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Model Drift and The Day 3 Problem
‘My AI model worked great, until…’
For years, AI has promised to transform organizations. But organizational business leaders know that—time and again—most AI projects struggle to deliver any value. Many projects fail right from the beginning. Others start strong, but degrade after a few days or weeks in production.
The problem is model drift — and it’s the number one reason AI programs fail to produce value.
Fortunately, it doesn’t have to be this way. Remediation is proven to overcome model drift and keep AI projects performant longer. It’s the missing key to unlocking AI’s potential.
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Read our white paper for answers to your most pressing questions about keeping AI models reliable:
![White paper page preview](https://www.striveworks.com/hs-fs/hubfs/Landing%20Pages/Model%20Drift%20and%20the%20Day%203%20Problem%20LP/White%20paper%20page%20spread.webp?width=800&height=800&name=White%20paper%20page%20spread.webp)
- “What is model drift (and why does it happen)?”
- “Why do AI models struggle to stay reliable?”
- “How does ‘Remediation’ keep AI models effective?”
- “What can I do to remediate my models today?”
Plus, learn how one government contractor used remediation to restore its AI project to deliver 1.1 million accurate predictions—in six weeks.
![White paper page preview](https://www.striveworks.com/hs-fs/hubfs/Landing%20Pages/Model%20Drift%20and%20the%20Day%203%20Problem%20LP/White%20paper%20page%20spread.webp?width=800&height=800&name=White%20paper%20page%20spread.webp)