Our team designs and builds AI-driven forecasting components directly into Workday Adaptive Planning — variance explainers, signal detection, and ML-driven planning intelligence that finance teams can actually use.
“AI in FP&A only creates value when it is architecturally integrated and explainable to finance users. We do not bolt AI onto existing models as a demo feature — we design it into the planning architecture from the start.”
Artificial intelligence in enterprise planning is only valuable when the outputs are explainable to the finance team making decisions. Our architects design AI components that surface variance drivers, flag forecast anomalies, and improve model accuracy — in language finance users understand.
We work with the Workday AI stack natively, integrating machine learning capabilities directly into Workday Adaptive Planning models. This means AI insights appear where finance already works — not in a separate tool that requires a data science team to maintain.
Variance explainers are one of the highest-value AI components we build. When actuals deviate from forecast, the system surfaces the primary drivers automatically — saving FP&A teams hours of manual analysis each close cycle.
Our team has built ML-assisted forecasting models across revenue, workforce, and operating expense planning. Each component is designed to improve over time as actuals data accumulates, and each is documented so your finance team understands how the model works — and can challenge it when needed.
Ready to discuss your planning architecture?
Talk to our team →Our architects will assess your current environment and give you a clear, honest view of what needs to change.