Make AI Projects Great Again

Artificial intelligence promises a lot - from breakthroughs in medicine to optimizing on-time pizza delivery. Sounds amazing, doesn’t it? Unfortunately, reality often tarnishes this perfect picture. Many AI projects end in disappointment, transforming budgets into expensive failures instead of transforming the world. A recent study conducted by the RAND Corporation sheds light on why this happens and offers insights into how to avoid turning AI projects into "everything and nothing." The study analyzed the reasons for AI project failures, interviewing over 65 experienced professionals and identifying five key problems. With over 80% of such projects failing, the report provides recommendations to help avoid pitfalls and implement AI effectively, especially in the government and private sectors.

Let’s start with a fundamental issue - not understanding why AI is being used in the first place. Teams often dive in with enthusiasm but lack a clear definition of the problem they’re trying to solve. The result? Solutions that may look impressive but aren’t necessarily effective. And speaking of problems, we can’t forget about data. AI thrives on data - lots of clean, well-prepared data. Yet, in most projects, data is like something lost in a swamp: messy, incomplete, and full of ambiguities. To paraphrase a classic saying, you can’t craft a golden statue out of garbage.

Another issue is blind devotion to technology. Many companies adopt AI not because they truly need it, but because it’s "what everyone is doing." After all, if the competitor down the road has AI, we must have it too, right? The problem is that doing something just because it’s trendy rarely ends well. Even when something sensible is designed, there’s often a lack of infrastructure to support AI systems effectively. Implementing solutions without a solid foundation is like building a house on sand - it looks great until it starts to crumble. It’s a pure case of FOMO (Fear of Missing Out), which often leads to spontaneous, poorly thought-out decisions.

Let’s not forget one of my favorite mistakes - overestimating what AI can do. Artificial intelligence is powerful, but it won’t solve every problem in the world. Trying to apply it where it simply doesn’t fit is a surefire way to doom a project. AI is a tool, not a magic wand… at least not yet.

So, how do we avoid these missteps? First, start by defining what you want to achieve. It sounds simple, but a lack of clarity is what derails most projects. Second, invest in data. Really, without it, AI is like a Ferrari without fuel - beautiful but useless. Also, remember that trends may work in fashion, but not necessarily in technology. AI should solve real problems, not just look good in an annual report.

Infrastructure is another crucial factor. Without it, even the best algorithm will perform like a seven-year-old phone - sometimes it works, sometimes it doesn’t, and when it does, it’s painfully slow. Finally, it’s important to have realistic expectations. AI isn’t a universal solution, and sometimes, a simpler approach, like process optimization - or something less trendy these days, like investing in human capabilities - might be a better option.

Without a doubt, artificial intelligence has immense potential, but only if we approach it thoughtfully. The RAND report reminds us that AI isn’t magic but a tool that requires planning, effort, and - brace yourself - common sense. If we put in the work, we might achieve something more than just spectacular large-scale failures. Let’s also remember the importance of human-centered action, which is often pushed aside. After all, even if robots controlled by autonomous AI sew shoes perfectly someday, there’s nothing more beautiful in human creations than those small imperfections, the little details that give soul to our work and redefine what it means to be human.