When companies focus on practical, user-centered implementation, AI can stop being an experiment and start having a real impact.

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.

When a software project stumbles—or ultimately fails—it can have a range of negative consequences, from lost and unrecoverable resources to a blow to team morale. It can be tempting to blame a failed ...