040 - Why Most AI Projects Fail (And How to Get Them Right)

In this episode of the Stacked Data Podcast, Harry sits down with Adam Sorka  from Hyper Cubed to tackle one of the biggest gaps in the industry right now:Why so many AI projects never make it past experimentation — and what it actually takes to deliver real value.Adam has built a reputation as a pragmatic (and often sceptical) voice in the AI space. In this conversation, he breaks down what’s really driving the current wave of AI adoption — and why much of it is still fuelled by hype, not outcomes.They explore how to properly identify and validate high-value AI use cases before writing a single line of code, what “AI readiness” actually means beyond buzzwords, and how to think about testing, governance, and risk in production systems.A big theme throughout is the role of humans in the loop — why removing them too early creates more problems than it solves, and how the best teams design AI systems that augment, rather than replace, decision-making.Finally, Adam shares how to measure real impact and what it takes to scale beyond a single successful use case — turning AI from a side experiment into a meaningful business capability.If you’re a data leader or practitioner trying to cut through the noise and build AI that actually delivers, this episode is packed with practical frameworks and hard-earned lessons.