Data Strategy with AI: From Vision to Business Transformation

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

In fact, according to Gartner, at least the 40% of AI initiatives fail to generate ROI.

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.

An AI data strategy is, in essence, a framework that enables you to transform data into strategic assets.

But it is not about accumulating information or deploying isolated models. The key is to connect data, algorithms and talent, generating sustainable value..

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

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.

La modernización con IA significa rediseñar el negocio bajo un paradigma AI-first. — Modernizing with AI means redesigning the business under an AI-first paradigm.

How do you build a business-aligned AI data strategy?

“An AI strategy is about thinking big, forcing yourself to think about the impact beyond the technology,” he says. Daniel Menal, Head of Data & AI at IT Patagonia.

For the expert, this is achieved by involving different actors (outside the IT area) in a small discussion table.

“This thinking raises questions about the business and paves the way for a roadmap broader than a mere technological perspective,” he notes. And he points out that the strategy cannot be centralized in a single person.

An effective strategy requires bi-directional alignment: The business must guide AI, and AI must feed back to the business.

To achieve this, three fundamental principles are generally recommended:

1. Clear and measurable objectives. An AI data strategy must answer specific business questions. For example: How to predict customer churn? How to optimize resource allocation?

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

3. Data governance and ethics. Confidence in models depends on the quality, traceability, and responsible use of data, with clear policies.

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.

Establecer una estrategia de IA implica pensar en grande y obligarse a pensar en el impacto más allá de la tecnología. — Establishing an AI strategy means thinking big and forcing yourself to consider the impact beyond the technology itself.

Getting Started: From Dispersed Data to Strategic Assets

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.

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.

In this sense, it is worth highlighting that converting data into a strategic asset requires a structured path that involves:

Assess organizational maturity. Many companies have information silos that limit a comprehensive view. A data maturity assessment is the starting point.
Define quick wins. Choose projects with immediate impact, such as demand forecasting, customer segmentation, or fraud detection. These cases often generate internal confidence and justify new investments.
Design the pipeline data with AI. This defines the ability to capture, process and make data available in real time. A pipeline Solid allows you to move from descriptive to prescriptive analytics.
Promote a model AI-first. AI should not be a complement, but the engine of new ways of working and decision-making.

The transformation does not begin with large technological deployments, but with measured and high-impact steps, which consolidate trust in data as the basis for intelligent decisions.

The gap between vision and actual capacity

“In many organizations, the challenge is understanding that the efficiency achieved with AI doesn't usually come from the outset. The technology needs to evolve, and finding the best business case is the main challenge,” says Daniel.

One of the biggest challenges is the dissonance between ambition and ability.

Boards often view AI as a disruptive force, but technical departments warn of issues such as:

Incomplete or low-quality data.
Lack of integration between legacy systems and new platforms.
Shortage of specialized talent in AI and data science.

To close this gap, organizations should:

1. Investing in team upskilling with AIIt's not just about training data scientists; it's about marketing, finance, operations, and other areas learning to use AI as a strategic tool.

2. Adopt a collaborative approachBusiness leaders and technical teams must design the strategy as a team.

3, Scale graduallyStart with pilots, learn from mistakes, and then scale across the entire organization.

True modernization occurs when the gap between business vision and technical capabilities becomes a bridge of collaboration, learning and progressive construction.

Una estrategia de datos con IA solo es exitosa si genera valor tangible. — An AI data strategy is only successful if it generates tangible value.

Metrics to measure the impact of the strategy

When asking about the metrics or indicators that are key to assessing whether the AI-powered data strategy is generating tangible value for the organization, the simplest way would be to refer to ROI.

But, from Daniel's perspective, there are other ways to measure impact. Some of them are:

The creation of new areas within the company.
The degree of maturity achieved in different areas.
The opportunity it gives companies to reconvert.

"These are results that aren't usually measured, but they are really important," he warns.

From this perspective, and taking into account that An AI data strategy is only successful if it generates tangible value., among the key indicators to consider can also be considered:

Speed of insights: average time from data capture to its use in strategic decisions.
Level of automation: percentage of critical processes managed with AI.
Maturity of the pipeline data with AI: degree of standardization, reliability and scalability of the data infrastructure.
Cultural adoption: number of non-technical teams using AI-based tools in their daily work.

Measuring success does not mean counting how many models are implemented, but Evaluate how data and AI translate into measurable improvements in outcomes, efficiency and organizational culture.

Turning data into an engine of innovation

Modernization with AI does not mean adding algorithms to traditional processes, but redesign the business under a paradigm AI-first.

A well-executed AI data strategy Turn data into an engine of innovation, capable of anticipating scenarios, optimizing decisions and opening new business models.

Organizations that manage to close the gap between vision and capabilities, relying on a pipeline solid, an ethical approach to data and the upskilling of their teams, will be better prepared to compete in an environment where competitive advantage depends on how knowledge is transformed into strategic action.

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