How to Vet an Enterprise AI Implementation Partner in 2026

Body: We all know the reality of the AI boom right now: building a cool wrapper or a proof-of-concept is easy. Getting a machine learning model to deliver actual business value in a production environment? That’s where projects die. Failure usually stems from poor data readiness, lack of governance, or partnering with an agency that understands algorithms but doesn't understand architecture or business logic. If your team is looking to bring in an external AI implementation partner—especially in booming tech hubs like Ahmedabad—here are 7 technical and strategic steps to vet them. Evaluate Business Logic Over Raw ML Skills The biggest reason AI projects fail is a lack of business alignment. A strong partner translates business bottlenecks into data problems. If they don't care about your ERP setup or your inventory cycles, they aren't the right fit. Check Their Production Experience Look for case studies where models actually hit production. Ask them about their MLOps stack. Have they