Data to action: activating Snowflake Intelligence with UiPath agentic automation

Organizations today are overwhelmed by data proliferation with customer data, operational data, and third-party market signals scattered across countless systems and tools. The result is siloed insights and fragmented actions across disconnected business systems.

Together, Snowflake and UiPath are democratizing agentic AI-driven decision making. Snowflake provides a unified data foundation to centralize and enrich enterprise and external intelligence. The UiPath Platform™ for agentic automation then activates that intelligence, turning insights in Snowflake into real-time, end-to-end business actions across the enterprise.

The integration of UiPath agentic automation with Snowflake Intelligence represents a major step forward in how organizations turn data into action. Together, we’re helping customers unlock governed, AI-driven insights and immediately operationalize them through automation—closing the gap between intelligence and execution across the enterprise.

Raghu Malpani, CTO, UiPath

With Snowflake Intelligence, we offer an always-on enterprise intelligence agent that drives smarter, faster decisions. Yet intelligence alone is only as powerful as the actions it enables. That’s why our collaboration with UiPath is so meaningful. Together, we’re eliminating the boundaries between data-driven insight and enterprise-wide execution.”

Baris Gultekin, VP of AI, Snowflake

With this collaboration, organizations can meet business objectives such as:

• End-to-end agentic automation: from natural language insights to automated process execution

• Operational agility: respond to risks, opportunities, and market shifts in real time

• Enterprise governance: maintain compliance and lineage from data to action

• Productivity gains: reduce manual intervention and human bottlenecks

These objectives can be leveraged across industry scenarios such as:

• Revenue reconciliation: merge financial ledgers, payment gateway data, and e-commerce transactions to detect discrepancies and automate reconciliation and reporting workflows.

• Demand forecasting and replenishment: combine point-of-sale (POS) data, inventory systems, and external signals (weather, holidays, promotions) in Snowflake to predict demand surges and trigger automated restocking through UiPath Platform.

• Supplier risk management: combine vendor performance metrics, shipment data, and external risk indices to detect potential disruptions and automatically reroute orders or alert buyers.

• Provider network optimization: combine enterprise provider metrics with production, preparation, and process (3P) quality and benchmark data to identify underperforming providers and automate credentialing or performance reviews.

Let’s look at an intelligent credit risk management and remediation use case. Consider a large financial institution that wants to monitor credit portfolio exposure in real time and respond proactively to emerging risk mitigation.

The organization has enterprise data in Snowflake: loan books, payment performance, collateral data, transaction histories, and credit models. ​​Additionally, with their third-party data in Snowflake Intelligence it can now access:

• Real-time credit bureau updates

• Macroeconomic data (e.g., interest rate movement, FX rates)

• News and social sentiment data on corporate browsers

To get to the outcome, the customer will:

1. Build data intelligence

Snowflake Cortex AI analyzes both enterprise and external datasets to identify credit exposures at risk. For example, flag accounts with declining collateral value or negative borrower sentiment in the last 30 days. The Cortex Agent then synthesizes structured loan data with unstructured text (market news, analyst notes) to provide risk scoring and context.

2. Activate agents

Using a UiPath Conversational Agent (accessible through Microsoft Teams or a web app) the risk officers can ask “Which corporate clients show increasing risk based on market sentiment and missed payments?”. The UiPath Agent queries Snowflake via the Cortex API, surfaces the insights, and offers next-best actions such as: notify the relationship manager, trigger a revaluation of collateral, and escalate to risk governance committee. This insight can also be designed to be proactive as opposed to a trigger.

3. Agent orchestration-driven process automation

UiPath Maestro™ will then enable orchestrating the end-to-end credit risk and remediation process, coordinating seamlessly across AI agents, robots and subject matter experts to-update the bank’s CRM and risk systems (e.g., Salesforce Financial Services Cloud, Moody’s Risk Confidence), automatically generate a risk case in ServiceNow or an internal workflow, schedule review meetings, and send alerts with attached data visualizations from Snowflake.

4. Set up a continuous feedback loop

The actions and outcomes (e.g., credit adjustments, repayment updates) can be logged back into the customer’s Snowflake data lake, continuously enriching the dataset that Snowflake Cortex AI uses for model retraining and future detection.

As a ​recommended ​​​Snowflake Intelligence partner, UiPath brings agentic automation to the very moment insight happens—empowering leaders to make faster, smarter, and more auditable decisions.

This partnership marks a turning point: enterprises can now connect their most trusted data to their most powerful agentic automation platform. The result is a business that thinks and acts at the same time—continuously, intelligently—and at scale.

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