Run AI-Powered Sales and Revenue Forecasts Using Chat and Natural Language Queries (NLQs)
Learn how to generate custom, AI-powered sales and revenue forecasts by using natural language queries with data from Oracle Fusion Cloud Enterprise Performance Management, Oracle E-Business Suite, PeopleSoft, and JD Edwards.
To maintain revenue and margin growth while navigating fluctuations in supply and demand, companies need agile forecasting capabilities that they can run on-demand and that can be managed by both technical and non-technical users. These solutions must instantly respond to market changes, align critical resources, and make data-driven decisions.
While traditional ERP and EPM systems provide a solid foundation for most business operations, these applications often need enhancements to address industry-specific use cases. These enhancements involve cumbersome steps to move, cleanse, and transform data, requiring data scientists or other expert technical resources to build, run, and manage these forecasting processes.
By complementing these systems with an AI-powered forecasting solution that integrates with diverse data sources, organizations can:
- Empower authorized business users to run sales and revenue forecasts on demand
- Democratize data access for nontechnical users, while freeing up valuable IT resources for strategic priorities
- Eliminate the need for extensive machine learning (ML) knowledge to generate forecasts, and use generative AI to interpret results
- Speed time-to-market with a simplified process that can generate in-place forecasts
Oracle Cloud Infrastructure (OCI) provides a highly-integrated set of services, including Oracle Autonomous Database (ADB), Oracle Cloud Infrastructure Data Integration, Oracle Cloud Infrastructure Data Science, and Oracle Cloud Infrastructure Generative AI.
You can seamlessly connected these services with ERP and EPM applications to build powerful forecasting tools.
OCI Generative AI provides a pluggable and adaptable forecasting framework that enables business users to easily run natural language queries (NLQs) to initiate custom forecasting jobs, analyze data, and produce business-critical insights.
In addition, advanced technical users can create custom models, which can be made available to non-technical business users to leverage in their forecasting tasks.
The architecture presented here illustrates a best-practice deployment pattern for running an AI-powered sales and revenue forecasting solution on OCI. While many OCI services shown can be replaced by open-source components, leveraging OCI services offers significant advantages.
Chief among these advantages is the ability to simplify code and ensure smooth, seamless deployments because of deep integrations between the listed services. For example, you can connect Data Science and Autonomous Data Warehouse with a single line of code, and OCI Data Integration provides built-in connectors for a large number of sources and targets so that no custom code is needed.