Determining candidate score and potential

  • Release version: Zurich
  • Updated July 31, 2025
  • 2 minutes to read
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    Summary of Determining candidate score and potential

    ERP Semantic Mining is a feature designed to generate scores that rank the potential for replatforming legacy ERP (Enterprise Resource Planning) systems onto the ServiceNow AI Platform. This scoring helps evaluate how well an application candidate aligns with supported ERP modules and data models. Starting with the Zurich release, ERP Semantic Mining is being prepared for future deprecation: it will no longer activate on new instances but will remain supported on existing ones.

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    Key Features

    • ERP Module Identification: Each candidate application is linked to an ERP module in ERP Semantic Mining, which guides the evaluation of potential scores based on matching remote and extraction tables.
    • Data Table Types:
      • Remote Tables: Obtain records by running scripts against external data sources.
      • Extraction Tables: Retrieve large data volumes via scheduled queries and use transform tables for processing data on the ServiceNow AI Platform.
    • Prerequisite Configuration: Administrators must configure ERP system connections using Zero Copy Connector for ERP before scoring.
    • Candidate Scoring: Scores reflect how well candidate tables and fields are supported by ServiceNow’s ERP models, indicating the readiness for replatforming without additional changes.
    • Similarity Assessment: Candidates are compared based on table and model similarities, with thresholds adjustable in system properties.

    How Scoring Works

    The candidate potential score is calculated using several metrics:

    • ERP Models Used: Counts how many ERP models the candidate’s remote and extraction tables correspond to.
    • Similar Candidates: Number of candidates exceeding similarity thresholds considering both table- and model-based similarity.
    • Supported Table Score: Ratio of supported custom app tables relative to total custom app tables, excluding technical or ServiceNow cluster tables.
    • Supported Table Usage: Proportion of supported tables actively used by the custom app.
    • Unsupported Model Penalty: Penalty applied based on unsupported operations in ERP models, normalized via a sigmoid function.
    • Unsupported Table Extensions: Counts custom app tables suggested as model extensions but not fully supported.
    • Model Inaccuracy: Number of supported ERP model tables not used by the app, also normalized via sigmoid function.

    Practical Implications for ServiceNow Customers

    This scoring mechanism helps customers assess which legacy ERP applications are best suited for migration onto the ServiceNow AI Platform by quantifying compatibility and potential effort. High scores imply easier, more immediate replatforming using existing ERP models and data integrations, while low scores indicate gaps that may require additional customization or development. Customers should ensure proper ERP connections via Zero Copy Connector and can adjust similarity thresholds to fine-tune candidate evaluation. Despite upcoming deprecation, this tool remains valuable for ongoing ERP replatforming initiatives during the Zurich release lifecycle.

    ERP Semantic Mining generates a score to rank the potential for replatforming legacy ERP (Enterprise Resource Planning) candidates onto the ServiceNow AI Platform.

    Important:
    Starting with the Zurich release, ERP Semantic Mining is being prepared for future deprecation. It will be hidden and no longer activated on new instances but will continue to be supported.
    Every candidate has an ERP module specified in the candidate details in ERP Semantic Mining. The ERP module is used to evaluate the potential score of the candidate for replatforming, as well as the remote tables and extraction tables the model contains.
    • Remote tables get their records from running an associated script against an external data source.
    • Extraction tables retrieve large amounts of data using a scheduled query, and use transform tables to process data for use on the ServiceNow AI Platform.
    Note:

    Admins must first configure the connection to the ERP system in Zero Copy Connector for ERP. For more information, see Working with ERP systems in Zero Copy Connector for ERP.

    High and low scores for candidate potential

    ERP Semantic Mining evaluates candidates based on how well their tables and fields are supported by the ServiceNow AI Platform.
    • A high potential indicates that ERP Semantic Mining can immediately use remote tables and extraction tables that match the ERP model for the application candidate without making additional changes.
    • A low potential indicates that the application candidate matches few of the remote tables and extraction tables in the ERP models in Zero Copy Connector for ERP.

    How scores are calculated

    The candidate potential score is calculated using the following metrics:
    • ERP models: The number of ERP models that the candidate uses remote tables and extraction tables from.
    • Similar candidates: The number of candidates with a similarity score above the threshold, which accounts for both table-based similarity and model-based similarity.

      The threshold can be adjusted in the System Properties [sys_properties] table, and the default OR condition can be changed to AND.

    • Supported table score: The ratio of the number of custom app tables that are supported by any ERP model relative to the number of custom app tables.
      Note:
      Tables from either the Technical or ServiceNow table clusters are ignored from these computations.
    • Supported table usage: The ratio of tables supported by the relevant ERP models that are used by the custom app.
    • Unsupported model penalty: A penalty for the number of unsupported operations on tables in ERP models. The number of unsupported operations is passed through a sigmoid function, so it ranges from 0.0 and 1.0.
    • Unsupported table extensions: The number of custom app tables that are also suggested as model extensions.
    • Model inaccuracy: The number of tables supported by relevant ERP models that aren’t used by custom apps, and are passed through a sigmoid function.