The world has entered a different phase of the global economy. It is no longer just about producing more, but about understanding better. In this new environment, companies that have managed to turn data into intelligent decisions have gained an advantage over those that remain anchored to analog models based on the knowledge and experience of one or several managers. The contrast can be clearly seen when comparing two recent cases. Palantir Technologies, a company based in Colorado specializing in artificial intelligence models applied to solving complex problems, has managed to quintuple its market capitalization in four years, reaching a value close to US$70 billion in 2025 with barely four thousand employees. In contrast, Atos, one of Europe’s largest IT services groups, went from more than 130 thousand employees to around 69 thousand after a deep restructuring associated with its late entry into the world of artificial intelligence, a process that even required intervention from the French state. The signal is clear: in the digital economy, value is no longer explained by the size of the organization, but by the intelligence embedded in its decisions.
More than an achieved state, Guatemala is undergoing a process of growing digitalization. With an estimated population of 18.6 million people in 2025, around 11.3 million have used the internet (60.8%), and the country registers 10.4 million active social media accounts. At the same time, official estimates indicate that around two-thirds of the adult population has at least one active bank account. Everything points to more than nine out of ten banked Guatemalans already participating in the digital world. This convergence reveals a relevant dynamic: the digital economy is beginning to overlap with the formal financial economy and opens a space for transformation that is advancing faster than many of the country’s traditional structures.
This logic of growing digitalization has already spread to economic activity. E-commerce in Guatemala has grown at rates close to 20% annually in recent years, driven by digital payments, intermediation platforms, and new consumption habits. At the same time, exports of modern services —software, BPO, analytics, and digital services— have recorded annual growth rates between 8% and 10%, according to data from AGEXPORT. This performance shows that a growing part of Guatemala’s productive apparatus already competes and generates value under the rules of the digital economy, even in an institutional environment that advances at a slower pace.
This gap between economic dynamism and the country’s institutional capacity is neither abstract nor new. It has already had concrete consequences in strategic sectors, and the case of the electricity sector is illustrative. Between 2010 and 2015, Guatemala managed to consolidate itself as a net exporter of electricity in the regional market, with annual surpluses exceeding 70 GWh. However, the lack of timely decisions in planning, regulation, and investment gradually eroded that advantage. In recent years, the country has gone from being a net exporter to importing between 200 and 300 GWh annually, reversing a position that had taken more than a decade to build. The message is clear: when institutions fail to accompany technological and productive change, even hard-earned advantages eventually dissipate.
The digital economy is not a homogeneous block or a trend adopted all at once. It is organized into successive layers, each with a different level of sophistication, productive impact, and barriers to entry. First comes digital infrastructure, which enables transactions and connectivity; then services and automation, which allow efficiency and scale; later, applied intelligence, where data is transformed into decisions; and finally, open innovation, which reduces uncertainty by integrating talent, technology, and continuous learning. These layers do not compete with one another; they complement each other. Understanding this sequence is key to identifying where each economy and each company currently stands within the process of digital transformation.
On this foundation, a second layer develops: digital services and process automation, where Guatemala has shown early maturity and a clear export vocation. At this level are solutions aimed at digitizing operations and automating tasks, enabling efficiency and scale. Companies such as BDG have deployed thousands of software robots (RPA) to automate administrative and operational processes, generating measurable productivity improvements. Others, such as Iungo, have worked on digitizing processes and customer service experiences, enabling local companies to operate with standards comparable to those of more sophisticated markets.
A third, more recent and decisive layer is that of advanced analytics and applied artificial intelligence, where data becomes a decision engine. Here it is no longer just about automating, but about optimizing and automating complex systems. In Guatemala, companies operating in this space are beginning to consolidate. Sento has developed solutions capable of transforming one hundred percent of contact center voice interactions into structured and actionable data. In the same layer is Improgress, a company of which I am CEO, whose recent growth responds to a deliberate strategy built on more than two decades of experience and a careful study of the international innovation ecosystem. In just one year, the company has tripled its professional talent base —now composed of 55 collaborators— and has more than doubled its revenue, reinvesting those surpluses into innovation and the development of new solutions.
As the digital economy advances toward more sophisticated layers, one decisive factor emerges clearly: talent. Solutions based on artificial intelligence, advanced analytics, and open innovation do not scale solely with capital or technology; they require people trained with judgment, intellectual autonomy, and the capacity for continuous learning. From the concrete experience of technology companies operating in these layers, the talent that integrates best and generates value most rapidly usually comes from university environments that have committed to academic rigor, individual responsibility, and early engagement with real-world problems. In our case, close collaboration with universities such as Universidad Francisco Marroquín, Universidad del Valle de Guatemala, and Universidad del Istmo has made it possible to incorporate technical professionals with a solid foundation and a culture compatible with highly complex environments. To fully participate in this new business culture, hand in hand with artificial intelligence, people capable of thinking, learning, and taking responsibility for results are required.
However, even with available talent, not all organizations are prepared to operate in these advanced layers. Applied artificial intelligence requires different organizational cultures: less hierarchical dependence, greater individual autonomy, clear responsibility for results, and a permanent willingness to learn. It is no coincidence that many initiatives fail not because of a lack of technology, but because of cultural resistance, especially at intermediate levels where change is perceived as a threat. In this context, the strategic decision is no longer whether to adopt these tools, but when and how to do so. Recent experience shows that the cost of experimenting and correcting early is usually lower than the cost of adapting late and under pressure. In the end, as in every technological race, the greatest risk is not failing, but being left behind while others move forward.
What is happening today in the digital economy is neither an isolated phenomenon nor exclusive to small countries. In just a few years, industrial and technological giants that once seemed untouchable have been overtaken by more agile competitors. Japanese and German companies that dominated sectors such as electronics and the automotive industry for decades have lost ground to actors that integrated data, intelligence, and continuous innovation earlier. High-end screens ceased to be Japanese and became Korean; electric vehicles are no longer defined by German tradition, but by the learning speed of companies such as Tesla or BYD. This process has accelerated in the United States, Europe, and Asia, and today extends to the rest of the world. For countries like Guatemala, the lesson is clear: the future does not necessarily belong to the largest or the oldest, but to those capable of incorporating intelligent value before everyone else. Everything will depend on the ability to learn quickly and the willingness to compete in the economy of the future: a decision that, as Guatemalans, we will have to make sooner rather than later.
