PHILADELPHIA, PA — Qlik® announced rapid adoption of its Qlik Predict solution, as enterprises increasingly embrace real-time, explainable forecasting to drive faster, more confident decision-making across industries. Built for enterprise-grade governance and scalability, Qlik Predict enables organizations to operationalize machine learning through a no-code interface accessible to business users across all functions.
The platform allows predictive models to be embedded directly into frontline workflows—helping companies forecast demand, anticipate customer churn, or improve patient outcomes without requiring advanced data science expertise.
“Executives are racing to turn AI from promise into performance where decisions happen hour by hour,” said Brendan Grady, Executive Vice President and General Manager of Qlik’s Analytics Business Unit. “Qlik Predict embeds trusted forecasts directly in workflows, so frontline teams build and use models through a no-code experience while IT retains governance and auditability. Customers are converting pilots into production outcomes at scale with faster decisions, clearer accountability, and measurable impact.”
Enterprises across industries are already realizing tangible results. SLA Software Logistik Artland reduced production deviations to 1%, while Steinemann, a leader in the meat processing industry, improved production forecast accuracy above 90%. Logistics Plus cut shipment errors from 20% to under 1%, and Village Roadshow, one of Australia’s top entertainment companies, uses Qlik Predict to optimize staffing, costs, and customer experience.
Powered by Qlik Cloud®, the solution integrates seamlessly with existing enterprise data sources. Key features include intuitive, code-free modeling; explainable AI through SHAP-based visualizations; and native workflow integration with alerts and scenario modeling capabilities—all designed to ensure AI-driven insights are actionable and accountable at the point of decision.
With adoption accelerating globally, Qlik Predict is positioning itself as a critical tool for organizations seeking to move beyond AI experimentation to measurable business performance at scale.
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