“AI will fix everything!”—said no one who’s actually tried running an AI project. According to MIT’s research, 95% of AI pilots end in failure. That means companies are pouring money into experiments that never get past the testing phase. But failure doesn’t have to be inevitable.
AI pilots flop because:
- Projects start with hype, not clear business outcomes
- Leaders delegate without buy-in, assuming AI will “just work”
- Data pipelines are messy, siloed, or incompatible
- Teams underestimate how hard it is to measure success
MIT’s findings stress that organizations need strong KPIs, leadership support, and iterative testing. AI isn’t plug-and-play; it’s experiment-and-refine.
I’ve seen how fragile data pipelines can be, and I know what happens when AI is bolted onto systems without planning. That’s why my approach is to help businesses start small, measure often, and scale only what works. My certifications in AI and cybersecurity just confirm that I stay current, but my experience makes sure the real-world fit works.
