AI Projects Are Failing at an Alarming Rate Enterprise AI adoption is accelerating. Budgets are growing. Boards expect measurable outcomes. Yet most AI initiatives fail...Read More The post Why 70% of ...

Five-minute evaluation tool helps enterprise teams benchmark data foundations, governance maturity, infrastructure ...

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

Why does traditional training fail tech teams? It's jarring to know that 78% of organizations abandon projects partway through because they didn't have employees with the necessary IT skills. Today, ...

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.

Your project is on schedule, until legal reviews take way longer than anticipated. You find out—too late—this exact situation happened with another a project a few years ago. Sound familiar?

American enterprises spent an estimated $40 billion on artificial intelligence systems in 2024, according to MIT research. Yet the same study found that 95% of companies are seeing zero measurable ...

This week, an exercise in separating truth from hype. I am old enough to remember when generative AI (genAI) was the best thing since sliced bread — destined to solve any and all problems. But CIO.com ...

Community driven content discussing all aspects of software development from DevOps to design patterns. Agile software development is one of the most proven approaches to building software and ...