Roughly 80% of AI projects fail to get into production. This is not due to flaws with the model. The reason is that most firms underestimate just how different a production-level AI system is from a proof of concept. Here are five indicators that the gap is closing on your firm’s roadmap.
1. Your AI prototype has been “almost ready” for two quarters
A working prototype is not the same as a production system. The difference between a good working prototype, which demonstrates good results on a toy dataset in Jupyter Notebook, and production-grade model, which is re-trained and continuously monitored – this is the stage where most internal AI projects fail. If your prototype has been on the verge of deployment for many quarters, then the problem is usually MLOps and deployment practices, not the model itself.
2. You are hiring generalist engineers to do specialist AI work
A good backend engineer doesn’t always mean a good machine learning engineer and vice versa. AI projects require knowledge of data engineering, machine learning models development, and MLOps. These are 3 different skill sets and usually not found together in one generalist hire.
3. There is nobody who tests for bias and accuracy before launch
Your team is able to explain how the model was trained, but unable to explain how the bias, edge case and accuracy was tested before actual users saw the results – this is a serious launch risk. And it is most likely to appear when you least expect it – in front of a customer or regulator.
4. You lack a retraining/monitoring strategy
An initially effective model will not be so forever; the data will change, user behavior will shift, and there will be more edge cases. The absence of the retraining and monitoring pipeline leads to the gradual decline of performance until the issue is recognized.
5. New requests for any AI-related functionality take months, not weeks
If the normal product features take two sprints but are related to AI features – two quarters, then the reason is likely to be not the complexity of the concept, but the availability of dedicated specialists. It is the clear sign that the roadmap is exceeding the capabilities of the team assigned to implement it.
The solution that a dedicated AI engineering team offers
The dedicated AI engineering team covers all aspects: data engineering, machine learning, and MLOps so that a project does not get stuck when moving from prototype to deployment phase. It should encompass the pipeline, monitoring, testing for bias and accuracy, and a retraining schedule integrated into the engagement process right from the beginning, and not added on as an afterthought after launch.
And if two or three of these signs sound all too familiar, the solution will certainly not be to “work harder with the current team.” Rather, it would be to bring in precisely the right kind of skill set that the roadmap itself calls for.
