AI in the balance: why 80% of projects fail and burn billions | Generative ai use cases in healthcare examples | Generative ai certification microsoft azure | Generative ai in finance pdf | Turtles AI

Most AI projects fail, generating billions in losses. The main reason is the lack of understanding of the real problems to be solved. A recent study by RAND Corporation highlights five reasons for failure and proposes seven remedies to mitigate the risks.

Key Points:

• 80% of AI projects fail, causing huge financial losses.
• The lack of clear identification of the problems to be solved is the main factor of failure.
• Inadequate data and insufficient infrastructure compromise the effectiveness of AI models.
• RAND Corporation proposes investments in infrastructure and greater awareness of the limits of AI as possible solutions.

In the technological world, AI is often considered one of the most promising and, at the same time, risky innovations. Despite the huge amount of capital invested, with companies like OpenAI predicting billion-dollar losses in the next year, new research highlights how the majority of AI-related projects fail, causing huge financial losses. A study conducted by the RAND Corporation, a non-profit think tank, revealed that approximately 80% of AI projects fail to achieve the desired results, with a number of very specific reasons behind these failures.

The main problem that emerges from the study is the lack of understanding on the part of stakeholders of the real problems that AI should solve. Often, in fact, company founders focus more on demonstrating the technological power of their solutions rather than on the actual value that these technologies could offer to end users. This approach, in addition to diverting attention from the primary objective, can lead to massive investments which, however, fail to translate into useful and functional products.

Another critical factor is the quality of the data used to train AI models. The lack of adequate data or the presence of distorted data can compromise the effectiveness of the model, leading to inaccurate results and consequent disinterest on the part of users. The availability of high-quality data is therefore fundamental to the success of an AI project, but companies often fail to guarantee it, thus reducing the chances of success of their product.

In addition to these problems, the inadequacy of technological infrastructure represents a further obstacle. Insufficient infrastructure can significantly slow down the process of training AI models, increasing the cost and time required to develop effective solutions. Attention to infrastructure is therefore crucial, not only to speed up development times, but also to ensure that models are trained on higher quality data.

Finally, the RAND Corporation has suggested a number of remedies to mitigate the risk of failure in AI projects. Among these, the importance of investing in adequate technological infrastructures and the need for greater awareness of the intrinsic limits of AI. Despite the progress achieved, AI is not an infallible solution and its limits must be well understood by those who develop it. Understanding these aspects can make the difference between the success and failure of a project.

This analysis offers a critical overview of an ever-evolving industry, where mistakes can be costly. With the right attention and strategy, however, it is possible to avoid failures and make the most of the potential offered by AI.