Why AI Projects Fail and How to Fix Them
AI is everywhere, yet most AI initiatives never deliver actual ROI. In this episode, Oliver King-Smith breaks down why so many AI projects fail and what companies can do differently. Listen to a grounded conversation about AI adoption challenges and resetting unsuccessful projects without losing progress.
Despite massive investment and attention, AI adoption remains painfully difficult. Industry research suggests that the vast majority of AI projects fail to reach production or deliver value. AI implementation challenges are rarely about the technology itself. They more often stem from unrealistic expectations, weak data foundations, and organizational blind spots. This episode explores how to spot failure early, how to fix AI projects before they collapse, and how in general get started with AI.
About the Speaker:
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Oliver King-Smith
Oliver King-Smith is a technical leader with experience delivering software and hardware projects focused on AI, machine learning, and IoT. He currently works on Data-Efficient Reinforcement Learning algorithms. With a background in mathematics, computer science, and AI, Oliver builds and leads technical teams and applies AI to solve control problems.
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Why listen?
- Understand why AI projects fail (and why the causes are rarely technical)
- Learn the most common mistakes that undermine processes from day one and lead to AI project failure
- Discover what “data readiness” actually means beyond buzzwords
- Gain practical advice on best practices in AI and moving from AI pilots to production systems
- Explore when private, secure AI ecosystems are essential
- Get a realistic framework for starting your first AI project without wasting months
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