Most enterprise IT projects fail, and it has been this way for decades, and this discussion with Roey Mechrez of BeyondMinds considers why this is the case. One of the primary reasons is the trade-off between building custom solutions and buying off the shelf products. This is doubly different with AI since the success of a model depends on the data and training, not to mention the maintenance and updates needed as issues arise. Data science teams need to invest significant time and model into infrastructure, rather than just jumping in to train the model. This is a similar challenge to DevOps, but the added dimension of models and data makes MLOps even more challenging.