed of AI

26 /20
Governance foundations are in place but inconsistently applied. Focus on automating policy enforcement and broadening lineage coverage.

Your recommended next steps

You have solid foundations but are leaving AI value on the table. Focused investment in your weaker dimensions will accelerate results disproportionately.

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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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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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