Even as we emerge from generative AI’s tire-kicking phase, it’s still true that many (most?) enterprise artificial intelligence and machine learning projects will derail before delivering real value.

The transition from experimental AI models to production systems exposes the true quality of training data. Edge cases that were absent during testing become frequent. Small inconsistencies in ...

Companies hate to admit it, but the road to production-level AI deployment is littered with proof of concepts (PoCs) that go nowhere, or failed projects that never deliver on their goals. In certain ...

Somewhere in your organization, an AI project is dying. Perhaps it's the recommendation engine that was supposed to boost sales by 30%. Maybe it's the predictive maintenance system that promised to ...