Data & AI: Consolidating an AI-first strategy

Philosophy AI-first It argues that artificial intelligence must cease to be an accessory resource for become the strategic core of organizations. 

This implies that every decision, process, and experience of people is influenced by the use of data and intelligent algorithms. 

In a context where competitiveness depends on the ability to anticipate and adapt, AI-first It's not a fad, but a path towards corporate sustainability.

In this article we ask for help from Daniel Menal, Head of Data & AI at IT Patagonia to analyze how to scale from isolated artificial intelligence projects to a culture AI-first, the main challenges and aspects to consider, and the role of leadership in fostering cultural transformation.

What does adopting a strategy entail today? AI-first

As Daniel explained to us, this philosophy has multiple meanings. 

Depending on the level of maturity of the organization, AI-first It can be represented as the actions taken internally to boost the business or reduce operating costs. But there are cases where it is strongly linked to how the company interacts with its end customers, especially in sales channels.

Adopt an approach AI-first means rethinking the way businesses are conceived. 

It's not just about using chatbots or recommendation systems. The key lies in integrate artificial intelligence into the very architecture of the organization. From how products and services are designed, to how internal resources and the commercial area are managed.

Today, AI-first This involves having a robust, flexible, and secure data infrastructure capable of centralizing dispersed information and ensuring its quality. 

It also demands incorporate artificial intelligence across the board, so that marketing, logistics, finance, or human resources can use AI tools such as strategic enablers. 

All of this is supported by an organizational culture that values data and by committed leadership that guides change.

Be AI-first It doesn't mean implementing isolated projects. It involves redesigning how business operations are conceived and executed. It consists of a shift in mindset that distinguishes those who innovate from those who merely experiment.

5 things to consider when implementing an approach AI-first

Implement an approach AI-first It's not just about deploying artificial intelligence solutions in isolation, but about transform the way the organization thinks, decides and operates. 

For artificial intelligence to become a true engine of competitiveness and not just a passing experiment, it is necessary to address certain key factors that ensure its effective and sustainable adoption. 

Below, we present five key aspects for the transition to a model AI-first be successful.

1. Robust, flexible, and secure data infrastructure

To scale with AI, it is essential to have access to clean, structured and unstructured data, with clear governance and security policies. 

Companies that do so, They report improved metrics for immediate access to internal data, greater regulatory compliance and lower failure rate in artificial intelligence initiatives. 

2. Cross-cutting integration of AI in all areas

Instead of having isolated artificial intelligence projects (chatbots, prediction, partial automation), adopt an approach AI-first This means that all areas of an organization use this technology as a strategic enabler to:

• Making decisions.
• Increase personalization.
• Achieve greater efficiency.

3. Data-driven and continuous learning culture

Simply incorporating technology is not enough: People need to understand, trust, and collaborate with AI.. That they know what data matters, how it is used, and what risks should be considered. 

Similarly, they should be trained in analytical skills. Furthermore, it is required Tolerance for error, rapid iteration, and adaptation to change.

4. Visible, ethical and committed leadership

Leadership doesn't just allocate resources. It also defines the vision, drives ethics, fosters cultural change, legitimizes AI experiments, and decides on long-term priorities. 

5. Governance, transparency and accountability

It is important to have standards that ensure models are fair, explainable, comply with privacy regulations, allow biases to be identified and mitigated, and that risks are actively managed. 

This includes both internal policies and alignment with external provisions or legal regulations.

From isolated projects to a culture AI-first

Most companies start by testing AI on specific projects: a chatbot, a predictive maintenance solution, a customer segmentation tool. 

While these cases add value, remaining at that level is insufficient to fundamentally transform the organization.

Building a culture AI-first demands moving from isolated to structural. 

This involves auditing the company's technological maturity, designing a strategic roadmap, and implementing pilot projects aligned with business objectives. Above all, it means democratizing access to artificial intelligence.

The creation of centers of excellence, continuous training, and ethical and regulatory integration from the outset are fundamental steps in that journey.

In short, a culture AI-first It is not achieved by multiplying experimental projects, but integrating AI as an everyday, reliable, and strategic resource for all areas of the organization.

In this sense, Daniel points out that there must be a strategic decision from the company's management positions, an executive motivation that drives the teams to apply it to their daily work. 

“Working with an open but carefully managed AI governance framework turns out to be a pretty successful approach. The important thing is not to limit the company to a suite of tools, but rather to give governance to everything that is activated, ensuring that they are all aligned with the company's objectives and security,” he says.

The following are some practical steps that allow you to move from isolated projects to a culture AI-first.

• Artificial intelligence maturity audit: assess where the bottlenecks are (infrastructure, data, talent, leadership, culture).
• Design a strategic roadmap: define key areas where AI can provide greater value, not only in immediate ROI but also strategic impact: customer experience, operational efficiency, product innovation.
• Create pilot projects aligned with the strategic visionThese pilot projects must have clear metrics, visible impact, and serve as a "proof of concept" for scaling up.
• Establish AI centers of excellence (CoE) or core team: an internal group that supports, guides, shares best practices, tools, and technical support for the different areas.
• Continuous trainingNot just technicians, but leaders, business teams, and people who understand what AI entails, what possibilities it offers, what its risks are, and how to manage them.
• Ethics and regulations from the start: Incorporate ethical frameworks, internal audits, regulatory compliance, and data governance from the earliest projects to avoid major problems when scaling up.

