What's in this article?
Deploying an AI model isn’t just about setting it up and walking away. It’s about ensuring it continues to perform well and solve the problem you set out to address. This is especially important for tasks like lead assignment where even a small change can have a big impact.
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AI Model Monitoring
Once a model is live, it’s easy to assume it will work perfectly forever. But that’s not the case. You must regularly check to see how well your model is doing. If you’re using predictive values to assign leads, for example, you need to make sure those assignments are helping your team.
Ask yourself:
- Are leads being distributed evenly?
- Is there an improvement in conversion rates?
These are critical metrics you can only evaluate through monitoring AI models, especially in the first few weeks. Ignoring this step can lead to missed opportunities and lower efficiency. For example, if certain leads aren’t being followed up on or conversion rates aren’t improving, your model may need some fine-tuning. Early AI model monitoring allows you to make minor adjustments before they become major issues.
Adjust and Update as Needed
Let’s say you notice that some team members are getting too many leads while others are getting too few. This imbalance can affect overall AI model performance. Keep a close eye on your model to catch these issues early and adjust your lead assignment strategy.
Maybe the model needs a tweak, or your strategy needs to be realigned. The key is to stay flexible and responsive.
Monitoring AI models isn’t just about finding problems; it’s about spotting successes. If certain adjustments improve conversions or customer engagement, replicate those strategies in other areas. This way, you can continually improve and build on your success.
The First Two Weeks Are Crucial
When you first deploy a model, pay extra attention during the initial days. This is when you can spot any immediate issues and make quick adjustments. Daily checks are recommended in the first two weeks to ensure everything runs smoothly.
Determine whether the model effectively solves the problem it was designed for. If you see results trending in the wrong direction, it’s much easier to make quick fixes during this initial phase.
As time goes on and the model proves to be stable, you can move to less frequent monitoring, like weekly or monthly checks. But don’t assume that everything will keep running smoothly forever. Regular reviews are essential to make sure the model continues to perform as expected, especially when new data or variables are introduced into the system.
Regular AI Model Updates Keep Models Accurate
Most AI models benefit from periodic updates. Monthly updates are recommended. As new data flows in, the model must learn from it to stay effective. This helps it adapt to changes in your data patterns and business needs. For example, if you have a spike in leads from a new marketing campaign, an updated model can better handle this influx and keep assignments fair and efficient.
You may update the model more frequently if you see significant shifts in your data or market conditions. Regular AI model updates ensure that your model is always operating with the most current and relevant information. This is particularly useful for sales and marketing teams that rely on up-to-date insights to make quick decisions.
Read More: Why AI Automation is the Game-Changer in Sales Dynamics
Keep an Eye on Long-Term Impact
After the initial monitoring phase, you should check your AI model performance periodically. This helps ensure that it’s still delivering the value you expect. Over time, your business goals or data might change, and your model should be able to adapt accordingly. Regular updates and monitoring will help you make the most of your AI model, keeping your lead assignments and other processes running smoothly.
Regularly check and update your models to ensure they continue to perform well. They will help your team achieve their goals and make the most out of your AI investment.