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 ...

Development refers to a deliberate process of economic, social, political, and institutional transformation aimed at ...

California might have to call 911 to save its emergency communications system upgrade project. Whether the call would actually get through is another matter. While the Golden State prides itself on ...

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 ...