It’s not the Pi—it’s how we use it.

More than 80% of corporate AI projects never make it out of the pilot phase or fail to deliver measurable value once deployed, according to RAND research. This failure rate is two times higher than ...

With half of all generative AI projects failing after the proof-of-concept stage, organisations are discovering that ...

Despite business leaders feeling 'AI-ready', the data tells a different story. Leaders must now take a pragmatic approach to ...

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?

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.

It is within this context that Madhusudan Nagaraja has been contributing independent advisory guidance as a member of the PMI Infinity Advisory Committee. PMI Infinity, launched in January 2024, is ...

The claim that “AI projects are failing” has become a familiar headline—and a valid one. But while the failure rate may be high, it’s not necessarily cause for alarm. In fact, understanding why these ...

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 ...

Enterprise applications are the lifeblood of modern business, driving operational efficiency, enabling smarter business decisions and reducing technical debt. Yet, many strategies continue to fall ...