GoKawiilOpinions expressed by Entrepreneur contributors are their own.
Listen to this post
Key Takeaways Implementing a new AI tool every time you make a slight update can actually yield a worse result, because after all the work that goes into the new model, the customer may not even see an improvement on their end.
A technically better model does not automatically make it a better business decision.
Founders often assume that every improvement in AI model accuracy deserves a production release. But when testing, deployment, monitoring and engineering labor are factored in, deploying a slightly better model can actually produce a worse business outcome.
Imagine your AI team has trained a new model that performs 0.2% better than the version currently serving customers. Naturally, the data scientists are pleased and the automated pipeline marks the candidate as superior, leading everyone to assume it should immediately replace the existing model. But that is when the real production work begins.
The candidate must pass rigorous security and integration tests before engineers can package it, deploy it into a test environment and validate its behavior. Furthermore, the team might need to run a shadow or canary release, update monitoring rules, document the changes and prepare a comprehensive rollback plan. By the time this new model finally reaches production, the company has spent significantly more than the original training cost, yet customers may never even notice the improvement.
This highlights one of the most expensive misunderstandings in applied artificial intelligence: A technically better model is not automatically a better business decision.
Accuracy and business value are not the same thing
Accuracy measures technical performance, whereas business value measures whether that performance actually improves an outcome your company cares about.
... continue reading