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Is your company ready for AI adoption? Beyond the hype: Talent, processes, and mindset

Artificial Intelligence is no longer a concept of the future—it’s a current, actionable tool delivering measurable impact across industries, from banking to retail.

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It promises to optimize operations, reduce costs, personalize experiences, and accelerate decision-making. Yet behind the excitement and optimistic headlines lies a critical question many organizations avoid asking honestly: Are we truly ready to adopt it?

Buying technology is not the same as adopting artificial intelligence strategically. Too often, AI investments end up as isolated efforts—disconnected from core business processes and unsupported by the internal capabilities needed to drive real change.

This article cuts through the hype and takes a closer look at the real factors that determine whether a company is genuinely prepared to turn AI from a powerful concept into a tangible competitive advantage.

AI Adoption: Excitement outpaces execution

Numerous recent reports reveal a concerning gap between intention and execution. According to IBM's 2024 AI Adoption Index, only 34% of companies have data structures that are ready for AI. McKinsey adds that just 11% have successfully scaled AI solutions with impact across business units. Meanwhile, Deloitte found that 74% of companies that started using AI lacked the necessary in-house talent.

The result? Initiatives that remain attractive proof of concepts but are ultimately unsustainable. The main causes: lack of strategic vision, fragmented data structures, and a disconnect between technology and business.

From Lab to Business: When a PoC Isn’t Enough

Many leaders bet on implementing AI as an experiment, without establishing the organizational conditions necessary for true adoption. Having a predictive model built in a lab environment adds no real value unless it’s integrated into daily decision-making flows. Moving from theory to practice requires recognizing that AI is not a finished product — it’s an evolving architecture.

Companies that successfully scale AI usually start with a clear roadmap: which processes it will impact, how success will be measured, who will lead the initiative, and what structural changes are needed to support it. Otherwise, AI becomes an expensive innovation line disconnected from tangible results.

Specialized talent: The overlooked factor in many strategies

The talent gap remains the most obvious bottleneck. Companies need more than data scientists. They require leaders who understand how to apply this technology to business models, engineers who ensure its scalability, and designers who build experiences around intelligent systems.

In response to this shortage, three effective strategies have emerged:

  1. Create internal AI academies to retrain existing talent.

  2. Forge partnerships with universities and research centers to accelerate upskilling.

  3. Collaborate with external experts who can progressively build in-house capabilities.

The difference between success and frustration often isn’t in the software itself but in the people who operate it, interpret it, and scale it.

Infrastructure matters: Without clean data, there’s no useful AI

One of the most frequent misconceptions is that AI models “just work” because the technology is powerful. In reality, the strength of any AI system depends—above all—on the quality of the data it’s built on.

According to Gartner (2024), in successful AI projects, 80% of the effort is dedicated solely to preparing, cleaning, and governing data.

Data silos, unstructured formats, and inconsistent versions don’t just slow down adoption—they put the entire model’s output at risk.

That’s why data governance—with clear standards, well-defined roles, and the right integration tools—is just as important as the algorithm itself.

Mindset: The invisible foundation that shapes everything

AI isn’t just about technology—it’s a fundamentally different way of thinking. Companies that aren’t willing to trust probabilistic models or that treat AI as a standalone innovation effort, rather than an operational extension, often fail to see meaningful results.

Internal communication also plays a critical role. Many employees fear AI will replace them, without realizing its greatest value lies in complementing—not replacing—human talent. The most successful organizations are those that craft clear, intentional narratives about how AI empowers people and redefines roles, rather than eliminates them.

Real-World cases: Lessons from those getting it right

BBVA scaled over 100 AI use cases because it started with the right foundations: strong data governance, continuous training, and a long-term vision. Falabella, on the other hand, spent three years preparing its infrastructure before deploying solutions in e-commerce and supply chain. Bayer’s case stands out for creating an AI Center of Excellence that brings together data science, business, and technology—achieving 25% gains in predictive maintenance efficiency.

All three examples share a common pattern: clear business vision, gradual preparation, and multidisciplinary teams. There are no shortcuts.

Conclusion: Is this just a trend—Or a new way to run a business?

Artificial intelligence isn’t just a passing trend—it’s an organizational discipline that’s reshaping how companies operate, make decisions, and connect with customers and their environment. Buying tools isn’t enough. Companies need to look inward and make the structural changes necessary to turn that investment into real value.

Organizations that are truly ready tend to share four key traits:

  1. A clear vision of how AI fits into their overall business strategy.

  2. A strong, well-governed data infrastructure aligned with business goals.

  3. Hybrid talent that bridges the gap between tech and business.

  4. A culture that doesn’t fear change—but welcomes it with time, space, and resources.

Action Steps for Companies Ready to Move from Pilot to Real Impact:

  • Honestly assess their technological and organizational maturity before launching any initiative.

  • Define and prioritize use cases based on real business impact and technical feasibility.

  • Create strategies to attract, retrain, or acquire AI-first talent.

  • Build clean, structured, and accessible data foundations.

  • Involve business leaders from the design phase through to evaluation.

AI holds real promise—but only for those who understand that adopting it is far more about culture and organization than just technology. Because in this game, it’s not the company with the best model that wins, but the one that knows how to embed it at the very core of its business.

Colombia

Bogotá Headquarters

Calle 93 #16-46 oficina 404 edificio Zenn Office PH

Spain

Madrid

Calle Conde de peñalver, 45, entre planta oficina 2, 28006, Madrid

USA

Miami-Florida

1000 Brickell Av, PMB 5137

Mexico

Mexico DF

Av. Rio Misisipi 49 Int. 1402, Cuauhtémoc

Panama

City of Panama

Calle 50, edificio, torre BMW, San Francisco

Ecuador

Quito

Andrade Marin 24, Quito 170518, Ecuador

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