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AI Readiness Score

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Feature Description

AI Readiness Score

Lumenore introduces the AI Readiness Score within the Data Dictionary, enabling users to objectively assess how well their data is prepared for AI-driven use cases across the platform. The score, calculated out of 100, evaluates critical metadata and contextual elements that directly impact AI performance—such as descriptions, business context, schema prompts, synonyms, and units of measure.
The score is color-coded for instant interpretation:

  • Red (Below 40): Low readiness, with visible gaps and cons highlighted
  • Yellow (41–75): Moderate readiness, showing improvement areas
  • Green (Above 75): High readiness, with strengths and pros surfaced

Each score component allows users to directly navigate to the relevant tabs and take corrective action. A universal “Improve Your Score with AI” button enables users to automatically enhance metadata using AI, accelerating readiness with minimal manual effort.
This ensures that datasets used in AI dashboards, AI Insights Boards, and Lumenore Ask Me are context-rich, well-governed, and optimized for accurate AI responses and insights.
To know more, click here.

End User Business Benefits

no code

AI-Ready Data Foundation

Instantly understand how prepared your data is for AI consumption using a transparent, standardized score.

Faster decision

Actionable Data Governance

Identify exactly what is missing and where to fix it through clickable score breakdowns and guided navigation.

increase accuracy

Higher AI Accuracy & Reliability

Richer metadata and stronger context lead to more precise AI insights, recommendations, and responses.

increase accuracy

Scalable AI Performance

Ensures consistent AI behavior and performance as data volumes and complexity increase.

increase accuracy

Proactive Data Quality Management

Early visibility into readiness gaps allows teams to fix issues before AI models or insights are impacted.

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  • Healthcare

    • Assess data readiness for clinical outcomes dashboards and patient care analytics.
    • Identify gaps in claims and provider data before deploying AI-driven population health models.
    • Ensure high-quality inputs for predictive models on readmissions or patient risk scores.

  • Manufacturing

    • Evaluate shop-floor sensor and machine data for predictive maintenance AI models.
    • Identify inconsistencies in production and supply chain data before AI optimization.
    • Improve data quality for AI-driven efficiency and cost-saving recommendations.

  • Contact Centers 

    • Score the readiness of call transcripts and interaction data for AI-based sentiment analysis.
    • Detect gaps in agent performance or escalation data before feeding into AI dashboards.
    • Ensure consistency in customer feedback data for AI-driven satisfaction insights.

  • Agritech

    • Assess readiness of crop yield and soil condition data for AI-driven farming models. 
    • Identify missing or inconsistent satellite and IoT sensor data.
    • Prepare structured, high-quality datasets for AI-based climate impact forecasts.

  • Retail & E-commerce

    • Score sales and returns data for AI-driven recommendation engines.
    • Evaluate promotional and campaign data for AI-powered personalization.
    • Ensure clean product categorization data for demand forecasting.

  • Banking & Financial Services

    • Assess loan, transaction, and portfolio data readiness for fraud detection AI.
    • Identify gaps in customer segmentation data before running AI-based risk scoring.
    • Improve data accuracy for AI-driven cross-sell and upsell models.

  • Government & Public Sector

    • Evaluate readiness of citizen service records for AI-driven service optimization.
    • Detect incomplete or inconsistent program performance data.
    • Improve public budget data for AI forecasting and policy planning.

application areas