"If we're waking up engineers at 3 am, we need to be confident that we're not reporting on false positives."
— Erin
Fortune 500 Retail
Retailers rely on AI systems for demand forecasting and inventory optimization, aiming to minimize waste, boost sales, and enhance customer satisfaction. Monitoring allows retailers to detect and fix data quality issues, such as drift or bias, before it leads to inaccurate predictions and significant risks to the business.
AI is the backbone of most retailers' personalization and recommendation systems, which are essential for delivering outstanding customer experiences. But when these AI models suffer performance degradation or bias, it can result in large-scale customer disappointment.
Retailers can leverage AI to streamline their supply chain operations, resulting in cost savings and faster delivery times. By monitoring AI models in real-time, they can proactively identify and address performance issues before they impact demand forecasting or transportation planning.
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As retailers increasingly rely on AI to support exceptional customer experiences, demand forecasting, and inventory management, there is a growing risk of bad data leading to inaccurate predictions or recommendations. To ensure the reliability and accuracy of AI-powered systems, companies need to have visibility into the health of their AI models and the quality of the data used to train and update them.
Retailers can ensure that they are providing the best possible customer experience, optimizing their inventory levels, reducing stockouts, and improving demand forecasting, by monitoring the performance of ML models over time. WhyLabs allows organizations to assess the health of this data in a privacy-preserving and cost-effective way without moving or duplicating the data. Every stakeholder, from data scientists to product managers and executives, has dashboards for transparency into the health of each AI application powering the business.
— Erin
Fortune 500 Retail