--- sourceDocument: Australia Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences Release : - australia ft:locale : - en-US ft:publication_title : - Australia Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Use cases # Use cases in Information Extraction skill {#ariaid-title1} Release version: Australia Updated November 5, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Use cases in Information Extraction skill The Information Extraction skill in ServiceNow enables customers to process unstructured documents and extract structured data using generative AI. Use cases define the types of documents to process and specify the exact information to extract, allowing AI to retrieve data accurately across varied document formats sharing common extraction requirements. Show full answer Show less ## Key Features * **Use Case Definition:** Each use case specifies a document type and the fields, tables, and questions that identify the target information within documents. * **Flexible Document Handling:** One use case applies to multiple document formats as long as they share the same data extraction needs. * **Components of a Use Case:** Includes use case records, fields (specific data points), tables (structured data in columns), questions (contextual answers from text or images), integrations, and workflows. * **Setup Process:** Involves naming the use case, defining extraction schema, configuring integrations and flows, and testing for accuracy. * **Guidelines for Precision:** Clearly define extraction requirements, use field prompts and descriptions tailored to your use case, follow recommended formatting, and iteratively adjust descriptions based on test results. * **Intelligent Extraction:** Generative AI identifies document structure dynamically instead of relying on rigid templates, enhancing adaptability but recommending human review before automating workflows. * **Workflow Automation:** Extracted data populates relevant ServiceNow workflows such as IT Service Management (ITSM), Field Service Management for Telecommunications, or ServiceNow Otto for HR Service Delivery (HRSD). Full automation mode can skip fulfiller verification. ## Common Use Cases * **Invoice Processing:** Extract line items, invoice numbers, totals, and vendor details from PDFs or scanned invoices. Example use with ServiceNow Otto for Accounts Payable Operations (APO). * **HR Onboarding:** Extract employee data like national IDs, tax declarations, and emergency contacts from onboarding documents. * **Field Service:** Extract details from purchase orders, maintenance sheets, or work orders to pre-fill service tasks. ## Practical Benefits for ServiceNow Customers * Enables automated extraction of structured data from varied document types, reducing manual data entry. * Supports process automation across multiple ServiceNow workflows, improving operational efficiency. * Guided setup and best practices help customers optimize extraction precision and relevance. * Human review is recommended to ensure data accuracy before full automation, balancing efficiency and quality. Use cases in the Information Extraction skill define what documents to process and what information to extract, so generative AI can accurately retrieve structured data from unstructured documents. A use case defines the document type to process and the specific information generative AI extracts from it. A single use case applies to all documents that share a common set of values to extract, even when those documents aren't in the same format.{#cu-use-cases__cu-use-cases-para-1} ## Common use cases {#cu-use-cases__section-common-use-cases} Invoice processing : Extract line items, invoice numbers, totals, and vendor details from PDFs or scanned vendor invoices. For an example implementation, see [ServiceNow Otto for Accounts Payable Operations (APO)](https://www.servicenow.com/docs/access?context=now-assist-apo&version=australia&pubname=australia-source-to-pay-operations&ft:locale=en-US). HR onboarding : Extract fields such as national IDs, tax declarations, and emergency contacts from employee documents. Field service : Extract data from purchase orders, equipment maintenance sheets, or work orders to pre-populate service tasks. ## Use case structure {#cu-use-cases__section-use-case-structure} A use case consists of the use case record and its related fields, tables, questions, any integrations, and workflows.{#cu-use-cases__cu-use-cases-para-2} * Fields identify specific information within documents relevant to predictions. * Tables extract information from cells within table columns. * Questions generate answers based on an analysis of the text and images within a document. {#cu-use-cases__ul_acq_xl1_ckc} ## Use case setup {#cu-use-cases__section-use-case-setup} Use case setup includes defining the use case name and details, specifying fields, tables, and questions, configuring integrations and flows, and testing the use case.{#cu-use-cases__cu-use-cases-para-3} The following guidelines help improve extraction precision and relevance.{#cu-use-cases__cu-use-cases-para-4} Identify your requirements : Clearly describe the specific field, table, or answer you want to extract. Use field prompts : Use the relevant field prompts and descriptions customized for your use case. This guides the extraction process. Follow the recommended format : To improve accuracy, follow the format provided in the on-screen Field Assistance tab. This produces consistent results. Adjust descriptions : Field descriptions may change during test executions. Reviewing changes helps improve your extraction results. Follow ## Use case--based extraction flow {#cu-use-cases__section-extraction-flow} Schema definition : You create a use case by defining a schema --- a set of specific fields, tables, and questions that you need from that document. Intelligent extraction : Rather than using rigid templates, generative AI uses the use case to identify the document's structure and extract the target information. Results may vary and human review is recommended before using extracted data in automated workflows. Workflow automation : After generative AI extracts the data, it populates the required fields in your ServiceNow® workflows, such as ITSM, Field Service Management for Telecommunication, or ServiceNow Otto for HRSD. Note: When full automation mode is enabled, fulfiller verification is skipped. **Related concepts** * [Predictions in Information Extraction skill](https://www.servicenow.com/docs/sJiQOwCZW9QrjoQcE2~X~w "A prediction is the value that the Information Extraction skill infers for each field, table, or question in a use case. Predictions drive automation decisions and can be applied automatically or reviewed by a person, depending on the use case configuration.") * [Automation modes](https://www.servicenow.com/docs/i6KzhgYWrc57LJ_LjceWaw "Automation modes in the Information Extraction skill determine whether predictions are accepted automatically or routed to a human agent for review before a document task completes.") **Related tasks** * [Set up a use case](https://www.servicenow.com/docs/7i2Se9AZ5Ls1TvXR7fZJPA "Create a use case to define the information to extract from a document for processing.") **Related reference** * [Content Understanding personas](https://www.servicenow.com/docs/_SDqe3OUlIshNOjwI~suHg "Personas define the roles that interact with Content Understanding and the actions each role performs.")