The data supports this trend. Companies that adopt artificial intelligence achieve productivity improvements of up to 7%, according to an analysis by the Bank of Spain, while international consulting firms like KPMG place that impact on operational efficiency between 20% and 45%. However, adoption remains limited in the Spanish business sector: only 2.9% of industrial SMEs currently use AI, according to the 2025 AI Adoption Barometer for Spanish SMEs, prepared by IndesIA in collaboration with Acciona and Informa.
This combination of high impact and low adoption paints a clear picture: a window of opportunity for companies that take the lead. “The companies that are making the most successful progress in this area share a common pattern. First, they start with a realistic assessment of their starting point: what data they have, how it is used, and to what extent it is aligned with business objectives. From there, they define a roadmap that prioritizes specific use cases—in areas such as operations, finance, or marketing—capable of generating tangible value in short timeframes. This progressive approach avoids isolated projects or pilot programs without continuity and facilitates the natural integration of analytics and AI into existing processes,” says Javier Tejada, co-president and head of Technology at the Spanish consulting firm h&k, which offers comprehensive solutions based on Microsoft technologies and artificial intelligence to more than 1,100 clients.
From Stored Data to Data That Drives Decisions:
Modernizing data platforms is another key factor for SMEs to achieve tangible results. By 2026, the focus will be on having architectures capable of working in real time, integrating multiple information sources, and scaling flexibly. These three factors are crucial for analytical and artificial intelligence models to be deployed with a real impact on the business.
Advanced analytics allows companies to anticipate demand, optimize operations, and adjust prices in real time. Sectors such as retail, energy, and logistics already use predictive models to improve margins and efficiency, while augmented analytics—with natural language queries and intelligent assistants—is democratizing access to data within organizations. "The leap is clear: we're going from looking at data in reports to having data act directly on the business, and that completely changes the game," Tejada points out.
This change also demands a profound technological upgrade. Traditional architectures are ill-equipped to respond in real time, which is driving the adoption of modern platforms capable of integrating streaming analytics, edge computing, and AI models embedded in processes.
For many SMEs, this step will be crucial: failing to modernize their data platforms can translate into a loss of agility, reduced responsiveness, and a disadvantage compared to more advanced competitors.
But the transformation is not only technological; it is also organizational. The companies that achieve the best results are those that accompany these technical changes with new ways of working: data governance, clear criteria for information quality, and greater involvement of business areas in the use and interpretation of data.
In this new scenario, analytics is no longer the exclusive domain of the IT department and is becoming integrated into the daily operations of key areas such as finance, operations, and marketing. This change also requires new professional profiles, capable of acting as a bridge between technology and business, and a cultural evolution that fosters data-driven decisions at all levels of the organization.
From its experience supporting hundreds of SMEs in their transition to AI-driven data models, h&k has observed that companies making data-driven decisions improve their operational efficiency and strengthen their competitiveness. "And they're not just more efficient," concludes Tejada, "they're more resilient, agile, and competitive. It's no longer so much about who digitizes first, but about who is able to extract real value from their data and translate it into operational decisions."
