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, Head of Analytics, Data & Engineering at MediaLab, 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.