Why Generative AI Projects Fail — and What’s Behind It

At IGF, we follow closely the news about AI and its use. Not a long time ago, Gen AI was considered something unreal and unusual that would change the world. It gave hope that it would change not only everyday life, but also industries. But, as we see nowadays, more and more AI projects just failed and didn’t reach anything. Just like a bubble, the AI hype burst. At IGF, we work with startups and investors who turn ambitious AI ideas into real products, and we’ve seen what makes the difference between success and failure. So, Today, we’re going to cover the topic of AI and AI projects and why they fail.

The statistics show that only 16% of AI initiatives ever scale across an organization, while two-thirds of leaders struggle to move their pilots to production. You might wonder why this happens. The answer is simple, and here are some of them:

• Short-term validation goals — pilots built to impress, not to scale.
• Unclear objectives — teams are unsure what “success” actually looks like.
• Data unreadiness — incomplete, biased, or inaccessible data.
• Lack of in-house expertise — no one to translate tech into value.
• Inflated expectations — hoping AI will fix what strategy hasn’t.

When creating prototypes, teams understand that it doesn’t mean that the final product will be the same. Also, the productivity of the product can vary. So, the prototype and reality often don’t equal. Nowadays, AI tools can make you confident, or at least make you feel confident. However, measurable outcomes are the things that indicate success. Without them, those gains are just theoretical. But the good news is that the problem is not in the technology, but in its structure.

The Shift Toward Smarter, Sustainable AI

Even though AI technology doesn’t seem promising, there is good news. Failure isn’t the end of the story, as there’s a new phase of AI innovation. And it’s an agentic system that acts anonymously. What’s that, you might ask? Let’s take a closer look at it! So, an agentic system has already changed the way businesses work. It works with massive datasets, analyzes them. More importantly, these systems act like humans, making real-time decisions. These systems don’t need a lot of human input, turning insights into action.

Steady and responsive AI technology is not just about good algorithms. It requires leadership and strategic vision that will lead the way. Of course, a robust data infrastructure is needed, as it lets technology scale and collaborate safely. These steps will allow technology to scale and reach the required performance. This is what makes even the most advanced technology valuable.

At IGF, we help startups and investors build AI projects that last. Our focus is on transforming ambitious concepts into viable solutions — from defining use cases and data strategies to guiding product growth.