95% of AI projects fail. Successful AI initiatives prioritize infrastructure, focused use cases, and integration capabilities. Major hurdles include poor system integration, insufficient skill sets, and challenges building in-house AI solutions. Organizations that successfully implement AI are far more likely to engage third-party AI providers. Many people are either enthusiastic about AI, oblivious to it, or skeptical of its promises. Current AI models can make major, even catastrophic, mistakes, which limits deployment in critical use cases. Practical business applications drive optimism about AI's potential. Focusing efforts on infrastructure and partnerships increases chances of deployment success.
When it comes to AI, most people fit in one of three camps: They are enthusiastic supporters of AI who believe it will rapidly transform everything, or they are still -- somehow -- oblivious to AI, or they are skeptics who find most AI promises are overblown and unrealistic, and that many AI solutions today are broken and unimpressive. Also: Gen AI disillusionment looms, according to Gartner's 2025 Hype Cycle report I tend to sit somewhere between an enthusiast and a skeptic.
I am extremely skeptical about most of the futuristic claims that tend to come from generative AI's biggest supporters, and I find the tendency of current AI models to make major (even catastrophic) mistakes a reason to pull back on the use of AI in most critical use cases. On the other hand, I am extremely excited about AI's potential, especially when it comes to many of the practical use cases we are seeing businesses focus on in our research.
This is why the recent study by MIT, which found that 95% of AI projects fail, was so interesting to me. My skeptical side exulted with an attitude of, "See, this is what happens when you fall for the hype."However, my enthusiast side saw how this study reinforces many of the trends Aberdeen research is showing for how businesses can successfully deploy AI.
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