Video · May 19, 2026
Demo: SensibleAI Forecast Express
About this video
SensibleAI Forecast Express gives Finance power users a self-service forecasting capability that connects directly to their OneStream cube, eliminating the need for custom data pipelines or technical intermediaries. Working from within a familiar cube view environment, users can configure data sources, select dimensions, pull up to seven years of historical data, and generate forecasts that write results back to the cube automatically as predictions complete. What sets Forecast Express apart is not just the speed of setup, but the depth of explainability built into every output. Feature contribution charts and periodic explanation views show exactly which drivers increased or decreased each forecast value, giving Finance teams the transparency needed to present AI-generated forecasts with confidence.
Key takeaways
- Forecast Express removes the technical barrier between Finance users and AI-powered forecasting. By sourcing data from and writing results directly back to the OneStream cube, power users can configure, run, and access forecasts entirely within their existing workflow. No custom data pipelines, no IT dependency, and no context switching required.
- More historical data directly improves model performance. The ability to pull up to seven years of actuals gives the model the breadth needed to identify seasonal patterns, cyclical trends, and long-run drivers that shorter time horizons would miss. For Finance teams preparing revenue forecasts across multiple entities and product lines, this depth translates into more reliable and defensible outputs.
- Built-in explainability makes AI forecasts auditable and actionable. The tug of war chart and periodic explanations page show feature contributions across the full forecast horizon, making it clear which drivers pushed a forecast value up or down in any given period. This transparency closes the gap between AI-generated insight and Finance-ready output, giving FP&A leads what they need to stand behind the numbers in front of leadership.
Video Transcript
Sensible AI Forecast Express. Let's assume I am a power user at Golfstream, a vertically integrated golf retailer. I'm preparing to kick off my revenue forecast for drivers in woods for our West Group entities.
It's January 2026, and I've just loaded my actuals for December 2025. Sensible AI Forecast allows me to source data from and write forecast results back to my cube without needing to write custom data pipelines.
Within my newly created project, I can configure my source data. Forecast Express allows me to select my cube and scenario and the time periods I want to pull my data from. Since more data generally means the better model performance, I'll pull in seven years of history.
I will select the rest of the members to pull data from, filtering results by pinning specific members, and selecting the actual line items I want to forecast for. With my dimensions selected, I can generate a preview of my data to ensure it's the data I want.
I can see the number of line items I'm forecasting for, and the amount of historical data I have for them. Once confirmed, I can submit. The new cube administration page allows me to configure the workflow responsible for loading my forecast results.
The workflow page displays the various workflows, transformation rules, and data sources in my application. Let's create some new ones for my project. I can see the existing data sources filtered by cube and scenario type.
I'll create a new one, providing a name, sensible AI forecast, and selecting my fin detail cube and forecast scenario type. Next, I will create a new transformation rule profile. Finally, for the workflow profile, I will provide a name, select my cube root workflow profile, and submit.
Any new sources will be created automatically based on my configurations, and my workflow profile is now ready to load my forecasting results. A streamlined output configuration allows sensible AI forecasts to write the results back to the cube as the prediction completes.
Now let's switch perspectives. As an end user, cube views are my home base, and my sensible AI forecast results are already waiting for me. It's never been easier to access my generated forecast insights.
Looking out a few months, let's drill down into the Phoenix Mach 10 forecast for April. Drilling back, I can load the prediction summary view from sensible AI forecast. With the full year's forecast in view, I'll hone in on April.
Below, the prediction explanation for my April forecast shows how the features impacted this result. I can view the tug of war chart to see the feature contributions across my forecast horizon, the features that increased or decreased my forecasted value.
The periodic explanations page shows an alternative view. I'll select a forecast start date and forecast name. For each feature group and the comprising features, I can see the contributions made by each across my forecast horizon.
Related Resources
Look into the future of Finance
Demo: Modern Financial Close
OneStream Developer Studio
