{"id":27872,"date":"2025-10-22T08:00:00","date_gmt":"2025-10-22T06:00:00","guid":{"rendered":"https:\/\/itpatagonia.com\/?p=27872"},"modified":"2026-03-12T18:07:02","modified_gmt":"2026-03-12T17:07:02","slug":"estrategia-de-datos-con-ia","status":"publish","type":"post","link":"https:\/\/itpatagonia.com\/en\/blog\/estrategia-de-datos-con-ia","title":{"rendered":"Data Strategy with AI: From Vision to Business Transformation"},"content":{"rendered":"

In recent years, the conversation about artificial intelligence has accelerated. However, many organizations find that investing in technology doesn't guarantee results. <\/p>\n\n\n\n

In fact, according to Gartner, at least the 40% of AI initiatives fail to generate ROI<\/a>.<\/p>\n\n\n\n

The real difference comes when AI is integrated into a well-defined data strategy that is not only technically robust, but also anchored in the business vision<\/strong>.<\/p>\n\n\n\n

An AI data strategy is, in essence, a framework that enables you to transform data into strategic assets. <\/p>\n\n\n\n

But it is not about accumulating information or deploying isolated models. The key is to connect data, algorithms and talent, generating sustainable value.<\/strong>.<\/p>\n\n\n\n

In this article we analyze how turning an organization's data into a strategic asset<\/a>. Also, what is typically the main gap between business vision and actual AI capabilities, and how can it be closed?<\/p>\n\n\n\n

In addition, we address the metrics and indicators that are key to assessing whether the AI-powered data strategy is generating tangible value for the company.<\/p>\n\n\n\n

\"La
Modernizing with AI means redesigning the business under an AI-first paradigm.\u00a0<\/figcaption><\/figure>\n\n\n\n
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How do you build a business-aligned AI data strategy?<\/strong><\/h2>\n\n\n\n

\u201cAn AI strategy is about thinking big, forcing yourself to think about the impact beyond the technology,\u201d he says. Daniel Menal<\/a>, Head of Data & AI at IT Patagonia<\/a>.<\/p>\n\n\n\n

For the expert, this is achieved by involving different actors (outside the IT area) in a small discussion table.<\/p>\n\n\n\n

\u201cThis thinking raises questions about the business and paves the way for a roadmap <\/em>broader than a mere technological perspective,\u201d he notes. And he points out that the strategy cannot be centralized in a single person. <\/p>\n\n\n\n

An effective strategy requires bi-directional alignment<\/strong>: The business must guide AI, and AI must feed back to the business. <\/p>\n\n\n\n

To achieve this, three fundamental principles are generally recommended:<\/p>\n\n\n\n

1. Clear and measurable objectives.<\/strong> An AI data strategy must answer specific business questions. For example: How to predict customer churn? How to optimize resource allocation?<\/p>\n\n\n\n

2. Designing scalable AI data models.<\/strong> A one-size-fits-all model isn't enough. Architectures are needed that support different use cases and adapt to the evolving business.<\/p>\n\n\n\n

3. Data governance and ethics.<\/strong> Confidence in models depends on the quality, traceability, and responsible use of data, with clear policies.<\/p>\n\n\n\n

Building a data strategy with AI means putting business objectives at the center. Designing an ecosystem of data and models that not only solve current problems, but can evolve alongside the organization<\/a>.<\/p>\n\n\n\n

\"Establecer
Establishing an AI strategy means thinking big and forcing yourself to consider the impact beyond the technology itself.<\/figcaption><\/figure>\n\n\n\n
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Getting Started: From Dispersed Data to Strategic Assets<\/strong><\/h2>\n\n\n\n

The first steps that an organization that wants to turn its data into a strategic asset should take should focus on Ensure that data is readable and understandable by the business, not just by technical areas<\/strong>. <\/p>\n\n\n\n

This is Daniel's analysis, which also states that when an organization reaches this milestone, it means it has matured its processes and quality tasks, among other factors that can turn data into information.<\/p>\n\n\n\n

In this sense, it is worth highlighting that converting data into a strategic asset requires a structured path that involves:<\/p>\n\n\n\n