How to build an AI that predicts ERP integration failures.

Everyone who works with integrations has faced situations where integration can break for different reasons, and the system or business analyst can spend hours chasing failures after they happen.

But what if we can predict integration failures before they occur?

Just as NVIDIA does in its NVIDIA Omniverse platform (https://www.linkedin.com/showcase/nvidia-omniverse/), where AI simulates robot collisions and system bottlenecks before real-world deployment, you can try those predictive steps in the ERP integration processes.

1. So, first of all, you can try to collect integration logs to the CSV file over the past few months. For example, I extracted the following:

• ERP type (NetSuite, Sage Intacct, etc.)
• API endpoints (/invoices, /payments, /customers, etc)
• Payload size
• Number of fields
• Response time
• HTTP status code
• Outcome (1 = success, 0 = failure)

These fields gave the model context on what happens under the hood of each request.

I attached the example of the CSV table.

1. The second step, you train an XGBoost classifier.

Using Google Colab (https://colab.research.google.com/), I trained the model on ~1000 rows of logs.
The model learned patterns like:

Payloads >500 KB + more than 3 custom fields + /invoice = 78% failure risk.

1. The third step, I tested new requests.

Before hitting the ERP API, I request metadata through the model. If the predicted risk is high, I pause, flag it, or reroute it.

Finally, you will receive:

a) Caught high-risk failures before they happened;
b) Reduced fire-fighting in production;
c) Gave business visibility into fragile integration conditions;
d) Boosted confidence during testing;