My main use case for Automation Anywhere AI Agent is to extract data from different invoices such as bills of lading, document automation, OCR extraction, and speech-to-text. In a recent project, a client sends me documents via their respective email, and I use document automation to extract invoice details such as invoice date, vendor name, amount, currency, PO number, and delivery note. After extracting the data, I receive the values in Excel format. Mostly, I use Automation Anywhere AI Agent for document automation where there is a GenAI prompt to extract both unstructured and structured data in a structured format. The GenAI prompt helps me extract only the relevant fields, not other fields.
AVP - Service Delivery Manager at a outsourcing company with 501-1,000 employees
Real User
Top 20
Jan 19, 2026
I am currently in POC mode implementing one of Automation Anywhere AI Agent solutions for a customer in the insurance industry. My customer wants to build Automation Anywhere AI Agent for making underwriting decisions. We are building the LLM for the underwriter team who gives the final confirmation on insurance policy processing. The underwriters review all the rules being run and if all the rules are passing properly through all stages, then the system gives the decision. This entire flow is being built with Automation Anywhere AI Agent.
Account Director at a tech vendor with 10,001+ employees
Real User
Top 10
Jan 17, 2026
The main use case for Automation Anywhere AI Agent in the organizations I work with is bank reconciliation and PO invoicing. The use cases depend on the segment. In one of the use cases, bank reconciliation was implemented, along with awarding POs or creating invoicing and raising it based on the PO that they used to receive from the customer. All of that documentation or work which was bulk in number and very repetitive for the organization normally fell into banking or manufacturing. These were two of the use cases which I have promoted or implemented at multiple places. For PO invoicing in manufacturing or banking, Automation Anywhere AI Agent was ingesting all invoices from email extracts and reading the line item data using the IQ bot. It was validating this data against the PO and GRN data from the SAP in the organization. It was autonomously resolving common mismatches, such as price variance within the tolerance or missing PO reference, by querying ERP and vendor master data. Exceptions such as quantity mismatch were routed through the automation bot with a recommended action. With respect to bank reconciliation, the agent compared the core banking transactions with the statements from multiple partner banks. It used artificial intelligence to auto-match transactions with date, amount, variations, and narration. The unmatched items were categorized and posted automatically where rules allow. I worked with a customer called SNS Group, which was my customer. I onboarded them on Salesforce. In both PO invoicing and bank reconciliation, the agent was limited to data capture. It applied policy-based reasoning to manage the tolerance limit and vendor SLAs, aging rules. It was learning from the historical resolutions, such as which vendor usually sends partial invoices and which banks post delayed fees. There are many use cases across industry, but these are two which I was part of the implementation team.
Co Founder at a media company with 10,001+ employees
Real User
Top 20
Dec 23, 2025
The primary use cases predominantly involve banking, including check validation, fraud management, and anti-money laundering. We extract these from the core banking system and pull them into the agent, which then provides a response that we return to the system. I predominantly try to integrate Automation Anywhere AI Agent with ServiceNow tools and Dynatrace for data tracing. However, the data is not moving correctly onto the integration platform.
Automation Anywhere AI Agent is a robust automation tool designed to enhance efficiency and streamline processes through intelligent automation capabilities, making it a reliable choice for enterprises seeking to leverage AI-driven technologies for superior operational outcomes.As an intelligent agent, Automation Anywhere AI Agent offers businesses a sophisticated platform for automating complex workflows with ease. Its advanced features allow organizations to achieve scale and precision in...
My main use case for Automation Anywhere AI Agent is to extract data from different invoices such as bills of lading, document automation, OCR extraction, and speech-to-text. In a recent project, a client sends me documents via their respective email, and I use document automation to extract invoice details such as invoice date, vendor name, amount, currency, PO number, and delivery note. After extracting the data, I receive the values in Excel format. Mostly, I use Automation Anywhere AI Agent for document automation where there is a GenAI prompt to extract both unstructured and structured data in a structured format. The GenAI prompt helps me extract only the relevant fields, not other fields.
I am currently in POC mode implementing one of Automation Anywhere AI Agent solutions for a customer in the insurance industry. My customer wants to build Automation Anywhere AI Agent for making underwriting decisions. We are building the LLM for the underwriter team who gives the final confirmation on insurance policy processing. The underwriters review all the rules being run and if all the rules are passing properly through all stages, then the system gives the decision. This entire flow is being built with Automation Anywhere AI Agent.
The main use case for Automation Anywhere AI Agent in the organizations I work with is bank reconciliation and PO invoicing. The use cases depend on the segment. In one of the use cases, bank reconciliation was implemented, along with awarding POs or creating invoicing and raising it based on the PO that they used to receive from the customer. All of that documentation or work which was bulk in number and very repetitive for the organization normally fell into banking or manufacturing. These were two of the use cases which I have promoted or implemented at multiple places. For PO invoicing in manufacturing or banking, Automation Anywhere AI Agent was ingesting all invoices from email extracts and reading the line item data using the IQ bot. It was validating this data against the PO and GRN data from the SAP in the organization. It was autonomously resolving common mismatches, such as price variance within the tolerance or missing PO reference, by querying ERP and vendor master data. Exceptions such as quantity mismatch were routed through the automation bot with a recommended action. With respect to bank reconciliation, the agent compared the core banking transactions with the statements from multiple partner banks. It used artificial intelligence to auto-match transactions with date, amount, variations, and narration. The unmatched items were categorized and posted automatically where rules allow. I worked with a customer called SNS Group, which was my customer. I onboarded them on Salesforce. In both PO invoicing and bank reconciliation, the agent was limited to data capture. It applied policy-based reasoning to manage the tolerance limit and vendor SLAs, aging rules. It was learning from the historical resolutions, such as which vendor usually sends partial invoices and which banks post delayed fees. There are many use cases across industry, but these are two which I was part of the implementation team.
The primary use cases predominantly involve banking, including check validation, fraud management, and anti-money laundering. We extract these from the core banking system and pull them into the agent, which then provides a response that we return to the system. I predominantly try to integrate Automation Anywhere AI Agent with ServiceNow tools and Dynatrace for data tracing. However, the data is not moving correctly onto the integration platform.