Is it possible to learn from our mistakes? I would like to believe so, but history seems to show that we keep repeating the same mistakes. Not long ago, we experienced the rise of “Dotcoms”, a phenomenon where numerous companies rushed to create web portals and offer online services without a clear vision, motivated by the fear of being left behind. Many of these projects failed spectacularly, and not because of technological deficiencies.
The cycle repeats. Every time a new technology with disruptive potential emerges, we fall prey to “Shiny Object Syndrome.” We allow ourselves to be dazzled by calls for innovation without reflecting on our true need or capacity to implement it. As in the past, failures are now abundant.
The harsh reality facing generative AI projects
This is the trap into which many entrepreneurs and companies have fallen, dazzled by the promises of generative artificial intelligence without understanding the true meaning of its implementation. Attracted by its attractiveness, without a solid strategy, they have initiated fragile projects that face difficulties due to lack of foundations and planning.
It is estimated that the percentage of artificial intelligence projects that fail is significantly higher than in other areas of information technology, reaching up to 80%. A recent RAND Corporation study identifies the main causes of these failures, which include poor project management and unrealistic expectations regarding AI capabilities. The prospects are not very encouraging either.
One of the latest Gartner studies reveals that at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025. Among the reasons are inadequate risk controls, increased costs and Lack of clarity on business value.
Learning from the past to ensure future success
The lesson that emerges from these failures, whether in the dotcom sector, cryptocurrencies, big data or, currently, in AI, is that technology alone does not ensure success. It is crucial that any generative artificial intelligence (GAI) initiative is supported by a well-defined strategy, clear objectives, and a deep understanding of both its capabilities and limitations.
It is estimated that the percentage of artificial intelligence (AI) projects that fail is significantly higher than in other areas of information technology, reaching up to 80%
This should also apply to the implementation of generative artificial intelligence in the processes of development companies. The technology promises to optimize software processes by shortening development times and reducing the need for large teams. However, it is essential to ask ourselves: Is it really contributing to improving productivity and quality or have we fallen into the “shiny object” trap?
As we have already mentioned, the implementation of technological innovations such as IAG in the business environment goes beyond an enthusiastic approach or the fear of being left behind, it will require a strategic approach and a set of clear steps that allow us to measure and ensure improvements. tangible.
One of the first and most important actions is to establish defined objectives. Companies must specify what problems they seek to solve and set goals that can be quantified, such as reducing development times or improving code quality.
Knowing our goals, it is also essential to understand the starting point through a preliminary analysis of the current process. Here, benchmarking becomes a key tool, both internally and externally. By comparing projects that have and have not used IAG, you can discern whether the improvements in performance and efficiency are real or only apparent.
Traditional metrics, such as delivery time or the amount of software generated, should be complemented by other indicators that evaluate the quality of development, its long-term sustainability, and the ability of AI to adapt to complex environments. To do this, having access to good data is essential. But is it really worth implementing a technology whose cost exceeds the benefit it offers? Examining the relationship between costs and benefits is crucial. It is also essential to also consider the expenses associated with the implementation, maintenance and training of the equipment.
The essence of success lies in the fusion of technology with a well-defined vision and constant and precise monitoring of results. Continuous monitoring of its impact allows adjustments to be made to processes and ensures that benefits are maintained or even increased over time. Companies that collaborate with clients to carry out this constant analysis and monitoring in order to verify whether the implementation of artificial intelligence is truly optimizing productivity and development quality. This allows us to evaluate whether the adoption of generative artificial intelligence represents a strategic decision that strengthens competitiveness, or if, on the contrary, it is a fad that does not justify its cost.
As was the case with dotcom companies, those organizations seeking to harness the true potential of generative artificial intelligence must be willing to make the necessary effort: careful planning, defining clear objectives, constant monitoring, adaptation and, above all, recognize that technology is just a tool. And it is only when this tool is properly integrated into processes that it truly not only shines but shines.