The surge in interest around artificial intelligence is a global phenomenon, but in Mexico there are specific organizational and data-related hurdles that must be overcome.
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Amid widespread excitement about AI and frequent announcements of large corporate investments in Mexico, one statistic stands out: only about 3% of the information produced within organizations in the country meets the minimum quality standards required to be useful for AI projects.
That warning comes from the consultancy X-Data in its ebook Data Readiness: How to Prepare Your Company’s Data to Implement Artificial Intelligence, where it emphasizes that the main barrier is not the technology itself but the state of the data that feeds it.
Companies are committing large sums to these technologies. For example, BBVA Mexico said it will invest 100 billion pesos over the next six years, directing those resources toward technology and AI to enhance customer experience and boost productivity. Likewise, cloud providers such as AWS and Microsoft have announced multimillion-dollar investments to build data centers that will support these capabilities.
In practical terms, X-Data estimates that 97% of the information organizations hold is unusable for AI models because it is disorganized, of poor quality, lacks structure, or simply isn’t relevant.
This issue becomes especially important when you consider projections that Mexico’s AI applications market could be worth around $450 million by 2025, according to the CIO Playbook from Lenovo and IDC, driven largely by the financial, manufacturing and retail sectors.
X-Data stresses that the starting point for AI is not the algorithm or the platform, but the condition and quality of the data.
Many firms begin AI initiatives drawn by the promise of automating tasks, tailoring services, or supporting smarter decision-making. Yet relatively few perform a thorough audit of their information assets before launching those projects.
Brad Calder, vice president of infrastructure at Google Cloud, has noted that preparing data for a successful AI rollout requires clear objectives for what the AI should achieve. He advises taking inventory of available data sources, assessing their quality and relevance, and conducting a diagnostic of the technology stack. That diagnosis should cover not only systems for storing and transforming data but also compliance with regulatory frameworks, he told Expansión in a recent interview.
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Mexican companies face significant challenges: in many organizations data lives in silos—across platforms and marketing teams, for instance—saved in different formats and without clear governance policies.
The absence of processes to integrate, clean and analyze that information prevents it from becoming useful input for algorithms. Experts warn that without high-quality data, even the most advanced AI solutions will produce inaccurate or irrelevant outcomes.
A report from the IBM Institute for Business Value finds that 45% of organizations express concerns about data accuracy or bias, creating a meaningful obstacle to adopting AI.
Additionally, 42% of respondents say they lack sufficient proprietary data to effectively customize generative AI models, underscoring the importance of strong data governance and strategies to raise both the quality and applicability of the data used in AI initiatives.
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Lucas Monroe tracks the latest trends in mobile and 5G innovation. His work spans hardware analysis, telecom breakthroughs, and ecosystem development in next-gen connectivity.