The role of leadership in fostering cultural transformation towards AI-first

No transformation of this magnitude happens spontaneously: leadership is the engine that defines the direction and legitimizes the changes.

“Those companies that actually get their teams to use technology in their daily work will experience real change; the rest will only do it to meet an OKR. This cultural change has to be generated from the basic structure of the pyramid upwards,” Daniel warns.

Indeed, on the path towards a model AI-first, Leaders have the task of communicating a clear vision, sponsoring projects, allocating resources, and managing the cultural resistance that inevitably arises.

Furthermore, they must to ensure that AI adoption is ethical and transparent, that data privacy is protected, bias is prevented, and fairness is ensured. 

This involves inspiring trust, not only within the company but also towards customers and strategic partners.

Leadership AI-first It is not just technological, but profoundly human: it is about inspiring people to trust artificial intelligence as an ally to innovate, learn and evolve collectively.

Some key issues to consider in terms of leadership:

a. Clear strategic vision and constant communication

Leadership must demonstrate what AI-first specifically means for each business unit, what the role of each team is, and where the company is headed in terms of artificial intelligence.

This needs to be communicated many times, with examples, internal use cases, and visible progress in terms of results.

b. Visible sponsorship of initiatives

Leaders need to support AI projects not only with resources. They must also be the first to use and evaluate the solutions, encourage experimentation, tolerate failures, and celebrate learnings.

c. Allocation of adequate resources

It is important that they manage the funding, infrastructure, training spaces, and time needed to incorporate new ways of working. Without these resources, any initiative will stall.

d. Change management and employee involvement

Developing a culture of active listening is key to identifying:

• Resistance to change and digital transformation.
• Fears associated with the potential loss of jobs.
• Technological ignorance

It is also essential to incorporate middle leaders and cultural ambassadors AI-first. 

At the same time, it is necessary to make transparent how the data will be used, and what the impact will be for employees and customers.

and. Ethics, trust, and responsibility

Leaders must be proactive in defining principles for the use of AI, avoiding bias, ensuring privacy, explaining automated decisions, and complying with regulations. 

This not only reduces legal and reputational risks, but also strengthens internal and external acceptance.

The main challenges of the approach AI-first

While the potential of the approach AI-first It is enormous; organizations face a number of obstacles that they must recognize and manage. 

“The main challenge is that, although there are success stories, the maturity and constant change of technologies means that A decision made today may be outdated or inefficient tomorrow. ”versus another new emerging technology,” Daniel points out.

Another key challenge is the fragmentation and low data quality, This limits the effectiveness of the models. This is especially true considering that many companies still operate with siloed, outdated, or inconsistent information. 

In addition to this, technological complexity and high costs associated with the development, maintenance and scaling of AI systems.

Also the shortage of specialized talent, This includes not only professionals with experience in data science, but also in data engineering, solutions architecture, ethics, and regulations. The lack of these profiles slows down full adoption.

On a cultural level, the resistance to change It remains one of the biggest obstacles. Many people perceive AI as a threat to their jobs or distrust automated decisions. 

Added to this is the need to build a strong governance, that ensures transparency, regulatory compliance and ethical use of artificial intelligence.

Finally, one cannot ignore the environmental impact of large AI models, whose energy consumption generates economic and reputational pressures.

In short, to be AI-first This involves facing challenges that go beyond technology: It encompasses cultural, ethical, regulatory, and environmental aspects.. 

Companies that manage these factors will be better prepared to capture all the value that AI can offer.

Current content and recent examples that reinforce this trend

A recent report from the General Catalyst group It identifies four critical areas for achieving real AI integration without “hitting a wall”: 

• Data infrastructure.
• Contextual specialization (adapting models to the specific operation of the company).
• Transformation of the workforce.
• Courageous leadership. 

Recent studies indicate that a high percentage of artificial intelligence initiatives fail or do not scale precisely because of a lack of intelligent data infrastructure, governance, and adequate access to data.

According to the white paper Intelligent Data Infrastructure is the Foundation of AI Success, Up to 20% of AI initiatives fail due to inadequate data infrastructure. Furthermore, the report states that many organizations face limitations in data access, compliance, and governance. 

The same applies to a Gravity analysis, which indicates that Between 70% and 85% of AI projects fail to meet their objectives. Often, failures are related to the quality, quantity, and integration of the data. 

On the other hand, the article Why Most Enterprise AI Projects Fail — and the Patterns That Actually Work, mentions that around 2025 42 out of % companies abandoned most of their AI initiatives, In many cases, this occurs during the proof of concept (PoC) phase due to problems related to infrastructure, data access, integration, and governance.

Ethical risks—bias, privacy, fairness, transparency, and regulatory compliance—are increasingly recognized as central themes in surveys led by technology companies.

AI-first: a competitive imperative

The approach AI-first It is no longer a futuristic option but an urgent strategy for organizations that want to lead, not just adapt. In fact, it has already become a competitive imperative. 

Integrating artificial intelligence into the strategic core of the business allows you to anticipate the market, personalize services, and respond quickly to the changing needs of customers and consumers.

However, this path is not without its challenges: it demands visionary leadership, investment in talent and infrastructure, ethics in the use of data, and a profound cultural transformation. 

Those organizations that manage to go through it will not only adapt to the present, but will set the agenda for the future.

Through our Data & AI team, we'll guide you so you can to get the most out of the data generated by your operation, with cutting-edge technologies and tools. 

Contact us and let's talk about how strengthen the data strategy and develop an approach AI-first in your company.