In 2026, artificial intelligence has moved beyond being a promise to become a core pillar of corporate strategy for many Spanish companies aiming to become data-driven organizations. According to a study by Boston Consulting Group, 81% of them place AI among their top three strategic priorities. At the same time, a report by ONTSI highlights that 78% of professionals are demanding training in digital technologies, including AI, in order to adapt to this new phase.
The trend is clear. However, behind these figures lies a less visible reality: while many organizations are advancing in AI, few are able to objectively measure its real impact. ROI, operational efficiency, output quality, the cost of errors, or user adoption levels remain major unknowns in many initiatives.
In this context, data is everything. When used effectively, it can transform the very essence of a data-driven organization and significantly boost results. But this only happens when data is applied through a truly data-driven approach—one based on clear metrics, well-governed models, and decisions grounded in evidence rather than intuition.
At Quantion, we have understood this for years. That is why we have adopted a comprehensive strategy based on Data Agents and generative AI, designed not only to innovate, but to generate measurable and sustainable value in real business environments.
Why Generative AI Changes the Rules of the Game
Generative AI represents a turning point because it enables organizations to extract maximum value from the data they already have. It facilitates access, interpretation, and exploitation of information, while accelerating the delivery of tangible results in much shorter timeframes than traditional approaches.
That said, this potential can only be realized when there is a solid foundation in place: well-structured data, modern architectures—such as Data Mesh or Lakehouse—and an infrastructure capable of handling continuous data flows without compromising quality, security, or governance.
Large language models have not only transformed how users access information; they have also raised the bar for how corporate data must be organized, documented, and contextualized. This leads to a key question: are organizations truly prepared, from a data perspective, to scale AI?
The data platform: the true foundation of data-driven AI
Talking about generative AI without talking about the data platform means staying at the surface of the problem. An organization may experiment with advanced models, but without a solid, well-governed, and business-aligned data foundation, the impact will be limited and difficult to sustain.
Being data-driven means much more than simply having data. It requires an architecture capable of integrating heterogeneous sources, defining shared business semantics, ensuring data quality, and consistently measuring the impact of decisions.
In this sense, the data platform is not a technical prerequisite, but a strategic asset. It is what allows AI to evolve from isolated use cases into cross-functional capabilities embedded in business processes and evaluated through business metrics. Without this foundation, generative AI risks becoming an eye-catching solution disconnected from operational reality.
Measuring AI: The Great Pending Challenge for Data-Driven Organizations
An increasing number of studies and real-world experiences converge on one point: the success of AI does not depend solely on the model, but on how its impact on the business is measured. Many initiatives remain at the proof-of-concept stage and fail to scale because there is no clear framework connecting AI with the organization’s data-driven reality—namely, a solid data platform, shared business semantics, and metrics to assess costs, returns, result quality, and user adoption.
To address this challenge—how to move from experimenting with AI to integrating it in a measurable way on top of a well-governed data foundation—we will be hosting another session of the Innovation Tech Leaders community on February 6.
At this event, Lluís Vicente Hernández, Head of Consulting and iX, and Rafael Giménez, Head of Data & AI at Quantion, will share practical approaches, key metrics, and lessons learned from real projects, where AI is built on consolidated data platforms and clear business rules to enable actionable decisions. The goal is to help organizations move beyond experimentation and objectively evaluate AI return, costs, quality, and adoption in production environments.
Data Agents: turning data into measurable business decisions
Among emerging innovations, at Quantion we see Data Agents as one of the most transformative applications. These intelligent agents are capable of converting business questions expressed in natural language into complex analyses, leveraging multiple data sources and business semantics defined by the organization itself.
For example, an executive might ask:
“How has profitability by product line evolved over the past year?”
A Data Agent does not simply translate this question into technical queries; it interprets what “profitability” means in that specific context, which rules should apply, and how to present the result in a clear and actionable way.
The real leap comes when questions evolve toward scenarios such as:
“Detect risk situations in my orders.”
Answering this requires understanding what constitutes a risk situation for that specific business—delivery delays, margin deviations, or recurring historical incident patterns—by incorporating business knowledge, rules, and context.
In this sense, Data Agents not only democratize access to data, but also make it possible to measure real impact: error reduction, analytical time savings, and improved decision-making. They are a clear expression of how AI, when supported by a solid and well-governed data foundation, can become a true operational capability.
Ultimately, being data-driven is not about adopting more technology, but about connecting data, platforms, and AI with measurable business decisions. Only then can data be transformed into real impact.
