Most discussions about artificial intelligence revolve around models, applications, and computational capacity, leaving the layer that sustains all of this in the background: data engineering. Its function was to ensure that information circulated between systems and was available for corporate analysis and applications. The rapid expansion of artificial intelligence has altered this scenario. Today, the ability to generate value with AI is directly related to data quality, availability, and governance, making data engineering an increasingly relevant piece in technology and business decisions.
In recent years, the race to adopt generative AI has led organizations across different sectors to accelerate investments in models, platforms, and intelligent applications. The results, however, depend less on the sophistication of the technology and more on the maturity of the structure that sustains these initiatives. Fragmented, inconsistent, or outdated information compromises system performance, increases operational risks, and reduces the return on investment.
The numbers help gauge the scale of this movement. According to IDC, global investments in infrastructure for artificial intelligence surpassed US$ 318 billion in 2025, more than double the amount recorded in the previous year. This data reinforces an increasingly present perception in the market: the adoption of AI requires not only computational capacity but also environments prepared to process, integrate, and make information available at scale.
The shift also appears in the priorities of business leaders. Among the main data and analytics trends for 2025, Gartner highlights the consolidation of data products, the evolution of metadata management, and the advancement of architectures aimed at integrating multiple information sources.
The growing demand for real-time decisions reinforces this movement. Customers expect more personalized experiences, executives need to respond quickly to market changes, and automated systems depend on updated information to operate efficiently. In this context, cloud architectures, continuous processing, and data visibility are gaining ground in corporate strategies.
There is also an important economic dimension to this discussion. McKinsey estimates indicate that generative artificial intelligence could add between US$ 2.6 trillion and US$ 4.4 trillion per year to the global economy. Capturing this potential, however, will depend less on the isolated adoption of tools and more on the ability of companies to transform data into strategic assets connected to operations and business objectives.
Much of the debate on artificial intelligence remains concentrated on the evolution of models and the speed of innovations. However, the ability to capture value from these technologies will be increasingly associated with building reliable, scalable, and well-governed data environments. Data engineering is stepping out of the background to become part of the strategic agenda of organizations.
* Nicolas Silberstein Camara, CTO and co-founder of Firecrawl; and Nicole Grossmann, AI specialist and Georgia Tech graduate.
