Real-time Analytics · Sales Automation
Steelcase needed a better way to support AI-driven pricing recommendations and approval automation because manual pricing submissions across tens of thousands of deal scenarios were too slow and inconsistent to support high-volume commercial selling.
Reported impact
Steelcase improved AI-driven pricing recommendations and approval automation with measurable results including 1.5K+ and 50% sales hours unlocked to identify more opportunities, while giving teams a faster path from governed data to production decisions.
Steelcase uses Databricks Data + AI Platform to support AI-driven pricing recommendations and approval automation. The implementation replaced a legacy state where manual pricing submissions across tens of thousands of deal scenarios were too slow and inconsistent to support high-volume commercial selling. Pricing submissions and product and discount data move through production data pipelines and land in Delta Lake and Feature Store, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. MLflow, Agent Bricks, and Serverless compute operationalize models, agents, and analytics that deliver real-time discount recommendations and automated approvals. Steelcase improved AI-driven pricing recommendations and approval automation with measurable results including 1.5K+ and 50% sales hours unlocked to identify more opportunities, while giving teams a faster path from governed data to production decisions.
Pricing submissions and product and discount data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver real-time discount recommendations and automated approvals.
Databricks provided the full-stack platform we needed, from feature engineering to model deployment to monitoring, allowing us to build an automated pricing solution unmatched in our industry.
Manual processes just couldn’t keep up with the pace of business or the scale of our data.
We needed a smarter, faster way to respond to complex pricing scenarios across thousands of deals,
Working in Databricks made it possible to handle massive data with speed and collaboration. The feature store was a game changer, allowing the model to respond in seconds, even with complex, multisource features,
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Traceability
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